SIK Report 849

Pasta as an example for structure and

dynamics of carbohydrate rich food materials

A literature review

Thomas Steglich

This literature review is part of a PhD work at

� SIK, the Swedish Institute for Food and Biotechnology

� Lantmännen Cerealia

� Chalmers University of Technology

� Swedish University of Agricultural Sciences

Academic Supervisor: Maud Langton

Industrial Supervisor: Annelie Moldin

Figures and images in this review are reproduced with permission

ISBN 978-91-7290-315-9

Göteborg, 2013

Contents

1. Introduction 7

I. PASTA: FROM RAW MATERIAL TO PRODUCT 9

2. Material 102.1. Durum wheat components . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1.1. Starch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.1.2. Protein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.1.3. Interaction of starch and gluten . . . . . . . . . . . . . . . . . . . . . 172.1.4. Non-starch polysaccharides/ Dietary �bres . . . . . . . . . . . . . . . 192.1.5. Minor components: lipids, colour pigments, ash . . . . . . . . . . . . 21

2.2. Alternatives to durum wheat and non-traditional ingredients . . . . . . . . . 22

3. Processing 263.1. Durum wheat processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2. Pasta production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.2.1. In�uence of processing on selected parameters . . . . . . . . . . . . . 323.3. Pasta cooking and post-cooking . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.3.1. In�uence of cooking conditions . . . . . . . . . . . . . . . . . . . . . 33

II. METHODS 35

4. Introduction Part II Methods 36

5. Analysing the microstructure 375.1. Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5.1.1. Light microscopy (LM) . . . . . . . . . . . . . . . . . . . . . . . . . 375.1.2. Confocal laser scanning microscopy (CLSM) . . . . . . . . . . . . . . 405.1.3. X-ray computed tomography (XRT) . . . . . . . . . . . . . . . . . . 425.1.4. Scanning electron microscopy (SEM) . . . . . . . . . . . . . . . . . 425.1.5. Transmission electron microscopy (TEM) . . . . . . . . . . . . . . . 445.1.6. Atomic force microscopy (AFM) . . . . . . . . . . . . . . . . . . . . 445.1.7. Image analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.2. Other methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6. Analysing the macrostructure 476.1. Rheological methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

6.1.1. Rheological properties of �ours and doughs . . . . . . . . . . . . . . 47Rapid viscoanalyser (RVA) . . . . . . . . . . . . . . . . . . . . . . . 48Falling number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Farinograph and amylograph . . . . . . . . . . . . . . . . . . . . . . 48Alveograph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4

6.1.2. Rheological properties of gluten . . . . . . . . . . . . . . . . . . . . . 486.1.3. Texture analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

6.2. Thermal analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496.2.1. Di�erential scanning calorimetry (DSC) . . . . . . . . . . . . . . . . 496.2.2. Dynamic mechanical analysis (DMA) . . . . . . . . . . . . . . . . . . 50

6.3. Other methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

7. Analysing water transport and mobility 517.1. Water transport and its modelling . . . . . . . . . . . . . . . . . . . . . . . 51

7.1.1. Water transport mechanisms . . . . . . . . . . . . . . . . . . . . . . 517.1.2. Theoretical kinetic models of water transport . . . . . . . . . . . . . 527.1.3. Kinetic models applied to pasta . . . . . . . . . . . . . . . . . . . . 53

Mixing and drying . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Rehydration process . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

7.2. Nuclear magnetic resonance and magnetic resonance imaging . . . . . . . . 567.3. Near-infrared re�ectance spectroscopy (NIR) . . . . . . . . . . . . . . . . . . 597.4. Hyperspectral imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597.5. Gravimetrical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607.6. Other methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

8. Analysing quality parameters 618.1. Colour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628.2. Texture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628.3. Composition analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

9. Concluding remarks 66

Bibliography 67

5

Abbreviations

AACC American Association of Cereal Chemists

AFM Atomic force microscopy

AX Arabinoxylans

BF Bright �eld

CLSM Confocal laser scanning microscopy (also known as CSLM)

DMA Dynamic mechanical analysis

DIC Di�erential interference contrast microscopy

DSC Di�erential Scanning Calorimetry

HMW-GS High molecular weight glutenin subunits

HT High temperature

LMW-GS Low molecular weight glutenin subunits

LM Light microscopy

LOX Lipoxygenase

LT Low temperature

MRI Magnetic resonance imaging

NIR Near-infrared re�ectance

NMR Nuclear magnetic resonance

OCT Optimal cooking time

QD Quantum dots

RS Resistant starch

SEM Scanning electron microscopy

TEM Transmission electron microscopy

TPA Texture pro�le analysis

VHT Very high temperature

WE-AX Water extractable arabinoxylans

WU-AX Water unextractable arabinoxylans

XRT X-ray tomography (also known as microtomography, m-CT)

6

1. Introduction

Pasta

Nowadays, pasta is an universal food mainly made from wheat, but also from rice andother cereals. Pasta has a century-old history with roots in China and Italy, however,the industrial revolution did not start before the 1950s (De Vita, 2009). Even in Italythe consumption of pasta rose �rst after this time, from being a food for feast days tonow everyday use. The consumption rose to top today about 25 kg per capita and year.For comparison: In Sweden 9 kg per capita and year are consumed1. Pasta is o�ered inmanifold ways - as fresh pasta, instant pasta, dried pasta or as noodles.However, it is still not completely understood how the cooking performance and textural

properties of pasta are formed and in�uenced by every step of the production chain -starting from the choice of the raw material over the production itself to end with how tokeep the product warm after cooking.

