i

STUDY ON KINETICS OF VINEGAR PRODUCTION

AND

MATHEMATICAL MODELLING ON ANTIOXIDANT ACTIVITY

OF FRUIT JUICE

THESIS SUBMITTED FOR PARTIAL FULFILMENT OF THE REQUIREMENT FOR

THE DEGREE OF

MASTER OF TECHNOLOGY

IN

FOOD TECHNOLOGY AND BIOCHEMICAL ENGINEERING

2013-2015

BY

SUMAN KUMAR SAHA

Examination Roll no.-M4FTB1502

REGISTRATION No. 112125 of 2010-11

Under the Guidance of

PROF. RUNU CHAKRABORTY

Professor and Head

Department of Food Technology and Biochemical Engineering

FACULTY OF ENGINEERING AND TECHNOLOGY

Jadavpur University

Kolkata-700032

ii

This Project is dedicated to My Beloved Parents

&

My senior Kaustav Chakraborty

iii

FACULTY OF ENGINEERING AND TECHNOLOGY

DEPARTMENT OF FOOD TECHNOLOGY AND BIOCHEMICAL ENGINEERING

JADAVPUR UNIVERSITY

KOLKATA-700032

Declaration of originality and compliance of

academic ethics

I hereby declare that this thesis contains literature survey and original research work

by the undersigned candidate, as part of my Master of Technology in Food Technology and

Biochemical Engineering studies.

All information in this document have been obtained and presented in accordance

with academic rules and ethical conduct.

I, also declare that, as required by these rules and conduct, I have fully cited and

referenced all materials and results that are not original to this work.

Name: Suman Kumar Saha

Examination Roll Number: M4FTB1502

Thesis Title: “Study on kinetics of vinegar production and mathematical modelling on

antioxidant activity of fruit juice”

Signature with date:

_____________________________

( SUMAN KUMAR SAHA )

iv

FACULTY OF ENGINEERING AND TECHNOLOGY

DEPARTMENT OF FOOD TECHNOLOGY AND BIOCHEMICAL ENGINEERING

JADAVPUR UNIVERSITY

KOLKATA-700032

Certificate of Recommendation

I hereby recommend the thesis entitled “Study on kinetics of vinegar production and

mathematical modelling on antioxidant activity of fruit juice” prepared under my

supervision by Suman Kumar Saha, student of M.Tech, 2nd year (Examination Roll no-

M4FTB1502, Class Roll no.-001310902002, Registration no.-112125 of 2010-11). The thesis

has been evaluated by me and found satisfactory. It is therefore, being accepted in partial

fulfilment of the requirement for awarding the degree of Master of Technology in Food

Technology and Biochemical Engineering.

----------------------------------------------- ------------------------------------------------

Prof. Runu Chakraborty Dean

Professor & Head Faculty Council of Engineering

Department of F.T.B.E & Technology

Jadavpur University Jadavpur University

v

FACULTY OF ENGINEERING AND TECHNOLOGY

DEPARTMENT OF FOOD TECHNOLOGY AND BIOCHEMICAL ENGINEERING

JADAVPUR UNIVERSITY

KOLKATA-700032

Certificate of Approval

This is to certify that Mr. Suman Kumar Saha has carried out the research work

entitled “Study on kinetics of vinegar production and mathematical modelling on

antioxidant activity of fruit juice” under the supervision of Prof. Runu Chakraborty,

at the Department of Food Technology and Biochemical Engineering, Jadavpur University.

I am satisfied that he has carried out this work independently with proper care and

confidence. I hereby recommend that this dissertation be accepted in partial fulfilment of the

requirement for awarding the degree of Master of Technology in Food Technology and

Biochemical Engineering.

I am very much pleased to forward this thesis for evaluation.

…………………………..

Prof. Runu Chakraborty

Professor & Head

Dept. of F.T.B.E

Jadavpur University

vi

ACKNOWLEDGEMENT

This thesis entitled “study on kinetics of vinegar production and mathematical

modelling on antioxidant activity of fruit juice” is by far the most significant scientific

accomplishment in my life and it would be impossible without people who supported me and

believed in me.

To begin with, I express my deepest regards, unbound gratitude with sincerest thanks

to my guide respected Prof. Runu Chakraborty (Professor & Head, Department of Food

Technology and Biochemical Engineering, Jadavpur University) without who’s efficient and

untiring guidance, my work on this practical would have remained incomplete. She has been

very kind and affectionate and allowed me to exercise thoughtful and intelligent freedom to

proceed with this project work and finally produce this thesis. Her words of encouragement

have left an indelible mark in my mind which I am sure would also guide me in future.

I take this opportunity to express my heartfelt gratitude to the respected Prof. Utpal

Raychaudhuri for his valuable advice, suggestions and encouragement during the course of

my work.

I am also thankful to other respected faculty members Prof. Lalita Gauri Ray, Prof.

Uma Ghosh, Dr. Paramita Bhattacharya and Dr. Dipankar Halder along with library,

laboratory staffs and my friends who have been always the source of motivation and

inspiration for me.

I would like to thank research scholar Mr. Kaustav Chakraborty for his valuable

guidance and tremendous assistance throughout the project work. I am deeply indebted to

him for his help throughout the work by providing fruitful suggestions and cooperations.

Last of all, I would like to express my heartfelt gratitude to my parents, who inspired

me in making this endeavour a success.

May, 2015 Suman Kumar Saha

vii

Contents 1. Antioxidant activity of Vinegar as compared to Source-an overview......................................... - 2 -

1.1 Introduction ........................................................................................................................ - 2 -

1.2 Vinegar from different sources ........................................................................................... - 4 -

1.3 Conclusion ........................................................................................................................... - 8 -

1.4 References .......................................................................................................................... - 9 -

2 Process optimization and kinetics study of vinegar production from Manilkara zapota ......... - 17 -

2.1 Introduction ...................................................................................................................... - 17 -

2.2 Materials and methods ..................................................................................................... - 18 -

2.2.1 Chemicals .................................................................................................................. - 18 -

2.2.2 Yeast culture Preparation ......................................................................................... - 19 -

2.2.3 Acetobacteraceti culture preparation....................................................................... - 19 -

2.2.4 Preparation of Fermentation medium for Ethanol Production ................................ - 19 -

2.2.5 Preparation of Fermentation medium ...................................................................... - 19 -

2.3 Analytical methods ........................................................................................................... - 20 -

2.3.1 Determination of Ethanol concentration .................................................................. - 20 -

2.3.2 Determination of acid ............................................................................................... - 20 -

2.3.3 Estimation of Biomass Concentration ....................................................................... - 20 -

2.3.4 Response Surface Methodology ............................................................................... - 20 -

2.3.5 FTIR study .................................................................................................................. - 21 -

2.3.6 Kinetic models ........................................................................................................... - 21 -

2.4 Results and Discussion ...................................................................................................... - 23 -

2.4.1 Response surface analysis of data ............................................................................ - 23 -

2.4.2 Microbial and product growth .................................................................................. - 25 -

2.5 Conclusion ......................................................................................................................... - 26 -

2.6 References ........................................................................................................................ - 27 -

3 Mathematical Modelling of growth of Acetobaceter aceti in Vinegar Fermentation reaction - 40 -

3.1 Introduction ...................................................................................................................... - 40 -

3.2 Microbial kinetics methods ............................................................................................... - 40 -

3.3 Material ............................................................................................................................. - 43 -

3.3.1 Chemicals .................................................................................................................. - 43 -

3.3.2 Yeast culture Preparation ......................................................................................... - 43 -

3.3.3 Acetobacter aceti culture preparation ...................................................................... - 44 -

3.3.4 Preparation of Fermentation medium for Ethanol Production ................................ - 44 -

viii

3.3.5 Preparation of Fermentation medium ...................................................................... - 44 -

3.4 Analytical methods ........................................................................................................... - 45 -

3.4.1 Determination of Ethanol concentration .................................................................. - 45 -

3.4.2 Determination of acid ............................................................................................... - 45 -

3.4.3 Estimation of Biomass Concentration ....................................................................... - 45 -

3.4.4 Statistical Analysis ..................................................................................................... - 45 -

3.5 Result and Disscussions .................................................................................................... - 46 -

3.6 Conclusion ......................................................................................................................... - 48 -

3.7 References ........................................................................................................................ - 49 -

4 Partial Least square modelling for Prediction of Antioxidant activity of Phenolic compounds- 58 -

4.1 Introduction ...................................................................................................................... - 58 -

4.2 Method ............................................................................................................................. - 60 -

4.3 Statistical Analysis ............................................................................................................. - 61 -

4.4 Results and Discussion ...................................................................................................... - 62 -

4.5 Conclusion ......................................................................................................................... - 65 -

4.6 References ........................................................................................................................ - 66 -

ix

List of Figures:

Figure 2-1: Response surface plot showing the effect of Temp, time and pH on vinegar

production ………………………………………………………………………………….- 31

-

Figure 2-2Variation of acetic acid vs biomass and substrate vs biomass for vinegar

production. .......................................................................................................................... - 32 -

Figure 2-3: Comparison of calculated values and the experimental data from our experiment -

33 -

Figure 3-1: Comparison of Monod, Moser and Haldene equation. .................................... - 52 -

Figure 3-2:Comparison of logistic and gompertz equation ................................................ - 53 -

Figure 3-3 Residual plot for logistic and gompertz equation ............................................. - 54 -

Figure 4-1 Difference between electron donating and withdrawing effect ........................ - 69 -

Figure 4-2 Rsquared, factor 1 vs factor 2 and VIP plot for compounds containg electron

withdrawing group .............................................................................................................. - 70 -

Figure 4-3 Rsquared, factor1 vs factor 2 and vip plot for compounds with electron donating

group ................................................................................................................................... - 71 -

x

Nomenclature:

S substrate concentration (gl-1

)

P product concentration (gl-1

)

t time (h)

X cell concentration (g dry weight (l)-1

)

X0 initial biomass concentration (gl-1

)

Xm maximum biomass concentration (gl-1

)

α growth associated product formation coefficient (gg-1

)

β non-growth-associated product formation coefficient (gg-1

h-1

)

γ,η parameters in Luedeking-Piret like equation for substrate uptake ( g S (g cells)-1

, g

S (g cells)-1

h-1

respectively)

μm maximum specific growth rate (h-1

)

Yx/s biomass yield

Yp/s product yield based on the substrate utilized

ms maintenance coefficient (g substrate (g cells-h)-1

)

st. stationary phase

qs rate of substrate utilization

qp rate of product utilization

k proportionality constant indicating growth rate

ε constant indicating toxicity and inhibitory characteristics

xi

Abstract

The thesis entitiled “Study on kinetics of vinegar production and mathematical

modelling on antioxidant activity of fruit juice” investigates potentiality of sapodilla fruit as

an ingredient for vinegar with rich phytochemical profile and mathematical modelling of

antioxidant activity of key antioxidant compounds present in fruit juice. Sapodilla is a prime

tropical fruit. It is normally eaten fresh, but sometimes it is served as candy, dehydrated

slices, jelly and juices. It is a rich source of phenolic antioxidants, which is responsible for

key health benefits such as- coronary heart disease, inflammation, ageing, cancer, free radical

production protecting properties. Although, being used for a chief source of gum, sapodilla is

still a un-utilized source for various popular fruit by-products like wine and vinegar. No

previous attempts were made to produce wine or vinegar by using sapodilla as an ingredient.

