Doctorate of Philosophy - University of Adelaide · Relationships between Berry Sensory Assessment...

Relationships between Berry Sensory Assessment and Wine Quality

in Vitis vinifera L. Shiraz

by

Sandra Milena Olarte Mantilla

Thesis submitted to School of Agriculture, Food and Wine

of the University of Adelaide

in fulfillment of the requirements for the degree of

Doctorate of Philosophy

July 2015

Relationships between Berry Sensory Assessment and Wine quality in Vitis vinifera L. Shiraz.

By:

Sandra Milena Olarte Mantilla

Supervised by:

Dr Susan E.P. Bastian, Associate Professor, School of Agriculture, Food and Wine. The University of Adelaide

Dr Cassandra Collins, Senior Lecturer, School of Agriculture, Food and Wine. The University of Adelaide

Dr Patrick G. Iland Visiting Research Fellow, School of Agriculture, Food and Wine. The University of Adelaide

Thesis submitted in fulfillment of the requirements for the degree of Doctorate of Philosophy

School of Agriculture, Food and Wine Faculty of Science, The University of Adelaide Waite Research Institute, Glen Osmond, SA 5064 Email: [email protected]

mailto:[email protected]

Table of Contents

Abstract ................................................................................................................................... ii

Declaration .............................................................................................................................. v

Journal of papers published as part of this research .............................................................. vi

Related publications and communications arising during candidature ................................ vii

Acknowledgements ................................................................................................................ ix

Abbreviations .......................................................................................................................... x

Chapter. 1 Introduction ......................................................................................................... 11

Chapter. 2 Published article: Review: Berry Sensory Assessment (BSA) concepts and practices for assessing winegrapes’ sensory attributes ......................................................... 18

Appendices Chapter. 2 ......................................................................................................... 31

Chapter. 3 Published article: Comparison of sensory attributes of fresh and frozen berries using Berry Sensory Assessment ......................................................................................... 49

Chapter. 4 Manuscript in revision: Relationships between grape and wine sensory attributes and compositional measures of cv Shiraz ............................................................................ 60

Chapter. 5 Submitted manuscript: Berry measures and wine volatile compounds and quality in Shiraz (Vitis vinifera L) are modulated by the use of rootstocks ..................................... 93

Chapter. 6 Discussion and future direction ......................................................................... 131

References (Introduction and Discussion and future direction) ......................................... 143

i

Abstract

Berry sensory assessment (BSA) is a technique that can help grapegrowers and

winemakers make decisions about harvest date and grape product allocation. BSA as a

structured technique has been used by grapegrowers, winemakers and researchers for the

last thirteen years. However the number of studies reporting results of the effect of

viticultural practices on berry sensory characteristics and wine quality is limited. This

thesis examined the BSA technique through four different studies.

Study one is a combined review literature review and industry survey paper in

which the methodology of Berry Sensory Assessment is presented and the research

conducted using BSA is discussed. It also presents the results of a survey channeled to

Australian grapegrowers and Australian and New Zealand winemakers about their

experience using BSA, their perceptions on its use and their suggestions for improving

the methodology. It was evident from the survey that 90% of grapegrowers and

winemakers use BSA and they want to understand the link between BSA and wine

quality. These results demonstrated the importance of BSA for wine producers and the

need for further improvement.

The aim of study number two was to determine if berry sensory attributes and

berry compositional variables could predict wine sensory attributes, wine compositional

variables and wine quality in Shiraz. The analyses of berry and wine sensory attributes,

compositional measures and wine quality using partial least squares regression and

Pearson’s correlations from two seasons identified several relationships between berry

ii

sensory attributes and wine sensory attributes and quality. A significant negative

relationship was identified between seed bitterness and wine savoury spice flavour for

the two seasons. The berry sensory attribute pulp detachment from the skin was

identified as a predictor of various wine sensory attributes (eg. the harder to detach skin

from the pulp the higher intensity for wine body colour, rim colour and dark berry

aroma) and wine quality scores in the season 2011.

The aim of study number three was to determine if berries stored at -20oC for

three months could be used instead of fresh berries to conduct BSA. Being able to

conduct BSA on frozen berries could help to reduce sensory fatigue in assessors by

allowing them to evaluate samples over a longer time period and to schedule BSA away

from the busy harvest period. The results of this study determined that sensory profile

from Shiraz berries differed in five sensory attributes – pulp sweetness, pulp fresh fig

flavour, skin colour extraction, skin bitterness and seed astringency - between fresh and

frozen berries at three times of harvest, preventing the evaluation of these five sensory

attributes in Shiraz frozen berries.

Study number four aimed to determine the effect of three rootstocks on sensory

and compositional differences of Shiraz grapes and wines in comparison to a non-

grafted control. The trial was conducted over two seasons. Berry and wine sensory and

compositional differences were found between the grafted treatments and the non-

grafted control. PCA was able to discriminate the wines from the four treatments in three

groups of aroma compounds (acetate esters, ethyl esters and higher alcohols) in both

iii

seasons. The results of wine quality scores from two seasons showed that the un-grafted

treatment had the lowest quality and 110 Richter and Schwarzmann the highest.

The findings from this study identified relationships between the sensory and

compositional variables in berries and wines that are affecting wine quality. It also

showed that that the use of rootstocks has an impact on berry and wine sensory and

compositional characteristics.

iv

Declaration

This thesis contains no material which has been accepted for the award of any other degree

or diploma in any university or other tertiary institution and to the best of my knowledge

and belief, contains no material previously published or written by another person, except

where due references has been made in text.

I give consent to this copy of my thesis when deposited in the University Library, being

available for loan and photocopying, subject to provisions of the Copyright Act 1968.

The author acknowledges that copyright of published works contained within this thesis (as

listed below) resides with the copyright holder of those works. I also give permission for

the digital version of my thesis to be made available on the Web, via the University’s

digital research repository, the library catalogue, The Australasian Digital Thesis Program

(ADTP) and also through web search engines, unless permission has been granted by the

University to restrict access for a period of time.

_______________________ _____________________

Sandra Milena Olarte Mantilla Date

v

Journal of Papers Published as part of this Research

Olarte Mantilla, S.M., Collins. C, Illand, P.G., Johnson, T.E. and Bastian, S.E.P. (2012)

Review: Berry Sensory Assessment: concepts and practices for assessing winegrapes’ sensory attributes. Australian Journal of Grape and Wine Research 18 (3): 245-225 Presented in Chapter 2

Olarte Mantilla, S.M., Collins. C, Illand, P.G., Kidman, C.M., Jordans, C. and Bastian, S.E.P. (2013)

Comparison of sensory attributes of fresh and frozen wine grape berries using berry sensory assessment. Australian Journal of Grape and Wine Research 19 (3): 349-357 Presented in Chapter 3

Olarte Mantilla, S.M., Collins. C, Illand, P.G., Kidman, C.M., Ristic, R., Hasted, A., Jordans, C. and Bastian, S.E.P.

Relationships between grape and wine sensory Attributes and Compositional Measures of cv. Shiraz. [Manuscript in revision - American Journal of Enology and Viticulture] Presented in Chapter 4

Olarte Mantilla, S.M., Collins. C, Illand, P.G., Kidman, C.M., Ristic, R., Boss, P.K.B., Jordans, C. and Bastian, S.E.P.

Wine quality in Shiraz (Vitis vinifera L) can be modulated by the use of rootstocks [Submitted Manuscript] Presented in Chapter 5

Each of these manuscripts is presented in the thesis in the form according to the author instructions for the associated journal. This thesis has been prepared following the University of Adelaide specifications for a PhD thesis by publication format

vi

Related Publications and Communications Arising During Candidature

Olarte Mantilla, S.M., Collins, C., Iland, P.G., Kidman, C.M., Ristic, R., Boss, P.K., Jordans, C., Bastian, S.E.P Finding relationships between BSA (Berry Sensory Assessment) and wine quality: Case study – Shiraz /Rootstocks. (seminar). 15th Australian Wine Industry Technical Conference. Sydney, July 2013.

Olarte Mantilla, S.M., Collins, C., Iland, P.G., Kidman, C.M., Ristic, R., Boss, P.K., Jordans, C., Bastian, S.E.P. Relationships between sensory profiles and instrumental measurements in raw fruit to predict quality of final product. 10th Pangborn Sensory Science Symposium (poster). Rio de Janeiro – Brazil, August 2013.

Olarte Mantilla, S.M., Collins, C., Iland, P.G., Kidman, C.M., Ristic, R., Jordans, C., Bastian, S.E.P. Effect of rootstocks on Shiraz berry and wine sensory characteristics (seminar). Crush symposium, Adelaide – November 2012.

Olarte Mantilla, S.M., Collins, C., Iland, P.G., Kidman, C.M., Ristic, R., Jordans, C., Bastian, S.E.P. Linking Berry Sensory Assessment (BSA) to Wine Quality (seminar). Treasury Wine Estates, Adelaide – November 2012.

Olarte Mantilla, S.M., Collins, C., Iland, P.G., Kidman, C.M., Ristic, R., Jordans, C., Bastian, S.E.P. Linking Berry Sensory Assessment (BSA) to Wine Quality (seminar). Hebrew, University of Jerusalem, Rehovot – Israel, September 2012.

Olarte Mantilla, S.M., Collins, C., Iland, P.G., Kidman, Jordans, C., Bastian, S.E.P. Effect of rootstock on Shiraz berry and wine sensory characteristics. 5th European Conference on Sensory and Consumer Research (poster). Bern- Switzerland, September 2012.

Olarte Mantilla, S.M., Collins, C., Iland, P.G., Kidman, Jordans, C., Bastian, S.E.P. Berry Sensory Analysis – Should frozen wine grape berries be used in sensory evaluations? 5th European Conference on Sensory and Consumer Research (poster). Bern- Switzerland, September 2012.

Olarte Mantilla, S.M., Collins, C., Bastian, S.E.P. Berry Sensory Assessment (BSA) – should frozen wine grape berries be used in sensory evaluations? Is BSA important for winemakers and grapegrowers? (poster). 17th International Symposium GiESCO. Asti (Alba) – Italy, September 2011.

vii

Olarte Mantilla, S.M., Collins, C., Bastian, S.E.P. Berry Sensory Analysis – Should frozen wine grape berries be used on sensory evaluations? Is BSA important for winemakers and grapegrowers? (seminar). Crush symposium, Adelaide – September 2011.

Olarte Mantilla, S.M., Collins, C., Kidman, C.M., Ristic, R., Bastian, S.E.P Wine quality and wine sensory assessment evaluation in grafted Shiraz (poster). 14th Australian Wine Industry Technical Conference. Adelaide, July 2010

viii

Acknowledgements

This thesis is dedicated to my family, my mum who has been every week listening patiently

providing advice, love and believe. To my dad even when he doesn’t say much, I know he

misses me and feels proud of me. To my siblings Diego and Mario and to my nieces and

nephew who bring me happiness with every smile. Thank you, you are the most important

part of my life.

I sincerely like to thank my supervisors, Dr Susan Bastian, Dr Cassandra Collins and Dr

Patrick Iland for their support, advice, patience and encouragement thorough this project.

I also want to thank Dr Paul Boss for his help in the last part of the project.

I want to acknowledge thankfully the funding received for my PhD project from the

Australian Grape and Wine Authority (AGWA). I also want to thank the South Australian

Research and Development Institute (SARDI) for allowing me to use their vineyard for my

research project.

I also would like to thank my colleagues at Adelaide University, particularly to Dr

Catherine Kidmann, Dr Renata Ristic, Dr Crystal Sweetman and pre Doctor Joanna

Gambetta for their friendship and advice.

It has been a long journey where some people have come and gone but I want to thank for

their support Oscar, David, Gabi, Vanessa, Roberta, Brad, Sofi a big thank you.

ix

Abbreviations

AGWA Australian Grape and Wine Authority ANOVA Analysis of Variance BSA Berry Sensory Assessment DA Descriptive Analysis CSIRO GC-MS HPLC ICPAES

Commonwealth Scientific and Industrial Research Organisation Gas Chromatography- Mass Spectrometry High Performance Liquid Chromatography Inductively Coupled Plasma Atomic Emission Spectrometry

LRI Linear Retention Index MFA Multi Factor Analysis PCA Principal Component Analysis PLS Partial Least Squares SARDI South Australian Research and Development Institute SO2 Sulphur Dioxiode TA Titratable Acidity VA Volatile Acidity

x

Chapter 1. Introduction

Grape composition and quality is the result of a combination of variety, season, site and

different viticultural practices that may include pest management, vineyard site selection,

bunch thinning and rootstock selection, among others. Wine quality is a product of grape

quality, applied winemaking practices during the vinification process and wine ageing. By

using instrumental measurements diverse studies have been able to evaluate components

that drive wine quality such as flavour compounds (Ferreira et al., 2009, San Juan et al.,

2012, Caputi et al., 2011, Rigou et al., 2014), phenolic compounds (Holt et al., 2008, Ristic

et al., 2007, Cortell et al., 2008, Cadot et al., 2012a) and alcohol content (Bindon et al.,

2013, King and Heymann, 2014). The definition of quality from an economic point of view

could be different depending of the type of goods. In the case of wine, the quality will be

evaluated in the process of consumption as it is an “experience good” (Dolgin, 2008).

However, wine quality is usually assessed by wine experts and researchers with extensive

wine training and experience. However it is important to mention that wine experts

sometimes have shown to disagree in their wine quality ratings (Hodgson, 2009). Wine

scoring systems are used and involve the description of all perceived characteristics of

appearance, aroma and flavour to give a value to the whole product. In terms of consumers,

only highly wine knowledgeable wine consumers can rate the quality of wines (Johnson

and Bastian, 2007), whilst the remaining consumers can only indicate their hedonic liking.

Descriptive Analysis (DA) is probably the most powerful sensory method used by

sensory professionals to evaluate different sensory characteristics of various products

(Lawless and Heymann, 2010). It has been widely used in the wine industry to evaluate the

intensity of sensory attributes in wines. Many studies have reported the use of DA to

11

evaluate the effect of different viticultural practices or vinification techniques such as;

canopy manipulation in Gewürztraminer (Reynolds et al., 1996, Ristic et al., 2013, Ruiz-

García et al., 2014), irrigation treatments in Cabernet Sauvignon (Chapman et al., 2005,

Casassa et al., 2013, Mendez-Costabel et al., 2014), varietal and regional sensory

characterisation (Varela and Gámbaro, 2006, Blackman et al., 2014, Cadot et al., 2012b,

Dreyer et al., 2013, García-Muñoz et al., 2014, Gómez García-Carpintero et al., 2012b,

Heymann et al., 2014, Hjelmeland et al., 2013, King et al., 2014, Llobodanin et al., 2014,

Muñoz-González et al., 2011, Reynolds et al., 2013, Tomasino et al., 2013, Vilanova et al.,

2013), vine vigour in Pinot Noir (Cortell et al., 2008), shading treatments in Shiraz and

Cabernet Sauvignon (Joscelyne et al., 2007, Ristic et al., 2007), vinification techiniques

(Buffon et al., 2014, Hopfer et al., 2012, Arfelli et al., 2011, Cejudo-Bastante et al., 2011,

Durner et al., 2010, Espitia-López et al., 2014, Ganss et al., 2011, García-Carpintero et al.,

2010, Gómez García-Carpintero et al., 2012a, González-Álvarez et al., 2014, Heymann et

al., 2013, Malherbe et al., 2013, Şener and Yildirim, 2013, Sokolowsky et al., 2014, Takush

and Osborne, 2012) and wine storage evaluation (Gómez Gallego et al., 2013, Wirth et al.,

2012).

Grape quality evaluation, in contrast to wine quality evaluation, is not very well

standardized. In Australia, medium to very large sized wineries fulfil their grape

requirement via grapegrowers who receive payment for the fruit after it goes through a

quality evaluation. In the Australian wine industry the quality of grape berries is defined as

the combination of grape berry maturity, purity and flavour/character (Allan, 2003).

Maturity and purity parameters such as total soluble solids, titratable acidity, uneven

ripening, and disease incidence can be easily assessed in the vineyard or at the winery.

12

However, flavour and variety requirements are difficult to quantify and have been found to

rely more on product requirements and winemaking styles (Allan, 2003). Limited

quantification has been achieved on flavour and variety characteristics of table grapes but

such measurement is very rare in wine grape berries. Commonly, grape growers and wine

makers go to the vineyard to collect grape samples and taste the grapes before harvest.

However this assessment has always been informal and non-structured. A response to the

lack of a structured berry quality sensory measurement was the creation of the berry

sensory assessment (BSA) by the Institut Cooperatif du Vin (ICV) (Rousseau and Delteil,

2000). In Australia the methodology was disseminated through national workshops and a

book published by Winter et al (2004), which was designed to be used as a guide to apply

the BSA methodology in the vineyard. From the time that BSA was presented to the wine

producers until the beginning of this project there was very little knowledge on the actual

level of use and acceptability of BSA in Australia. There was no communication between

wine producers and researchers on how the methodology had helped industry or how the

methodology could be improved. For this reason, as part of this PhD study, a survey of

grape growers and wine makers was conducted to evaluate the uptake and usefulness of

BSA, and to gather the input they could provide on what needed to be improved.

Since the release of BSA, some studies have employed this methodology with a range

of objectives. It has been used to evaluate vineyard variations and locations in France using

Cabernet Franc berries (Le Moigne et al., 2007), and to compare berries and wines from six

Shiraz blocks in McLaren Vale (Davidson Viticultural Consulting Services, 2006). Sensory

evaluation was also conducted on frozen Semillon berries to evaluate the effect of

defoliation and bunch thinning (Lohitnavy et al., 2010). More recently BSA has used to

13

evaluate the effect of temperature and water stress in grape berries (Bonada et al., 2013,

Sadras et al., 2013). Some valuable work defining the relationships between berry sensory

and wine characteristics has been provided by the Davidson Viticultural Consulting

Services using Shiraz (2006), by Rousseau (2001) in Chardonnay, by Pozzo Di Borgio and

Rousseau (2004) in Grenache, and by Winter et al. (2004) in Shiraz and Merlot. However,

in some of these studies a short four point category scale was used which was similar to the

one the wine producers would use. The use of such a short four point category scale could

have some methodological limitations as it might affect sample discrimination by leaving

assessors with only two possible answers in cases when assessors show the tendency to

avoid scale ends (Meilgaard et al., 2007). Another limitation was that in some of these

studies the assessor number was less than five which could lead to loss of information when

explaining treatment effects (King et al., 1995). Knowing the relationships between berry

sensory attributes and wine quality is important because it would allow wine producers to

decide when to harvest based on the style of their desired wine product. Therefore,

determining and understanding these relationships was the focus of this study.

Research to reveal relationships between berry sensory attributes and wine quality

requires the sensory evaluation of different grape varieties grown in different wine regions.

Such variability will result in a significant number of samples, which can generate fatigue

for sensory assessors and time pressure for researchers as the grapes will need to be

assessed in a short period of time. A solution to alleviate these difficulties for assessors and

researchers is to freeze grapes at the time of harvest and conduct their sensory evaluation in

a less busy period. Previous research has indicated that sensory response to freezing and

thawing varies depending on the type of fruit, freezing period length and thawing

14

conditions. For example, strawberries stored for 6 and 10 days at 1oC under carbon dioxide

or mixed air showed higher ratings for off odour, fermented odour and musty odour than

fresh strawberries (Shamaila et al., 1992). Blueberries kept at -4oC for 15 days in non-

perforated films had similar sensory ratings to fresh fruit (Chiabrando and Giacalone,

2008). Grapes for juice production stored over 9 months at -28 oC beforehand increased

bitterness and astringency perceptions and reduced perception of fresh aromas and flavours

(Flora, 1976). Sensory attributes and chemical composition of wines made from frozen

Cabernet Franc and Cabernet Sauvignon must stored at -18 oC for 28 days after harvest did

not differ from those made with fresh must (Schmid et al., 2007). However, the effect of

freezing and subsequent thawing process on wine grape sensory characteristics is not yet

known. Consequently one of the aims of this study was to determine whether there were

any sensorial and compositional differences between fresh and frozen Shiraz berries stored

at -20 oC for three months.