Objective

The main aim of this PhD project is to study the interdependence of raw materials, processconditions, structure and �nal quality properties of carbohydrate-rich food systems withthe emphasis on pasta products.The vision of the project is in the long run to study which microstructure properties are

· Important for properties in pasta in ready-to-eat meals

· Needed for a pasta which can be prepared in the microwave without prior cooking

· Needed for a pasta with a high level of dietary �bres and wholegrain

The main goal is to control the pasta quality by choice of raw material and components, howthe process can be adjusted depending on quality demands, as well as to create structuresstarting from certain raw material conditions.

· To design microstructures in order to create desired texture properties

· To control mass transport at di�erent heating pro�les

· To design composite material with de�ned quality properties

This literature review shall sum up the current knowledge on (durum) wheat and dietary�bre as a raw material, the manufacturing process as well as on modi�cations of the rawmaterial or process to improve the quality of pasta products. Furthermore, methods toanalyse certain parameters such as microstructure and water migration shall be describedas well.

1http://www.internationalpasta.org/index.php?cat=22&item=7&lang=2, accessed 02/2012.

7

.

Part I.

PASTA: FROM RAW MATERIAL

TO PRODUCT

9

2. Material

Generally, dried pasta is produced by mixing milled durum wheat, water and optionalingredients such as eggs, tomato, spinach or other functional ingredients (Cubadda, 1993).However, in several countries some restrictions apply. For instance, it is only allowed touse durum wheat for dried pasta sold in Italy, France or Greece; to use common wheatsis considered to be an adulteration (for fresh pasta, however, it is allowed; Peressini et al.,2000; Sissons, 2008).Kratzer (2007) de�nes pasta processing from a material science point of view as a trans-

formation of a "cell structured natural composite of the major components starch andprotein and minor constituents [...] into a biopolymer composite material consisting of acontinuous protein phase with starch granules as a �ller." The major components shall bedescribed in this chapter. The composition of starch and protein are described in indi-vidual subsections. As they are often analysed at the same time, their in�uence on pastaquality is described in a subsequent subsection.

2.1. Durum wheat components

On a global scale, bread or common wheats (Triticum aestivum) are the most commoncultivated wheat crops with a share of about 95 % (Fuad and Prabhasankar, 2010). Pastacan be made of common wheat, however the preferred crop are durum wheats (Triticumdurum) (Cubadda, 1993). From the wheat crop, only the seeds - the kernels or grain - areused for food production. The main parts of a grain are the bran, the starchy endospermand the germ (Figure 2.1A shows a schematic illustration of a common wheat grain; deviantto the illustration durum wheat has no hairs of brush).The bran consists of several protective cell layers from the pericarp (epidermis, hypo-

dermis, endocarp), testa, nucellar and aleurone layers (Kill and Turnbull, 2001; Figure 2.1B+C). The endosperm consists mainly of starch granules and protein bodies, which aregrouped in a cellular structure surrounded by thin cell walls. It is the energy storage ofthe grain. The share of starch increases within the endosperm from the outer regions ofthe subaleurone layer to the central region. The germ is the embryo of the grain and thusis rich in vitamins, minerals, antioxidants and dietary �bre (Manthey and Schorno, 2002).To achieve homogeneous �ours only the endosperm is used and thus bran and germ are

often removed during milling. However, they are used in speciality products such as whole-grain food products (Manthey and Schorno, 2002). The remaining endosperm is milledto �our and for common and durum wheat these �ours are called farina and semolina,respectively. There compositions are given in Table 2.1. The numbers shown representonly an average, as quality characteristics vary within common and durum wheat types,especially concerning protein content. In general, only varieties of common wheat with arather high protein content can be used for pasta making (Cubadda, 1993). Alternativesto durum wheats are mentioned in Chapter 2.2. Several methods have been developed toassess the pasta making quality of grains which include visual appearance, defects or theweight of 1000 kernels and after milling parameters such as ash content, speck count, colouror particle size distribution besides the analysis of the quantity of the major components(Sissons, 2008).

10

Figure 2.1.: Structure elements of a common wheat grain. (A) illustration of a grain, (B)light micrograph of an embedded section (length of scale bar is unknown),(C) �uorescence micrograph from bran layers. Figures reproduced from Slavinet al. (2000), Autio and Salmenkalliomarttila (2001) and Poutanen (2012).