In the present study, we aimed at producing sapodilla vinegar. The ability of sapaodilla to act

as ingredient and micro-organism to sustain in sapodilla were monitored by measuring pH,

time, temperature, product formation, substrate formation and microbial growth. Also,

antioxidant activity of phenolic compounds were measured by studying various key

molecular descriptors which would allow the prediction of antioxidant activity of other

compounds that is similar to tested compounds.

Chapter 1 deals with the review about the difference in antioxidant activity and

antioxidant compound profile of vinegar as compared with their sources. Fruit contains

numerous compounds as antioxidants which degrades and changes during fermentation.

Several new different compounds are also produced. This results in change in antioxidant

activity and profile of vinegar. In this chapter, changes in profile of different key classes of

antioxidant compounds in vinegar vs fruit is discussed.

xii

Chapter 2 deals with the potency of sapodilla as an ingredient for vinegar. Cultivated

worldwide, Sapodilla is a key fruit with several key antioxidant compounds and high

antioxidant activity. However, it is still unutilized as a potential source for fruit by-products.

Vinegar is a widely popular food condiment and is mainly produced form fruit. As a fruit,

sapodilla can be used to produce fruit vinegar with unique flavour and rich antioxidant

activity. But, production of vinegar should be optimized for successful exploitation of

sapodilla. Response surface methodology (RSM) is a statistical tool for optimization of

multivariate system. In this study, RSM was utilized to optimize vinegar production using

sapodilla with pH, temperature and time as process conditions.

Chapter 3 deals with ability of microorganism Acetobacter aceti to survive in

fermentation medium containing sapodilla. Besides C and N sources, microorganism requires

several key ingredients for proper growth; any compounds should not act as inhibitor of

growth of microorganism. In this study, microbial population growth was studied and

modelled with several different equation. Key conclusion on survival ability in a specific

media can be drawn using these equations.

Chapter 4 deals with analysing antioxidant activity of several antioxidant compounds

related to each other on the basis of chemical structures. Antioxidant property is influenced

by underlying molecular mechanisms which also effects other properties. Thus, identifying

these properties will allow proper analysis of antioxidant activity and prediction of

antioxidant activity of similar unknown compound. Partial Least square (PLS) was used to

statistically analysed the variation of antioxidant property and other molecular descriptors to

find reliable models for forecasting.

xiii

- 1 -

CHAPTER 1

- 2 -

Antioxidant activity of Vinegar as compared to Source-an overview

1.1 Introduction Vinegar, a popular acidic food condiment is produced mainly from various fruits and

cereals, by the biochemical action of Acetobacter and gluconobacter groups of bacteria. The

mild acidic flavour of vinegar is due to presence of acetic acid, the chief chemical produced

during acetous fermentation, at 4-10 percent level. Apart from its use as condiment, vinegar

has prominent usage in food preservation, pharmaceutical, therapeutic field[1], [2].

As defined by Joint FAO/WHO food standards programme, vinegar production is a

double fermentation process. In the first step, saccharomyces species converts fermentable

sugars to ethanol that is oxidized by acetobacter species bacteria in the next step to yield

acetic acid. An initial high sugar concentration, typically 10% (w/v) or more, and an acidic

pH favour ethanol production by yeast during anaerobic periods of ethanolic fermentation. In

acetous fermentation, alcohol dehydrogenase (ALD) catalyzes oxidation ethanol to

aetaldehyde, which in the subsequent step is oxidized to acetic acid by aldehyde

dehydrogenase (ALDH).

C2H5OH + NAD

CH3CHO + NADH + H+ (catalyzed by ALD)

RCHO + NAD++ H2OR COOH +NADH+ H

+ (catalyzed by ALDH)

Acetic acid bacteria (AAB) are aerobic, gram-variable, nonspore forming cells that

have an optimum pH of 5-6.5 for growth. Twelve genera of bacteria, including Acetobacter,

Gluconobacter, Acidomonus, Asaia, Kozakia, Saccharibacter species, are included into AAB

that are capable to oxidize sugars and alcohols into organic acids as final products. Fruits and

flowers are the natural habitat of AAB[3]. Each kind of vinegar involves unique combination

of organism, resulting in a different yield of acetic acid of variable quality. In traditional

production system of “surface culture method”, organism grows on the media surface. Long

- 3 -

time is needed for complete fermentation, but resultant vinegar is of high quality. The longer

fermentation period allows accumulation of “mother of vinegar”, a nontoxic slime composed

of yaest and acetic acid bacteria, possessing numerous unsubstantiated health benefits. In

contrast, modern submerged system has shorter production duration of 24-48 hrs. First, the

liquid is oxygenated by agitation and, subsequently, the bacteria culture is submerged

permitting rapid fermentation[4].

Phenolic compounds may act as antioxidants in different ways, such as direct reaction

with free radicals, scavenging of free radicals, increasing transfomation of free radicals to the

compounds with much lower reactivity, chelation of pro-oxidant metals (mainly iron),

delaying or strengthening activities of many enzymes. Fresh fruit extracts are an excellent

source of polyphenolic compounds. Epidemiological studies suggested that consumption of

red fruit juices such as grape, different berry juices and pomegranate correlate with reduced

risk of coronary heart disease, stroke, certain types of cancers and ageing. For this reason, it

is believed that the consumption of fruit and vegetables, rich in bioactive compounds, is

linked with the increase in resistance against such diseases. The beneficial effects of fruit and

vegetables are becoming increasingly appreciated [5], [6].

The fermented fruit grape products – wine (alcoholic) and vinegar (alcoholic and

acetic fermentations) – are also rich in polyphenols. Evidence of a negative association

between coronary heart disease (CHD) mortality and vinegar consumption has suggested

possible protective effects of vinegar [7] .Brewed vinegar, a commonly used condiment of

food, also has medicinal uses by virtue of its physiological effects, such as promoting

recovery from exhaustion, regulating blood glucose, blood pressure, stimulating the appetite,

and promoting calcium absorption. As a fermented product of fruit juices rich in antioxidant

and phenolic compounds, vinegar is being investigated for potential health benefits to human

health [8].

- 4 -

The aim of the study is to examine how antioxidant activity of vinegar differs from

that of their source-corresponding fruit, in terms of phenolic, flavonoid and antioxidant

compound profile and antioxidant activity.

1.2 Vinegar from different sources

1.2.1 Grape Vinegar

Grape, one of the most popular and widely available fruit, is a fruiting berry of

deciduous woody vines of the genus Vitis. It can be eaten as raw or in other forms like jam,

jelly, seed extract, raisins, wine, vinegar. Color of grape can be white, purple, black, dark

blue, yellow, green, orange and it is a major determiner of nutritional profile of fruit. Grape

seeds and skins are a good source of polyphenolic tannins which imparts astringency[9].

Grape juice is also a good source of flavonoids that is responsible for improvement of the

endothelial function, increase of the serum antioxidant capacity, protection of LDLs against

oxidation, decrease of native plasma protein oxidation, and reduction of platelet aggregation

[7]. In addition to epicatechin present as main polyphenolic antioxidants, catechin, gallic acid

and procyanidins are the other major antioxidants. These compounds possess hydroxyl,

peroxyl, superoxide and DPPH adical scavenging activity. Not only color of grapes, but also

the species of grapes, location, prevailing climatic condition and postharvest handling so

influence the phenolic content and antioxidant activity. Thus, Catechin and epicatechin

contents of V. Vinifera grapes were higher than in V. rotundifolia grapes, but the latter

contained more gallic acid. In general, grape seeds had much higher monomeric flavonol

contents than skins. Catechin and epicatechin concentrations in Chardonnay grape skins were

3 times higher than in Merlot grape skins [9]. Also red grape juice, with higher tannins,

possesses higher oxygen radical absorbance capacity (ORAC-FL) value than white grape

juice.

- 5 -

During acetic fermentation, phenolic compounds with high antioxidant activity may

be degraded to new phenolic compounds with lower antioxidant activity. This leads to

reduction in radical scavenging activity of vinegar. The ORAC-FL value was decreased from

14.6-25.0 μ mol of trolox equivalent/ml to 4.5-11.5 μ mol of trolox equivalent/ml[7].

Type of Wood of the barrel and ageing time also influence the phenolic compound

amount and profile of vinegar, and thus, antioxidant activity is influenced [10]. For balsamic

vinegar, significant increase was observed for samples aged in cherry, chestnut and oak wood

barrel and for chestnut the increase is most significant [11]. Chestnut releases a higher

concentration of gallic acid and, therefore, the formation of gallic ethyl ester is more likely in

chestnut barrels. But, the concentration of catechin and resveratrol were decresed [12].

Increase in ageing time allows the release of important phenolic compounds, especially

aldehydes. Four compounds, namely 5-hydroxymethylfurfuraldehyde (HMF), 2-

furfuraldehyde, proto- catechualdehyde and vanillin, were affected by ageing time [11]. The

study by Natera et al have confirmed the influence of ageing in wood on phenolic and volatile

compound profile of vinegar [13].