An important component that could affect grape quality is the selection of rootstock, a

decision that is commonly taken based on the vineyard soil chemical and sanitary historical

records. Rootstock importance has been well documented in different viticultural aspects

such as phylloxera and nematode management (Bioletti et al., 1921, Boubaias, 1966,

Buchanan and Whiting, 1991, Mccarthy and Cirami, 1990), salinity and soil acidity

management (Cirami, 1993, Hepaksoy et al., 2006, Zhang et al., 2002), and fertilization and

yield (Delas et al., 1991, Keller et al., 2001b). However, few studies show how

compositional changes in grapes and wines from different rootstock treatments are reflected

in sensory characteristics. In an early study, assessors found no significant differences in

wine quality scores between Chardonnay wines produced from either different rootstock

15

treatments or ungrafted vines (Ewart et al., 1993). However, in a later study, flavour and

wine intensity scores for Cabernet Sauvignon wines produced from vines grafted on Teleki

5C were found to be significantly higher than those for wines made with berries from

ungrafted vines (Gawel et al., 2000). Although these studies provided valuable preliminary

information, the sensory characteristics evaluated were limited to quality score and general

flavour and aroma intensity attributes. Evaluation of the effects of rootstocks on grape and

wine aroma, taste and flavour including mouthfeel sensory characteristics is crucial for

wine producers and researchers, as it will provide wine producers additional criteria for

choosing the most appropriate rootstock for their own vineyard conditions. A rootstock trial

was therefore conducted to determine if sensory differences could be found between wines

and berries from different rootstocks versus a non-grafted control treatment.

This thesis is presented in five chapters.

Chapter 2 – Berry sensory assessment (BSA) review and report of use by grape

growers and winemakers.

This chapter is a combined review – survey paper in which the methodology of Berry

Sensory Assessment is presented and the research conducted using BSA is discussed. It

also contains the results of a survey channelled to grapegrowers and winemakers about

their experience using BSA, their perceptions on its use and their suggestions for improving

the methodology.

Chapter 3. – BSA of fresh and frozen Shiraz berries

This chapter discuss the comparison of BSA and compositional measurements performed

on fresh and frozen Shiraz berries.

16

Chapter 4.– Relationships between grape and wine sensory attributes and

compositional measures.

This chapter covers the results of the search for relationships between berry sensory

attributes, and wine chemistry, sensory properties and wine quality

Chapter 5. – The effect of rootstocks on berry and wine sensory and composition.

Chapter four presents the results of the sensory and compositional evaluation of grapes and

wines from Shiraz grafted treatments and the non-grafted control.

Chapter 6. – General discussion

Finally, a discussion based around the synthesis of the results of the four studies is

presented in chapter five.

17

Chapter 2. Published Article - Australian Journal of Grape and Wine

Research

Review: Berry Sensory Assessment (BSA): concepts and practices for assessing

winegrapes’ sensory attributes

Chapter two covers a recount of the development of BSA as a methodology for

grapegrowers and winemakers. The methodological approach and outcomes of studies

evaluating the effects of viticultural practices on berry sensory attributes and relationships

between berry sensory attributes and wine sensory attributes are critically reviewed.

The second part of Chapter two is the report of a survey addressed to the Australian

grapegrowers and winemakers. The main aim of the survey was to clarify whether they use

BSA or not as an approach to determine grape harvest date. The grapegrowers and

winemakers were also asked to provide suggestions and recommendations on how to

improve the BSA as a methodology to better serve their needs.

Appendix 1 which contains supporting online material for Chapter 2 and the

surveys presented to grapegrowers and winemakers is included immediately at the end of

this chapter.

18

Olarte Mantilla, S.M., Collins. C, Illand, P.G., Johnson, T.E. & Bastian, S.E.P. (2012). Review: Berry Sensory Assessment: concepts and practices for assessing winegrapes’ sensory attributes. Australian Journal of Grape and Wine Research, 18(3), 245-255.

NOTE:

This publication is included on pages 19 - 34 in the print copy of the thesis held in the University of Adelaide Library.

It is also available online to authorised users at:

http://dx.doi.org/10.1111/j.1755-0238.2012.00203.x

Wine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvest

This survey is part of a GWRDC funded PhD research project of Sandra Milena Olarte Mantilla from The University of Adelaide. Her PhD supervisors are Drs Sue Bastian and Cassandra Collins. Sandra is a member of the Wine Science and Business Group of the School of Agriculture, Food and Wine and may be contacted on 0404183730 or at [email protected].

The purpose of this survey is to find out what type of berry sensory assessment wine makers are using. The information generated in this survey, along with the other components of the project, will assist in determining the correlation between grape berry quality assessment in the vineyard and the quality assessment of the wine.

This survey will take aproximately 15 minutes to complete. Please provide your details, however they will remain confidential in a secure data base and will not be divulged to any other person. All data will be reported in aggregate and your anonymity is guaranteed. This survey has been reviewed and approved by the Human Research Ethics comitee of The University of Adelaide.

Introduction

Appendix 1b: Winemakers survey

35

Wine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvestPart 1. Questions related to vineyard activity

1. Please provide the following information. The information provided willonly be read by the researchers in charge of the project.

*

Name

Position

Company

2. What is the postcode where your winery is located?*

3. What are the sources of fruit that you use to make wine? Please choosejust one answer.

4. What is the area of your own vineyard/s that you source your fruit from?Please select by clicking the appropriate circle.

5. What is the area of the contract vineyard/s that you source your fruitfrom? Please select by clicking the appropriate circle.

Personal vineyard only (go to question 4 and skip question 5)

Contract growers only (skip question 4 and go to question 5)

Combination of both (go to question 4 and then to question 5)

Small ≤10 Hectares

Medium 10-30 Hectares

Large 30-100 Hectares

Very large > 100 Hectares

Small ≤ 10 Hectares

Medium 10-30 Hectares

Large 30-100 Hectares

Very large > 100 Hectares

36

Wine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvest6. What are the major red wine grape varieties you source to make wine?You may select more than one response.

7. What are the major white wine grape varieties you source to make wine?You may select more than one response.

8. In relation to the most crushed variety in your winery over the last 5vintages, what quality levels of grapes did you achieve? Please divide the quality over 3 levels totaling 100%. If you didn't make wine in any of the vintages, please choose the N/A option from the variety drop down menu.

Variety Top quality Middle quality Bottom quality Total

2009

2008

2007

2006

2005

Shiraz

Cabernet Sauvignon

Merlot

Pinot Noir

Grenache

Petit Verdot

Ruby Cabernet

Mataro

Cabernet Franc

Sangiovese

Other/s

N/A

Other varieties (please specify)

Chardonnay

Semillon

Sauvignon Blanc

Sultana

Riesling

Colombard

Muscat Gordo Blanco

Pinot Gris

Vedelho

Viognier

Other

N/A

Other varieties please specify

37

Wine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvestWine grape berry sensory assessment for grape quality at harvest9. We define Grape Berry Sensory Assessment as any form of visual, aromaor taste evaluation of wine grape berries. This could involve evaluation of berry colour, aroma and the flavour, taste and mouth feel of some/all berry parts.

Do you use any wine grape berry sensory assessment?

Please indicate your answer by clicking on the circle.

*

Yes

No (Please go to question 19 by selecting the "next" button on the bottom of the page)

10. If you answered "Yes" to question 9 please select from the list the redgrape varieties on which you perform berry sensory assessment.

11. If you answered "Yes" to question 9 please select from the list the whitegrape varieties on which you perform berry sensory assessment.

Shiraz

Cabernet Sauvignon

Merlot

Pinot Noir

Grenache

Petit Verdot

Ruby Cabernet

Mataro

Cabernet Franc

Sangiovese

Other/s

N/A


Chardonnay

Semillon

Sauvignon Blanc

Sultana

Riesling

Colombard

Muscat Gordo Blanco

Pinot Gris

Verdelho

Viognier

Other/s

N/A


38


12. Why do you use grape berry sensory assessment? You may select morethan one response.

13. If you use grape berry sensory assessment, please select which winegrape berry sensory assessment/s you use from the list below. You may select more than one response.

Harvest date decision

Grade fruit quality for product allocation

Detect and control disease outbreaks

Evaluate sunburn damage

Other

Please specify other

Tasting of sugar and acidity in grape berry

Winter, Whiting and Rousseau Methodology (Berry Sensory Assessment in Australia)

Phenolic ripeness assessment of grape skin by texture analysis

Ripeness assessment of grape seed and/or skin by colour visual determination

Other


39


14. How did you learn about the wine grape berry assessment that youuse? You may select more than one response.

15. What do you think are the benefits of the grape berry sensoryassessment that you use? You may select more than one response.

I have created it myself

Berry sensory assessment from the vineyard that I source my fruit from

Industry workshop

From a book

From a wine maker mentor

In house technique

University

Other


Achieve the grade of berries that I want

Higher quality wine

Appropriate price paid for grapes

Other


40


6. What other methods do you use to determine that your grape berr16.are ripe enough and of sufficient quality to make the desired type of wine? You may select more than one response.

17.7. Do you think the wine grape berry sensory assessment that you usebe improved?

Degrees Brix measures

Titratable acidity measures

After some visits to the vineyard I decide when to pick

Viticultural advice

Other

N/A

Yes


18. If you answered yes to question 17, please indicate what steps could betaken to improve this methodology. You may select more than one response.

After answering this question please go to question 21 by selecting the "next" button on the bottom of the page.

More simplified system

More training

The berry sensory assessment could be used more frequently before harvest

Learning how to obtain a representative sample from the vineyard

Reduce cost of the methodology

Understanding the link between berry sensory assessment and wine quality

Other


41


19. Only answer this question if you answered "No" to question 9. Whydon't you use any wine grape berry sensory assessment?

20. Only answer this question if you answered "No" to question 9. How doyou determine that your grape berries are ripe enough and of sufficient quality to make the desired type of wine? You may select more than one response.

Too time consuming

I don't know any assessment techniques

I don't think it is important to use this technique

Other

Please specify

Degrees Brix measures

Titratable acidity measures

After some visits to the vineyard I decide when to pick

Viticultural advice

Other

Please specify

21. We define Wine Sensory Assessment as any form of visual, aroma ortaste evaluation of wine. This involves the evaluation of wine colour, aroma and the flavour, taste and mouth feel of wine.Do you perform sensory assessment on the wine that you make?

Yes


42


22.2. Please confirm, do you use berry sensory assessment on the grathat you source to make wine?

Please indicate your answer by clicking on the circle.

Yes


23.3. Do you compare the results of the wine sensory assessment withearlier berry sensory assessment?

Yes


24.4. How often does the resultant wine quality match the expectatcreated by the berry sensory assessment undertaken previously?

Never

25% of the time

50% of the time

75% of the time

100% of the time

Part 2. Questions related to winery activity.

43


25. What is the size of your winery? Please choose only one response.

26. In relation to the most produced wine variety in your winery over thelast 5 vintages, what quality levels of that wine did you achieve? Please divide that quality between 3 levels so that they total 100%. If you didn't make wine in any of the vintages, please choose the N/A option from the variety drop down menu.

Variety Top quality Middle quality Bottom quality Total

2009

2008

2007

2006

2005

Less than 20 tonnes per year

20 to 49 tonnes per year









20000 or more tonnes per year

Please specify other variety and the year

end of surveyThank you for your participation. A summary of the findings from this survey will be sent to you in the future. We

reiterate that your anonymity is guaranteed.

44

This survey is part of a GWRDC funded PhD research project of Sandra Milena Olarte Mantilla from The University of Adelaide. Her PhD supervisors are Drs Sue Bastian and Cassandra Collins. Sandra is a member of the Wine Science and Business Group of the School of Agriculture, Food and Wine and may be contacted on 0404183730 or at [email protected].

The purpose of this survey is to find out what type of berry sensory assessment grape growers are using. The information generated in this survey, along with the other components of the project, will assist in determining the correlation between grape berry quality assessment in the vineyard and the quality assessment of the wine.

This survey will take aproximately 5 minutes to complete. Please provide your details, however they will remain confidential in a secure data base and will not be divulged to any other person. All data will be reported in aggregate and your anonymity is guaranteed. This survey has been reviewed and approved by the Human Research Ethics comitee of The University of Adelaide.

Introduction

Appendix 1b: Grapegrowers survey

45

1. Please provide the following information. This information will only be used by theresearchers in charge of the project and will allow us to put you into a draw of a year subscription of the Australian Journal of Grape and Wine Research.

2. What is the postcode where your main vineyard is located?

3. Do you taste your wine grape berries in the vineyard before harvest? Pleaseindicate your answer by ticking the box.

Part 1. Questions related to vineyard activity

*

Name

Position

Company

Telephone

*

Yes (go to question 5)

No (go to question 4)

4. If you answered NO to question 3; why do you not taste your grape berries beforeharvesting?. Please indicate your answer by ticking the adequate box.

Too time consuming

I don't know any grape berry sensory assessment techniques

I don't think it is important to use any grape berry sensory assessment

Other

Other (please specify)

5. If you answered YES to question 3; do you believe tasting the berries beforeharvest assists you in assessing the quality of your grapes.

Yes

No

46

page two

6. If you answer YES to question 3; how did you learn about the technique that youuse to taste the grape berries?. You may select more than one response.

7. If you answered YES to question 3; do you think the technique that you use totaste your grape berries before harvest can be improved?

I have created it myself

From the winery that I sell fruit to

Industry workshop

From a book/article

From a viticulturist mentor

In-house technique

University

Other


Yes No

8. If you answered yes to question 7, please indicate what steps could be taken toimprove this methodology. You may select more than one response.

More simplified system

More training

The grape berry tasting technique could be used more frequently before harvest

Learning how to obtain a representative sample from the vineyard

Reduce cost of the methodology

Understanding the link between berry sensory assessment and wine quality

Other


end of surveyThank you for your participation. A summary of the findings from this survey will be sent to you in the future. We

reiterate that your anonymity is guaranteed.

47

NOTE: Statements of authorship appear in the print copy of the thesis held in the University of Adelaide Library.

Chapter 3. Published Article – Australian Journal of Grape and Wine

Research

Comparison of sensory attributes of fresh and frozen berries using Berry Sensory

Assessment

The results of the survey reported in Chapter two emphasized that an extensive

number of grapegrowers and winemakers have been using BSA together with other

methodologies as a way to assess grape maturity and to determine harvest date, which

justified our research.

The review of BSA presented in Chapter two highlighted that very little research

has been done to evaluate the effect of viticultural practices on berry sensory attributes.

Obtaining a deeper understanding of the relationships between berry sensory attributes and

wine quality was also in alignment with grapegrowers’ and winemakers’ proposition,

expressed in the survey, as the main way to improve BSA.

In general, elucidation of the relationships between BSA and wine quality may

require the evaluation of a large number of samples which might result in assessors’ palate

fatigue. To overcome this problem it was necessary to find an alternative procedure that

allows researchers to conduct BSA with a manageable number of samples for the assessors.

The following paper compares berry sensory attributes and compositional variables

of Shiraz fresh berries and berries frozen at -20°C for three months.

49

Olarte Mantilla, S.M., Collins. C, Illand, P.G., Kidman, C.M., Jordans, C. & Bastian, S.E.P. (2013). Comparison of sensory attributes of fresh and frozen wine grape berries using berry sensory assessment. Australian Journal of Grape and Wine Research, 19(3), 349-357.

NOTE:

This publication is included on pages 50 - 58 in the print copy of the thesis held in the University of Adelaide Library.

It is also available online to authorised users at:

http://dx.doi.org/10.1111/ajgw.12041

NOTE: Statements of authorship appear in the print copy of the thesis held in the University of Adelaide Library.

Chapter 4. Manuscript in revision - American Journal of Enology and

Viticulture

Relationships between grape and wine sensory attributes and compositional

measures of cv Shiraz

Literature review of BSA and the survey among Australian and New Zealand

grapegrowers and winemakers described in Chapter 2 concluded that elucidation and

understanding of the relationships between berry sensory attributes and wine quality was

of the foremost importance to improve BSA and provide more applicability for wine

producers.

The following paper examines relationships between berry sensory attributes and

berry compositional variables with wine sensory attributes, wine quality scores and wine

compositional measures. Partial Least Squares Regression and Pearson’s correlations

were used as the statistical tools to determine relationships between sensory and

compositional variables of berries and wines during two seasons. The established

relationships are discussed and recommendations to improve BSA arising from these

relationships are proposed.

60

Relationships between Grape and Wine Sensory Attributes and Compositional

Measures of cv. Shiraz

Sandra M. Olarte Mantilla1, Cassandra Collins1, Patrick G. Iland2, Catherine M. Kidman1,3, Renata Ristic1, Anne Hasted4, Charlotte Jordans1 and Susan E. P. Bastian1* 1School of Agriculture, Food, & Wine, University of Adelaide, Waite Campus, PMB1,

Glen Osmond, South Australia 5064, Australia 2Patrick Iland Wine Promotions Pty Ltd, PO Box 131, Campbelltown, South Australia

5074, Australia 3Wynns Coonawarra Estate, Memorial Drive, Coonawarra, SA 5263, Australia

4Qi Statistics Ltd, United Kingdom

*Corresponding author: Dr Sue Bastian, tel: +61 8 83136647 fax: +61 8 83137116,

e-mail [email protected]

Acknowledgements: The authors wish to thank all the assessors that participated in the

grape and wine sensory evaluation. This research was funded by the Australian Grape

and Wine Authority. The University of Adelaide is a member of the Wine Innovation

Cluster (www.wineinnovationcluster.com) Adelaide, South Australia. The berry and

wine sensory tastings were performed with the approval of The University of Adelaide

Human Research Ethics Committee.

Abstract: Relationships between sensory attributes, compositional measures and wine

quality scores of Shiraz grapes and wines were evaluated for two seasons, 2009-2010

and 2010-2011. The sensory profile of berries and wines were evaluated by Descriptive

Analysis (DA) and wine quality was assessed by an expert panel. In this study berry

sensory attributes were better predictors of wine sensory and compositional variables

than the combination of berry sensory attributes and berry compositional variables.

Partial least squares (PLS) regression analysis and Pearson’s correlation revealed a

negative relationship between seed bitterness and wine savory spice flavor for both

seasons. In the 2011 season pulp detachment from the skin was correlated with wine

61

sensory attributes such as rim color, fresh dark berry flavor and savory spice flavor and

in turn with wine quality score. Correlations between wine sensory attributes and wine

pigmented polymers and wine total tannins were identified in both seasons. These

findings are important for grapegrowers and winemakers as an insight into grape

sensory attributes that may have an impact on wine quality.

Keywords: Berry sensory assessment, descriptive analysis, wine sensory attributes,

seed bitterness, wine quality score, savory spice, pulp detachment.

Introduction

Berry sensory assessment (BSA) is a grape tasting methodology used by grape and wine

producers to evaluate sensory characteristics of the whole berry and of the individual

berry parts (skin, pulp and seeds). Interest in wine grape sensory evaluation has

increased in the last decade, due in part to the introduction of wine producers and

researchers to BSA as a practical methodology (Rousseau and Delteil 2000, Winter et

al. 2004, Olarte Mantilla et al. 2012). Recent research has explored the effect of leaf

removal (Lohitnavy et al. 2010), temperature (Sadras et al. 2013), water stress (Bonada

et al. 2013) and vineyard location (Le Moigne et al. 2007) on grape sensory attributes as

determined by BSA.

In a recent survey, members of the wine industry highlighted that “understanding the

relationships in the grape berry-wine continuum” was key knowledge needed to better

use BSA methodology (Olarte Mantilla et al. 2012). Winemakers and grapegrowers

reported that understanding the relationships of the berry-wine continuum may enable

them to predict optimum harvest time for improved wine quality; may make it easier to

allocate fruit to low or high grade end products as well as determine practices to better

62

manage berry flavor development and composition in the vineyard to make wines of

desired characteristics.

Some insights into the relationships between BSA and wine sensory characteristics

have been provided by the work of Davidson Viticultural Consulting Services for Shiraz

(2006); Rousseau (2001) for Chardonnay; Pozzo Di Borgio and Rousseau (2004) for

Grenache and Winter et al. (2004) for Shiraz and Merlot. The work for Shiraz showed

that high green character sensory scores for berries and wines were associated with

treatments with higher yield and less than 50% bunch exposure (Davidson Viticultural

Consulting Services 2006). Another finding from this study was that berries with higher

sensory scores for berry fruit flavor and flavor length gained higher scores for jam

aroma and body in the corresponding wine. A positive correlation was found between

the BSA pulp fruit characters attributes and wine body for Chardonnay (Rousseau 2001,

Winter et al. 2004).