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Table 2.1.: Selected representative properties of durum wheat (semolina) and commonwheat (farina) �ours

Unit Durumwheat

Commonwheat

Sources

Carbohydrates [%] 72.8 78.0 Anonymous (2010)

Damaged starch [%1] 11-12 7-10 Eliasson and Larsson (1999)

Amylose [%1] 26-28 23 Vansteelandt and Delcour (1999); Baik and Lee (2003)

[%1] 0-40 Sissons (2008)

Protein [%] 12.7 10.6 Anonymous (2010)

[%] 11-16 Kill and Turnbull (2001)

Gluten proteins [%1] (60)-80 Gil-Humanes et al. (2011)

Gliadin:Glutenin 0.6-0.86 0.72 Rao et al. (2001); Aravind et al. (2011); Létang et al. (1999)

Water [%] 12.7 10.5 Anonymous (2010)

Dietary �bre [%] 3.9 1.9 Anonymous (2010)

Lipids [%] 1.0 0.5 Anonymous (2010)

Ash [%] 0.8 0.4 Anonymous (2010); Eliasson and Larsson (1999)

Kernel size length [mm] 7 Troccoli et al. (2000)

Kernel sizeperimeter

[mm] 15 Troccoli et al. (2000)

Granule A-type [mm] 13-16 22-36 Soh et al. (2006), Pérez and Bertoft (2010)

Granule B-type [mm] 3-5 2-3 Soh et al. (2006), Pérez and Bertoft (2010)

Granule C-type [mm] < 1 Wilson et al. (2006)

1Percentage of speci�c major component

2.1.1. Starch

Granular organisation

On the nanoscale, starch is composed of the two polysaccharides amylose and amylopectinand are both based on the monomer (1->4) a-D-glucans. These two polysaccharides aresynthesized in granular form, leading to an ultrastructure of starch existing over severallength scales (from nm to up to 40 mm) as it is illustrated in Figure 2.2 (Pérez and Bertoft,2010). They are formed during biological synthesis together with non-starch moleculessuch as phosphates and lipids (Conde-Petit, 2003; Tang et al., 2006).Amylose is a long-chain molecule with a molecular weight of 105 to 106, which is not or

only slightly branched and has partly a helical and double-helical structure (Delcour et al.,2010). The highly branched amylopectin (1->6 linkages) with a high molecular weight of107 to 108, in contrast, is constituted in major parts of crystalline and amorphous lamellaes(length of about 10 nm, Figure 2.2B, Tang et al., 2006). The crystalline lamellaes consistsof the double-helical side chains of the amylopectin, whereas the amorphous lamellaes arethe branching zones of amylopectin. The extent of crystallinity is dependent on the amountof amylose and is ranging between 15 % for high-amylose starches and 40-50 % for waxy,i.e. non-amylose starches (Copeland et al., 2009). It is also dependent on moisture content,with an observed maximum of crystallinity at 27 % (Blazek, 2008). The crystallinity isbased upon the close arrangement of six or seven double-helices. Two types of crystallinitystructure patterns can be distinguished in wheat starch, as it can be analysed by X-ray di�raction (Figure 2.2A and B, Blazek, 2008; Tang et al., 2006). Type A has morestructured double-helices than type B and di�ers also in the chain length of amylopectin

12

and in the amount of structural water within the crystal. Wheat as other cereals tend tohave a crystallinity pattern of type A (Copeland et al., 2009). The crystallinity of nativestarch granules can be shown with polarized light, as the crystallinity leads to birefringence,and that can be seen as a so called Maltese cross (Pérez and Bertoft, 2010) .The next higher level of structure are blocklets, which are formed of several lamellae

amylopectin molecules (length up to 120 nm) (Gallant et al., 1997). These ordered struc-tures are interrupted both by amylose, which is mostly surrounding the blocklets and byslightly branched amylose-like (LC) amylopectin. The LC amylopectin molecules (up to 13% of all amylopectin) can have a length of several blocklets (Tang et al., 2006). Accordingto the same authors, these semi-crystalline blocklets are the basic unit in starch granuleformation and exist in two forms, 'normal' and 'defective' blocklets. They can be correlatedto the highest level of starch organisation within the granule, the observed growth rings.These growth rings are now seen as being a none-continuous structure with alternating hardand soft shells, consisting of crystalline and semi-crystalline regions of a size of 120-400 nm(Blazek, 2008; Pérez and Bertoft, 2010). The hard shells are formed of normal blocklets(common wheat: 100 nm wide) and the soft shells of the defective blocklets (25 nm wide)(Pérez and Bertoft, 2010). The defective organization comes from non-branching amylo-pectin molecules (Tang et al., 2006). These zones of defective blocklets could be seen asa correspondent to the amorphous channel concept of Gallant et al. (1997). For commonwheat, the existence of channels has been demonstrated using CLSM (Kim and Huber,2008). Channels were present in type-A- and type-B-granules (de�nition: see next subsec-tion) and in waxy and normal common wheat starchs. Hydration ceased the visualisationof the channels. For durum wheat starch, channels were yet not presented. Their existencecan be assumed, however, as during starch heating amylose is leaking within the granuleboth to the surface and the inner groove region (shown in pasta by Heneen and Brismar,2003).Blocklets have yet not been extracted and characterized, but there is evidence for a level