Produced as a result of malliard reaction, melanoidins could be responsible for high

antioxidant activity of vinegars [14]. Melanoidins are materials formed by interactions

between reducing sugars and compounds possessing a free amino group, such as free amino

acids and the free amino groups of peptides. High molecular weight melanoidins synthesizes

and accumulated during ageing of vinegar, especially balsamic vinegar [15], [16]. They can

account for upto 50% of antioxidant activity of aged vinegar.

1.2.2 Jujube Vinegar.

Native to south asia, Jujube is a deciduous shrub of Rahmnaceae family. In China and

adjacent area, for treatment related to respiratory, gastrointestinal, anti-inflammatory and

- 6 -

urinary diseases, jujube and it’s seed is prescribed [17]. “Fruit of life” contains several

important classes of phytochemical such as polysaccharides, phenolics, flavonoids and

saponins responsible for several biological activities [18]. Kamiloglu et al, [19] found total

phenolic content of jujube genotypes selected from turkey has phenolic content ranged from

25 to 42 mg GAE g-1

DW. For jujube genotypes from India, Koley et al 2011 found total

phenolic content varied twofold from 172 to 328.61 mg GAE/100 gm. After fermentation,

phenolic content decreased from 56.21 mg % to 45.75 mg %. This 18.6% decrease is

consistent with decrease occurred in other vinegars during acetous fermentation. However,

increase in flavonoid content from that of juice indicates synthesis during acetous

fermentation or liberation from cell wall [20].

1.2.3 Persimmon Vinegar

Persimmon has long been medically used for bronchial, paralysis and other blood

related chronic diseases due to presence of important phenolic compounds specially tannins

[21], [22]. DPPH radical scavenging and Radical scavenging activity of persimmon seed

extract are comparable with that of grapes, due to higher tannin concentration of 577.37 mg/

100 g as compared to 535mg/ 100 g in grapes [22]. The total phenolic content can reach upto

67mg GAE/g extract, depending upon the genotypes and various other environmental factors

[23]. The antioxidant activity and total phenol index for persimmon vinegar are 1601 μ mol

TE/kg and 324 mg gallic acid/kg which higher than white and redwine vinegar [21].

Persimmon vinegar has shown significantly higher DPPH radical scavenging activity of 52%-

higher than apple vinegar (11%), rice vinegar (40%) and pomegranate vinegar (35%) [24],

[25].

1.2.4 Pomegranate Vinegar

- 7 -

Pomegranate or Punica granatum, is a deciduous, red-rounded fruit bearing shrub of

Lythraceae, native to South asia stretching from Iran to India. Presence of several key

bioactive compounds such as hydrolysable tannins, monomeric anthocyanins, 3glucosides,

3,5 diglucosides and hydroxyl-cinnamic acids is responsible for several key biofunctions

(Lansky et al,1998, Du et al,1975, Nawwar et al, 1994a, Gil et al, 2000). [26]found the total

phenolic content of pomegranate juice was 1387 mg GAE/l, same as berry fruits. After

acetous fermentation, the phenolic content slightly reduced to 1254 mg GAE/l which is

higher than vinegar produced form other constituents [25]. A 9% decrease is lowest of all

vinegar but white wine which has almost similar change in polyphenolic content. The

phenolic content higher than rabbit-eye blueberry vinegar [26].

1.2.5 Strawberry Vinegar:-

Widely recognized for its characteristics aroma and unique flavor, strawberry is an

evergreen shrub of genus Fragaria [27]. It have shown presence of key minerals, vitamins,

antioxi- dants and secondary metabolites [28]. Presence of 224 mg GAE/g fresh tissue weight

of phenolic content results in an EC50 of 9.7 mg/ml [27]. During acetous fermentation,

phenolic content decreases from 2000 mg GAE/kg to 1000-2377 mg GAE/kg, depending on

treatment levels, which has resulted in a DPPh capacity of 6000-14000 μ mol TE/kg.

Anthocyanin loss can be attributed to polymerization and condensation reaction with other

phenols. Vinegar stored in glass barrel has lowest nutritional quality while those in cherry has

highest nutritional parameters[29].

1.2.6 Tartary Buckwheat vinegar

Native to east asia, tartary buckwheat is a food plant in the genus fagopyrum and

mainly consumed as tea, sprouts or milled products [30]–[32]. Due to presence of pheneolic

and flavonoid compounds such as rutin, quercetin, phenyl propanoid glycosides and catchins

- 8 -

and also important phytosterols, fagopyrins, it is used for aging, hypocholesterolemic and

antidiabetic activities[30], [32], [33]. Highest flavonoid and phenolic content of 22.6 and

12.99 mg/g dry weight is recorded in raw seeds. Higher free phenolic phenolic acid content

may indicates suitability of bran for therapeutic usage [34]. Rich in phenolics and flavonoids

especially rutin, tartary buckwheat vinegar shows good DPPH radical scavenging activity,

having IC50 value of 17 mg/ml. Flavonoid and phenolic content have decreased during

acetous fermentation. However, numerous different volatile compounds, including

antioxidant compounds like furfural and 5-methyl furfural, have appeared after fermentation

that improves radical scavenging potential of vinegar. Buckwheat Vinegar is rich in

tetramethyl pyrazine, or Ligustrazine, which is being investigated for potential inhibitor of

platelet aggregation [35].

1.3 Conclusion

Fruit is the main source of phenolic compounds in vinegar. During acetic

fermentation, antioxidant activity of vinegar is usually reduced from fresh fruit due to change

in phenolic and other antioxidant compound profile. Compounds with higher antioxidant

activity are converted to compounds with lower antioxidant activity by microoganisms.

Severel new compounds produced during processing and ageing in barrels can compensate

for lost activity of vinegar.

- 9 -

1.4 References

[1] S. Ji-yong, Z. Xiao-bo, H. Xiao-wei, Z. Jie-wen, L. Yanxiao, H. Limin, and Z.

Jianchun, “Rapid detecting total acid content and classifying different types of vinegar

based on near infrared spectroscopy and least-squares support vector machine,” Food

Chem., vol. 138, no. 1, pp. 192–199, 2013.

[2] P. Saha and S. Banerjee, “OPTIMIZATION OF PROCESS PARAMETERS FOR

VINEGAR,” Internatinal J. Res. Eng. Technol., vol. 02, no. 09, pp. 501–514, 2013.

[3] N. Saichana, K. Matsushita, O. Adachi, I. Frébort, and J. Frébortová, “Acetic acid

bacteria : A group of bacteria with versatile biotechnological applications,” Biotechnol.

Adv., 2014.

[4] M. Gullo, C. Caggia, L. De Vero, and P. Giudici, “Characterization of acetic acid

bacteria in ‘traditional balsamic vinegar,’” Int. J. Food Microbiol., vol. 106, no. 2, pp.

209–212, 2006.

[5] D. S. Dimitrijevic, D. A. Kostic, G. S. Stojanovic, A. N. A. S. Mitic, M. N. Miti, and

A. S. Dordevic, “Phenolic composition , antioxidant activity , mineral content and

antimicrobial activity of fresh fruit extracts of Morus alba L .,” J. Food Nutr. Res., vol.

53, no. 1, pp. 22–30, 2014.

[6] K. Gündüz and E. Özdemir, “The effects of genotype and growing conditions on

antioxidant capacity, phenolic compounds, organic acid and individual sugars of

strawberry,” Food Chem., vol. 155, pp. 298–303, 2014.

- 10 -

[7] R. M. Callejón, M. J. Torija, A. Mas, M. L. Morales, and A. M. Troncoso, “Changes of

volatile compounds in wine vinegars during their elaboration in barrels made from

different woods,” Food Chem., vol. 120, no. 2, pp. 561–571, 2010.

[8] A. Sugiyama, M. Saitoh, A. Takahara, Y. Satoh, and K. Hashimoto, “Acute

cardiovascular effects of a new beverage made of wine vinegar and grape juice,

assessed using an in vivo rat,” Nutr. Res., vol. 23, no. 9, pp. 1291–1296, 2003.

[9] Y. Yilmaz and R. T. Toledo, “Major Flavonoids in Grape Seeds and Skins:

Antioxidant Capacity of Catechin, Epicatechin, and Gallic Acid,” J. Agric. Food

Chem., vol. 52, no. 2, pp. 255–260, 2004.

[10] M. C. García Parrilla, F. J. Heredia, and A. M. Troncoso, “Sherry wine vinegars:

Phenolic composition changes during aging,” Food Res. Int., vol. 32, no. 6, pp. 433–

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[11] A. B. Cerezo, W. Tesfaye, M. E. Soria-Díaz, M. J. Torija, E. Mateo, M. C. Garcia-

Parrilla, and A. M. Troncoso, “Effect of wood on the phenolic profile and sensory

properties of wine vinegars during ageing,” J. Food Compos. Anal., vol. 23, no. 2, pp.

175–184, 2010.

[12] A. B. Cerezo, W. Tesfaye, M. J. Torija, E. Mateo, M. C. García-Parrilla, and A. M.

Troncoso, “The phenolic composition of red wine vinegar produced in barrels made

from different woods,” Food Chem., vol. 109, no. 3, pp. 606–615, 2008.

[13] R. Natera, R. Castro, M. de Valme García-Moreno, M. J. Hernández, and C. García-

Barroso, “Chemometric studies of vinegars from different raw materials and processes

of production.,” J. Agric. Food Chem., vol. 51, no. 11, pp. 3345–51, May 2003.

- 11 -

[14] F. Masino, F. Chinnici, A. Bendini, G. Montevecchi, and A. Antonelli, “A study on

relationships among chemical, physical, and qualitative assessment in traditional

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“Evaluation of antioxidant activity and total phenols index in persimmon vinegars

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CHAPTER 2

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Process optimization and kinetics study of vinegar production

from Manilkara zapota

2.1 Introduction

Native to Mexico and Central America, Sapodilla (Manilkara zapota) belongs to the

family Sapotaceae and is an evergreen, glabrous tree, 8-15 m in height. It is cultivated in all

tropical countries including Indian subcontinent. The fruit is a fleshy berry, generally

globose, conical or oval with one or more seeds. The fruit generally weighs about 75–200 g,

ranging from 5 to 9 cm in diameter. The fruit has a thin rusty brown scurfy skin and a

yellowish brown or red pulp with a pleasant, mild aroma and an excellent taste[1] The seeds

of M. zapota are aperients, diuretic tonic and febrifuge. Stem bark is astringent and febrifuge.