Not unlike the previous studies mentioned above, the aim of this research was to

investigate whether relationships between berry and wine sensory attributes and

compositional variables could be identified for Shiraz grapes and wines. However, in

contrast to this earlier research, our data collection encompassed two seasons’ of

objective berry and wine sensory evaluation utilizing a formal sensory science

technique; Descriptive Analysis (DA), as opposed to routine wine industry berry and

wine sensory assessment procedures. Additionally, the current research has gathered

berry and wine compositional measures and determined any existing relationships

between the berry chemistry and sensory measures with wine composition, sensory and

quality scores using multivariate statistics including partial least squares (PLS)

regression analysis.

63

Materials and Methods

Vineyard. Berry samples were sourced from a Shiraz, clone BVRC30 (Vitis vinifera

L.) vineyard in the Barossa Valley. The experimental site was planted in 2001 at the

South Australian Research and Development Institute (SARDI) research station located

at Nuriootpa, Barossa Valley, South Australia. The vineyard was established at 1481

vines per hectare and trained to a bilateral cordon. Vines were planted at 2.25 m and 3.0

m vine and row spacing, respectively. Vines were spur pruned to 40 nodes per vine. The

soil type is fine sandy loam. A horizon overlying a red brown B horizon (Northcote et

al. 1954). All treatment vines were drip-irrigated and received 1.3 ML/ha (128 mm) in

2010 and 0.6 ML/ha (56 mm) in 2011 of irrigation during the growing season. The

irrigation schedule was based on soil moisture probes. Growing season rainfall

(September to April) was 267 mm in 2010 and 516 mm in 2011. Long term annual

rainfall for the site is 500 mm (Bureau of meteorology 2013).

Experimental design Twelve sampling sites (each comprised of seven vines) were

randomly selected across the vineyard to collect berry samples. One hundred berries per

sampling site were also collected randomly on a weekly basis from veraison until

harvest to perform grape maturity measures (Total Soluble Solids (TSS), pH and

Titratable Acidity (TA)). Berries were collected from different sections of the cluster –

top, middle and bottom – and from both sun exposed and non-exposed sides. Fruit from

each site was harvested when a maturity stage of ~25.3 Brix (2010) and ~22.8 Brix

(2011) was reached. The reasons to harvest the fruit based on TSS measurement were to

be consistent with grape producers harvest criteria and to ensure that any sensory

differences were due to the sample differences and not to differences in sugar content.

For each of the 12 sites, all clusters from the 7 vines were harvested and pooled. A

sample of 35 kg of fruit from each sampling site was collected for sensory evaluation,

64

chemical analysis and wine making. From each of the 12 samples, 4.5 kg

(approximately 15 clusters) of grapes were put aside for sensory and compositional

analysis. These clusters were broken into smaller clusters of approximately 6 berries.

Two kg were allocated to BSA and were kept at 4oC overnight after collection and then

equilibrated at room temperature for two hours prior to being presented in plastic weight

boats to panelists for sensory assessment. This process was performed for both seasons.

Wine making. Wine was made separately from each of the 12 sampling sites.

Clusters affected by Botrytis cinerea were excluded (out of necessity this sorting

procedure was performed only on the 2011 season grapes due to higher disease levels

from higher rainfall). Twenty five kg of fruit per sampling site were transported to the

Hickinbotham Roseworthy Wine Science Laboratory, University of Adelaide

winemaking facility. Grapes were stored at 4˚C overnight and the day after de-stemmed

and crushed using a combined crusher/de-stemmer (Enoitalia, ENO-15, Italy) into 30L

plastic fermenters (Ampi, Australia). During crushing 50mg/L of sulphur dioxide (SO2)

was added as a 20% solution of potassium metabisulphite (PMS) to each of 12 sampling

units musts. Tartaric acid additions were made to the must prior to yeast inoculation to

adjust the pH to 3.5. The musts were all inoculated on the same day with AWRI 796

yeast (Maurivin™, Toowoomba, NSW, Australia) following dose and preparation as

per manufacturer’s instructions. Diammonium phosphate (0.5g/L) was added as well at

the time of yeast inoculation when the ferments were between 18-20oC. Ferments were

hand plunged three times per day and co-inoculated with malo-lactic bacteria

(Oenococcus oeni) when the ferments were approximately 8 degrees Baumé (oBé).

Once primary fermentation reached approximately 2oBé the ferments were pressed with

a 130L capacity bladder press in 2010 (Diemme 130L, Laboratory press, Italy) and a

20L bladder press in 2011 (Zambelli, Hydro 20, Italy) into 10L glass demijohns. When

65

primary fermentation was completed, the ferments were racked off gross lees into 5L

glass vessels avoiding ullage with the addition of glass marbles. Additions of SO2 (to

reach 80ppm Total SO2), copper sulfate (1.5 mL per 0.75 L bottle of 1mg/L aqueous

copper sulfate solution) and tartaric acid (1 g/L if pH was higher than 3.55) were made

to ferments that had completed malo-lactic fermentation (<0.05 g/L malic acid) and then

kept at 0oC for 3 weeks for cold stabilization. After cold stabilization the wines were

filtered using a pad filter (Colombo-Rover pump & 6 pad filter, Italy) with 0.8 μm Z6

cellulose filter pads (Ekwip, NSW, Australia) and bottled into 375ml bottles with metal

screw cap closures. The wines were then stored at a constant temperature of 15oC for

future wine sensory and chemical evaluations.

Sensory Evaluation. Sensory evaluation of the twelve berry and wine samples was

performed using DA and conducted at The University of Adelaide, Waite campus

sensory facilities. Training sessions were divided into two stages: initial training in the

open plan focus group room and final training and evaluation sessions in computerized,

individual booths under fluorescent light with a light temperature of 6500oK, in the

sensory laboratory. Fizz software, version 2.47b (Biosystèmes, Couternon, France) was

used to collect the sensory ratings and to generate a randomized balanced presentation

order of the samples for each assessor. One minute breaks after each sample and five

minute breaks every three samples were enforced and panelist used citrus pectin

solution (for berry tasting) (1g/L, Sigma-Aldrich Co., St Louis, MO, USA) and plain

water crackers (Home Brand, Woolworths Limited, SA, Australia) to alleviate palate

fatigue as described in Olarte et al. (2013).

BSA: training. Fourteen assessors in the 2010 season (21-72 years old, 11 women

and three men) and eleven assessors in the 2011 season (20-32 years old, eight women

66

and three men) were selected for the DA panel based on their experience in sensory

evaluation, motivation and time availability. Assessor training for both seasons was

conducted as described previously (Olarte Mantilla et al. 2013). The first part of the

training was mainly focused on evaluating assessors’ ability to identify the basic taste

qualities – sweetness, acidity and bitterness and the astringent mouthfeel sensation.

Assessors were also presented with taste quality ranking exercises in grape juice as

described in Olarte Mantilla et al. (2012) to evaluate their ability to discriminate

between intensities. The ranking exercises were also used to establish low and high

anchors for those tasting qualities in the relevant grape part – skin, pulp and seed. The

second stage of training included the evaluation of berry sensory attributes of Shiraz

berries from the University of Adelaide Coombe vineyard. In this part of the training the

assessors had the opportunity to familiarize themselves with the tasting procedure and

to generate new or remove redundant sensory attributes as relevant to Shiraz berry

samples used in this investigation.

BSA: final evaluation. Two 2-hr sessions (conducted over 2 weeks), were required to

evaluate the berries from season 2010, as all of the samples did not reach the

predetermined levels of TSS at the same time. Berry sensory assessment of the 2011

samples was performed in two 2-hr sessions within a week. Assessors evaluated 25

attributes of 12 randomly presented samples twice (each in a separate session) resulting

in a total of 24 samples per assessor following the procedure described previously

(Olarte Mantilla et al. 2013). The attribute - color extraction was not evaluated in 2011

samples as in 2010 comparison with the color evaluating scale was observed to be

affected by individual assessors’ saliva characteristics.

Wine sensory evaluation: training. Sensory evaluation of wines was performed

using DA. Fifteen assessors in season 2010 (23-44 years old, seven women and eight

67

men) and twelve assessors in season 2011 (20-38 years old, nine women and three man)

were selected for the DA panel based on their experience in sensory evaluation,

motivation and time availability. Assessor training in 2010 was conducted in eight 2-hr

sessions, while in 2011 it was conducted in four 2-hr sessions. The training sessions

involved ranking exercises of taste attributes, astringency and identification of aroma

standards. During the training sessions the assessors were also presented with a number

of the trial wines (four per session in 2010 and six per session in 2011) to develop wine

appearance, aroma, flavor, mouth feel and aftertaste descriptors. Once assessors had

tasted the twelve wine samples the descriptors were collated and by consensus a final

list of descriptors was created. Twenty three descriptors were generated for the 2010

wines; they included two attributes for appearance, eight for aroma, six for mouth feel,

six for flavor and one for aftertaste. Twenty five attributes were generated to describe

the 2011 wines’ sensory characteristics. From those 25 attributes three were used to

describe wine appearance, nine aroma, six mouth feel, six flavor and one aftertaste.

Lists with the 2010 and 2011 wine attributes and definitions are presented in Table 1.

Wine sensory evaluation: final assessment. Final evaluation of the wines for both

seasons was conducted in two 2-hr sessions over two weeks. The assessors evaluated 12

samples in duplicate resulting in 24 samples per assessor (12 samples per session). The

assessors were presented with 30 ml per sample served in clear INAO (ISO standard)

215 ml tasting glasses covered with a petri dish. Reference standards were also

available throughout the formal evaluations (Table 1) and assessors revised them before

starting the assessment and were encouraged to do so again during the breaks or as

required. Wine quality evaluation: expert panel. In addition to DA sensory evaluation

of the wines, a sensory evaluation was conducted to determine wine quality. Wine

experts were recruited based on their wine knowledge and experience and were asked to

68

evaluate the quality of the wines under blind conditions using the Australian wine show

20 point scoring system (Rankine 1990, Dunphy and Lockshin 1998). Eight wine

experts participated in the wine quality evaluation in 2010 and 12 in 2011; each

performed within a month after the DA sensory evaluation was completed. Wine quality

evaluation was conducted at The University of Adelaide, Waite campus sensory facility

booths under the same room conditions described above for DA. A random balanced

presentation design was used to present the wines in duplicate for each season to the

wine experts, that resulted in 22 (2010, only one bottle was available of 2 samples) and

24 (2011) wines being presented. Thirty mL of each wine sample was presented to the

expert assessors served in clear INAO (ISO standard) 215 mL tasting glasses covered

with a petri dish and identified with a three digit code. Paper score cards were provided

to rate the 12 wine samples quality scores.

Berry chemistry. One hundred and fifty berries were randomly sampled from each

of the twelve sampling sites. One hundred were crushed, centrifuged and the

supernatant retained to determine TSS as degrees Brix, using a digital refractometer

(ATAGO® Pocket – α, Japan), pH and TA measurements were made using a pH meter

and autotitrator (CRISON, CompacT TITRATOR, Spain) as described in Iland et al.

(2004). The remaining fifty berries were kept at -20oC for 6 months to conduct total

anthocyanins and phenolics measures of berries following the methodology described in

Iland et al. (2004) (Supplemental Table 1).

Wine chemistry. Standard chemical measurements (SO2, pH, TA, volatile acidity

(VA), alcohol and residual sugar (RS)) were performed on the wines at the time of

sensory evaluation, following the methodologies described in Iland et al (2004)

(Supplemental Table 1).

69

High Performance Liquid Chromatography (HPLC) of Wine Samples. HPLC

analysis was used to determine concentrations of anthocyanins, tannins and pigmented

polymers in wines from both seasons following the methodology described in Eglinton

et al. (2004). Wines were clarified by centrifugation at 8,049 g for 4 minutes. A wine

supernatant volume of 20 μL was run through a polymeric column (100Å, 5μm, 250 x

4.6 mm; Polymer laboratories, UK) and the same material guard cartridge at a flow rate

of 1mL/min. Individual anthocyanins and pigmented polymers were identified at 520nm

and quantified as malvidin-3-glucoside equivalents based on a calibration curve using

different concentrations of malvidin-3-O-glucoside chloride (Extrasynthese, Genay,

France). Using the same procedure, tannins were identified at 280 nm and quantified as

catechin equivalents from a standard curve prepared with catechin (Sigma-Aldrich)

(Supplemental Table 1).

Statistical analyses. A two way mixed model analysis of variance (ANOVA) with

random assessors for each season’s data of BSA and wine sensory scores was executed

to identify sensory attributes significantly different between the 12 berry samples and

between the 12 wine samples. The relationships between the sensory attributes of the

berry and wine samples were determined by using the mean scores of each significant

attribute from all the assessors. Sensory attributes that were significantly different in the

ANOVA together with the compositional data were then grouped by modality and

subjected to Multiple factor analysis (MFA) to determine their contribution to the

samples difference. Three individual MFAs were conducted per season for the

relationships between berry sensory attributes with 1) wine sensory attributes, 2) wine

compositional variables (Supplemental table 1) and 3) wine quality score to explain the

data variability and to determine if it was possible to conduct PLS analysis. Three more

MFAs were conducted with the addition of berry compositional variables. For the

70

variability to be explained the MFA RV coefficients had to be equal or higher than 0.6

(data not presented). After conducting the three MFA analyses RV coefficients were

found to be higher than 0.6 (data not presented), making PLS regression analysis

possible (except for the data set combining berry sensory attributes, berry compositional

variables and wine quality scores for season 2010, MFA RV < 0.6).

PLS regression was performed for each season to identify berry sensory attributes

and berry compositional variables that could have an impact on 1) wine sensory

attributes, 2) wine compositional variables and 3) wine quality scores. After conducting

all PLS analyses Q2 coefficients were compared to test if the combination of berry

sensory attributes and berry compositional measures produced a more reliable

prediction model than berry sensory attributes alone. After performing the PLS analysis,

sensory attributes that had a variable importance for prediction (VIP) equal or higher

than 0.8 were included in the prediction model. Models that had a Q2 cumulative value

higher than 0.4, were considered suitable to act as predictors of wine sensory attributes,

wine compositional measures and wine quality scores.

In addition Pearson’s correlation was conducted for each season with all the berry

and wine sensory attributes and compositional variables irrespective if they were

significantly different or not. ANOVA, MFA, PLS and Pearson’s correlation analyses

were performed using the statistical package XLSTAT version 4.02 2012 (Addinsoft

SARL, Paris, France).

Results and discussion

Berry and wine sensory evaluation, composition and wine quality evaluation.

Statistical analyses were conducted for both sessions from the 2011 BSA but on session

two data only from the 2010 BSA. The reason for this decision was that the ANOVA of

71

the 2010 season BSA from session one, showed significant differences for some

assessors in their scoring levels, resulting in uncertainty in estimation of the sample

mean scores.

The BSA revealed ten significantly different attributes in 2010 and six attributes in

2011 (P≤0.10) (Table 2) with only one attribute, seed bitterness, that was different in

both seasons.

Wine sensory evaluation of 12 samples showed 11 significantly different attributes in

2010 and 14 in 2011 (P≤0.10) (Table 2). Consequently, seven wine sensory attributes

were found to be different in both seasons: rim color, body color, fresh red berry aroma,

fresh dark berry aroma, alcohol flavor, tannin quantity and tannin quality. Furthermore,

only wine quality scores showed significant differences between the 12 samples in the

2011 season (P≤0.10), but not in the 2010 season (Table 2).

Relationships between berry sensory attributes and berry compositional

variables with wine sensory attributes. PLS regression from the 2010 season

generated a prediction model where the combination of four berry sensory attributes

(pulp fresh fig flavor, pulp prune flavor, skin acidity and seed bitterness) were able to

predict one wine sensory attribute (wine savory spice flavor) with high Q2 (0. 5) and R2

(0.83) coefficients (Table 3). Pulp prune and fresh fig flavor plus seed bitterness were

negative predictors for the wine savory spice flavor model while skin acidity was a

positive predictor of wine savory spice flavor (Figure 1A).

The consecutive runs of PLS regression of berry and wine sensory attributes from

season 2011 determined that the combination of four berry sensory attributes (pulp

acidity, pulp detachment from the skin, seed astringency when crushed and seed

bitterness) were able to generate prediction models for three wine sensory attributes

(rim color, fresh dark berry flavor and savory spice flavor). Satisfactory Q2s’ (>0.40)

72

and R2s’ (>0.60) values were obtained for the individual prediction models of rim color,

fresh dark berry flavor and savory spice flavor (Table 3). The combination of pulp

acidity, seed astringency when crushed and seed bitterness negatively and pulp

detachment from skin positively predicted these three wine sensory attributes (Figure

1B).

In the 2010 season, a similar prediction model was identified when either berry

sensory attributes were used to predict wine sensory attributes or when berry sensory

plus compositional measures were included in the PLS regression (that is the Q2

coefficient remained the same) (Table 3). No berry compositional variables were able to

predict wine sensory attributes, when used as a combined predictor with berry sensory

attributes in season 2010.

PLS regression using the 2011 berry sensory attributes and compositional variables

determined that the same berry sensory attributes were able to generate a prediction

model for wine sensory attributes than when only berry sensory attributes were used.

Pulp acidity, pulp detachment from skin, seed astringency when crushed, seed bitterness

and one berry compositional variable (berry color) were part of the prediction model for

wine rim color and wine fresh dark berry flavor. However, the Q2 coefficients of the

models for wine rim color and wine fresh dark berry flavor decreased after the inclusion

of compositional measures in the PLS analysis. In contrast to the rim color and wine

fresh dark berry flavor prediction model, it was not possible to obtain a model for wine

savory spice flavor with the combination of berry sensory attributes and berry

compositional variables (Table 3). Pulp acidity, seed bitterness and seed astringency

when crushed had a negative relationship and pulp detachment from the skin and berry

color had positive relationships with rim color and fresh dark berry flavor (Figures 1C).

73

Seed bitterness was found to be negatively correlated with wine savory spice flavor

in both seasons (r = -0.57 p = 0.05 in 2010, r = -0.71, p = 0.01 in 2011). Wine savory

spice attribute was defined by the tasters as a pepper-like sensation. To our knowledge

no previous studies have reported this relationship. However this wine attribute is

associated with rotundone, a compound that is formed in the berry and accumulates

exclusively in the exocarp and its content in the wine could be up to 10 % of the content

in berries depending on the skin contact regime during fermentation (Caputi et al.

2011). Rotundone is usually found in high concentrations in Shiraz wines (Herderich et

al. 2012), but may vary in berries sampled across different sections of a vineyard

(Scarlett et al. 2014). Rotundone content in berries has also been shown to increase as

the distance of the berries to the vegetal parts is shorter (Zhang et al. 2013). Although

Scarlett et al. (2014) did not find a close relationship between berry rotundone content

and vine vigor they suggested that rotundone content could be altered due to the effect

of vineyard variability and temperature. Understanding pepper sensations in Australian

Shiraz wines is of importance, as anecdotally it is an attribute often associated with

premium Shiraz wines (Herderich et al. 2012) and has been demonstrated to impact

wine consumer preference (Lattey et al. 2010).

Evaluation of different seed characteristics is part of the berry sensory protocol

presented by Winter et al. (2004) that is used by Australian wine producers (Olarte

Mantilla et al. 2012). However, evaluation of seed bitterness is not included in the

protocol. Seed bitterness was shown to be negatively associated with both the fresh dark

berry sensation and the pepper-like sensation in wine, which indicates that seed

bitterness is an important attribute to include in berry sensory analysis score sheets.

Grape seeds have been shown to be highest in bitter intensity compared to all other

berry parts (Brossaud et al. 2001) but bitterness in seeds does not necessarily translate to

74

bitterness in wine; a relationship which may be confounded by oenological impacts. In

the current study seed bitterness showed no significant relationship with bitterness in

wine, albeit a negative relationship with wine dark berry and savory spice flavor.