of structure between the growth rings and amylopectin lamellae and blocklets are o�eringa valuable model for the position of amylose in the granules (Pérez and Bertoft, 2010).Within the granule, amylose is randomly distributed and radial orientated. Its contentis richer at the periphery of the granules. A smaller part of amylose may be involved inlipid complexes and there exists larger amylose molecules as well, which are non-leachableand which are thought to interact with the double-helical zones of amylopectin (Pérezand Bertoft, 2010). The aforementioned starch-lipid complexes consists of helical amyloseincluding fatty acids, monoglycerides or linear alcohols, while amylopectin do probablyform these complexes only weakly or not at all (Blazek, 2008).A further structural element is currently disputed: On the surface of the granules, pores

might be visible and be connected to the amorphous channels. Tang et al. (2006) callthe pores artefacts, while Pérez and Bertoft (2010) claim their existence. Furthermore,there might be a correlation between the number of pores and the digestibility of thestarch granule (Pérez and Bertoft, 2010). Undisputed are the components on the starchgranule surface. Surface lipids (free lipids and starch-amylose complexes) and proteins canmoderate the starch functionality (Blazek, 2008). Delcour et al. (2000a) demonstrated,that the removal of surface proteins led to accelerated hydration of starch granules.

Kernel organisation

Starch granules are organised within the native kernel in a cellular structure. Wheatstarch is commonly said to have a bimodal distribution with larger oval type-A-granulesand smaller round type-B-granules (Soh et al., 2006). Some researchers claim that thereexists a third category of type-C-granules, which are even smaller than type B (Wilson

13

A

B

Figure 2.2.: (A) Starch organisation over several length scales � from granular to molecularlevel (reproduced from Gallant et al., 1997).(B) Detailed scheme of the organisation of amylopectin, amylose, lipids andminor components in blocklets (reproduced from Tang et al., 2006).

14

et al., 2006). C-granules make up to 80 % of all granules when counted in numbers, butare negligible when counted as volume percentage. The volume share of A- and B-granulesin durum wheat is according to the measurements done by Wilson et al. (2006) between80-90 % A-granules and 10-20 % B-granules, respectively. While A-granules start to growsoon after the anthesis, B- and C-granules start to grow later. The average chain length ofamylopectin molecules is shorter in B-granules than in A-granules (Copeland et al., 2009).Changing the ratio of A:B granules towards more B-granules results in higher and fasterwater absorption, because B-granules have a higher surface to volume ratio (Soh et al.,2006). According to a summary of Soh et al. (2006), it is not fully clear whether a change inthe content of A- and B-granules leads to a change in the content of amylose in total. Somestudies stated that A-granules compared to B-granules contained 4-10 % more amylose,while other studies could not detect signi�cant di�erences. Probably caused by a higherlipid content in B-granules, the gelation temperature is higher in B- than in A-granules(Delcour et al., 2010).

Comparison of durum wheat starch and common wheat starch

Zweifel (2001) summarised the outcome of several publications, which studied the di�er-ences between starches from durum wheat and common wheat (see also Table 2.1). Indurum wheat starch the amylose content is slightly higher than in common wheat starch,and shows a lower gelatinisation temperature (Delcour et al., 2010; Vansteelandt and Del-cour, 1999). Furthermore, durum wheat starch has a higher water binding capacity. Zweifel(2001) concluded that these data suggests a less compact starch granule structure in durumwheat. However, durum wheat lacks a certain protein (puroindoline), which induces astronger interaction of proteins with the starch granule surface and is making the kernelsvery hard (Delcour et al., 2010). This may be the reason, that starch damage duringmilling is higher in durum wheat than in common wheat. Compared to common wheat,especially the type-A-granules of durum wheat are smaller and the volume share of themare higher (Wilson et al., 2006). Finally, according to Vansteelandt and Delcour (1999),durum wheat starch and common wheat starch show about the same protein content, butdi�er in lipid content.

2.1.2. Protein

The heterogeneous wheat protein is classi�ed according to extractability into four fractions:water soluble albumins, globulin (soluble in salt solution), gliadins (soluble in ethanol) andglutenins (soluble in dilute acids, Troccoli et al., 2000). Albumins and globulins representthe minor part (15-20 %) and have emulsifying properties. The major part of wheatproteins consists of gliadin and glutenin Delcour et al. (2012). The latter two proteinsform the composite gluten, which is seen as probably being the most important factor indetermining pasta quality as they can form a viscoelastic network.Glutenin is further separated into high and low molecular weight subunits (HMW-GS and

LMW-GS, respectively). LMW-GS has a ratio of 60-80% of all glutenins and its molecularmass is in the magnitude of 3-5*105(Sissons, 2008). The equivalent value for HMW-GS is8-12*105, while the molecular mass of glutenin can exceed 108. This indicates that gluteninis a huge polymer, bounding together the subunits by intermolecular disulphide bonds andit is formed in a helical structure (Sissons, 2008; Zweifel, 2001).Gliadins (MW 2.5-7.5*105) are classi�ed based on their electrophoretic mobility into a-,

b-, g- and w-gliadins (Kontogiorgos, 2011; Sissons, 2008). The monomeric Gliadins containonly intra disulphide bonds and the interaction with the gluten polymer is happening vianon-covalent forces (Sissons, 2008).