The leaves and bark are used as medicine to treat cough, cold, dysentery and diarrhoea.

Antimicrobial and antioxidant activities are also reported from the leaves of M. zapota. The

major constituents isolated from fruits of M. zapota are polyphenols (methyl chlorogenate,

dihydromyricetin, quercitrin, myricitrin, (+)-catechin, (-)-epicatechin, (+)-gallocatechin, and

gallic acid[2]. The antioxidant activity of sapodilla fruit has been reported to be very high in

the ABTS assay (3396 mg kg-1

; ~76 μmol TE g-1

DW) [3]. Sapodilla has stronger nitric oxide

scavenging activity and inhibitory effects against tumor cell proliferation than pomegranate,

apple, dragon fruit, and grape [4]. Phenolic compounds are the main source of antioxidant

activity of sapodilla[5]. Although Sapodilla is cultivated mainly for its edible fruit, it is also

the source of chicle, the principle ingredient in chewing gum [1]. The protein content of

sapodilla is very low (0.4–0.7 g per 100 g pulp)[6]. Fruit is becoming popular throughout the

world. It is normally eaten fresh, but sometimes it is served as candy, dehydrated slices, jelly

and juices[7].

Production of vinegar with improved phytochemical attribute has been key interest

area for the research for past decades. Besides traditional benefits from acetic acid, vinegar is

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now being investigated for other potential benefits arising from ingredients such as fruits,

spices used for seasoning that can be used. Contemporary vinegars such as fruit vinegar,

herbal vinegar, vinegar seasoned with spices and cereal vinegars has exhibited their ability to

prohibit and alleviate several chronic diseases such as free radical induced cell damage,

arthritis, gastrointestinal disorder etc. Fruits such as sapodilla may provide an ideal ingredient

for production of vinegar which may exhibit similar medicinal property as that of sapodilla

and can be easily available and taken by masses.

Response surface methodology (RSM) is an efficient experimental strategy to

determine optimal conditions for a multivariable system rather than by the conventional

method, which involves changing one independent variable while keeping the other factors

constant. These time consuming methods are incapable of detecting the true optimum.

Response surface methodology has been successfully used to model and optimize

biochemical and bio- technological processes related to food systems [8]. To our knowledge,

there have been no studies on the response surface optimization of vinegar production from

sapodilla [9].

The aim of this study is to optimize the physical parameters for improved productions

of herbal vinegar from sapodilla. Also, substrate utilization and product formation kinetics of

vinegar production will be studied.

2.2 Materials and methods

2.2.1 Chemicals

Dextrose, calcium carbonate (GR), KH2PO4, K2HPO4, MgSO4.7H2O, FeSO4.7H2O

and urea were purchased from Merck, India. Yeast extract, malt extract, tryptone, agar and

peptone were obtained from Himedia, India.

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2.2.2 Yeast culture Preparation

Stock culture of Saccharomyces cerevisiae (NCIM 3315) was obtained from the

National Chemical Laboratory (NCL), Pune, India. The culture medium consisted of 3 malt

extract, 10 glucose, 3 yeast extract and 5 peptone (g/l). The organisms were grown at a

temperature of 300C and pH 6.5. The incubation period was 45 hours. After incubation, the

culture was stored at 40C in a refrigerator.

2.2.3 Acetobacter aceti culture preparation

Stock culture of Acetobacter aceti (NCIM 2116) was obtained from the National

Chemical Laboratory (NCL), Pune, India. The composition of the culture medium: 10

tryptone, 10 yeast extract, 10 glucose, 10 calcium carbonate and 20 agars (g/l). The

organisms were grown at a temperature of 300C and pH 6.0. The incubation period was 24

hours. After incubation, the culture was stored at 40C in the refrigerator.

2.2.4 Preparation of Fermentation medium for Ethanol Production

Sapodilla (Manilkara zapota) was purchased from market in Kolkata. These were

preserved at -500C in an ultra-low temperature Freezer (Model C340, New Brunswick

Scientific, England).The fermentation medium consisted glucose 10, urea 3, KH2PO4 0.5,

K2HPO4 0.5, MgSO4.7H2O 0.5, FeSO4.7H2O 0.01 (g/l). The fermentation process was carried

out in a 250 ml flask; 100 ml of fermentation media were inoculated with yeast culture. The

pH and temperature were adjusted to 5.5 and 320C for each experiment. The incubation time

was 10 days and the flask was made airtight by paraffin paper for maintaining anaerobic

conditions.

2.2.5 Preparation of Fermentation medium

After ethanol fermentation, 120 g/l of sterile sugar was added to the medium and

inoculated with Acetobacter aceti starter culture. The temperature and pH were adjusted as

- 20 -

per the experiments. The incubation time was 140 hours and flask was agitated at 150 rpm to

maintain an aerobic condition. Samples were withdrawn with a sterile injection syringe at

predefined interval for analysis.

2.3 Analytical methods

2.3.1 Determination of Ethanol concentration

A 5 ml fermented sample was centrifuged (Remi C-24, Mumbai, India) at 3500 g for

10 minutes. The supernatant solution was used to determine the ethanol concentration by gas

chromatography (Agilent Technologies: GC system-7890A gas chromatography, column-

Agilent JKWDB-624 with column ID- 250μm, length- 60m and film length-1.4μm). The

ethanol content was calculated by the GC peak areas.

2.3.2 Determination of acid

Acetic acid concentration was quantified by a HPLC system (JASCO, MD 2015 Plus,

Multiwave length detector) equipped with absorbance detectors set to 210 nm. The column

(ODS-3) was eluted with 0.01 (N) H2SO4as the mobile phase at a flow rate of 0.5 ml/min and

a sample injection volume of 20 μl. Standard acetic acid (Merck, India) was used as an

external standard.

2.3.3 Estimation of Biomass Concentration

The dry weights of mycelium were obtained after centrifuging the broth samples at

1100 g for 20 minutes. The harvested biomass was then washed with deionized water, dried

for 8 h at 1050c, cooled in desiccators and weighed [10].

2.3.4 Response Surface Methodology

Natural vinegar production from sample was studied and the process was optimised

with Response surface methodology (RSM). Different types of RSM designs include 3-level

factorial design, central composite design (CCD), Box-Behnken design (BBD) and D-optimal

- 21 -

design. Among all designs, CCD is the most widely used response surface designed

experiment and allows us to efficiently estimate first and second order terms. A 3-factor, 3-

level design would require a total of 20 unique runs. Hence, CCD was applied to optimise

vinegar production with time, temperature and pH were the independent variable. Th factors

and their respective coding is given in table 2.1. These parameters have been optimised on

the basis of the highest yield of vinegar from the sample. A 3-factor, 3-level CCD design with

3 centre points was created using Design Expert 7 (2008, USA) and given in table 2.2. The

design was used to explore quadratic response surfaces and constructing second-order

polynomial model. The nonlinear quadratic model is given as:

Y=b0+b1x1+b2x2+b3x3+b12x1x2+b13x1x3+b23x2x3+b11x12+b22x2

2+b33x3

2 (1)

Where Y is the measured response associated with each factor level combination; b0 is an

intercept; b1 to b33 are the regression coefficients and x1, x2 and x3are the independent

variable.

The polynomial equation for the response was validated by the statistical test called

ANOVA (Analysis of Variance), for determination of significance of each term in equation

and also to estimate the goodness of fit. Response surfaces were drawn for experimental

results obtained from the effect of different variables on the acetic acid concentration in order

to determine the individual and cumulative effects of these variables [11].

2.3.5 FTIR study

A Fourier-transform infrared (FT-IR) spectrum of the fermented vinegar on KBr discs

was recorded in FTIR-8400S (Shimadzu, Japan). The scanning range covered 400-4000 cm-1

with resolution of 4 cm-1

[12].

2.3.6 Kinetic models

Product formation

- 22 -

The kinetics of product formation by a microorganism was based on Luedeking and

Piret equations which combine both growth-associated and nongrowth-associated

contributions [13].

𝑑𝑃

𝑑𝑡= 𝛼

𝑑𝑥

𝑑𝑡+ 𝛽𝑥 (6)

According to this model, the product formation rate depends on both the instantaneous

biomass concentration, x, and growth rate, dx/dt, in a linear manner and α and β may be

identified with energy used for growth and maintenance, respectively. At stationary phase (dx

/ dt= 0) and (x = xm), Luedeking-Piret kinetics of batch culture imply:

β =(𝑑𝑝

𝑑𝑡)𝑠𝑡

𝑥𝑚(7)

The product formation is growth associated when α ≠ 0 and β = 0. The integrated form of Eq.

(6) using P = 0 (t = 0) expresses P as a function of t [14].

P = 𝛼𝑥0(𝑒𝜇𝑚𝑡

(1−(𝑥0𝑥𝑚

)(1−𝑒𝜇𝑚𝑡))− 1) + 𝛽(

𝑥𝑚

𝜇𝑚) ln(1 − (

𝑥0

𝑥𝑚)(1 − 𝑒𝜇𝑚𝑡)) (8)

Thus, Eq. (8) can be written in the form:

P = αX + K (9)

Substrate utilization

The substrate utilization kinetics was based on Luedeking-Piret like equation which

considers substrate conversion to cell mass, to product and substrate consumption for

maintenance.

𝑑𝑠

𝑑𝑡= - 𝛾

𝑑𝑥

𝑑𝑡− 𝜂𝑥 (10)

At stationary phase (dx / dt= 0) and (x = xm), η can be obtained using the following equation:

- 23 -

η = (-(ds / dt))st./ xm(11)

Integrating the equation (10) using s = so (t =0) yields the following equation [14], [15]:

s = so – (𝑥0𝑥𝑚𝑒

𝜇𝑚𝑡)

𝛾(𝑥𝑚−𝑥0+𝑥0𝑒𝜇𝑚𝑡)+ (

𝑥0

𝛾) − (𝜂

𝑥𝑚

𝜇𝑚) ln(

(𝑥𝑚−𝑥0+𝑥0𝑒𝜇𝑚𝑡)

𝑥𝑚) (12)

2.4 Results and Discussion

2.4.1 Response surface analysis of data

The maximum amount of acetic acid was produced in run 19 and the amount was 5.89

at pH 6.0 for 10 days of fermentation at 280c. The minimum amount of acetic acid was

produced in run 1 and the amount was 3.12 at pH 4.0 for 6 days of fermentation at 240c. This

is similar to optimized parameters of palm vinegar production [10]. The experimental data are

analysed using R (version 3.10, Austria) and given in Table 2.2. For a model to become

significant, it should have a high model F value and low lack-of fit F value. Lack-of fit

compares the residual error to pure error and it is not desirable [16]. So, a small F value and

high P value for lack-of fit term are desired. The obtained model has F value of 63.78 and

lack-of fit F value of 4.04, both of these values indicate the suitability of model (table 2.3).