Previous studies have shown that wine dark fruit perception can have either a positive

relationship (Joscelyne et al. 2007, Bindon et al. 2014) or a negative relationship

(Chapman et al. 2005, Lattey et al. 2010, Cadot et al. 2012) with bitterness perception in

wine (Bindon et al. 2014). Factors enhancing bitter sensation in wine includes: alcohol

content (Vidal et al. 2004, Oberholster et al. 2009), low molecular weight phenols

(Casassa and Harbertson 2014, Gonzalo-Diago et al. 2014) and extended maceration

(Yokotsuka et al. 2000). The relationships between seed bitterness, savory spice and

wine dark berry sensation found in this study should be used only as potential indicators

of dark berry flavor and savory spice in wine until further elucidation of the diverse

factors likely involved in those complex relationships can be determined.

In 2011 several relationships were revealed between pulp detachment from the skin

and rim color ( r= 0.76, p=0.00), body color ( r= 0.75, p=0.01), fresh red berry aroma

(r= -0.81, p=0.00), dried fruit aroma ( r= 0.66, p=0.02), alcohol aroma ( r= 0.64,

p=0.03), acidity ( r= 0.67, p=0.02), alcohol flavor ( r= 0.59, p=0.05), body ( r= 0.61,

p=0.03), tannin quantity ( r= 0.78, p=0.00), tannin quality ( r= 0.73, p=0.006) fresh dark

berry flavor ( r= 0.72, p=0.01), dried fruit flavor ( r= 0.78, p=0.00) and savory spice

flavor ( r= 0.58, p=0.05). It is possible that some of these relationships could be

coincidential, therefore further research needs to be conducted to confirm the causative

effect of pulp detachment for some of these sensory attributes. However, a recent study

showed that heat stress regimes may significantly affect, pulp detachment and berries

grown under the heat tents were softer and contained pulp which was easier to detach

from the skin (Bonada et al. 2013), while wines had less anthocyanins, total tannins and

75

total phenolic content (Bonada et al, unpublished data, 2014). This further demonstrated

a positive relationship between difficulty of pulp detachment from the skin and

compositional parameters related to wine quality including color, flavor and mouthfeel.

In contrast to our findings in Shiraz, three previous studies that evaluated Shiraz

(Winter et al. 2004) Grenache (Pozzo Di Borgo and Rousseau 2004) and Chardonnay

(Rousseau 2001) berries and the corresponding wine from different maturity levels

showed that higher levels of quality in Grenache and Chardonnay wines were achieved

when the pulp was easier to detach from the skin. Grenache wines were more full

bodied and had higher prune, confectionary and pepper aroma character scores, but less

red fruit characteristics (Pozzo Di Borgo and Rousseau 2004), whilst Chardonnay wines

were rated higher for white fruits and confit fruit aromas (Rousseau 2001). The

correlations found in Shiraz (Winter et al. 2004) highlighted that when the pulp is more

difficult to detach from the skin the wines will have a higher intensity of vegetal

aromas.

The finding of our and the above mentioned studies agree that ‘pulp detachment from

the skin’ is an important berry sensory attribute to include in BSA score sheets.

However, the direction of the relationship of this berry sensory attribute with wine

composition and sensory attributes may vary depending on methodological differences

for example, berry maturity at sample harvest or grape quality.


variables with wine compositional variables. Only in the 2011 season but not for the

2010 season, could pulp detachment from the skin be used to positively predict wine

76

total tannins (Table 3); a model that is also supported by Pearson’s correlation

coefficients (r= 0.82, p=0.00).

After the inclusion of berry compositional variables to the PLS regression for the

2011 season, a prediction model was generated where the combination of one berry

sensory attribute (pulp detachment from skin) and one berry compositional variable

(berry color), were able to predict one wine compositional variable (wine total tannins)

(Figure 2). Both skin detachment and berry color were found to be positive predictors of

wine total tannins (Figure 2). The model Q2 coefficient (0.68) obtained after the

inclusion of compositional variables was lower than the model Q2 (0.78) without

compositional variables.


variables with wine quality score. PLS regression using berry sensory attributes to

predict wine quality scores did not produce a prediction model for the 2010 season as

Q2 and R2 were below the threshold to consider accepting a model (Table 3).

However, PLS regression for the 2011 season generated a prediction model where

pulp detachment from skin was a positive predictor of wine quality scores (Table 3).

Pearson’s correlation also produced a good correlation for pulp detachment from the

skin and wine quality scores (r= 0.81, p=0.00). Previously, Shiraz berries with pulp that

was more difficult to detach from the skin were graded higher in the industry allocation

of low and high grade fruit (Jordans et al, unpublished data 2014).

Although the range of wine quality scores in each study was not large, it was greater

in 2011 (14.0 to 15.6) than in 2010 (14.6 to 15.6). The Q2 coefficients generated for the

model did not change after the inclusion of compositional variables. No berry

compositional variables were able to predict wine quality scores, when used as a

combined predictor with berry sensory attributes in either of the seasons.

77

Relationships of wine sensory attributes with wine compositional variables.

Wine quality scores were positively correlated in both seasons with wine rim color,

wine body color, wine body, wine tannin quantity, wine length and wine dried fruit

flavor (data not shown). Wine quality scores were also positively correlated to wine

polymeric pigments and wine tannins (Table 4) in agreement with findings of Ristic et

al (2010).

Various wine sensory attributes (body color, body and tannin quantity) were also

positively correlated with wine polymeric pigments (M3G eq/L) and total tannins (CAT

equ/L) in both seasons (Table 4). Cortell et al (2008) reported positive correlations in

Pinot Noir wines between the sensory attributes chemical aroma and earthy aroma and

wine pigmented polymers and total tannins.

Interestingly, wine total tannins had positive correlations with fresh dark berry aroma

and flavor and inverse correlations with fresh red berry aroma and flavor (Table 4).

More recently Casassa et al (2014) found positive correlations between small polymeric

pigments and red and dark wine aroma in treatments where moderate regulated deficit

irrigation was applied.

Model reliability: berry sensory attributes and compositional measures as

predictors? When the Q2 coefficients generated from either berry sensory attributes or

the combination of berry sensory attributes and compositional variables for the three

wine parameters (sensory attributes, quality scores and compositional variables) were

compared, the result was that in all the cases the Q2 coefficient did not change. Overall

the models generated were more reliable when they were generated solely from berry

sensory attributes. This is indicative that berry sensory attributes alone can reliably

predict wine sensory attributes and wine quality scores. The limitation of this study was

a low number of samples, however it is expected that model reliability will increase if a

78

larger number of samples are used and if the range of values for the investigated

parameters was wider.

Conclusion

The results of our study were able to show for the first time that across two seasons,

Shiraz grape berry seed bitterness was a negative indicator of savory spice in wine,

which is an important aroma known to impact wine consumer preference for Shiraz

wines. Pulp detachment from the skin showed important relationships with wine quality

scores, wine sensory attributes and wine compositional measures, although statistically

significant differences were only found in one season. Investigation of this attribute

over more seasons is required to increase confidence in the possible utilization of this

attribute as a wine quality predictor.

The implied importance of seed bitterness may help grapegrowers and winemakers to

identify grape parcels that could potentially produce pepper-like flavor in wines.

Furthermore pulp detachment from the skin can potentially assist with the identification

of fruit parcels that may generate wines with more color, dark grape flavor and better

tannin structure and possibly of higher quality.

The results of this study have shown that there are relationships between berry

sensory attributes and wine sensory attributes that are responsible for wine quality in

Shiraz wines. However more research needs to be conducted using a wider range of

grape quality to determine what berry sensory attributes are influencing wine quality.

Research using other grape varieties needs to be carried out to determine if the

relationships are translatable, or if some of these relationships are variety specific.

Confirmation of these relationships will not only help wine producers to determine

grape flavor potential in the vineyard and in turn achievable wine styles; but it could

79

also help producers and researchers to develop vineyard practices and means to

manipulate berry sensory characteristics to obtain particular wine styles.

80

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186 Figure 1. PLS correlations plots showing berry sensory attributes predicting wine 187

sensory attributes in 2010 (A), berry sensory attributes predicting wine sensory 188 attributes in 2011 (B) and both berry sensory attributes and compositional variables 189 predicting wine sensory attributes in 2011 (C). Vectors with closed markers are 190 berry sensory attributes and compositional variables and vectors with open markers 191 are wine sensory attributes. 192

Pulp fresh ripe fig flavor

Pulp prune flavor

Skin acidity

Seed bitterness

Savory spice flavor

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1

t2

Correlations with t on axes t1 and t2

Pulp acidity

Pulp detachment of skin

Seed astringency when crushed

Seed bitterness Rim color

Fresh dark berry flavor

Savory spice flavor

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1

t2

A

B

C

Pulp acidity


Seed astringency when crushed

Seed bitterness

Color /g berry mass

Rim color

Fresh dark berry flavor

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

-1.1 -0.85 -0.6 -0.35 -0.1 0.15 0.4 0.65 0.9

t2

t1

84

193 Figure 2. Correlations plot showing the combination of berry sensory attributes and 194

compositional variables that predict wine compositional variables for the season 195 2011. Vectors with closed markers are berry sensory attributes and compositional 196 variables and vectors with open markers are wine compositional variables. 197


Colour /g berry weight

Wine total tannins

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1

t2

t1

Correlations with t on axes t1 and t2

85

Visual, aroma, mouth feel, flavor and after taste sensory attributes of Shiraz, Table 1.198 wine, extreme end and mid-scale word anchors and tasting instructions. 199

Attribute Definition and standards

Instructions Scale anchors

Wine appearance Assess wine rim and color using the color scales provided

Rim color Wine rim color while glass is tilted compared to color scales made with colored paints. †

1 to 9 using different combinations of colored paints

Body color Wine body color while glass is tilted compared to color scales made with colored paints. †

1 to 9 using different combinations of colored paints

Transparency‡

Density of color pigments Transparent, intermediate, opaque

Wine aroma Take the lid from the glass, smell the wine and evaluate the aroma intensity of your sample for the following attributes.

Fresh red berry Fresh raspberry (3 ripe pieces mashed) and/or strawberry (1 ripe piece dice up)

Low, moderate, intense

Fresh dark berry Fresh blackberry (4 ripe pieces mashed), blueberry (6 pieces squashed) and/or plum (1/2 blood plum)


Floral Fresh violet flowers (10 fresh violets) Low, moderate, intense Confectionary§ Raspberry cordial ( Home Brand,

Woolworths, Bella Vista, NSW, Australia) Low, moderate, intense

Green¶ Green aroma unripe fruit (green plum) and/or stalks


Dry fruit Dry prunes, figs and/or raisins Low, moderate, intense Savory spice Cracked black pepper, dry thyme and/or

nutmeg Low, moderate, intense

Sweet spice Liquorice, aniseed or any other sweet spice


Alcohol

Perception of Alcohol aroma Low, moderate, intense

Wine mouth feel Take a sip of wine and evaluate the following attributes after swirling the wine in your mouth for 10 seconds

Acidity Acidic perception Low to very acidic Alcohol Perception of alcohol warmth Low, moderate, intense Body Mouth feel perception on palate Low to full Tannin quantity Drying sensation of tannins after

expectorating wine Low to high

Tannin quality

Grain size of tannins after expectorating wine

Talc, satin, velvet, felt

Wine aftertaste Length

Time that wine remains on palate after expectorating

Short (≤2 seconds), 3 seconds, 5 seconds, long (≥7seconds)

Wine flavor Take a sip of wine and evaluate the following attributes after swirling the wine in your mouth for 10 seconds

Fresh red berry Same definition and standard as aroma Low, moderate, intense Fresh dark berry Same definition and standard as aroma Low, moderate, intense Confectionary Raspberry cordial ( Home Brand,

Woolworths, Bella Vista, NSW) Low, moderate, intense

Dry fruit Dry prunes, figs and/or raisins Low, moderate, intense Green Green aroma unripe fruit (green plum)

and/or stalks Low, moderate, intense

Savory spice Cracked black pepper Low, moderate, intense Sweet spice Same definition and standard as aroma Low, moderate, intense

†Cadmium red deep hue paint (Liquitex®, Piscataway, NJ, USA) to Mix of 36% Cadmium (Liquitex®, Piscataway, NJ, USA) + 32 % 200 perylene violet (Winsor & Newton, London, England) + 32% dioxazine purple (Liquitex®, Piscataway, NJ, USA) 201 All standards were prepared for the 2010 and 2011 DA in black glasses with 30ml of Shiraz wine made from same vineyard in 2009 and 202 2010. ‡ Transparency was not evaluated in 2010. §Confectionary was defined as red and black berry jam for 2010 wines. ¶Green aroma was 203 not an attribute evaluated in 2010 wines. 204

86

Mean values of the sensory ratings for fresh Shiraz berries sensory attributes, Table 2.205 wine sensory attributes and wine quality scores from twelve samples from season 206 2010 and 2011. 207

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 P

2010

Berry sensory attributes

Pulp sweetness 8.3 9.7 9.8 9.8 10.1 10.2 9.6 10.0 10.3 9.5 9.3 9.3 0.09

Pulp fresh ripe fig flavor 4.8 4.5 4.5 4.6 4.6 4.7 4.8 6.2 5.1 3.7 4.8 4.6 0.10

Pulp prune flavor 3.3 3.7 4.7 3.9 3.0 4.1 4.2 4.8 3.9 3.6 3.8 3.0 0.05

Skin disintegration 6.6 7.3 7.5 6.5 6.9 6.4 5.3 6.7 6.0 7.5 7.0 5.6 0.01

Skin acidity 4.0 3.5 3.1 4.1 4.3 3.8 3.7 2.8 3.2 3.8 3.8 3.4 0.09

Skin astringency general 4.1 6.0 6.0 4.5 4.7 4.7 4.6 5.2 5.2 6.0 4.3 3.6 0.01

Skin tannic intensity 3.8 5.8 5.8 5.3 4.7 5.7 4.5 5.1 4.9 6.0 4.4 4.9 0.01

Skin grain size of tannins 3.1 5.3 5.0 4.4 4.2 4.8 4.0 4.4 4.7 5.3 3.7 4.6 0.07

Skin astringency resalivation 3.6 5.1 5.4 4.5 4.0 4.8 3.9 4.0 4.1 5.3 4.2 4.5 0.08

Seed bitterness 3.5 3.8 4.6 3.9 4.7 3.5 5.0 5.5 5.1 4.2 3.6 5.0 0.05

Wine sensory attributes

Rim color 7.1 6.3 6.6 9.6 9.3 9.0 7.6 8.2 8.9 8.1 8.8 8.9 <.00

Body color 8.0 6.9 7.0 10.7 10.6 10.3 8.5 9.3 9.8 9.6 9.8 10.0 <.00

Fresh red berry aroma 8.2 9.0 8.5 6.9 7.5 6.7 8.3 8.4 8.2 7.1 6.9 7.0 0.01

Fresh dark berry aroma 7.7 7.0 7.4 8.4 7.9 8.7 7.3 7.7 8.2 8.6 8.6 9.0 0.05

Sweet spice aroma 4.3 4.7 5.3 5.5 4.6 5.6 4.9 4.5 5.0 5.3 4.4 5.9 0.10

Alcohol flavor 7.6 7.1 7.1 7.3 7.8 7.9 5.9 7.3 7.1 7.4 7.8 7.8 0.03

Body 7.3 7.1 7.2 8.5 8.5 8.4 6.9 7.3 8.1 8.0 7.8 7.6 0.10

Tannin quantity 6.1 5.6 5.6 7.0 6.5 6.5 5.1 5.5 6.3 6.2 6.5 6.0 0.07

Tannin quality 7.5 6.7 6.5 7.7 7.9 7.5 5.2 6.6 7.6 7.7 7.1 7.1 0.00

Fresh Red Berry flavor 7.5 8.0 8.3 6.5 7.6 7.0 8.2 8.0 8.0 6.6 7.4 6.6 0.04

Savory spice flavor 5.6 5.1 5.0 5.4 5.6 5.2 4.5 4.3 4.8 5.7 5.5 5.5 0.10

Wine quality score 14.7 14.8 14.6 15.6 15.3 15.6 14.6 15.1 15.2 14.8 14.5 15.1 0.18 2011

Berry sensory attributes

Pulp acidity 7.4 7.2 7.0 5.6 5.6 6.1 5.9 6.0 4.9 5.7 5.8 6.6 0.00

Pulp detachment from skin 5.1 4.8 4.6 5.6 6.6 6.5 5.4 5.0 5.1 5.3 5.9 5.6 0.09

Skin disintegration 7.3 6.7 7.1 6.6 7.3 7.1 6.2 5.9 6.6 6.1 6.9 6.7 0.07

Seed color 6.9 7.0 6.8 6.5 6.7 6.3 6.8 6.7 7.4 7.6 7.2 7.5 0.03

Seed astringency when crushed 8.9 8.8 9.1 8.4 7.3 7.8 8.3 8.0 6.9 7.6 7.4 7.8 0.03

Seed bitterness 6.1 6.7 6.8 6.4 4.6 5.1 5.0 3.8 4.7 5.2 4.4 5.0 0.00

Wine sensory attributes

Rim color 4.3 5.3 5.0 7.4 8.3 8.3 6.3 6.2 7.0 8.1 8.3 8.6 <.00

Body color 5.0 6.3 6.8 9.4 10.0 10.0 8.2 7.3 7.9 9.4 9.9 10.5 <.00

Fresh red berry aroma 9.3 9.5 8.9 8.3 7.7 7.7 8.8 9.1 9.3 8.5 7.5 7.5 0.06

Fresh dark berry aroma 5.8 6.3 5.9 8.0 7.0 7.9 7.2 7.1 6.9 7.5 8.4 8.1 0.01

Savory spice aroma 4.9 5.0 5.0 5.7 5.4 5.5 6.4 6.2 5.5 6.0 7.1 7.0 0.04

Alcohol aroma 5.9 5.6 5.3 5.9 6.0 6.8 5.7 5.7 5.1 6.3 7.1 7.0 0.01

Acidity 5.6 7.2 6.0 7.2 7.5 7.7 6.6 6.5 7.0 7.0 6.8 7.2 0.04

Alcohol flavor 5.7 6.5 5.9 5.7 6.9 7.2 6.1 5.7 6.1 7.1 7.8 7.5 0.00

Body 4.5 5.6 4.9 6.8 6.1 7.3 6.8 5.4 6.1 7.0 6.7 7.4 <.00

Tannin quantity 4.5 5.2 5.3 6.6 6.9 7.3 5.4 5.0 4.9 6.6 6.9 7.1 <.00

Tannin quality 5.1 5.5 5.4 6.2 6.5 7.5 5.7 5.2 4.5 6.8 6.5 6.9 <.00

Length 7.1 8.0 7.7 8.7 8.8 9.0 7.8 6.9 7.0 9.3 8.6 9.9 <.00

Fresh dark berry flavor 5.6 5.8 6.0 7.3 7.7 7.7 7.5 6.8 7.2 7.1 8.7 7.8 0.00

Savory spice flavor 5.3 4.9 5.4 6.4 6.7 6.6 7.0 6.7 5.7 7.1 7.0 6.3 0.01

Wine quality score 14.5 14.1 14.0 15.1 15.1 15.6 15.1 14.3 14.5 14.9 14.8 15.5 0.00 Significant P-values at 10% level are highlighted in bold letters 208

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Wine sensory attributes and wine quality score from seasons 2010 and 2011 Table 3.209 predicted from berry compositional variables highlighting goodness of prediction. 210

Combination of berry attributes/variables involved in predicting models (independent variables)

Predicted wine attribute(s)/variable(s) after PLS regression (dependent variables)

Model Q2 Model R2

2010 Berry sensory attributes

Pulp fresh ripe fig flavor (-) Pulp prune flavor (-) Skin acidity (+) Seed bitterness (-)

Savory spice flavor 0.75 0.84

2010 Berry sensory attributes and berry compositional Pulp fresh ripe fig flavor (-) Pulp prune flavor (-) Skin acidity (+) Seed bitterness (-)

Savory spice flavor 0.75 0.84

2011 Berry sensory attributes Pulp acidity (-) Pulp detachment from skin (+) Seed astringency when crushed (-) Seed bitterness (-)