15

A B

Figure 2.3.: Organisation of gluten proteins in wheat. (A) Stained section of a wheat seed.Lower image is a magni�cation of the upper image. Protein bodies (PB) arestained in blue and are shown in the starchy endosperm and aleurone layer(AL). Bar corresponds to 500 mm and 50 mm, respectively. Reproduced fromGil-Humanes et al. (2011). (B) Structure of hydrated gluten over several lengthscales. Reproduced from Kontogiorgos (2011)

In the dry grain the gluten is accumulated in protein bodies (Figure 2.3A) which arequantitatively and qualitatively uneven distributed in the kernel (Tosi et al., 2011; Gil-Humanes et al., 2011). Protein concentration is higher in the sub-aleurone cells and lowerin the central starchy endosperm cells. Furthermore, two types of protein bodies (smalland large) are formed in the starchy endosperm. Small bodies are rich in gliadins andLMW-GS whereas large protein bodies are rich in HMW-GS (Tosi et al., 2011). Someprotein bodies can even form a continuous matrix enclosing starch granules.Hydrated gluten forms a continuous network. Kontogiorgos (2011) suggest a new hier-

archical model for the structure of the hydrated gluten (Figure 2.3 B). At molecular levelindividual glutenins and gliadins are interacting via various physical and covalent forces.Depending on the conditions of e.g. ratio of protein fractions and hydration status, therehappens a transition from �brils to a continuous phase where gluten polymers form sheets.These resulting sheets can be seen as the building block of the gluten network and it aggreg-ates may be embedded. The side-by-side arrangement of the sheets gives a nanoporousstructure with �con�ned� water entrapped into the sheets and interacting strongly withthe gluten matrix and �bulk� water surrounding it. Between the sheets nanocapillaries areformed. Depending on the packing of the sheets, a three-dimensional network is formed.Finally, the gluten network on the macroscale can show various morphologies.Speaking of rheological properties, glutenin is responsible for elastic properties in a

dough, while gliadin acts as a plasticizer being responsible for viscous properties, suchas dough extensibility (Sissons et al., 2007). The term gluten strength is a measure for thebalance between the two gluten subfractions, i.e. between viscosity and elasticity (Sissons,2008). Gluten strength can be measured with the gluten index method and a higher glutenindex indicates a stronger gluten (see also Chapter 6.1.2). There seems to be a tendencyfor a higher share of glutenin in stronger gluten varieties (Rao et al., 2001).

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2.1.3. Interaction of starch and gluten

Hydrothermal induced changes: Glass transition and gelatinisation/denaturation

In its native state and at room temperature, both starch and gluten are in a glassy state andare organised in crystalline structure. They are very limited in their water uptake with adecreasing solubility for the starch components from amylopectin, to amylose and amylose-lipid complexes (Conde-Petit, 2003). Depending on temperature and water content starchand gluten can change from a glassy state to a rubbery state reversibly (Figure 2.4) viaa so called glass transition. A second, irreversible transformation can occur at highertemperatures: Starch gelatinisation and protein denaturation.

Starch When su�cient moisture is available, native starch gelatinise during heatingabove the gelatinisation temperature, loosing its crystallinity and structural organization(Copeland et al., 2009). At the same time there is a signi�cant increase in molecular watermobility (Cuq et al., 2003). Water is absorbed �rst to the amorphous zones and if thereare channels, water gets �rst to the inside and di�uses out from the inside (Copeland et al.,2009; Fannon et al., 2004). This hydration process can be quite fast, with a half time of 7s(Lemke et al., 2004). The loosely bound amylose molecules start to leach out of the granule.Starch molecules can swell to a certain amount, before they are disrupted, caused by theinduced stress. Both glass transition and gelatinisation lead to a considerable increase inviscosity. Observing the melting process of starch using DSC measurements, an additionalpeak appears at higher temperatures than the peak corresponding to starch gelatinisation.This is attributed to the melting of amylose-lipid complexes (Conde-Petit, 2003, not shownin Figure 2.4). The shown temperature curves may in reality not be as exact as they arepresented. Because of the heterogeneity of starch and gluten, the transitions are occurringin a certain temperature range (Cuq et al., 2003).