The second-order polynomial equation for the measured response is given below:-

Y=5.67+0.24x1+0.064x2-0.093x3-0.055x1*x2-0.012x1*x3+0.068x2*x3-0.056x12-0.57x2

2-

1.06x32

(2)

The R2 value provides a measure of how much variability in the observed response

values can be explained by the experimental values and their interactions. A R2 value of

0.9829 indicates that 98.29% of the variability in the response could be explained by the

model. A positive value for regression coefficients represents an effect that favours the

optimization, while a negative value indicates an antagonistic effect.

- 24 -

By studying the regression coefficients for vinegar production (table 2.4), it can be

concluded that only Time (x1), Time2(x1

2), temperature

2(x2

2) and pH

2 (x3

2) are the only

significant variable as they each has a p value<0.005. Values of “Prob>F” less than 0.0500

indicate model terms are significant while values greater than 0.1000 indicate the model

terms are not significant [17]. It can also be concluded that temperature, pH and all of the

interactions are insignificant variable. Among the significant variable, pH2 is the most

important terms followed by temperature2 and time

2, as it has highest t value.

Figure 2.1 (a)-(c) shows the surface response plot for optimization of the conditions

for acetic acid fermentation. Surface plots were based on regression equation, holding three

variables constant at the level of zero while varying the other two within their experimental

range. The effect of temperature and time, pH and temperature and time and pH on the acetic

acid production is shown in fig 2.1 (a)-(c). The graph shows optimum point for acetic acid

production was 5.698813, the optimum pH, temperature and time being 5.888989,

28.1678930c and 10.888222 days.

The stationary point thus obtained is a maximum as all eigenvalues are negative (-

0.5272164, -0.5930815, -1.0665203). The largest eigenvalue (-1.0665203) corresponds to the

eigenvector (0.115930, -0.061246, 0.991367), the largest component of which (0.99136735)

is associated with pH; similarly, the second-largest eigenvalue (-0.5930815) is associated

with temperature. The third eigenvalue (-0.5272164) associated with time. These reiterate the

fact that acetic acid production is more sensitive to changes in pH than other two variables.

This fact can be rationalised by considering the stability of microorganism in the zone for

independent variables defined in the experiment. The zone of optimum pH stability for this

microorganism falls within the experimental pH range, whereas for temperature and time the

optimum stability zone encompasses the experimental zone.

- 25 -

2.4.2 Microbial and product growth

Saccharomyces cerevisiae, the organism used in the study, showed a normal growth

trend. It had a distinct exponential growth phase and stationary phase . As vinegar is a

primary metabolite, it was mainly formed during exponential growth phase. All experimental

data were analysed with R 3.1.0 (2013, Austria).

Product formation

Fitting the experimental data to Luedeking-Piret kinetics equation yielded the value of

parameters as follows: α=8.9625 g/g of biomass, β=0.1291 g/mg of biomass.h-1

. A plot of

acetic acid vs biomass concentration given in fig 2.2 a, will give the value of α and K. The

equation representing the relationship between the rate of product formation and microbial

growth is given as:

P=9.042X-4.597

The fitting of the results was satisfactory. A large α value compared to β indicates that the

synthesis of vinegar is primarily a growth associated type. In this model, α is the growth

associated product formation coefficient and can be associated with the product on biomass

yield (Yp/x).

Substrate Utilization

In vinegar bio-synthesis, glucose is converted to acetic acid by Saccharomyces

cerevisiae during exponential growth phase. A plot of substrate concentration and time given

in fig. 2.2 b will give value of S0, δ, γ. Fitting the experimental data to equation (12) yielded

the value of parameters as follows: S0=20.0837 g/l, Yx/s=0.08779 gg-1

, ms=0.1369 gg-1

day-1

.

The fitting of results was satisfactory.

- 26 -

Acetic acid is a powerful bacteriostatic, more in the undissociated acid from than

anion, can exhibit growth uncoupling action on microorganisms[18], [19]. High penetration

capability, owing to non-polar nature, allows it to accumulate into cytosol which could

reduce the cytoplasmic pH . Bacteria maintains cytoplasmic pH by extruding H+ by means of

the membrane H+-ATPase in a process energized by glycolytically generated ATP [20], [21].

Upon accumulation of acids, to maintain ΔpH microorganism produce more ATP for H+-

ATPese which reduces growth rate [22]. This, increasing ATP unavailability ceases growth.

Product formation rate increases initially, but, reduction in growth rate slows the product

formation rate.

2.5 Conclusion

Fermentation is a very complex process, and it is often very difficult to obtain a

complete picture of it. The response surface methodology based on a three variable CCD was

used to determine the effect of pH, time and temperature on acetic acid production. The

optimum pH, temperature and time were 5.89, 26.180C and 10.89 respectively for the highest

yield of acetic acid (5.70%). The model parameters Xm, X0, μm, α, β, S0, Yx/s, ms were

determined. Model has established that acetic acid is a growth-associated product with high α

value. Analysis of data substantiated the inhibitory effect of the vinegar on the growth of

Acetobacter aceti. Growth uncoupling effect of this weak acid is mainly responsible for this

inhibitory action.

- 27 -

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[17] C. Chen and F. Chen, “Study on the conditions to brew rice vinegar with high content

of γ-amino butyric acid by response surface methodology,” Food Bioprod. Process.,

vol. 87, no. 4, pp. 334–340, Dec. 2009.

[18] G. Wang and D. I. Wang, “Elucidation of Growth Inhibition and Acetic Acid

Production by Clostridium thermoaceticum.,” Appl. Environ. Microbiol., vol. 47, no. 2,

pp. 294–8, Feb. 1984.

[19] R. Bar, J. L. Gainer, and D. J. Kirwan, “An Unusual Pattern of Product Inhibition:

Batch Acetic Acid Fermentation,” vol. XXIX, pp. 796–798, 1987.

- 30 -

[20] A. A. Herrero, “End-product inhibition in anaerobic fermentations,” Trends

Biotechnol., vol. 1, no. 2, pp. 49–53, May 1983.

[21] D. J. Clarke, F. M. Fuller, and J. G. Morris, “The proton-translocating adenosine

triphosphatase of the obligately anaerobic bacterium Clostridium pasteurianum. 1.

ATP phosphohydrolase activity.,” Eur. J. Biochem., vol. 98, no. 2, pp. 597–612, Aug.

1979.

[22] J. J. Baronofsky, W. J. Schreurs, and E. R. Kashket, “Uncoupling by Acetic Acid

Limits Growth of and Acetogenesis by Clostridium thermoaceticum.,” Appl. Environ.

Microbiol., vol. 48, no. 6, pp. 1134–9, Dec. 1984.

- 31 -

Figure 2-1: Response surface plot showing the effect of a)Temp and time b) pH and time and

c)pH and temp on vinegar production.

- 32 -

Figure 2-2 Variation of a) acetic acid vs biomass and b)substrate vs biomass for vinegar

production.

- 33 -

Figure 2-3: Comparison of calculated values and the experimental data from our experiment

- 34 -

Table 1 Variables in the Central composite Design

Variables Coded levels

-1 0 1

Time 6 10 14

Temperature 24 28 32

pH 4 6 8

- 35 -

Table 2 Central composite design matrix of 3 test variables, the observed response and

predicted values

Run Time Temperature pH Experimental

value

Predicted

value

1 6 24 4 3.12 3.16

2 14 24 4 3.94 4.00

3 10 28 6 5.67 5.67

4 10 28 6 5.69 5.67

5 14 32 4 3.96 3.88

6 6 32 4 3.18 3.26

7 10 28 6 5.57 5.67

8 6 32 8 3.56 3.45

9 10 28 8 4.23 4.52

10 14 32 8 3.67 3.59

11 6 28 6 4.91 4.87

12 10 32 6 4.97 5.16

13 14 28 6 5.12 5.35

14 10 28 4 4.8 4.7

15 10 28 6 5.75 5.67

16 10 28 6 5.82 5.67

17 10 24 6 5.03 5.03

18 6 24 8 3.05 3.08

19 10 28 6 5.89 5.67

20 14 24 8 3.56 3.43

- 36 -

Table 3 Summary of the analysis of variance result for the response models

Source Sum of Squares df R Square F value Prob>F

Total Model 18.7569 9 0.9829 63.78 <0.0001

Residual Mean square

Lack of Fit 0.2619 5 0.05238 4.04 0.1487

Pure Error 0.064883 5 0.01298

Total Error 0.326785 10 0.03267

Eigenvalues Eigenvectors

Time Temperature pH

-0.5272 0.8002 -0.5856 -0.1298

-0.5931 0.5885 0.8083 -0.018878

-1.0665 0.1159 -0.0613 0.991367

- 37 -

Table 4 Statistical significance of the regression coefficients for vinegar production of vinegar

by A. aceti

Estimate Std. Error VIF

Intercept 5.67 0.062 1.00

x1 0.24 0.057 1.00

x2 0.064 0.057 1.00

x3 -0.093 0.057 1.00

x1:x2 -0.055 0.064 1.00

x1:x3 -0.12 0.064 1.00

x2: x3 0.068 0.064 1.00

x12

-0.56 0.11 1.82

x22

-0.57 0.11 1.82

x32

-1.06 0.11 1.82

- 38 -

- 39 -

CHAPTER 3

- 40 -

Mathematical Modelling of growth of Acetobaceter aceti in Vinegar

Fermentation reaction

3.1 Introduction

Acetic acid fermentation is one of the oldest biochemical processes, have been known

to ancient civilizations for thousands of years. During acetic acid fermentation, ethanol is

oxidized to acetic acid by Acetobacter:

C2H5OH+O2 CH3COOH+H2O

Vinegar production involves the conversion of ethanol to acetic acid by microbial

cells and strongly influenced by the difficulties to carry on acetic acid bacteria cultures [1].