Rim color Fresh dark berry flavor Savory spice flavor

0.62 0.63 0.45

0.77 0.72 0.73

Pulp detachment from skin (+)

Total tannins 0.73 0.77


Wine quality score 0.58 0.66

2011 Berry sensory attributes and berry compositional Pulp acidity (-) Pulp detachment from skin (+) Seed astringency when crushed (-) Seed bitterness (-) Color mg/g berry mass (+)

Rim color Fresh dark berry flavor

0.61 0.59

0.77 0.72

Pulp detachment from skin (+) Color mg/g berry mass (+)

Total tannins 0.68 0.77


Wine quality score 0.58 0.66

Variables, and Q2 and R2 coefficients highlighted in italic letters correspond to the 2011 season, non-highlighted correspond to 211 2010 season. Positive (+) and negative (-) symbols indicate the direction of the relationships found for each berry sensory 212 attribute/compositional variable with each of the wine sensory attribute/compositional variable/wine quality score. 213

88

Correlations between wine sensory attributes and wine compositional variables Table 4.214 from twelve samples from season 2010 and 2011 215

r indicates Pearson’s coefficient correlation.216

2010 2011

Wine pigmented polymers Wine tannins Wine pigmented polymers Wine tannins r P r P r P r P

Wine quality score 0.65 0.02 0.77 0.00 0.79 0.00 0.82 0.00 Rim color 0.71 0.01 0.83 0.00

Body color 0.58 0.05 0.78 0.00 0.79 0.00 0.86 0.00

Fresh red berry aroma -0.67 0.02 -0.84 0.00

Fresh dark berry aroma 0.69 0.01 0.80 0.00

Dried fruit aroma 0.64 0.03 0.66 0.02

Body 0.82 0.00 0.91 <0.00 0.78 0.00 0.78 0.00

Tannin quantity 0.61 0.03 0.78 0.00 0.71 0.01 0.84 0.00

Tannin quality 0.87 0.00 0.77 0.00

Length 0.92 <0.00 0.70 0.01

Fresh red berry flavor -0.71 0.01 -0.63 0.03

Fresh dark berry flavor 0.67 0.02 0.75 0.01

Dried fruit flavor 0.83 0.00 0.73 0.01

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Supplemental Table 1. Berry and wine compositional measures of Shiraz from twelve samples 217 from season 2010 and 2011. 218

219 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12

2010

Berry compositional

TSS (Brix) 24.8 24.9 24.2 26.1 24.8 27 25.5 25.6 25.5 23.9 25.4 26.4

pH 3.9 3.9 4 3.9 3.8 4 3.9 4 3.9 3.9 3.9 4

TA (g/L) 4.8 4.5 4.4 4 4.5 4.4 4.7 4.2 4.1 5.3 5.2 4.5

Total phenolics (au units/g berry mass) 1.08 1.12 1.21 1.25 1.52 1.39 1.29 1.49 1.11 1.04 1.19 1.30

Color mg/g berry mass 0.95 0.92 1.15 1.30 1.37 1.39 1.14 1.34 0.93 0.84 1.07 1.16

Berry mass (g/berry) 1.2 1.00 1.00 0.9 0.8 0.9 0.9 0.5 1 0.9 0.8 1

Wine compositional

pH 3.6 3.4 3.4 3.5 3.5 3.4 3.4 3.5 3.5 3.4 3.5 3.6

TA (g/L) 6 7.48 7.25 6.44 6.57 8.12 7.4 6.77 7.22 7.42 6.92 7.34

Alcohol (%V/V) 14.2 14.4 14.4 14.6 14.5 14.7 13.2 13.6 14.1 14.1 14.3 14.4

Total anthocyanins (M3G eq/L) 268 270 277 360 355 340 291 318 379 338 300 273

Total pigmented polymers (M3G eq/L) 35 33 30 52 37 49 32 38 42 49 36 29

Total tannins (CAT equ/L) 643 637 578 784 786 787 534 668 734 793 648 716

2011

Berry compositional

TSS (Brix) 22.2 22.5 23.1 22.4 22.8 23.2 23.1 23.4 23 22.7 22.8 22.7

pH 3.5 3.5 3.5 3.7 3.6 3.5 3.8 3.5 3.6 3.4 3.5 3.7

TA (g/L) 6.4 6.4 6.3 5.8 5.4 6.8 5.8 5.5 5.6 6.1 6.1 5.9

Total phenolics (au units/g berry mass) 0.67 0.73 0.62 0.74 0.69 0.69 0.77 0.74 0.76 0.75 0.74 0.76

Color (mg/g berry mass) 0.68 0.72 0.61 0.77 0.76 0.8 0.75 0.76 0.78 0.76 0.76 0.71

Berry mass (g/berry) 1.4 1.4 1.5 1.6 1.5 1.6 1.2 1.1 1.2 1.3 1.3 1.4

Wine compositional

pH 3.3 3.2 3.4 3.4 3.4 3.4 3.4 3.5 3.4 3.4 3.4 3.4

TA (g/L) 7.02 7.61 7.04 7.73 7.61 8.04 7.35 7.94 7.69 8.59 7.12 7.65

Alcohol (%V/V) 12 12 12 12.4 12.8 12.9 12.1 12.1 11.8 13 13.4 13.4

Total anthocyanins (M3G eq/L) 219 189 206 200 207 214 212 214 214 212 246 228

Total pigmented polymers (M3G eq/L) 23.4 25.9 23.4 31.6 30.5 34.6 29.3 31.4 27.3 26.9 30.4 35.3

Total tannins (CAT equ/L) 394 456 411 549 564 601 481 495 453 461 523 582

220

90

NOTE: Statements of authorship appear on pages 91 - 93 in the print copy of the thesis held in the University of Adelaide Library.

Chapter 5. Submitted manuscript - Journal of Agricultural and Food

Chemistry

Berry measures and wine volatile compounds and quality in Shiraz (Vitis vinifera L)

are modulated by the use of rootstocks

Grape material utilized to compare compositional and sensory measures of fresh

and frozen berries in Chapter 3; plus to examine relationships between berry and wine

sensory attributes and compositional variables in Chapter 4, was sourced from a Shiraz

rootstock trial. Having selected a rootstock trial for those two experiments also provided us

with the opportunity to evaluate the sensory characteristics of both berries and wines

sourced from Shiraz vines grafted to rootstocks and vines grown on their own roots. This

study would assist in the advancement of current knowledge about the potential use of

rootstocks to modulate wine quality in the vineyard.

The following paper examines berry and wine sensory attributes and compositional

variables from Shiraz vines grafted to three rootstocks and vines grown on their own roots.

Wine data was evaluated and multivariate analysis was utilized to determine which sensory

attributes and compositional variables, including volatile flavour chemistry, had the highest

impact on wine quality rating.

94

Berry measures and wine volatile compounds and quality in Shiraz (Vitis vinifera

L) are modulated by the use of rootstocks

Sandra M. Olarte Mantilla1, Cassandra Collins1, Patrick G. Iland2 Catherine M. Kidman1,3, Renata Ristic1, Paul K. Boss4, Charlotte Jordans1 and Susan E. P. Bastian1

1School of Agriculture, Food, & Wine, University of Adelaide, Waite Research Institute, PMB1, Glen Osmond, South Australia 5064, Australia

2 Patrick Iland Wine Promotions Pty Ltd, PO Box 131, Campbelltown, South Australia 5074,

Australia

3Wynns Coonawarra Estate, Memorial Drive, Coonawarra, SA 5263, Australia

4CSIRO Agriculture Flagship, PMB2, Glen Osmond SA 5064, Australia

Corresponding author: Dr Sue Bastian, fax +61 8 83037116,

e-mail [email protected]

ABSTRACT: Sensory and compositional measures were conducted on berries and wines of Vitis vinifera L. cv Shiraz vines grown on own roots or grafted to three different rootstocks. The study was conducted in an experimental rootstock vineyard in the Barossa Valley, South Australia, during two growing seasons (2009/10-2010/11). Wines produced with fruit from vines grown on their own roots were characterized by red berry aroma, whereas wines produced using grapes grown on vines grafted to either 110 Richter or Schwarzmann rootstock had a darker rim and more intense body color, dark berry aroma, red berry flavor and, more intense and coarser tannins. The elements Mn, Mg and B were higher in juice and wines from rootstock treatments, whilst Na was higher in juice and wines produced from vines grown on their own roots. The majority of the acetate esters were higher in the wines made from vines on their own roots in both seasons. The highest wine quality scores were obtained for 110 Richter wines and the lowest for own roots wines in both seasons. This study demonstrated that the use of rootstocks can have a positive effect on wine composition, sensory properties and wine quality.

Keywords: Rootstocks, berry sensory assessment, fresh berries, wine, Shiraz, own

roots, Schwarzmann, 110 Richter, 1103 Paulsen, aroma, flavor.

INTRODUCTION

Rootstocks are an important management strategy to combat viticultural challenges

such as nematodes, phylloxera, salinity, drought and acidic soils.1 The use of rootstocks

has also been reported to influence scion vigor, fruit set and ripening rate.2 Despite the

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reported benefits of using rootstocks, only 15% of Australian vineyards are planted on

rootstocks.3 The low rootstock adoption rates observed in Australia may be partially due

to; reported adaptation of nematode biotypes to rootstocks resistance factors,

uncertainty of the resistance of rootstocks to the different Australian phylloxera

biotypes and incompatibilities between rootstock and scion.1

Previous rootstock research has been able to identify differences in grape and

wine compositional parameters such as pH, K and phenolic compounds between

different rootstocks and non-grafted vines.4-6 Grafted vines displayed increased vigor7, 8

which was associated with diminished wine quality compared to wines of non-grafted

vines.9 However, evaluation of five rootstocks and three scions under deficit irrigation

showed that some rootstocks can perform better than vines on own roots by producing

wines with more anthocyanin and less K concentration.10 Earlier research of wines from

two rootstocks grafted to different grape varieties (five white varieties and five red)

showed that rootstocks can affect the concentration of volatile compounds of wines and

thus the flavor profile of wines,11 but no sensory or compositional comparison was

made between wines from grafted and ungrafted vines. Differences in aroma potential

(as phenol-free glycosyl-glucose concentration) were found in wines of Cabernet

Sauvignon grafted to two different rootstocks and subjected to three irrigation

regimes.12

However, studies showing how compositional changes in grapes and wines from

different rootstock treatments and non-grafted vines influence sensory characteristics

are scarce. In an earlier study, the assessors found no significant differences in wine

quality scores from Chardonnay wines produced from different rootstock treatments and

ungrafted vines.13 However, in a later study, wine flavor and aroma intensity scores

from Cabernet Sauvignon wines produced from vines grafted on Teleki 5C were found

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to be higher than the vines grown on their own roots.14 Although these studies provided

valuable information, evaluated sensory characteristics were limited to quality score and

general flavor and aroma intensity attributes.

This study aimed to evaluate the chemical composition and sensory properties of

berries and wines produced from Shiraz vines grown on own roots or grafted on three

different rootstocks.

MATERIALS AND METHODS

Plant Material, Experimental Design and Winemaking. Berry samples were

sourced from a Shiraz (clone BVRC30) rootstock trial vineyard planted in 2001 at the

South Australian Research and Development Institute (SARDI) Station located at

Nuriootpa, Barossa Valley, South Australia. Vine spacing, vineyard soil type and

irrigation conditions were described previously in Olarte Mantilla et al.15

Three North American Vitis rootstocks were selected for the experiment due to the use

and importance of these within the Australian wine industry: 110 Richter (Vitis

berlandieri X Vitis rupestris), 1103 Paulsen (Vitis berlandieri X Vitis rupestris) and

Schwarzmann (Vitis riparia X Vitis rupestris). Shiraz rootstock treatment were grafted

on these three rootstocks while vines grown on own roots (Vitis vinifera) were used as a

control. The experiment used a split plot design, where each treatment and control

consisted of 10 rows (plot). In each plot, three replicate subplots of seven vines were

selected for monitoring and sample collection.

From each treatment replicate 100 berries were randomly collected on a weekly basis

after veraison until harvest (EL stages 35 to 38)16 to conduct maturity measurements

(TSS, TA, pH). Grapes for berry sensory assessment (BSA), compositional measures

and winemaking were harvested when they reached a maturity stage of ~25o Brix in

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2010 and ~23o Brix in 2011. Grapes in 2011 were harvested at a lower Brix level, as the

high amount of rain experienced during this season put the fruit at an increased risk of

being spoiled by diseases. Berries and bunches from the seven vines were harvested

separately, weighed and the mass calculated as yield (kg) per meter of cordon.

Subsamples of the pooled bunches were taken for sensory evaluation, winemaking and

chemical analysis in both seasons as described in Olarte Mantilla et al.15

Berry Chemistry.

i) Basic Measures, Ionic Composition and Total Anthocyanins. Samples of 100

berries were randomly taken from each of the treatment replicates. Each sample was

then crushed, centrifuged (1767 g for 5 min) and the supernatant retained to measure

TSS, pH and TA using the methodology by Iland et al.17 A 10 mL aliquot from the

supernatant of centrifuged juice was used to analyze the elemental concentration (Fe,

Mn, B, Cu, Mo, Co, Ni, Zn, Ca, Mg, Na, K, P, S, Al, Ti, Cr, Cd, Pb, Se) in each sample

using Inductively Coupled Plasma Atomic Emission Spectrometry (ICPAES).18 The

analysis was performed by Waite Analytical Services (The University of Adelaide,

Adelaide, Australia). Berry total anthocyanins measures were conducted on 50 berries,

following the methodology described in Iland et al.17

ii) Flavan-3-ol Monomers and Tannin Subunits. The concentrations of flavan-3-ol

monomers and tannin subunits in grape skins and seeds were determined by High

Performance Liquid Chromatography (HPLC), following the procedure of Kennedy and

Jones.19 Fifty berry samples for all treatment replicates were weighed, skins and seeds

separated from the pulp and their mass and the seed number recorded. The skins were

ground under liquid nitrogen with an electric grinder (IKA®A11 basic, IKA® Works

(Asia), Selangor, Malaysia). Quantification of flavan-3-ol monomers, terminal and

extension units was conducted as previously described in Ristic et al.20 The skin and

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seed samples were extracted in 70% aqueous acetone for 24 hours, followed by removal

of acetone under reduced pressure and freeze drying. Flavan-3-ol monomers were

separated and quantified using a method by Peng et al 21 and HPLC, Agilent 1100

(Waldbronn, Germany) with a polyestyterene divinylbenzene column (PLRP-S, 250 x

4.6 mm, 100 Å, 5μm, 250 x 4.6 mm; Polymer laboratories). Flow rate and mobile

phases used were the same as described by Ristic et al.20 Tannins were identified at 280

nm and quantified as catechin equivalents from a standard curve prepared with catechin

(Sigma-Aldrich, USA).

After the freeze-drying process skin and seed extracts were subjected to a

phloroglucinol degradation procedure to isolate tannin subunits as described in Kennedy

and Jones.19 Phloroglucinol extracts were analyzed using the same HPLC equipment

described above using a C18 column (RP-18 250 x 4.6 mm, 100 Å, 5μm, 250 x 4.6 mm;

Hibar®). Injection volume and mobile phases were the same as described in Kennedy

and Jones.19 Tannin subunit peaks were identified at 280nm and quantified using the

area of a known catechin concentration (Sigma-Aldrich, USA). Concentrations of

terminal and extension subunits were calculated from published molar extinction

coefficients.19

Wine Chemistry.

i) Basic Measures and Ionic Composition. Basic chemical measurements (SO2, pH,

TA, volatile acidity (VA), alcohol and residual sugar) were performed on the wines at

the time of sensory evaluation, following the methodologies described in Iland et al.17

Wine trace elements (Fe, Mn, B, Cu, Mo, Co, Ni, Zn, Ca, Mg, Na, K, P, S, Al, Ti, Cr,

Cd, Pb, Se) were analyzed by Waite Analytical Services (University of Adelaide,

Adelaide, Australia) using Inductively Coupled Plasma Atomic Emission Spectrometry

(ICPAES).

99

ii) Phenolic Compounds. Anthocyanins, tannins and pigmented polymers

concentrations of the wines from both seasons were determined using HPLC following

the methodology by Eglinton et al 22, as previously described in Olarte Mantilla et al.15

iii) Aroma Compounds. The wines were analyzed using a non-targeted solid phase

micro extraction (SPME) gas chromatography (GC) mass spectrometry (MS) method, to

determine and semi-quantify the aroma compounds present in the head space. Wine

samples from both seasons were analyzed in September 2011 using the same equipment

and methods as described in Keyzers and Boss.23 Samples were analyzed at two

different dilutions with sterile water; 1:100 or 1:2 to a final volume of 10mL. The

samples were spiked prior to analysis with the internal standard d-13 hexanol (1:100

dilution, 1.15μg; 1: 2 dilution 9.2 μg). Analysis of 2010 samples was conducted on wine

samples that had been kept frozen at -20oC for 12 months. Analysis of 2011 wine

samples was performed on unopened bottles kept at 15˚C at the same time as the

sensory evaluation was conducted. Confirmation or prediction of the identity of each

volatile compound was made in three ways: (1) comparison of linear retention indices

(LRI) calculated from a series of n-alkanes (C8-C26) run after the wine analysis and the

LRI of authentic standards from an in-house library of 371 compounds, (2) comparison

of linear retention indices (LRI) calculated from a series of n-alkanes (C8-C26) run after

the wine analysis and LRI reported in the literature, (3) comparison of mass spectra of

the volatile compound with mass spectra found in spectral libraries (US National

Institute of Standards and Technology-05a (NIST-05a) and the Wiley Registry 7th

Edition). The relative amount of all detected compounds was calculated using the total

peak area of the internal standard d13-hexenol.

Sensory Evaluation. Sensory evaluation of the berry and wine samples was performed

using Descriptive Analysis (DA). In both seasons berry and wine sensory evaluations

100

comprised training and evaluation sessions. Rating of 25 berry sensory attributes 24 (24

in season 2011) was performed by 14 assessors in the 2010 season and 11 assessors in

the 2011 season following the procedure described in Olarte Mantilla et al.15

Rating of the 25 wine sensory attributes was performed by 15 assessors in season

2010 and 12 assessors in season 2011 following the procedure described in Olarte et

al.15

Wine Quality Evaluation: Expert Panel. Quality evaluation of all wines from both

seasons was conducted using the Australian wine show 20 point scoring system25, 26 as

described previously in Olarte Mantilla et al.15 Eight wine experts participated in the

wine quality evaluation in 2010 and 12 in 2011.

Statistical Analysis. One way ANOVA was conducted for each of the berry and

wine compositional and chemical variables to identify significant differences between

the rootstock treatments and the own roots control for each season. A two way mixed

model ANOVA with assessors as random effect for each season’s data of BSA and

wine sensory scores was executed to identify sensory attributes significantly different

between the rootstocks and the own roots control berries and between the wine samples

for each season. Principal component analysis (PCA) was performed on the sensory

attributes and compositional variables that were found to be significantly different in the

ANOVA analysis for each of the seasons between the four treatments. Means for the

PCA were normalized by dividing them by the maximum value observed for any of the

treatments including the control for each variable. Consequently all values were

between 0 and 1. For the PCA sensory attributes and wine quality scores were loaded as

main variables and compositional variables as supplementary variables. ANOVA and

PCA analyses were performed using the statistical package XLSTAT version 4.02 2012

(Addinsoft SARL, Paris, France).

101

RESULTS AND DISCUSSION

Vine Growth Measures and Rootstock Performance.

Yield, pruning mass and the ratio data presented in this study (Table 1) are part of the

rootstock trial measurements collected by Kidman et al.27 Yield was not significantly

different between the four treatments in both seasons, whilst the pruning mass (kg/m

cordon) was significantly higher in the own roots treatment compared to the other three

rootstocks (Table 1). In 2011 only the yield to pruning mass ratio (FM:PM) was

significantly higher in 110 Richter than the other three treatments. Both 110 Richter and

Schwarzmann were in the optimal range of FM:PM (5-10) in the 2010 season according

to Kliewer and Dokozolian,28 while all other treatments could be considered as under-

cropped under the same guideline. This difference in pruning mass and FM: PM ratio

were more likely to be due to the inherent vigor characteristics of each of the rootstocks.