Gluten Through the addition of water, gluten transforms from the glassy state to becomerubbery, elastic and is forming a network through inter-molecular bonds (Kill and Turn-bull, 2001). Already at a water content of 15 %, the glass transition occurs below roomtemperature and hence gluten is in the rubber state at the conditions used for pasta doughpreparation. At temperatures above 60 °C, again as a function of moisture, hydrated glu-ten is forming three-dimensional aggregates through the establishment of covalent bonds(protein-protein cross links; Sissons, 2008). This thermosetting is irreversible (Cuq et al.,2003). Gluten bond formation both during processing and cooking is described in moredetail elsewhere (Wagner et al., 2011; Bock and Seetharaman, 2012).

In starch and gluten, the structural transformations occur at similar moisture and tem-perature conditions. Hence, the transformations are often competitive in regard to water.This can lead to an uneven water distribution among the components in pasta e.g. duringcooking (Petitot et al., 2009a). It is interesting to note, that while starch becomes solubleduring gelation, gluten becomes during the network formation insoluble (Pagani et al.,2007). Therefore, the kinetics of these processes determine the extent of starch swelling.

Technological studies describing the e�ects of starch and gluten in pasta processing

To understand the in�uence of the wheat components on pasta quality, several authorscarried out reconstitution studies. That means, they fractioned the semolina into gluten(or even several gluten fractions) and starch (variation in starch granule distribution) andcombined the fractions in varying compositions together (Delcour et al., 2000a,b; Sissonset al., 2002). Sissons et al. (2007) altered the gluten composition and showed that glutenin

17

Figure 2.4.: State diagram for starch and gluten. Tg, glass transition temperature; Tm,melting point; Tgelat, gelation temperature of starch; Tr, minimum temperat-ure for protein thermosetting. Reproduced from Cuq et al. (2003)

increased and gliadin decreased dough strength in a dough made of both reconstituted �ourand semolina base. HMW-GS increased the dough strength of the base, while LMW-GSdecreased it. The dough strength changes did not alter spaghetti texture, however.An increased share of 32-44 % B-granules resulted in an improved pasta quality, with

an increased �rmness, reduced stickiness and reduced cooking loss (Soh et al., 2006). Thedough strength decreased above a share of 32 % B-granules. The authors speculated, thattoo many B-granules would need too much water creating an imbalance in the dough inwater distribution. Another study concentrated on the e�ect of starch digestion. For ashare of 40 % B-granules, they found slightly lower starch digestion compared to the control(Aravind et al., 2011).To increase the amylose content, Soh et al. (2006) used high-amylose maize starch.

Therefore, the results could be di�erent, when using amylose of durum wheat instead.However, they showed that increased concentrations of amylose led to a more extensibledough, lower water uptake of the pasta and increased �rmness. According to the authors,higher amounts of amylose lead to more tightly packed starch granules, which could beunder swelling more resistant to deformation. They concluded that the decreased wa-ter uptake might change the sensory perception negatively. The observations on varyingamylose content is in accordance with studies from Gianibelli et al. (2005) and Vignauxet al. (2005), which found inferior cooking properties for reduced amylose content. Anexplanation could be that starch granules of low amylose content deteriorate physicallymore easily and during cooling they form aggregates, but no network. This would resultin a soft structure (Tan et al., 2006).The reconstitution method in itself has some limitations, as through fractioning the

material properties are changed. Sissons et al. (2002) showed, that the dough strengthincreased in a reconstituted pasta sample compared with an non-reconstituted sample.Another approach was used by Wood (2009). Chickpea-forti�ed spaghetti was produced

and compared with standard durum wheat spaghetti to study the mechanisms for pastaquality. Her �ndings were, that pasta �rmness is in�uenced more by the composition andcontent of gluten than of protein content in total. Additionally, the protein-polysaccaridematrix rather than the starch composition is important for cooking loss, while cooking lossand stickiness do not necessarily correlate very strongly. Finally, increased protein andamylose contents decreased pasta stickiness.

18

2.1.4. Non-starch polysaccharides/ Dietary �bres

Classi�cation of dietary �bres

Dietary �bre is a not well de�ned term according to Lunn and Buttriss (2007). It is acollective term for a mixture of substances di�ering in chemical and physical compositionwhich all exert some kind of physiological e�ect. This physiological e�ect is mostly somesort of reduced digestibility by the human digestion. Dietary �bre includes soluble andinsoluble �bres, but also resistant starch and additives such as guar gum (BeMiller, 2010).Dietary �bre are di�erentiated according to their solubility in a bu�er at a de�ned pHor according to their fermentability in an in-vitro system representing human alimentaryenzymes (Dhingra et al., 2011, see also Figure 2.5, Lunn and Buttriss, 2007). Insoluble�bres are cellulose, hemicellulose and the non-carbohydrate cell component lignin. Com-mon soluble �bres are pectin, gums, mucilages, but also inulin and beta-glucan (Dhingraet al., 2011) Resistant starch (RS) is a category for undigested starches and starch-degradedproducts. There are four sources for RS: Physically inaccessible starch for amylase as it isentrapped in a food matrix (RS1), Starch granules resisting hydrolysis because of its nature(e.g. uncooked starch; RS2), retrograded starch (RS3) and modi�ed starch inaccessible toenzymes (RS4) (BeMiller, 2010). For RS, an extended review on the e�ect of processingon the content of RS in cereals was written by Alsa�ar (2011).Important physico-chemical properties of dietary �bres include particle size, surface area