High acid concentrations decrease the pH down to 2 which strongly affect microbial growth

and can lead to cell death. Besides the acidic environment, the medium contains also ethanol,

known as an inhibitory substrate. These unfavourable conditions make difficult the growth of

the bacteria. Apart from the study of microbial behaviour in a specific media, microbial

kinetics will help to optimize substrate concentration in order to yield high amount of vinegar

that will improve overall productivity. To model acetic acid production, it is therefore

important to begin to describe microbial kinetics [2], [3].

3.2 Microbial kinetics methods

The specific growth rate, µ (h-1

), is defined as the ratio between the biomass

production rate, rX (g/l.h), and the biomass concentration, X (g/l), equation (1).

µ = rX × 1

𝑋 (1)

When carried out in a batch culture, the biomass balance is expressed as equation (2).

Μ=1

𝑋×

𝑑𝑋

𝑑𝑇 (2)

- 41 -

The product and substrate balances in batch culture lead to the expression of the acetic

acid production rate, rP, and the ethanol consumption rate, rS.

rP=𝑑𝑃

𝑑𝑇(3)

rS=−𝑑𝑆

𝑑𝑇(4)

P and S represent the product (acetic acid) and the substrate(ethanol) concentration (g/L).

The specific acetic acid production rate, νp , is defined as the ratio between rP and X and

describes the acetic acid production rate per one unit of biomass. It is expressed in gm. Acetic

acid per g biomass per hour, and can be calculated by expression 5.

νP=1

𝑋×

𝑑𝑃

𝑑𝑇(5)

An analytical or numerical solution of the mass balances is possible once the function µ has

been specified [2].

Out of the several available kinetics models, the structured models consider

intracellular metabolic pathway that is difficult to apprehend without proper knowledge of

invivo reaction rates of implied enzymes. Unstructured growth models, simpler between the

two, describes growth rate as a function of initial microbial population.

𝑑𝑋

𝑑𝑇=f(x) (6)

Unstructured model proposes:

1. The biomass concentration and the rate of cell mass production are proportional.

2. The cells need substrate and can continuously synthesize metabolic products even

after the growth has finished.

3. The evolution of the biomass throughout the culture time (growth rate) presents an

asymptote as upper limit (saturation level) different for each substrate or level of

substrate used [4].

- 42 -

Monod equation describes specific growth rate is proportional to concentration of nutrient

presents at a limiting concentration.

𝜇 =𝜇𝑚𝑎𝑥

(𝐾𝑠+𝑆)× 𝑆 (7)

However, Moser and Haldene proposed two different kinetic models, in order to overcome

the limitations of the monod models, which takes into account the effect of K* and Ki,

provided that K*>1, and Ki is the inhibition constant [5]–[7].

𝜇 =𝜇𝑚𝑎𝑥

(𝐾𝑠+𝑆2)× 𝑆2 (8)

𝜇 =𝜇𝑚𝑎𝑥×𝑆

(𝐾𝑠+𝑆+𝑆2

𝐾𝑖) (9)

The more simplified form of 7,8,9 is [5]:-

1

𝜇=

𝐾𝑠

𝜇𝑚𝑎𝑥×

1

𝑆+

1

𝜇𝑚𝑎𝑥 (10)

1

𝜇=

𝐾𝑠

𝜇𝑚𝑎𝑥×

1

𝑆2+

1

𝜇𝑚𝑎𝑥 (11)

1

𝜇=

𝐾𝑠

𝜇𝑚𝑎𝑥×

1

𝑆2+

1

𝜇𝑚𝑎𝑥+

𝑆

𝐾𝑖(12)

Sigmoid function can also be used to conceptualise the growth pattern, taking lag time phase

into the consideration, occurring in different culture media.

Subtrate independent models assume, when nutrients are present in abundant,

population growth is proportional to the population.

𝑑𝑥

𝑑𝑡= 𝜇𝑥

- 43 -

Growth curves contain a final phase in which the rate decreases and finally reaches zero, so

that an asymptote (A) is reached.

However, when population tend to reach the maximum, owing to reduced nutrient

content, the population growth tend to fall which is measured by entity introduced in

substrate independent model and proportional to population[8], [9].

Logistic (14) and Gompertz (15) are the most popular sigmoid equation used to model

microbial growth.

𝜇 = 𝜇𝑚 × (1 −𝑥

𝑥𝑚) (14)

𝜇 = 𝜇𝑚 × log (𝑋𝑚

𝑋)(15)

Gompertz and logistic, both being substrate independent model, can be successfully used to

analyse effect of population on the growth[10]–[12].

3.3 Material

3.3.1 Chemicals

Dextrose, calcium carbonate (GR), KH2PO4, K2HPO4, MgSO4.7H2O, FeSO4.7H2O

and urea were purchased from Merck, India. Yeast extract, malt extract, tryptone, agar and

peptone were obtained from Himedia, India.

3.3.2 Yeast culture Preparation

Stock culture of Saccharomyces cerevisiae (NCIM 3315) was obtained from the

National Chemical Laboratory (NCL), Pune, India. The culture medium consisted of 3 malt

extract, 10 glucose, 3 yeast extract and 5 peptone (g/l). The organisms were grown at a

temperature of 300C and pH 6.5. The incubation period was 45 hours. After incubation, the

culture was stored at 40C in a refrigerator.

- 44 -

3.3.3 Acetobacter aceti culture preparation

Stock culture of Acetobacteraceti(NCIM 2116) was obtained from the National

Chemical Laboratory (NCL), Pune, India. The composition of the culture medium: 10

tryptone, 10 yeast extract, 10 glucose, 10 calcium carbonate and 20 agars (g/l). The

organisms were grown at a temperature of 300C and pH 6.0. The incubation period was 24

hours. After incubation, the culture was stored at 40C in the refrigerator.

3.3.4 Preparation of Fermentation medium for Ethanol Production

Sapodilla (Manilkara zapota) was purchased from market in Kolkata. These were

preserved at -500C in an ultra-low temperature Freezer (Model C340, New Brunswick

Scientific, England).The fermentation medium consisted glucose 10, urea 3, KH2PO40.5,

K2HPO4 0.5, MgSO4.7H2O 0.5, FeSO4.7H2O 0.01 (g/l). The fermentation process was carried

out in a 250 ml flask; 100 ml of fermentation media were inoculated with yeast culture. The

pH and temperature were adjusted to 5.5 and 320C for each experiment. The incubation time

was 10 days and the flask was made airtight by paraffin paper for maintaining anaerobic

conditions.

3.3.5 Preparation of Fermentation medium

After ethanol fermentation, 120 g/l of sterile sugar was added to the medium and

inoculated with Acetobacter aceti starter culture. The temperature and pH were adjusted as

per the experiments. The incubation time was 140 hours and flask was agitated at 150 rpm to

maintain an aerobic condition. Samples were withdrawn with a sterile injection syringe at

predefined interval for analysis [13].

- 45 -

3.4 Analytical methods

3.4.1 Determination of Ethanol concentration

A 5 ml fermented sample was centrifuged (Remi C-24, Mumbai, India) at 3500 g for

10 minutes. The supernatant solution was used to determine the ethanol concentration by gas

chromatography (Agilent Technologies: GC system-7890A gas chromatography, column-

Agilent JKWDB-624 with column ID- 250μm, length- 60m and film length-1.4μm). The

ethanol content was calculated by the GC peak areas[14] .

3.4.2 Determination of acid

Acetic acid concentration was quantified by a HPLC system (JASCO, MD 2015 Plus,

Multiwave length detector) equipped with absorbance detectors set to 210 nm. The column

(ODS-3) was eluted with 0.01 (N) H2SO4as the mobile phase at a flow rate of 0.5 ml/min and

a sample injection volume of 20 μl. Standard acetic acid (Merck, India) was used as an

external standard.

3.4.3 Estimation of Biomass Concentration

The dry weights of mycelium were obtained after centrifuging the broth samples at

1100 g for 20 minutes. The harvested biomass was then washed with deionized water, dried

for 8 h at 1050c, cooled in desiccators and weighed.

3.4.4 Statistical Analysis

Fitting to the model and parametric estimations calculated from the results were

carried out by minimisation of the sum of quadratic differences between observed and model-

predicted values, using the curve-fitting module provided by scipy.stats module. Module was

used to evaluate the significance of the parameters estimated by the adjustment of the

experimental values to the proposed mathematical models and the consistency of these

equations. The results were visualised with Matplotlib. The models were compared on the

- 46 -

basis of standard error between obtained value and predicted value, which reduces as fitting

of model become good.

3.5 Result and Disscussions

Experimental data for glucose and biomass concentration during the growth phase of

A. aceti were used for determination of different kinetic parameters. Monod, Moser,

Andrews. Considering cell dry weight as microbial concentration values (X) and glucose

substrate as limiting substrate concentration (S), values of μ and other inhibiting parameters

were determined from eq (10,11,12). The calculated value of different kinetic parameters is

given in table 1.

All the models predicted that the reaction has negative μmax and Ks value, which

indicates reduction of microbial population during “growth phase” despite the abundance of

nutrients even after growth phase. Although high level of fitting has been achieved, as

evidenced by low sum of square of residuals, the calculated values of μ do not correlated with

experimental parameters at all fig 3-1. Inhibition of growth is resulting from accumulation of

acetic acid, a mild bacteriostatic, within the cell and is most prominent during later phase of

fermentation. Thus high initial microbial population and low final population at the end of

growth phase is resulted which produces an artificial value of kinetic parameters, that do not

corroborate experimental conditions.A negative value of μmax is a result of relative lower

population at the end of growth phase, which also supports the theory of severe inhibition of

microbial growth by product. Hence, substrate dependent models like Monod or moser

cannot provide ideal framework for proper modelling of A. aceti growth during vinegar

production. For application of these equations, none but amount of limiting nutrient must

influence the growth of microorganism [9].