110 Richter has been described as being able to induce moderate vigor to the scion,

Schwarzmann low to moderate vigor and 1103 Paulsen moderate to high vigor.29

However, the vigor of grafted vines or those grown on own roots could be influenced by

interactions with the variety/clone, soil, climate and viticulture practices.30

Berry Composition, Berry Sensory Attributes and Rootstock Performance.

Berry Composition. TA, pH and °Brix measures in grape juice were not

significantly different between the four treatments in both seasons (Table 2). No

rootstock effect was observed by Harbertson and Keller 10 for °Brix and TA of grape

juice from Chardonnay, Merlot and Shiraz scions. Berry color (concentration of total

anthocyanins per gram berry mass) was lower in own roots than 110 Richter and 1103

Paulsen in the season 2011.

The concentration of total tannins per gram berry mass was higher in 2010 (1.48 –

2.00 mg/g berry mass) than in the 2011 season (0.85 – 1.28 mg/g berry mass; Table 2).

102

In both seasons, the highest concentration was observed for fruit grown on 1103

Paulsen rootstock and the lowest for 110 Richter. Skin tannins were not affected,

consequently any difference in berry tannins were mainly driven by seed tannins. It

appears that seeds of the grapes from 110 Richter-grafted vines synthesized and broke

down less tannins because the seed number and seed mass per berry were similar to

other treatments. This result agrees with recent findings in Shiraz grapes where total

tannin concentration of 1103 Paulsen was higher compared to five other rootstocks and

vines grown on their own roots.10

B and Cu concentrations in grape juice were significantly higher in fruit from 1103

Paulsen-grafted vines in both seasons. Higher concentrations of Na in juice were

associated with the own roots treatment in both years (Figure 1a and 1b), which has

been reported before,6, 31 although the Na concentration in grapes from Shiraz vines

grafted to 1103 Paulsen was significantly higher than the other two rootstocks in 2010

(Table 2). Higher concentrations of other cations such as Ca, Mg, Mn and B were

particularly associated (Figure 1a and 1b) with the treatments in both seasons,

particularly with the 110 Richter and 1103 Paulsen rootstock treatments in both seasons.

An earlier study did not show significant differences in Mg and Ca juice concentration

between fruit grown on own roots vines and other rootstocks.32

BSA. Four berry sensory attributes were significantly different between the four

treatments in the 2010 season and seven in the 2011 season (Table 3). Only seed

bitterness was significantly different in both seasons; in 2010 the highest value was

perceived in berries from 1103 Paulsen and in 2011 in berries from the own roots

treatment. Seed bitterness was correlated with seed tannin in the 2010 (Figure 1a) and,

in season 2011, seeds from the own roots treatment were not only perceived as more

bitter but also more astringent when crushed (Figure 1b). Pulp detachment from the skin

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was higher in the 110 Richter rootstock fruit than other treatments in season 2011

(Table 3). This attribute was found to be positively associated to berry color

(concentration of total anthocyanins per gram berry mass), FM:PM ratio and juice Mn,

Ca and Mg and negatively associated to total tannins, seed tannins, and juice

concentration of Cu and Na (Figure 1b).

Wine Composition and Rootstock Performance. pH, TA, Alcohol, VA, SO2. No

significant differences for wine pH, TA, VA and SO2 were found between the four

treatments in both seasons (Table 4). Wine produced from the 1103 Paulsen-grafted

fruit had the lowest alcohol content in both seasons (Table 4).

Anthocyanins, Tannins and Polymeric Pigments. Total anthocyanin concentration

was significantly higher in 110 Richter-grafted (351 mg/L) wines than those produced

from vines on own roots (271 mg/L) in the 2010 season. In the 2011 season, total

pigmented polymer and tannin concentrations were significantly higher in wines from

the 110 Richter rootstock treatment (32 and 571 mg/L) than those from own rooted

vines (24 and 470 mg/L for pigmented polymer and tannins respectively) (Table 4).

ICPAES. Significant differences in wine elemental concentration were observed

between the four treatments in both seasons. Wines made from the own roots treatment

were significantly higher in Na in both seasons (Table 4), which is in agreement with

findings in a previous study in Shiraz in the Nuriootpa region.6 Mg and Mn was lower

in own roots wines in both seasons (Table 4).

Wine Aroma Composition and Rootstock Performance. From the non-targeted

head-space volatile compound analysis, 152 compounds were identified in the

chromatograms (data not shown). Of these 152 compounds only 65 were found to be

significantly different between treatments, 42 compounds in 2010 and 41 compounds in

2011 (Table 5). The 65 significantly different compounds were distributed in twelve

104

chemical groups: acetate esters (ten), acids (one), higher alcohols (eighteen), aldehydes

(three), alkane hydrocarbons (one), esters (seven), ethyl esters (fourteen), fatty acids

(two), ketones (two), methyl esters (two), monoterpenes (two), nor-isoprenoids (three)

(Table 5).

Season 2010 had more significantly different compounds belonging to the alcohols,

fatty acids, methyl esters and monoterpenes chemical groups whereas significantly

different compounds in the 2011 season were mainly acetate esters, ethyl esters and nor-

isoprenoids (Table 5). The differences in the treatment effects on wine volatile

compound composition could be affected by climatological differences in the two

growing seasons, as season 2010 was warmer and dryer compared to the 2011 season.27

Most of the compounds belonging to the acetate esters group were significantly

different in both seasons (Table 5). The own roots treatment was characterized by

containing higher concentrations of acetate esters (pentyl acetate, hexyl acetate, (E)- and

(Z)-3-hexenyl acetate) compared to the rootstock treatments (Table 5). The acetate

esters found to be significantly different due to the rootstock treatment are generally

associated with apple, fruity, green and banana aromas (Table 6).

Ethyl ester concentration was significantly higher in Schwarzmann and the own roots

treatment compared to the other two treatments in the 2010 season (Table 5). However,

in the following season Schwarzmann was significantly higher in most of the ethyl ester

compounds than the own roots treatment and 1103 Paulsen (Table 5). Recent research

evaluating concentrations of acetate esters and ethyl esters found that sensory

perception of “red” and “dark” berry aromas in red wine may be due to higher

concentrations of some ethyl esters.41 A relationship between pruning mass, acetate

esters and ethyl esters was identified in this study in both seasons (Figure 2a and 2b).

This relationship may suggest that concentrations of acetate esters in the wine can be

105

associated with fruit from vines of higher vigor such as the own roots treatment and

higher concentration of ethyl esters in wine was associated with fruit harvested from

lower vigor vines grown on rootstocks. In the case of ethyl esters, a recent study

evaluating wine aroma compounds from high and low vigor own roots Pinot Noir vines,

found that ethyl esters were higher in wines from low vigor than from high vigor

vines.42 Although Song et al 42 concluded that concentrations of acetate esters were

lower in low vigor vines only one compound (hexyl acetate) out of the five acetate

esters identified was significantly different.

The compounds from the nor-isoprenoid group were significantly different only in

the 2011 season. (E)-Damascenone, one of the identified nor-isoprenoids, was found in

higher concentrations in wines from Schwarzmann-grafted fruit than those from 110

Richter and the own roots control (Table 5). (E)-Damascenone is associated with rose,

candy and citrus aromas in Shiraz wines, (Table 5) and has been shown to decrease

when sun is excluded from bunches during development.43 In our study, own roots vines

had significantly higher pruning mass in both seasons than the rootstock treatments

(Figure 2a and 2b) and were observed to have bunches with lower sun exposure.

Interestingly, increasing the levels of (E)-Damascenone increases the perception of

fruity characters imparted on wines by ethyl esters. 44

The majority of higher alcohol compounds that were significantly different in both

seasons were higher in the wines made from fruit grown on vines grafted to either 1103

Paulsen or Schwarzmann. However, more often than not, in one season the compounds

were higher in wines from one rootstock treatment and in the second season, in wines

from another rootstock treatment (Table 5).

Wine Sensory Attributes and Rootstock Performance. In both seasons ten wine

sensory attributes were significantly different amongst the wines produced from the

106

rootstock treatments (rim color, body color, fresh red berry aroma, fresh dark berry

aroma, alcohol aroma, alcohol flavor, body, tannin quantity, tannin quality and fresh red

berry flavor; Table 7). Wines made from berries of the own roots treatment had lighter

rim and body color, and a higher rating of fresh red berry aroma in both seasons,

whereas wines made from berries from the 110 Richter and Schwarzmann rootstock

treatments were rated higher for fresh dark berry aroma, alcohol aroma, alcohol flavor,

body, tannin quantity, tannin quality in both seasons.

Fresh dark berry aroma was positively associated with ethyl-2-methylbutanoate

concentration in the 2011 season (Figure 2b), which is in agreement with Pineau et al.41

Other ethyl esters such as ethyl furoate, ethyl phenylacetate and ethyl 4-

hydroxybutanoate were also positively associated with dark berry aroma in both seasons

(Figure 2a and 2b).

Wines from the 1103 Paulsen treatment had lower alcohol aroma and flavor sensory

ratings and high red berry flavor in both seasons (Table 7). In the 2011season, wines

made from the own roots control and 110 Richter-grafted vines had the lowest

concentration of (E)-damascenone and the 110 Richter wines had the highest

concentration of ethyl hexanoate which was positively associated with the sensory

perception of red berry flavor (Figure 2b).

Schwarzmann wines had a good association between the sensory ratings for alcohol

and measurements for alcohol in both seasons (Figure 2a and 2b). Fresh red berry aroma

showed a positive association between these acetate esters and the significantly higher

fresh red berry aroma sensory ratings in the wines in both seasons (Figure 2a and 2b).

Wine Quality Evaluation, Rootstock Performance and Recommendations for

the Wine Industry. In both seasons the quality scores were higher for 110 Richter

wines (15.5 and 15.2) and lower for the own roots treatment (14.6 and 14.2) in 2010 and

107

2011 respectively, whilst the 1103 Paulsen wines performed better in 2010 and the

Schwarzmann wines in the 2011 season (Figure 3). The observation that the 110 Richter

rootstock can increase the perceived quality of wines was noted in a previous study

using Cabernet Sauvignon, although only in one out of four evaluated seasons.45

However, in another study no significant differences were found in quality scores

between wines made from grafted and own rooted Chardonnay vines.13, 45

Various sensory and compositional outcomes appear to be responsible for the higher

quality scores given to 110 Richter wines and the lower scores for own roots wines.

Detaching the pulp from the skin was significantly more difficult in fruit from the

110 Richter-grafted vines than those on own roots and 1103 Paulsen-grafted vines in the

2011 season (Table 2). This attribute was found to be strongly positively correlated

(r=0.813, p <0.001) to wine quality score in the same season in agreement with a

relationship analysis conducted previously.15 Pulp detachment from the skin was also

correlated with berry color (concentration of total anthocyanins per gram berry mass),

the ratio FM: PM ratio and juice Mn, Ca and Mg but negatively correlated to total berry

tannins, seed tannins, and juice concentration of Cu and Na (Figure 1b).

Pulp sweetness in 2010 and pulp acidity in the 2011 sensory ratings were

significantly different between 110 Richter and own roots berries. This translated to

berries with sweeter pulp and berries with less acidic pulp being associated with a

higher wine quality score (Figure 1a and 1b). This aligns with a Merlot berry sensory

quality profile where berry pulp with higher ratings for sweetness and lower ratings for

acidity which were allocated to the higher wine quality potential.46

Higher concentrations of Mg, Mn and B in the juice and wine from the 110 Richter

treatment were associated to higher quality scores (Figures 1a - 2b), whereas higher

concentrations of Na were correlated to the lower quality scores of the wines from the

108

own roots controls (Figure 1a - 2b). A recent study showed that high concentrations of

Na in juice from Chardonnay resulted in high concentrations of Na in the corresponding

wines with higher ratings of salty taste and viscosity (soapiness) but lower fruit intensity

and likely the resultant quality.47

Concentrations of acetate esters, ethyl esters, higher alcohols and nor-isoprenoids

were identified as the groups of volatile compounds differentiating wines from the own

roots treatment with those wines from the rootstocks. Moreover interactions between

the volatile compounds, alcohol concentration and phenolic concentration, found in this

study and previously reported in the literature, 48 were important factors to understand

the sensory differences between the treatments. It appears that the higher quality score

given to wines from the 110 Richter treatment in both seasons was the result of the

combination of wine sensory ratings and compositional data of color, tannin and dark

berry aroma and body rather than higher concentration of any particular aroma

compounds compared to the other treatments.

In this study we were able to identify sensory and compositional differences in the

berries and wine from Shiraz vines grown on different rootstocks and the own roots

control. These differences could have been driven by the effects of the rootstocks on the

vigor (using pruning mass as an indicator) of the vines. This may explain the high wine

quality scores given to the 110 Richter treatment wines in both seasons and low scores

given to the wines made from fruit grown on own roots. It is interesting that the

rootstock 110 Richter performed well in our study and also in another study with

Cabernet Sauvignon.45 This would indicate that it is worthwhile including 110 Richter

in future rootstock studies. The use of a multidisciplinary approach in this study allowed

the identification of relationships between sensory attributes such as “pulp detachment

from the skin” that were associated with different berry compositional variables and the

109

wine quality score of the 110 Richter treatment. However future studies should include

more sites and scions to test the robustness of this knowledge across different varieties,

rootstock types, climate, soil characteristics and management practices.

ACKNOWLEDGMENTS

The authors wish to thank all the assessors that participated in the grape and wine

sensory evaluation. We thank Sue Maffei and Emily Nicholson for their technical

contribution in the GC-MS sample analyses. This research was funded by the Australian

Grape and Wine Authority. The University of Adelaide is a member of the Wine

Innovation Cluster (www.wineinnovationcluster.com) Adelaide, South Australia. The

berry and wine sensory tastings were performed with the approval of The University of

Adelaide Human Research Ethics Committee.

ABREVIATIONS USED

BSA, berry sensory assessment; TSS, total soluble solids; TA, titratable acidity;

ICPAES, inductively coupled plasma atomic emission spectrometry; HPLC, high

performance liquid chromatography; SO2, sulphur dioxide; VA, volatile acidity; SPME,

solid phase micro extraction; GC, gas chromatography; MS, mass spectrometry; LRI,

linear retention index; DA, descriptive analysis; PCA, principal component analysis;

FM:PM, yield to pruning mass ratio.

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116

Table 1. Vine growth measures for Shiraz vines grafted on three different rootstocks and an own roots control for seasons 2010 and 2011, Barossa Valley, South Australia

Own roots

110 Richter

1103 Paulsen Schwarzmann P

2010 Yield (kg/m cordon) 3.6 3.2 2.3 3.6 ns Pruning mass ( kg/m cordon) 1.0 a 0.5 b 0.7 b 0.6 b 0.030 Yield: Pruning mass 3.9 6.2 4.0 6.1 ns Yield Tonnes/Ha 11.9 a 9.6ab 6.7 b 10.8 ab ns

2011 Yield (kg/m cordon) 3.5 3.3 3.3 3.3 ns Pruning mass ( kg/m cordon) 2.2 a 1.1 b 1.4 b 1.2 b 0.018 Yield: Pruning mass ( kg/m cordon) 1.7 c 3.3 a 2.6 b 2.6 b 0.005 Yield Tonnes/Ha 11.6 9.8 8.2 9.9 ns

Vales are means of 3 field replicates. Analysis of variance to compare data: different letters within a row indicate significant differences for Fisher’s LSD at p <0.05, n=3.

117

Table 2. Physical and chemical measures for Shiraz berries and juice from vines grafted on three different rootstocks and an own roots control for seasons 2010 and 2011, Barossa Valley, South Australia.

2010 2011

Own roots

110 Richter

1103 Paulsen

Schwarzmann P Own

roots 110 Richter

1103 Paulsen

Schwarzmann P

Berry Berry mass (g) 1.06 0.86 0.79 0.90 ns 1.44 ab 1.59 a 1.17 c 1.34 bc 0.0004 Berry color (mg/g berry mass) 1.01 1.35 1.14 1.02 ns 0.67 b 0.78 a 0.76 a 0.75 ab 0.013 Seed number per berry 2.55 2.53 2.24 2.57 ns 2.04 1.85 2.03 1.87 0.033 Seed mass per berry (g) 0.07 0.07 0.06 0.07 ns 0.06 a 0.06 a 0.06 a 0.05 b 0.003 Total berry tannins (mg/g berry mass) ‡ 1.63 b 1.48 b 2.00 a 1.67 b 0.002 1.20 a 0.85 b 1.28 a 1.16 a 0.002 Skin tannins (mg/g berry mass) ‡ 0.76 0.70 0.95 0.77 ns 0.23 0.26 0.35 0.30 ns Seed tannins (mg/g berry mass) ‡ 0.87 b 0.78 b 1.05 a 0.90 b 0.001 0.97 a 0.59 b 0.93 a 0.86 a 0.001

Juice TSS (°Brix) 24.6 26.0 25.5 25.3 ns 22.60 22.80 23.17 22.7 ns pH 3.93 3.9 3.97 3.93 ns 3.48 3.61 3.62 3.52 ns TA (g/L) 4.56 4.31 4.32 5.03 ns 6.38 5.99 5.65 6.03 ns Fe (mg/L) 2.00 3.78 3.18 3.82 ns 2.05 1.62 1.63 1.55 ns Mn (mg/L) 0.49 c 0.81 ab 1.03 a 0.66 bc 0.001 0.31 b 0.43 a 0.40 ab 0.33 b 0.006 B (mg/L) 11.0 b 11.7 b 14.7 a 9.4 c <0.000

6.55 c 7.41 b 8.16 a 6.92 bc 0.0004

Cu (mg/L) 1.95 b 1.75 b 2.60 a 2.04 b 0.001 0.79 b 0.60 c 0.95 a 0.62 c <0.000 Zn (mg/L) 0.73 ab 0.56 b 0.88 a 0.83 ab 0.043 0.26 0.16 0.18 0.18 ns

Ca (mg/L) 73.3 b 98.8 a 111.8 a 97.3 ab 0.007 70.2 b 97.7 a 66.5 b 67.5 b 0.0004 Mg (mg/L) 83.2 c 123.1 b 142.4 a 91.6 c <0.000

87.2 b 113.1 a 112.0 a 110.3 a <0.000

Na (mg/L) 32.2 a 14.4 b 34.2 a 12.1 b 0.0003 26.1 a 7.0 c 8.8 c 14.7 b <0.000 K (mg/L) 2366 2466 2766 2700 ns 2006 2063 2100 2133 ns

P (mg/L) 158 bc 220 a 193 ab 155 c 0.001 155 145 140 133 ns S (mg/L) 105 111 110 118 ns 92.9 ab 80.4 c 97.2 a 83.57 bc 0.004 Al (mg/L) 2.24 4.51 4.01 4.93 ns 0.99 1.20 1.05 1.58 ns

Vales are means of 3 field replicates. Different letters within a row indicate significant differences for Tukey’s HD at p <0.05. Expressed as mg catechin equivalents/g berry mass.

118

Table 3. Ratings of significantly different sensory attributes of Shiraz berries from vines grafted on

three different rootstock treatments and an own roots control for seasons 2010 and 2011, Barossa Valley, South Australia.

Own roots

110 Richter

1103 Paulsen

Schwarzmann P

2010 Pulp sweetness 9.2 b 10.0 a 10.0 a 9.4 ab 0.081 Skin acidity 3.5 ab 4.0 a 3.2 b 3.6 ab 0.079 Seed crushability 10.1 ab 9.4 b 10.4 a 10.6 a 0.011 Seed bitterness 4.0 b 4.1 b 5.2 a 4.3 ab 0.044

2011 Pulp acidity 7.2 a 5.7 b 5.6 b 6.0 b 0.002 Pulp detachment from skin 4.8 b 6.2 a 5.2 b 5.6 ab 0.029 Skin disintegration 7.0 a 7.0 a 6.2 b 6.6 ab 0.057 Seed color 6.9 ab 6.5 b 6.9 ab 7.4 a 0.012 Seed astringency when crushed 9.0 a 7.8 b 7.8 b 7.6 b 0.013 Seed bitterness 6.5 a 5.4 b 4.5 b 4.9 b 0.005 Seed ease of tongue resalivation 7.8 a 7.2 ab 7.1 b 7.1 b 0.068

Values are means of n-3 replicates. Different letters within a row indicate significant differences for Fisher’s LSD at p <0.1.