characteristics, porosity and hydration properties such as swelling and water retentioncapacity (Meuser, 2008). Also the structure of a dietary �bre will determine its properties.E.g. cellulose can bind rather low amounts of water (due to its �bril structure) whereasarabinoxylans have a high capacity to bind water. These physico-chemical properties arealso dependent on environmental conditions (temperature, pH, ionic strength) and theyare modi�ed during processing (grinding, drying, heating, extrusion; Dhingra et al., 2011).

Dietary �bres in the wheat grain

Non-starch polysaccharides can be found in the wheat grain in the germ, the bran and in thecell walls of the endosperm (Sissons, 2008). The cell walls consist of cellulose, hemicellulose,lignin, and b-glucan. A major component within hemicellulose are arabinoxylans (AX)which are divided into water extractable (WE-AX) and water unextractable (WU-AX)(BeMiller, 2010; Sissons, 2008). Wheat bran is an important example for the category ofdietary �bre. It is often used as an ingredient in nutritional-value-added products as it iscomposed of about 48 % dietary �bre, 16 % starch, 18 % proteins, 5 % fat, 5 % sugars and6 % ash (Meuser, 2008). Bran consists of several tissues with varying properties and thestructure has been shown in more detail by Surget and Barron (2005).

Figure 2.5.: Suggested overview to classify dietary �bre by Lunn and Buttriss (2007)

19

Technological studies describing the e�ects of various �bres in pasta processing

Due to its nutritional pro�le, there is a great interest to learn more about how wheat brana�ects products. Only recently, several studied the e�ects of bran and bran fractions beingincorporated into pasta/ noodles (Aravind et al., 2012a; Chen et al., 2011; Chillo et al.,2008; Kaur et al., 2012; Shiau et al., 2012; Sudha et al., 2012; West et al., 2013a) or bread(doughs) (e.g. Almeida et al., 2013; Curti et al., 2013; Majzoobi et al., 2013). However,bran often induces in pasta strong aromas (due to phenolic acids) and a changed texturewith increased cooking loss and decreased �rmness (West, 2012). What induces the texturechange is not fully understood. Tudorica et al. (2002) argue for fresh pasta that soluble�bres are included into the protein network of the pasta, while insoluble �bres such as branare disrupting it. In the case of dried pasta, SEM images showed that the bran particleswhere not in contact with the protein matrix and thus disrupted the network (Bustos et al.,2011a; Manthey and Schorno, 2002) Others argue that it might depend on the amount andtype of �ber (germ particles destroyed the protein network to a larger extent than branparticles; Aravind et al., 2012a) and on the process conditions (Villeneuve and Gelinas,2007). One recent study rejects the theory of gluten network destruction for the case ofbread (Noort et al., 2010). Instead, the authors argue that the �bres interact physically orchemically with the gluten and thus hinders gluten aggregation.Several approaches have been reported to counteract the deteriorating e�ect of bran

inclusion. Heat treated bran (Sudha et al., 2011), wholegrain �our milled to rather largeparticle sizes (Gauthier et al., 2006) and pasta dried at high temperatures (West et al.,2013b) helped to remain the desired texture. A review discusses in a broader contextvarious techniques to increase the bioactive potential of wheat bran (e.g. through milling,fermentation or enzymes; Mateo Anson et al., 2012).According to de Noni and Pagani (2010) it has yet not be shown, that individual enzymes

in�uences the quality of pasta. However, added endoxylanases (EC 3.2.1.8) hydrolysed thexylan backbone in arabinoxylans and could thus convert some of the water unextractableAX to water extractable AX forms. High doses of the enzyme led to less leaching of solubleAX during pasta cooking in water and retaining thus a higher amount of soluble dietary�ber in the product (Ingelbrecht et al., 2001; Brijs et al., 2004). Increasing the amount ofWE-AX, increased the water absorption, but the pasta texture was not in�uenced (Turneret al., 2008). During cooking of pasta, only minor amounts of AX were released into thecooking water, but this amount can increase during overcooking (Sissons, 2008).Besides the addition of further ingredients to the dough, also technological variations

can in�uence the amount and e�ect of dietary �bres. Coarsely and �nely grounded dietary�bre-rich �ours will show a di�erent behaviour during rehydration (Gauthier et al., 2006;Meuser, 2008; Shiau et al., 2012). During thermal treatments such as cooking, �bre-proteincomplexes are formed in wheat bran increasing the amount of dietary �bre, too (Dhingraet al., 2011). Meuser (2008) reminds that only highly puri�ed dietary �bres will have alight colour and will be free from an unpleasant taste. Unpuri�ed �bres, instead, are oftenof yellow to brownish colour and have a bitter to characteristic �avour.Extrusion can increase the water solubility of dietary �bre. Whether this has a signi�cant

e�ect on wheat bran �bre seems to depend on process conditions and material propertiessuch as particle size (Robin et al., 2012).Resistant starch is heat sensitive and its content might increase slightly during extru-

sion, however decreases signi�cant during cooking of pasta (Gelencsér et al., 2010; Alsa�ar,2011). During storage, the RS content of food products can be modi�ed by applying tem-perature cycles (Alsa�ar, 2011). For a gelatinised starch, the temperature cycle between4°C and 30°C led to the formation of amylopectin crystals resulting in a reduced digestib-ility.