- 47 -

In these cases, sigmoid growth equations such as logistics and gompertz can be

utilized to model microbial growth. These equations include growth inhibition factors, often

proportional to population. The variation of Logistic and gompertz equation is shown in fig

3-2. It shows gompertz growth equation, with same μmax and Xm, have higher microbial

population value. The experimental value of μmaxand Xm for logistic and gompertz equation

are given below:

Logistic: μmax:-0.2554 h-1

, Xm:-1.258998 gm/l, residual sum of square:-8.99×10-6

Gompertz: μmax:-0.1496 h-1

, Xm:-1.43887 gm/l, residual sum of square:- 4.56×10-5

Logistic equation has lower residual sum of square which indicates better capacity of Logistic

equation over Gompertz to predict values for microbial population within the experimental

range. The observed result is close to those obtained for palm vinegar fig 3-3 [13]

Gompertz and Logistic function both assumes growth is slowest at initial and final

phase, but differs in the approach of both asymptote by the curve. Thus, Gompertz equation

may be termed as a special case of generalised Logistic function. But, Logistic equation

assumes the symmetrical approach by the curve, whereas logistic equation assumes right

hand asymptote approaches much more gradually than left hand [11]. Carefully examining

the models, we can see that models over-predict the initial and final population, with the

values being higher for gompertz equation, and under-predict during middle phase and the

value is again for gompertz equation. Even during end phase, the limiting nutrient

concentration is present in adequate amount. This signifies the growth inhibition by acetic

acid is as equal powerful as initial lag phase and can be termed as “secondary lag phase”.

During this secondary lag phase, microorganisms, in order to acclimatize with adverse

situations, ends growth phase prematurely and divert additional ATP to maintain proton

pump[15], [16]. Acetic acid can exert growth uncoupling effect by lowering pH which

- 48 -

microorganism try to oppose with “proton pump” or H+-ATPase [17], [18]. Being a

bacteriostatic and nonpolar, acetic acid can easily accumulate in cytosol and reduce pH below

a level at which bacteria has to reduce growth to maintain ΔpH with H+-ATPase [19], [20].

3.6 Conclusion

Vinegar fermentation study was carried out to study the growth of A. aceti in the

fermentation media. Substrate dependent kinetics models failed to account for the

experimental data and observations. Substrate independent model such as logistic and

gompertz can be used for modelling of microbial growth. Substrate inhibition by acetic acid

sets in a secondary lag phase which ends growth phase prematurely.

- 49 -

3.7 References

[1] K. R. Patil, “Microbial Production of Vinegar ( Sour wine ) by using Various Fruits,”

Indian J. Appl. Res., vol. 3, no. 8, pp. 602–604, 2013.

[2] C. Pochat-Bohatier, C. Bohatier, and C. Ghommidh, “Modeling the kinetics of growth

of acetic acid bacteria to increase vinegar production: analogy with mechanical

modeling,” Proc. Fourteenth Int. Symp. Math. Theory Networks Syst. - MTNS 2000,

2000.

[3] D. Cantero and J. M. Gomez, “Kinetics of substrate consumption and product

formation in closed acetic fermentation systems,” Bioprocess Eng., vol. 18, pp. 439–

444, 1998.

[4] J. A. vazquez and M. A. Murado, “Unstructured mathematical model for biomass ,

lactic acid and bacteriocin production by lactic acid bacteria in batch,” Chem.

Technol., vol. 96, no. August 2007, pp. 91–96, 2008.

[5] F. Ardestani, “Investigation of the Nutrient Uptake and Cell Growth Kinetics with

Monod and Moser Models for Penicillium brevicompactum ATCC 16024 in Batch

Bioreactor,” Iran. J. Energy Environ., vol. 2, no. 2, pp. 117–121, 2011.

[6] G. C. Okpokwasili and C. O. Nweke, “Microbial growth and substrate utilization

kinetics,” African J. Biotechnol., vol. 5, no. 4, pp. 305–317, 2005.

[7] N. Debasmita and M. Rajasimman, “Optimization and kinetics studies on

biodegradation of atrazine using mixed microorganisms,” Alexandria Eng. J., vol. 52,

no. 3, pp. 499–505, 2013.

- 50 -

[8] J. Liu, L. Weng, Q. Zhang, H. Xu, and L. Ji, “Short communication A mathematical

model for gluconic acid fermentation by Aspergillus niger,” vol. 14, pp. 137–141,

2003.

[9] M. Elibol and F. Mavituna, “A kinetic model for actinorhodin production by

Streptomyces coelicolor A3(2),” Process Biochem., vol. 34, no. 6–7, pp. 625–631,

Sep. 1999.

[10] M. H. Zwietering, I. Jongenburger, F. M. Rombouts, and K. van ’t Riet, “Modeling of

the bacterial growth curve.,” Appl. Environ. Microbiol., vol. 56, no. 6, pp. 1875–1881,

1990.

[11] C. Winsor, “Gompertz Curve as a Growth curve,” in national acdemy of Sciences,

1984, vol. 173, no. 2, pp. 253–258.

[12] D. A. Mitchell, O. F. Von Meien, N. Krieger, F. Diba, and H. Dalsenter, “A review of

recent developments in modeling of microbial growth kinetics and intraparticle

phenomena in solid-state fermentation,” vol. 17, pp. 15–26, 2004.

[13] S. Ghosh, R. Chakraborty, G. Chatterjee, and U. Raychaudhuri, “Study on

fermentation conditions of palm juice vinegar by response surface methodology and

development of a kinetic model,” Brazilian J. Chem. Eng., vol. 29, no. 3, pp. 461–472,

Sep. 2012.

[14] K. Chakraborty, J. Saha, U. Raychaudhuri, and R. Chakraborty, “Optimization of

bioprocessing parameters using response surface methodology for bael (Aegle

marmelos L.) wine with the analysis of antioxidant potential, colour and heavy metal

concentration,” Nutrafoods, vol. 80, no. 1, pp. 51–64, 2015.

- 51 -

[15] A. A. Herrero, “End-product inhibition in anaerobic fermentations,” Trends

Biotechnol., vol. 1, no. 2, pp. 49–53, May 1983.

[16] D. J. Clarke, F. M. Fuller, and J. G. Morris, “The proton-translocating adenosine

triphosphatase of the obligately anaerobic bacterium Clostridium pasteurianum. 1.

ATP phosphohydrolase activity.,” Eur. J. Biochem., vol. 98, no. 2, pp. 597–612, Aug.

1979.

[17] G. Wang and D. I. Wang, “Elucidation of Growth Inhibition and Acetic Acid

Production by Clostridium thermoaceticum.,” Appl. Environ. Microbiol., vol. 47, no. 2,

pp. 294–8, Feb. 1984.

[18] N. V Narendranath, K. C. Thomas, and W. M. Ingledew, “Effects of acetic acid and

lactic acid on the growth of Saccharomyces cerevisiae in a minimal medium.,” J. Ind.

Microbiol. Biotechnol., vol. 26, no. 3, pp. 171–7, Mar. 2001.

[19] J. J. Baronofsky, W. J. Schreurs, and E. R. Kashket, “Uncoupling by Acetic Acid

Limits Growth of and Acetogenesis by Clostridium thermoaceticum.,” Appl. Environ.

Microbiol., vol. 48, no. 6, pp. 1134–9, Dec. 1984.

[20] R. Bar, J. L. Gainer, and D. J. Kirwan, “An Unusual Pattern of Product Inhibition:

Batch Acetic Acid Fermentation,” vol. XXIX, pp. 796–798, 1987.

- 52 -

Figure 3-1: Comparison of Monod, Moser and Haldene equation.

- 53 -

Figure 3-2:Comparison of logistic and gompertz equation

- 54 -

Figure 3-3 Residual plot for logistic and gompertz equation

- 55 -

Table 1: Values of parameters for Monod,Moser and Haldene models

Model

Parameter

Monod Moser Haldene

μmax -0.0428 -0.16295 -0.0056256

Ks -24.734 -773.57 -10.371

Ki - - -38.543

- 56 -

- 57 -

CHAPTER 4

- 58 -

Partial Least square modelling for Prediction of Antioxidant

activity of Phenolic compounds

4.1 Introduction

Reactive oxygen species (ROS) is responsible for inflammation, aging, fibrosis,

carcinogenesis, neurological, cardiovascular diseases and cancers- a number of chronic

diseases rapidly spreading among world population and leading to increasingly higher work-

power, capability and life loss. Normal body defense system maintains a healthy balance of

ROS in the body, mainly for growth factor stimulation, control of inflammatory responses,

regulation of various cellular processes including differentiation, proliferation, growth,

apoptosis, cytoskeletal regulation, migration; but excessive production may be the result of

imbalanced cellular respiration and enzyme systems[1], [2].

Mitigation of Reactive oxygen species (ROS) stress is partially achieved by

application of antioxidant, any compounds capable of preventing or removing oxidative

damage to other molecules. Vitamins, minerals, enzymes and many other different classes of

compounds can act as antioxidants and thus can be used therapeutically or as medicine in

treatment of various diseases.

Natural fruits and vegetable, an important contributor to daily antioxidant intake by

human, are a rich source of various phytochemical compounds and therapeutically used

throughout the world for centuries [3]. Among various phytochemical compounds, phenolic

acid remains an important one owing to its growth controlling and radical scavenging effect.

The phenolic acids of plant-origin are predominantly of C6-C3 (phenypropanoid) type; but

C6-C1 (phenylmethyl) is predominantly formed by microbes (Sarakanen & Ludwig, 1971). A

vast array of 8000 different phenolic compounds can be broadly classified into two classes-

simple phenol and polyphenols; first class contains single phenol unit whereas latter contains

- 59 -

multiple subunits . Simple phenol is further classified into hydroxyl-benzoic structure and

hydroxyl-cinnamic structure[4].

Antioxidants can directly scavenge free radicals, chelate metals, activate antioxidant

enzymes, inhibit oxidases, mitigate nitric acid oxidation stress and improve antioxidant

activity of low MW antioxidants. Direct scavenging of radicals can occur via 3 different,

nonexclusive mechanism of hydrogen abstraction (HAT), proton coupled electron transfer

(PCET) and sequential proton coupled electron transfer (SPLET)[5].

Hydrogen atom transfer (HAT): R + ArOH RH + ARO. ,

One electron transfer (SPLET): ArOH ArO-+H

+

R + ArO-

R- + ArO

.

R-+H

+ RH

Proton coupled Electron transfer (PCET):R+ArOH R-+ArOH

+.

ArOH+.