119

Table 4. Compositional measures for Shiraz wines from vines grafted on three different rootstocks and an own roots

control for seasons 2010 and 2011, Barossa Valley, South Australia. 2010 2011 Own

roots 110 Richter

1103 Paulsen Schwarzmann P Own

roots 110 Richter


pH 3.44 3.48 3.43 3.52 ns 3.3 3.4 3.43 3.4 ns

Titratable acidity (g/L) 6.91 7.04 7.13 7.23 ns 7.22 7.79 7.66 7.79 ns

Alcohol wine (%v/v) 14.3 a 14.6 a 13.6 b 14.3 ab 0.008 12.0 c 12.7 b 12.0 c 13.3 a 0.0001

Volatile acidity (g/L) 0.41 0.24 0.36 0.28 ns 0.52 0.60 0.59 0.57 ns

SO2 total (mg/L) 62 52 57 61 ns 38 29 32 37 ns

Total anthocyanins (mg/L)† 271 b 351 a 329 ab 303 ab 0.042 205 207 213 229 ns

Total pigmented polymers (mg/L)† 33 46 37 38 ns 24 b 32 a 29 ab 31 ab 0.028

Total tannins (mg/L)‡ 619 785 645 719 ns 420 b 571 a 476 ab 522 a 0.007

Fe (mg/L) 1.18 ab 1.44 ab 1.12 b 1.49 a 0.019 1.39 ab 1.12 bc 1.53 a 0.97 c 0.001

Mn (mg/L) 0.64 c 1.06 b 1.3 a 0.8 c <0.0001 0.67 c 0.84 b 0.99 a 0.71 c <0.0001

B (mg/L) 11.0 bc 12.0 b 15.7 a 9.5 c <0.0001 8.5 b 8.9 b 11.8 a 6.9 c <0.0001

Cu (mg/L) 0.25 b 0.12 b 0.56 a 0.14 b 0.002 0.25 0.1 0.16 0.08 ns

Zn (mg/L) 0.40 0.40 0.57 0.36 ns 0.33 0.28 0.35 0.34 ns

Ca (mg/L) 74 62 69 63 ns 87 a 72 c 79 b 68 c <0.0001

Mg (mg/L) 100 d 152 a 139 b 114 c <0.0001 103 d 125 b 137 a 109 c <0.0001

Na (mg/L) 32.7 a 16.5 b 19.2 b 17 b <0.0001 33.8 a 11.8 b 14.1 b 11.8 b <0.0001

K (mg/L) 1246 1123 1036 1230 ns 1103 a 973 ab 910 b 983 ab 0.012

P (mg/L) 204 b 250 a 223 ab 163 c 0.001 263 a 233 ab 230 ab 206 b 0.005

S (mg/L) 153 145 156 148 ns 133 a 117 b 137 a 117 b 0.001 Values are means of 3 wine replicates per treatment. Different letters within a row indicate significant differences for Tukey’s HS at p <0.05, n=3. †Expressed as mg malvidin-3-glucoside/L. ‡ Expressed as mg catechin equivalents/L.

120

Table 5. Mean relative concentration values of aroma compounds found significantly different on Shiraz wines from vines grafted on three different rootstocks an own roots control for seasons 2010 and/or 2011, Barossa Valley, South Australia.

2010 2011

Compound PCA compound code

Own roots

110 Richter

1103 Paulsen

Schwarzmann P Own roots

110 Richter

1103 Paulsen

Schwarzmann P

Acetate esters

Propyl acetate AE1 100 b 103 b 147 a 75 b 0.002 88a 71ab 76a 50b 0.003 Butyl acetate AE2 9.7 a 8.9 ab 6.1 c 7.3 bc 0.002 6.4a 4.1b 4.1b 3.2b 0.01 Isoamyl acetate AE3 1841188 1183532 1085005 1464183 ns 899492a 635629ab 363645b 446872b 0.007 Pentyl acetate AE4 5.7a 4.1b 2.6c 3.5bc 0.0001 2a 1.1b 1.2b 0.6b 0.001 Hexyl acetate AE5 1391a 875b 561c 920b < 0.0001 347a 208b 162b 200b 0.001 (E)-3-Hexenyl acetate AE6 56a 34b 16c 28b < 0.0001 12a 6.9b 5.2b 6.5b 0.001 (Z)-3-Hexenyl acetate AE7 224 a 120 bc 79 c 126 b < 0.0001 98a 48b 36b 58b 0.0002 Heptyl acetate AE8 57a 21b 11b 25b 0.002 130b 128bc 123c 143a < 0.0001 Isoamyl lactate AE9 260 215 289 262 ns 304b 362a 356ab 393a 0.006 2-Phenylethyl acetate AE10 2672a 2162ab 955c 2058b < 0.0001 443a 365ab 188b 331ab 0.011

Acids Octanoic Acid A1 2370 1892 1650 2058 ns 1771ab 2116a 1409b 1894ab 0.042

Alcohol 1-Propanol AC1 150 142 240 143 ns 144ab 108b 367a 97b 0.02

1-Butanol AC2 55 ab 52ab 41b 68a 0.036 47 40 36 48 ns 1-Pentanol AC3 10.4b 10.9ab 12.5a 11.3ab 0.031 29 8.4 11.8 9.7 ns 4-Methylpentanol AC4 51ab 55ab 40b 67a 0.006 43c 54ab 47bc 61a 0.001 2-Heptanol AC5 5.8c 7b 8.7a 6.7bc < 0.0001 5.9 4.8 7.2 9.1 ns 3-Methyl-1-pentanol AC6 115b 134ab 70c 165a 0.0001 81b 107b 77c 138a < 0.0001 1-Hexanol AC7 1457ab 1387b 1633a 1506ab 0.038 1378b 1364b 1298c 1523a < 0.0001 (E)-3-Hexen-1-ol AC8 32b 31b 37a 30b 0.0002 26 24 24 26 ns 3-Ethoxy-1-propanol AC9 31b 36b 61a 32b 0.0020 26 34 40 27 ns (Z)-3-hexenol AC10 331 302 371 326 ns 443b 365c 361c 569a < 0.0001 (E)-2-Hexen-1-ol AC11 15 18 33 23 ns 27ab 20ab 30a 17b 0.029 1-Octen-3-ol AC12 39 44 48 48 0.041 34 33 32 31 ns 1-Heptanol AC13 389 292 332 297 ns 248b 305a 180c 315a 0.0001 6-Methyl-5-hepten-2-ol AC14 9.6 8.2 15.9 10.6 ns 4.0b 10a 4.1b 3.8b 0.003 2-(Methylthio)-ethanol AC15 4.6ab 4.4ab 5.9a 3.8b 0.015 14 11 12 12 ns 3-(Methylthio)-propanol AC16 35ab 29b 15c 43a 0.0003 19b 24b 19b 42a 0.001

121

Table 5. Continued

2010 2011

Compound PCA compound code

Own roots

110 Richter

1103 Paulsen Schwarzmann P Own

roots 110 Richter


Alcohols (higher alcohols) Benzyl alcohol AC17 25b 27b 42a 28b 0.001 21 24 21 25 ns 2-Phenylethyl alcohol AC18 7329ab 7775ab 6600b 8273a 0.028 4879b 5853ab 4517b 7998a 0.004

Aldehydes (R)-2-Octenal AD1 6.6b 8.9a 9.2a 9.3a 0.015 3.7 3.9 4.2 4 ns Furfural AD2 13 15 10 16 ns 9.2b 15.3ab 11.1b 21.7a 0.005 Safranal AD3 1.6b 2.7a 2.8a 2.6a 0.006 1.1 1.3 1.5 1.2 ns

Alkane hydrocarbons Decane AH1 42a 17b 33ab 20ab 0.038 47 44 41 51 ns

Esters Isoamyl propanoate E1 72ab 78ab 54b 86a 0.02 69 53 55 89 ns Propyl hexanoate E2 19.5ab 17.3b 36.4a 19.1b 0.02 21 19 17 15 ns Isopentyl hexanoate E3 68 50 63 51 ns 52bc 65ab 39c 70a 0.0002 Propyl octanoate E4 12.3ab 9.2b 17.1a 7.2b 0.006 10 9.1 7.9 8.4 ns Isobutyl octanoate E5 6.4a 4.3ab 5.1ab 4.1b 0.041 6.4 6.6 4.4 6.4 ns Ethyl 3-methylbutyl succinate E6 384 364 323 399 ns 95b 125ab 81b 155 a 0.016 Ethyl hexyl succinate E7 8.7 7 9.7 8.1 ns 2.3ab 2.5a 1.7b 2.9a 0.005

Ethyl esters Ethyl 2-methylbutanoate EE1 31 33 26 38 ns 37c 48ab 42bc 56a 0.003 Ethyl isovalerate EE2 60 63 51 70 ns 72c 95ab 83bc 102a 0.0005 Ethyl hexanoate EE3 92158 72184 83883 67190 ns 119782ab 145377a 104998b 129676ab 0.008 Ethyl hex-5-enoate EE4 23.7a 17.2b 24.5a 19.9ab 0.012 25b 21b 20b 35a 0.0004 Ethyl heptanoate EE5 74 66 125 92 ns 91a 109a 59b 111a 0.0001 Ethyl octanoate EE6 57426a 41921b 47088ab 38238b 0.002 3773ab 45253a 29684b 38602a 0.004 Ethyl nonanoate EE7 39a 22b 29b 24b 0.001 57 46 41 47 ns Ethyl 3-(methylthio)propionate EE8 14.7 14.4 17.6 8.8 ns 3b 3.3b 2.9b 5.2a 0.002 Ethyl furoate EE9 34b 34b 19c 50a < 0.0001 31b 34ab 34ab 48a 0.031 Ethyl decanoate EE10 8551a 8029ab 4859b 7906ab 0.031 7438 8219 5338 7837 ns Ethyl phenylacetate EE11 25.4b 28.9ab 22.1b 36.4a 0.014 16b 23ab 14b 32a 0.002 Ethyl 9-decenoate EE12 243 173 203 165 ns 198a 201a 121b 200a 0.022 Ethyl 4-hydroxybutanoate EE13 25.3ab 21.6ab 12.2b 27.3a 0.028 11 25 13 32 0.048 Ethyl 2-hydroxy-3-phenylpropanoate EE14 4.4 5.1 3.7 6.3 ns 4.9b 7.0ab 4.5b 10a 0.012

122

Table 5. Continued

2010 2011 Compound PCA

compound code

Own roots 2010

110 Richter 2010

1103 Paulsen

2010

Schwarzmann 2010

P Own roots 2011

110 Richter 2011

1103 Paulsen

2011

Schwarzmann 2011

P

Fatty acids Isovaleric acid F1 91ab 90ab 79b 104a 0.014 130 130 146 181 ns Nonanoic acid F2 2.6a 2.6a 0.7b 1.9ab 0.027 2 1.8 1.8 2.7 ns

Ketones 3-Hydroxy-2-butanone K1 25ab 14b 30a 13b 0.01 14 8.2 15.3 10 ns 4-Hydroxy-3-methylacetophenone K2 6.5 7.4 7.6 8.7 ns 3.6 3.1 7 3.8 0.048

Methyl esters Methyl hexanoate ME1 31b 32ab 42a 30b 0.021 39 43 35 37 ns

Methyl decanoate ME2 21a 14ab 15ab 12b 0.026 89 73 63 66 ns Monoterpenes

Citronellol M1 20b 24b 31a 26ab 0.005 16 17 19 19 ns trans-Geraniol M2 20a 14b 22a 17ab 0.038 17 21 16 22 ns

Norisoprenoids Unknown norisoprenoid N1 13 17 14 18 ns 7.0b 8.7b 10ab 13a 0.004

(Z)-Damascenone N2 11.1 8.9 12.1 11.1 ns 2.9b 3.4b 4.3ab 6.1a 0.021 (E)-Damascenone N3 221 171 224 210 ns 83b 99b 118ab 148a 0.015

Values represent the peak area of each compound in the samples, quantified with a selected ion, relative to the internal standard (d13-hexanol). Analysis of variance to compare data: different letters within a row indicate significant differences for Tukey’s HD at p <0.05 within a year, n=3. Mean values are relative amounts expressed in µg/L.

123

Table 6. Aroma compounds found significantly different in Shiraz wines from vines grafted on three different rootstocks and an own roots control in seasons 2010 and/or 2011, Barossa Valley, South Australia.

Linear retention indexa Compound name

Method identificationb

Quantifier ion Odor description

Acetate esters

975 Propyl acetate A 61 Fruity‡

1079 Butyl acetate A 56 Fruity33

1124 Isoamyl acetate A 70 Banana34

1167 Pentyl acetate A 61 Fruity‡

1263 Hexyl acetate A 84 Red berry35

1298 (E)-3-Hexenyl acetate C 67 Sharp fruity, green, banana, pear‡

1303 (Z)-3-Hexenyl acetate A 67 Fresh, green, sweet, fruity, banana, apple, grassy‡

1362 Heptyl acetate A 70 Fruity, apple36

1561 Isoamyl lactate B3 45 Banana, fruity with a ripe estry nuance‡

1807 2-Phenylethyl acetate A 104 Flowery, rose37 jammy, plum, floral 35

Acids

2074 Octanoic Acid A 60 Butter, almond35

Alcohols (higher alcohols)

1048 1-Propanol A 59 Alcohol, ripe fruit 33

1159 1-Butanol A 56 Medicinal33

1242 1-Pentanol A 70 Pungent, fermented, bready, yeasty‡

1303 4-Methylpentanol A 56 Fruity, berry35

1309 2-Heptanol A 83 Savory, coconut35

1316 3-Methyl-1-pentanol A 56 Fusel, cognac, cocoa, green, fruity‡

1346 1-Hexanol A 69 Floral, green, spice35

1359 (E)-3-Hexen-1-ol A 67 green‡

1366 3-Ethoxy-1-propanol A 59 NA

1374 (Z)-3-Hexenol A 67 Grass, solvent35

1395 (E)-2-Hexen-1-ol A 57 Mushroom, grass35

1440 1-Octen-3-ol A 57 Earthy, green, oily, vegetative and fungal‡

1441 1-Heptanol A 70 Musty, pungent, leafy green, fruity‡

1452 6-Methyl-5-hepten-2-ol A 95 Green, coriander‡

1523 2-(Methylthio)-ethanol C 61 Sulfurous, meaty‡

1710 3-(Methylthio)- propanol A 106 Raw potato, garlic38

1874 Benzyl alcohol A 79 Fruity, floral34

1908 2-Phenylethyl alcohol A 91 Floral, Rose35

Aldehydes

1407 (R)-2-Octenal A 45 Fresh cucumber, banana waxy green leaf‡

1458 Furfural A 96 Almond,39 earthy, wood35

1635 Safranal C 107 Woody, spicy, phenolic, camphoreous, medicinal, herbal‡

Alkane hydrocarbons

996 Decane A 71 NA

Esters

1185 Isoamyl propanoate A 57 Estry, fruity, apple, banana, fresh green, tropical‡

1302 Propyl hexanoate A 117 NA

1449 Isopentyl hexanoate A 99 Fruity, pineapple, pungent, sour, cheesy‡

1508 Propyl octanoate A 145 Coconut, gin‡

1542 Isobutyl octanoate C 127 Fruity, green, oily, floral‡

1891 Ethyl 3-methylbutyl succinate C 101 NA

2040 Ethyl hexyl succinate C 101 NA

Ethyl esters

1055 Ethyl 2-methylbutanoate A 102 Strawberry, berry,40 candy, apple35

1073 Ethyl isovalerate A 88 Red fruits,38 violet35

1223 Ethyl hexanoate A 99 Green apple, fruity, strawberry34, 38, 40

1269 Ethyl hex-5-enoate C 68 Fruity, pineapple‡

1318 Ethyl heptanoate A 88 Pineapple, banana, berry, cognac, green, seedy‡

1416 Ethyl octanoate A 101 Melon, wood35

124

Table 6. Continued

Linear retention indexa Compound name

Method identificationb

Quantifier ion Odor description

1525 Ethyl nonanoate A 88 Waxy, cognac, apple, banana, tropical‡

1556 Ethyl 3-(methylthio)propionate A 74 Baked apple, floral35

1614 Ethyl furoate A 95 Fruity, floral‡

1630 Ethyl decanoate A 88 Smoky, cheese35

1679 Ethyl phenylacetate B3 88 Fruity, fatty‡

1777 Ethyl 9-decenoate A 91 Floral, honey, rosy, balsamic, dark chocolate, anise seed‡

1798 Ethyl 4-hydroxybutanoate C 87 Caramel‡

2249 Ethyl 2-hydroxy-3-phenylpropanoate C 91 NA

Fatty acids

1689 Isovaleric acid A 60 Cheese, musty38

2169 Nonanoic acid A 60 Waxy, dirty, cheesy, cultured dairy nuance‡

Ketones

1281 3-Hydroxy-2-butanone A 45 Buttery, cream33

2189 4-Hydroxy-3-methylacetophenone C 135 NA

Methyl esters

1178 Methyl hexanoate A 74 Blueberry, fruity, floral35

1583 Methyl decanoate A 74 Fruity, floral‡

Monoterpenes

1755 Citronellol A 81 Floral, rosy, sweet, citrus‡

1838 trans-Geraniol A 69 Floral, sweet, rosy, fruity and citronella-like, citrus‡

Norisoprenoids

1705 Unknown norisoprenoid D 192 NA

1750 (Z)-Damascenone C 121 NA

1810 (E)-Damascenone A 121 Rose, candy, citrus35 a Linear retention index (LRI) calculated from retention relative to the retention of a series of n-alkanes (C8-C26). b A, identity confirmed by matching mass spectra and LRI with that of authentic standards; B, identity confirmed by matching mass spectra and LRI with that of LRI reported in the literature; C, tentative identification based on comparison with mass spectral libraries; D, was not possible to determine identity. Super indices in odor descriptors are the corresponding literature reference number. ‡ Aroma compounds odor descriptors from the good scents company http://www.thegoodscentscompany.com. NA Not available

125

Table 7. Intensity ratings of sensory attributes found significantly different in Shiraz wines from vines grafted on three different rootstock treatments and an own roots control for seasons 2010 and 2011, Barossa Valley, South Australia.

Own roots

110 Richter

1103 Paulsen

Schwarzmann P

2010 Rim color 6.7 c 9.3a 8.2 b 8.6 b <.0001 Body color 7.3 c 10.6 a 9.2 b 9.8 b <.0001 Fresh red berry aroma 8.6 a 7.1 b 8.3 a 7.0 b 0.004 Fresh dark berry aroma 7.3 c 8.4 ab 7.7 bc 8.7 a 0.020 Alcohol aroma 6.0 ab 6.6 ab 5.9 b 6.7 a 0.069 Alcohol flavor 7.3 ab 7.6 a 6.8 b 7.7 a 0.063 Body 7.2 b 8.5 a 7.4 b 7.8 ab 0.068 Tannin quantity 5.7 b 6.7 a 5.6 b 6.2 ab 0.095 Tannin quality 6.9 ab 7.7 a 6.5 b 7.3 ab 0.090 Fresh red berry flavor 7.9 ab 7.1 bc 8.1 a 6.9 c 0.046

2011 Rim color 4.9 c 7.0 b 7.4 b 8.3 a <.0001 Body color 6.0 c 8.6 b 9.0 b 9.9 a <.0001 Transparency 5.4 c 7.0 b 8.5 ab 9.4 a <.0001 Fresh red berry aroma 9.3 a 8.2 b 8.8 ab 7.8 b 0.053 Fresh dark berry aroma 6.0 b 7.0 a 7.7 a 8.0 a 0.013 Savory spice aroma 4.9 c 6.0 ab 5.6bc 6.7 a 0.008 Alcohol aroma 5.6 b 5.6 b 6.1 ab 6.8 a 0.007 Alcohol flavor 6.0 b 6.3 b 6.3 b 7.5 a 0.001 Acidity 6.3 b 7.2 a 6.9 ab 7.0 a 0.057 Body 5.0 c 6.8 a 6.0 b 7.0 a <.0001 Tannin quantity 5.0 c 5.8 b 6.1 ab 6.9 a 0.000 Tannin quality 5.3 b 5.9 b 5.9 b 6.7 a 0.005 Length 7.6 b 8.2 b 7.8 b 9.2 a 0.001 Fresh red berry flavor 5.8 b 7.2 a 7.5 a 7.8 a 0.004 Savory spice flavor 5.2 b 6.7 a 6.3 a 6.8 a 0.011

Values are means of 3 wine replicates;Different letters within a row indicate significant differences for Fisher’s LSD at p <0.1.

126

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Figure 3. Mean quality ratings for Shiraz wines from vines grafted on three different rootstocks and an own roots control for seasons 2010 and 2011, Barossa Valley, South Australia. Column values within each year followed by the same letter are not different at p < 0.100. Error bars indicate ± SE (n=3).

13.013.213.413.613.814.014.214.414.614.815.015.215.415.615.816.0

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b

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ab b

c

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129

NOTE: Statements of authorship appear on pages 130-131 in the print copy of the thesis held in the University of Adelaide Library.

Chapter 6. Discussion and future direction

6.1 Background of the study

Monitoring of grape condition and maturity is usually conducted several times before

harvest as a routine task by grapegrowers and winemakers. Depending on the size of the

vineyard and winery the level of the evaluated variables can vary; it can range from just

tasting berries in the vineyard, or conducting measurements of juice total soluble solids

(TSS), pH and titratable acidity (TA) if a laboratory is available. Large wineries may

include measurements of berry colour, aroma compounds and tannins as part of the

evaluation. However, independent of the size of the winery the common denominator of

these evaluations is the tasting of the berries. Tasting of berries before harvest has been

done routinely for centuries, but not until a decade ago was a structured methodology

designed for grapegrowers and winemakers as a common procedure (Rousseau and Delteil,

2000). However the question arising after tasting the fruit is always: “How do the

characteristics tasted in the berries impact on the resultant wine composition, sensory

properties and quality?”

To answer that question the main aim of this study was to determine if there were

relationships between BSA and wine quality. However, before continuing any further with

this study we needed to know if wine producers were using BSA. It was also important to

hear what improvements the grape and wine producers believed could be introduced to the

methodology in order to assist with the evaluation of fruit before harvest and if the main

aim of our study aligned with their suggestions. Following their validation of the first aim

of our study, the question was raised as to whether sensory differences could be found by

assessors when tasting fresh berries as opposed to tasting the berries after being stored for

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three months at -20°C. This strategy was formulated to facilitate the evaluation of a large

number of berry samples in a period outside vintage. A rootstock trial was chosen as a

suitable field trial to perform these evaluations. As such, an additional research question

was formulated: Can grape and wine sensory and compositional differences be found

between vines grafted to different rootstocks and vines grown on their own roots?

6.2 Use and suggested improvements of BSA by grapegrowers and winemakers

An online based survey was designed to determine if Australian grapegrowers and

winemakers were using the BSA methodology that was introduced and disseminated

through workshops and a book (Winter et al., 2004). From the 2250 Australian and New

Zealand wineries contacted by emails (listed in the 2008 wine industry directory data base),

the 25-question survey (Chapter 2, Appendix 1b) was answered by 344 winemakers from

~14% of the contacted wineries. A definition for BSA was provided in the questionnaire to

ensure that winemakers were familiar with the procedure and to capture as many answers as

possible (Question 9, Chapter 2, Appendix 1b). A separate eight question survey (Chapter

2, Appendix 1c) for Australian grapegrowers was also designed and the survey link sent via

the different regional grapegrowers associations as there was no available database for

Australian grapegrowers. Unfortunately, only 70 replies from South Australia and one from

Victoria were received. Despite the low number of respondents from the grapegrowers

surveyed, most winemakers (93%) and grapegrowers (97%) that answered said that they

used BSA (Chapter 2).

Although most winemakers indicated that they used BSA, the methodologies varied

from simply tasting grape sugar and acid through to evaluations such as phenolic ripeness

assessment by grape skin texture and seed/skin ripeness by colour assessment (Figure 4a,

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Chapter 2). Interestingly, the BSA methodology presented by Winter et al (2004) was the

one that was the least used by the winemakers (Figure 4a, Chapter 2). The outcome of the

survey demonstrated that more work needs to be carried out to promote the use and

understanding of BSA as a structured methodology among grapegrowers and winemakers.

Various ways of improvement of BSA were suggested by grapegrowers and winemakers.

They highlighted understanding the link between BSA and wine quality and more training

and frequency of BSA use (Figure 4c, Chapter 2). As such, they validated the aim of this

research.

6.3 Comparison of sensory attributes of fresh and frozen wine grape berries using

BSA

One of the limitations of using BSA is that it has to be performed on fresh berries, which is

not always possible, particularly when a study involves data collection from different grape

varieties, treatments and seasons to determine these relationships. Having a large number of

samples for tasting would generate fatigue in assessors and pressure for researchers. This

situation could be ameliorated if the BSA is performed on frozen berries in a less busy

period rather than on fresh berries. In our study, and as reported previously (Morris et al.,

1983, Threlfall et al., 2006, Spayd et al., 1987, García et al., 2011) freezing had an effect on

pH (higher than fresh berries) and TA (lower than fresh berries) for most of the harvest

time points (Table 4, Chapter 3). The changes in pH and TA were somewhat an indication

of possible sensory changes in berries after frozen storage. After conducting BSA on fresh

and frozen Shiraz berries it was found that five sensory attributes were consistently

different at the three times of harvest (Table 6, Chapter 3). However, these findings may be

134

specific to Shiraz berries only. Further studies are required to determine the relevance of

these findings to other varieties.

Shiraz grape berries stored frozen for 3 months at -20o C were characterised by

having a more sweet pulp and pulp with higher ripe fig flavour. The skin of the frozen

berries was less bitter and the rating for colour extraction was lower than in fresh berries.

Seeds from the frozen berries were found to be more astringent after being crushed. The

importance of these findings was assessed by determining if any of these attributes, or any

others that did not change on freezing were related to wine attributes. Pulp sweetness was

the only attribute out of the five berry sensory attributes that changed after freezing which

had an effect on wine quality. High pulp sweetness ratings were associated with higher

wine quality in 110 Richter (Chapter 5).

As reported in chapter 3, pulp detachment from the skin was a berry sensory

attribute that showed important relationships with wine sensory attributes and wine quality

scores in the 2011 season. Sensory ratings for pulp detachment from the skin were not

different at any of the harvest time points between fresh and frozen berries. This suggests

that frozen berries could be used to assess pulp detachment from the skin and further that

the data could be used to predict wine quality. This would require more detailed evaluation

of multiple Shiraz samples across a number of seasons. Further studies are required to

determine the relevance of these findings to other varieties as well.

Although our study determined that when berries are stored at -20oC five berry

sensory attributes differed from those from fresh berries at three times of harvest other,

storage conditions need to be explored to determine an optimal storage condition that does

not affect berry sensory attributes. Snap freezing followed by storage at -24°C treatment for

135

up 6 months with raspberries was found to be a treatment that retained sensory

characteristics similar to that of fresh fruit for late season raspberry cultivars (González et

al., 2002). A recent study compared sensory attributes of fresh Shiraz berries with berries

kept under three frozen storage treatments ( -20°C, -80°C and liquid nitrogen snap freezing

followed by -80°C) for eight weeks. In this study it was found that ten sensory attributes

differed between the treatments (Jiang et al, unpublished data 2014). However the ratings

of the berries that were snap frozen were closer to those of the fresh berries, suggesting that

snap freezing of berries is an alternative that should be explored and optimized to conduct

BSA on frozen berries (Jiang et al, unpublished data 2014).

6.4 Relationships between Grape and Wine Sensory Attributes and Compositional

Measures of cv. Shiraz

To determine relationships between berry sensory attributes and wine quality, 12 Shiraz

grape and the corresponding wine samples were subjected to BSA, wine descriptive

sensory analysis and wine quality evaluation.

An inverse relationship between seed bitterness and wine savoury spice flavour was

identified in both seasons. Pearson correlation (Table 4, Chapter 4) and PLS regression

(Table 3, Chapter 4) identified pulp detachment from the skin as an attribute that was

correlated to wine quality scores, wine sensory attributes and some compositional variables

in the 2011 season. Confirmation of this relationship in the future may be conducted by

having different levels of canopy density that will alter bunch shading (Ristic et al., 2007,

Joscelyne et al., 2007). Seed bitterness could then be evaluated in the different treatments

and in turn intensity of savoury spice and rotundone concentration in the wines.

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Shiraz berries in which it was more difficult to detach the skin from the pulp

produced wines with more intense sensory attributes and higher quality scores, in

agreement with other studies in Shiraz where pulp detachment from the skin was evaluated

(Bonada et al., 2013, Jordans et al., 2013) The correlation between “pulp detachment from

the skin” and wine sensory attributes was documented previously, in studies of Chardonnay

(Rousseau, 2001), Grenache (Pozzo Di Borgo and Rousseau, 2004) and Shiraz (Winter et

al., 2004). In Chardonnay and Grenache it was found that quality was correlated to pulp

that was easier to detach from the skin (Pozzo Di Borgo and Rousseau, 2004, Rousseau,

2001). In Shiraz, pulp that was more difficult to detach from the skin correlated to wines

with high vegetal aromas (Winter et al., 2004).

The varied relationships of pulp detachment from the skin with wine attributes

found among the different studies could be due to the methodological approach used to

evaluate the relationships between berries and wine. All studies that found that the more

difficult it was to detach the pulp from the skin were correlated to desired wine sensory

attributes, assessed fruit and the resultant wines that were at only one level of ripeness.

Whereas the studies that found that the easier it was to detach the pulp from the skin was

correlated to desired wine sensory attributes were conducted evaluating berries from

different maturity levels and the corresponding wines.

Differences in pulp detachment from the skin in Shiraz berries from different

quality levels may be due to berry shriveling in Shiraz, which is correlated with cell death

(Tilbrook and Tyerman, 2008). Jordans et al (2013) found that berry cell death in Shiraz

berries defined as high quality grade by industry coincided with pulp that was more

difficult to detach from the skin. However that relationship was not found in another study

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in Shiraz where the effect of heat was evaluated on berry shriveling and berry sensory

attributes (Bonada et al., 2013).

Seed tannins concentration had an inverse correlation with pulp detachment from

the skin, whilst pulp juiciness had a positive correlation to berry tannins. To the best of our

knowledge these correlations have not been identified before.

In this study a relationship between wine quality scores and wine tannins and wine

pigmented polymers concentration was identified in agreement with previous research

(Ristic et al., 2010). Consistent relationships across the studied seasons were identified

between wine compositional variables and wine sensory attributes. Wine tannins

concentration had a positive correlation with various wine sensory attributes (Table 4,

Chapter 4) (rim colour, body colour, fresh dark berry aroma, dark fruit aroma, tannin

quantity, tannin quality, length, fresh dark berry aroma and fresh dark berry flavour) in both

seasons which agrees with previous relationships identified by Smith et al (2007). However

we observed a positive relationship between wine tannin concentration and dark berry

aroma and a negative relationship between wine tannins and red berry aroma, which to the

best of our knowledge has not been identified before.

Our study highlighted strong relationships of “pulp detachment from the skin” with

berry and wine compositional variables, wine sensory attributes and wine quality scores.

However, knowledge of the mechanisms influencing “pulp detachment from the skin” is

still unknown therefore future work studying this topic is needed. Future studies could

investigate structural configuration and enzymatic processes occurring in the berry at

different stages of berry maturity and particularly when shriveling takes place.

Relationships between berry sensory attributes and compositional variables with

wine quality, wine sensory attributes and wine compositional variables in Vitis vinifera L.

138

Shiraz were highlighted in this study. Future research in other red and white cultivars

would provide a better understanding of the relationships of seed bitterness and pulp

detachment from the skin with the different berry and wine sensory and compositional

variables. More research can help to determine if these relationships are specific to Vitis

vinifera L. Shiraz or if relationships in other cultivars can be identified. Confirmation and

identification of new berry sensory attributes and wine quality relationships can help wine

producers and researchers to evaluate and implement vineyard practices to modulate berry

sensory attributes to obtain a targeted wine profile. Future research including very

distinctive fruit quality levels from different grape growing regions may provide

information about relationships of other berry sensory attributes with wine quality.

However all relationships found as a result of scientific research needs to be tested by the

grapegrowers to determine the key berry sensory attributes to be monitored for their own

vineyards and plots.

This study was able to identify relationships between berry and wine sensory

attributes and compositional variables using two statistical methodologies - Pearson’s

correlation and Partial least squares regression. However, a possible limiting factor in this

study was the reduced number of samples. Reliability and confidence of modeling as a

prediction tool increases with the number of samples, so future studies should include

larger number of samples. Eight would be the minimum number of field treatments with its

respective replicates required to conduct PLS analysis (Anne Hasted, personal

communication). Another factor that could have reduced reliability of the model was the

pooling of the field replicates for all berry evaluation and for winemaking. By pooling the

field replicates variability of the samples was reduced and as we didn’t have a large number

of samples reliability of the model could have been compromised.

139

6.5 Shiraz wine quality as affected by rootstocks: berry and wine sensory attributes

and compositional variables.

The effect of rootstocks on berry compositional differences and sensory properties were

identified. The investigated rootstocks were; 110 Richter, 1103 Paulsen and Schwarzmann

while an own roots treatment was used as a control treatment. Mineral composition in both

juice and wines was affected; grape juice from the own roots treatment had lower

concentrations of Mg, Mn and B and high concentrations of Na in both seasons (Table 2,

Chapter 5). Cu concentration in juice was higher in 1103 Paulsen in both seasons. Similar

composition was reflected in wines; Mn concentration in wine was lower in Schwarzmann

and in the own roots treatment and higher in 1103 Paulsen. The legal limits of Mn and Cu

in wine for export to China are 2 mg/L and 1mg/L, respectively (National Health and

Family Planning Commision of the People's Republic of China, 2011). As yet a general

recommended daily intake for Mn has not been established by the World Health

Organization as data showing Mn toxicity is not reliable (Nikfardjam et al., 2012). This is

noteworthy as China is an important export market for Australia and other wine producing

countries (Australian Grape and Wine Authority, 2014). As mentioned before, wine made

from own roots vines had lower concentrations of Mn but wines from 1103 Paulsen had the

highest concentration from all the rootstocks evaluated in our study (1.3 mg/L in 2010 and

0.99 mg/L in 2011). Wine making practices have been shown to affect content of Mn in

wines (Soto Vázquez et al., 2011) but also different grape cultivars can provide higher

content of Mn to wines (Nikfardjam et al., 2012) however no effect has been shown nor

evaluated of Mn on wine sensory attributes. In order to avoid potential high levels of Mn

where soil can be a source of this element, strategies such as use of rootstocks that extract

less Mn should be considered.

140

Sodium concentration was higher in own roots wines than the rootstock treatment

wines as reported previously (Walker et al., 2010). Wines produced from grapes grown

under saline conditions have been described as being salty, soapy and with less fruit

expression (De Loryn et al., 2014). In our study, higher concentrations of Na in wines from

own roots vines did not result in assessors describing the wines as salty or soapy but they

had lower intensity ratings for dark berry aroma and body. It has been suggested that under

conditions of high levels of salinity in soil and/or water, vineyards should be established

using vines grafted to rootstocks tolerant to salinity (Dry, 2007).

Berry phenolic composition: 1103 Paulsen had the highest berry tannin

concentration in both seasons, which was driven by seed tannins concentration in season

2010 and to some extent by skin tannins in 2011 (Table 2, Chapter 5). However higher

berry tannin concentration did not translate to higher concentration of tannins in the wines.

A recent study showed that wines produced from berries with high anthocyanin

concentration were high in anthocyanins and tannin concentration, irrespective of whether

the berry tannin concentration was low or high (Kilmister et al., 2014). This agrees with the

result of our study where 110 Richter wines had high anthocyanins and tannins

concentrations even when it had the lowest berry tannin concentration than any other

treatment.

Berry sensory attributes were significantly different across the seasons but only seed

bitterness was different in both seasons (Table 3, Chapter 5). Seeds from 1103 Paulsen

were more bitter in season 2010 whilst the seeds from the own roots treatments were more

bitter in 2011. A correlation between seed tannin concentration and seed bitterness was

found in 2010 season, but not in the 2011 season. Although no differences in TSS, pH and

TA were found among the rootstocks, assessors perceived berries from 110 Richter and

141

1103 Paulsen as having more sweet pulp in 2010 and having less acidic pulp in 2011 which

was linked with enhanced wine quality. This agrees with an established target profile for

Merlot wines where grapes are desired with higher sensory ratings of pulp sweetness and

lower pulp acidity in order to produce higher wine quality (Rousseau, 2001). In the second

season berries from 110 Richter had a pulp that was more difficult to detach than 1103

Paulsen and Schwarzmann. No other previous study, to the best of our knowledge, has

identified sensory differences of berries from different rootstocks and own roots vines.

In our study lower alcohol content was observed in 1103 Paulsen wines in both

seasons. Increasing levels of alcohol in wines are a source of concern first for consumers’

health (Grant, 2010, Macavoy, 2010) and extra cost in wine purchases as in some countries

tax is applied to wines with high levels of alcohol (De Barros-Lopes et al., 2003). Wine

producers are also affected because they have to manage wines with high levels of alcohol

which can be detrimental on wine sensory properties and quality (Gawel et al., 2007).

Using 1103 Paulsen or other rootstocks that produce wines with lower alcohol content

could be used in conjunction with other strategies for reducing alcohol, such as the use of

different yeast strains (Tilloy et al., 2014, Quirós et al., 2014, Contreras et al., 2014) or de-

alcoholisation (King and Heymann, 2014) by wine producers looking to lower wine alcohol

content.

Wines from the own roots treatment were characterized in both seasons by having

higher concentrations of most acetate esters than the rootstock treatments. Most ethyl esters

were associated with 110 Richter and Schwarzmann and the higher alcohols were

associated to 1103 Paulsen and Schwarzmann (Figure 2a and 2b, Chapter 5). Some of the

aroma descriptors reported by the literature for the acetate esters and ethyl esters that were

found different between the treatments are similar (Table 6, Chapter 5). However,

142

descriptors for ethyl esters were associated with red berries or with notes of caramel,

chocolate and candy (Table 6, Chapter 5). More importantly, aroma descriptors for ethyl

esters such as ethyl-2-methylbutanoate has been associated with dark berry aroma (Pineau

et al., 2009) and in our study was associated with higher ratings of dark berry aroma in the

2011 season. However in a recent study red berry aroma in commercial wines was not

associated with concentrations of ethyl esters (Sáenz-Navajas et al., 2015). Although more

studies involving aroma volatile composition are needed to add to the understanding of the

effect of the use of rootstocks on wine quality, our study provided key information by

revealing that there are differences in volatile composition in wines produced from vines

grafted to different rootstocks.

Wine sensory evaluation using Descriptive Analysis was able to identify sensory

differences between the four treatments. The wines from the own roots treatment were rated

as having lighter rim and body colour, higher ratings of fresh red berry aroma, lower rating

of dark berry aroma and lighter body in both seasons than the rest of the treatments (Table

7, Chapter 5). Experts perceive Shiraz wines having lower ratings of attributes such as

color intensity, body and dark berry aroma to be of lower quality (Lattey et al., 2010).

These sensory and compositional differences in berries and wine resulted in own

roots having the lowest wine quality scores in both seasons. 110 Richter had the highest

quality scores, which has been reported before for Cabernet Sauvignon rootstock trial wines

(Foot et al., 1989).

Sensory and compositional differences in berry and wine from Vitis vinifera L.

Shiraz vines grafted to three rootstocks and on its own roots were identified in this study

and resulted in differences in wine quality scores. Studies including more rootstocks and

more cultivars should be conducted to determine what the performance of other cultivars

143

under different rootstocks would be and if the differences observed in this study are only

exclusive to Shiraz. Rootstocks are known to behave differently depending on the soil

conditions, therefore future studies examining the effect of rootstocks on wine quality

should consider replication in different soil conditions and climate.

144

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