ArO.+H

+

R-+H

+ RH

Selection of individual pathway depends on structure of phenolics and specially

characteristics and placement of chemical moieties relative to OH group, only group capable

of donating H+ ion to radicals for rendering them into harmless quantity[6], [7]. Hence,

quantification of antioxidant activity of individual compounds includes study of the

scavenging pathway, placement and characteristics of OH group and other chemical moieties.

Pro-oxidant activity, an area of concern for antioxidants, is observable only if the

respective compound is present at higher level. A way of ensuring successful application of

an compounds as an antioxidants is to determine the antioxidant activity. Several molecular

- 60 -

properties are found to influence the antioxidant activity which is quantitatively and

qualitatively studied by using Structure activity Relationship (SAR). SAR allows prediction

about antioxidant activity of compound based on molecular property it share with other

structurally similar compounds[8].

4.2 Method

A wide range of in vitro methods using different artificial species such as 2,2´-

azinobis-3 ethylbenzothiazoline-6-sulfonic acid (ABTS), 1,1´-diphenyl-2-picrylhydrazyl

(DPPH), N, N-dimethyl-p-phenylendiamine (DMPD) has been employed to assess

antioxidant activity . DPPH assay employs DPPH free radical that shows a characteristic UV-

vis spectrum with maximum of absorbance close to 515 nm (methanol) FIG 1. Antioxidant

activity of compound is proportional to decrease of absorbance upon addition. It is easy to

perform, highly reproducible and comparable with other assay methods.There are various

ways to express assay results e.g.- TEAC, EC50, antiradical Power, TEC50, AE [9] .

There are various ways to express assay results.

1. TEAC- Trolox Equivalent Antioxidant Capacity is the antioxidant capacity of a given

substance compared to that of the standard antioxidant Trolox, an analogous

hydrosoluble of Vitamin E

2. EC50- It expresses the amount of antioxidant needed to decrease the radical

concentration by50%.

3. Antiradical Power:- ARP=1 ÷ EC50

4. TEC50- It espresso the time at equilibrium reached with a concentration of antioxidant

equal to EC50

- 61 -

5. AE- Antioxidant Efficacy comprises both electron or hydrogen atom-donating ability

and rate of their reaction towards the free radicals

The antioxidant activity data for several simple phenols are taken from Brand-Williams et al.

1995 and Villano et al. 2007[10], [11].

4.3 Statistical Analysis

PLS is a widely used chemometric method for multivariate calibration which was

developed around 1975 by Herman Wold and then introduced into chemometrics by Svante

Wold. A partial least squares regression (PLSR) model was used to evaluate the importance

of molecular properties as determinants of the antioxidant activity of simple phenols. Five

molecular parameters namely- Refractivity, Refraction index, surface tension, density and

polarizability were selected and calculated using ACD labs molecular property plugin for

ACD 3D viewer (ACD Labs, 2012). PLSR is a generalization of multiple linear regression

and it is particularly useful for analysing data with numerous, correlated and independent

variables [12]. It is a method to relate a matrix X to a vector Y or to a matrix Y. In the PLS

analysis, X space was projected to a hyperplane and the PLS factors were extracted to replace

the original X space. In this process, each PLS factor was produced by linear combination of

the selected predictor variables. By choosing a number of factors, the number of dimensions

could be reduced significantly and antioxidant activity was regressed on these extracted PLS

factor. All variables were manipulated with mean centering and scaling to unit variance and

the model was trained with data from samples. Percent variation accounted for by PLS

factors are used to obtain the appropriate number of components of each PLSR model [13].

A PLSR regression model is thought to provide significant and good predictions when

high percentage of predictor and response variation can be accounted for by fewer factors,

reducing the chances of overfitting [14]. The variable importance plots (VIP) can be used to

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explain contribution of each redictors in fitting the PLS model for both

predictors and response. Thus, it is possible to determine which molecular property has most

strongly influence on antioxidant activity. In general, an independent variable with a VIP

value greater than 1 is thought to be most relevant and significant for explaining the

dependent variable, whereas a value less than 0.5 indicates that the variable does not

significantly explain the dependent variable. In the interval between 0.5 and 1, the importance

level depends on the VIP value. If a predictor has a relatively small coefficient (in absolute

value) and a small value of VIP, then it is a prime candidate for deletion[15].

After optimizing for number of variables and components with validation, the PLS

model was applied to predict the antioxidant and Hotelling T2 for each observation was

derived to check the confidence level of predictability. A large value of Hotelling T2 would

indicate that the observation was suspected to be an outlier, possibly leading to a poor

prediction. After the trained model was internally validated, it was applied to an external test

sample with known cytotoxicity and the PRESS between predicted and observed cytotoxicity

was calculated to test the external applicability of the model [16]. All of the analyses were

conducted using the PLSR procedure implemented in SAS University Edition (SAS Institute

USA).

4.4 Results and Discussion

Based on characteristics of chemical moieties, other than OH group, present in a

compound, the simple phenols can be divided into 2 different classes- compound containing

electron withdrawing group and compound containing electron donating group. An electron

donating group releases electron density to a conjugated π system, whereas an electron

withdrawing group withdraws electron density from it. Thus, electron donating groups make

system more nuclephilic. On the other hand, electron withdrawing groups makes system more

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electrophilic which slows electrophilic substitution reaction[5]. Traditionally, electron

withdrawing groups are associated with poor antioxidant activity that is difficult to apprehend

experimentally fig. 4-1.

Development of PLS model

Electron withdrawing group

The performance of the model including 5 molecular properties and ARP value was

satisfactory. 3 PLS factors were able to accounted for 99.33% variation of predictor variable

and 97.62% variation in responses (Fig 2a). The plot in Fig 4-2 of the proportion of variation

explained (or R square) makes it clear that there is a plateau in the response variation after

three factors are included in the model. The correlation loading plot summarizes many

features of this two-factor model:

The X-scores are plotted as numbers for each observation. Vanillin (3), Vanillic acid

(4) and γ resorcylic acid (5) are found to remain closed together, separated from

phenol (1) and coumaric acid (2) present at periphery, which indicates presence of

electron donating group modifies the antioxidant activity.

The loadings show how much variation in each variable is accounted for by the first

two factors, jointly by the distance of the corresponding point from the origin and

individually by the distance for the projections of this point onto the horizontal and

vertical axes. The position of ARP (AR) in an area between 50-75% shows additional

factors are needed for proper explanation of response variation.

Projection interpretation can be to relate variables to each other. Thus, polarizability

(v5) is found to be highly positively correlated with ARP, and Refraction index (v2) is

negatively correlated. Other variables have very little correlation with ARP as

evidenced by their grouping around bottom centre of the circle.

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The variance importance plot in fig 4-2 can be used to find out relative importance

of predictor variables on response variables. Refraction index (v2) and refractivity (v1) have

the higher influence than surface tension and density which have lowest influence[13].

The resultant PLS regression equation is:-

ARP=-3.619 + 0.025×Refractivity + 0.691×Refraction index - 0.00036×Surface tension +

0.416×Density + 0.0641×Polarizibility (R2=0.9762)

In table 1, value for ARP, predicted ARP, PRESS and T2 are given for each

observation. Model does not hold good for phenol as indicated by higher PRESS and T2 value

[17].

Electron donating group

The performance of the model including 5 molecular properties and EC50 value was

satisfactory. 2 PLS factors were able to accounted for 95.65% variation of predictor variable

and 97.88% variation in responses (Fig 4-3). The plot of the proportion of variation explained

(or R square) makes it clear that there is a plateau in the response variation after two factors

are included in the model. The correlation loading plot summarizes many features of this

two-factor model:

Protocatechuic acid (2), Caffeic acid (3) and ferulic acid (4) are found to remain

closed together, separated from gallic acid (1) and caftaric acid (5).

The position of EC50 (EC) at apposition close to 100% line shows these factors are

sufficient for proper explanation of response variation.

Projection interpretation can be to relate variables to each other. Thus, polarizability

(v5) and refractivity (v1) is found to be highly positively correlated with ARP, while

others are negatively correlated.

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The variance importance plot (4-3) can be used to find out relative importance of

predictor variables on response variables. Refraction index (v2) and refractivity (v1) have the

higher influence than surface tension and density which have lowest influence [18], [19].

The resultant PLS regression equation is:-

EC50=116.1977 + 0.199×Refractivity – 59.934×Refraction index - 0.084×Surface tension -

8.18×Density + 0.491×Polarizibility (R2=0.9788)

In table 2, value for EC50, predicted EC50, PRESS and T2 are given for each

observation. Model is satisfactory for compounds as indicated by low PRESS and T2 value.

Similarity between influential predictor variables along with their importance in VIP

plot indicates not only structural similarity among the compounds but also the existing

similarity between reaction mechanism. Presence of chemically different group can only

influence the rate of H+ atom donation, but they cannot alter reaction mechanism at least for

phenolic class of compounds[20].

4.5 Conclusion

Partial least square was applied to found regression equation to predict ARP and EC50

values for compounds containing electron withdrawing and electron donating group. Results

indicates same predictors variables namely-refractivity, refraction index and polarizability

influence antioxidant activity of both classes. Surface tension and density have no effect and

so can be neglected. The resultant equations show good predictability of response variables,

and thus can be utilized to predict values for other compounds belong in these classes.

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4.6 References

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Figure 4-1 Difference between electron donating and withdrawing effect

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Figure 4-2 Rsquared, factor 1 vs factor 2 and VIP plot for compounds containing electron

withdrawing group

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Figure 4-3 Rsquared, factor1 vs factor 2 and vip plot for compounds with electron donating

group

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Table 1: Prediction of ARP value

Compound ARP Predicted ARP PRESS T2

Phenol 0.002 0.00167 6.44 3.1998

Coumaric acid 0.02 0.02482 -0.439 3.1561

Vanillin 0.05 0.0749 -0085 2.0276

Vanillic acid 0.17 0.1325 0.05642 0.5409

Resorcylic acid 0.36 0.3681 -0.261 3.0757

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Table 2: Prediction of EC50 value

Compound EC50 Predicted EC50 PRESS T2

Gallic acid 5.1 4.35 2.293 1.891

Protocatechuic acid 11.1 10.77 0.5280 0.664

Caffeic acid 12.1 14.045 -2.540 0.137

Ferulic acid 24.7 23.861 3.518 2.246

Caftaric acid 20.4 20.38 0.5998 3.062

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