20

Finally, also non-dietary �bre components can e�ect the digestibility: Amylose-lipid-complexes have been shown to have a lower digestibility. They occur not only naturally,but can also be formed during gelatinisation of starch when lipids are present (Alsa�ar,2011).

2.1.5. Minor components: lipids, colour pigments, ash

Lipids exists in the wheat grain in two forms: Starch bound lipids, forming amylose-inclusion complexes and non-starch lipids, which can be divided into free and bound lipids(Sissons, 2008). 64 % of all lipids in semolina are free lipids, which appear to be evenlydistributed in the protein network. These free lipids in the endosperm are mainly trigly-cerides and other nonpolar lipids such as hydrocarbons, mono- and diglycerides as well asfree fatty acids (Pomeranz, 1988). An extensive list of the individual types of wheat lipidscan be found in a review on lipids in bread making (Pareyt et al., 2011).Amylose within the amylose-lipid-complexes is prevented from leaching during gelatinisa-

tion and thus may reduce stickiness in starch (Blazek, 2008). According to Sissons (2008),not much is known about composition changes of lipids during pasta processing. However,it is known, that lipids interact during dough mixing especially with the gluten networkand that a removal of all lipids led to a pasta with increased stickiness and cooking loss(Sissons, 2008).The Colour pigments, giving pasta its yellow colour, are named carotenoids and are

grouped into carotenes, unsaturated hydrocarbons and xanthophylls (major component islutein; Sissons, 2008; Troccoli et al., 2000). Carotenoids are located in the outer layer ofthe kernel and to lesser extent in the endosperm. The insensitivity of the carotenoids in thepasta products is in�uenced by several factors such as storage time and process conditions,but the most important factor is the activity of the oxidative degradation by lipoxygenase(LOX) (Troccoli et al., 2000). LOX oxidises free fatty acids such as carotenes. Also theactivity of peroxidase in wheat has been reported, leading to a brown colour (Troccoli et al.,2000) and peroxidase may also a�ect rheological properties (Pomeranz, 1988). According tothe same authors, innumerable other enzymes can be found in wheat, but which probablyhave no great impact in cereal processing. For instance, in pasta production, a-amylase isof minor importance (Sissons, 2008). Amylase is existing mainly in preharvest sproutedgrain and even if this grain is used, the enzyme activity is further reduced during dryingand cooking.Another category of components is ash, the residual after combusting the wheat. Ash

consists mainly of minerals and the ash content varies within the kernel, with signi�canthigher amounts in the bran as in the endosperm (Troccoli et al., 2000). The ash contentis most important in milling, as the content is correlated to semolina yield. Furthermore,ash content in�uences the colour of the semolina, with more ash giving a browner colour(Troccoli et al., 2000). The ash content is in general higher in durum wheat than incommon wheat (Pagani et al., 2007).They are no components in itself, but mycotoxins are toxic metabolites from fungi present

in durum wheat grains, too. The content depends on the growing conditions and my-cotoxins can mainly be found in the bran layer (Brera et al., 2013). Adequate cleaning,debranning and milling processes can reduce the mycotoxin content (Cheli et al., 2013).Drying and �nally cooking of pasta further reduces the mycotoxin content (Brera et al.,2013).

21

2.2. Alternatives to durum wheat and non-traditionalingredients

The oldest enrichments were probably colouring plant material from tomato, spinach orcarrots (Cubadda, 1993). Also the usage of egg has a long history. Nowadays, there aremore widespread applications available. A list of tested supplemental �ours is given inTable 2.2. This table also includes a short-list of available dietary �bres, as well as somerecently tested enrichments. In the comprehensive review on non-traditional ingredientswritten by Fuad and Prabhasankar (2010), the authors included a chapter on additives suchas emulsi�ers, organic acids or alginate. As using additives in dried pasta is prohibited inEurope by law, they will not be covered in detail.Three main drive forces could be identi�ed within the research for supplements or re-

placements to durum wheat pasta:

· Substitute durum wheat with local available grains

· Increase nutritional value

· Decrease allergenicity, in particular replace gluten

In markets with a developed pasta production, such as the United States or the EuropeanUnion, mainly durum wheat is used for pasta production. However, especially researchersin non-traditional pasta consuming countries such as Iran or India are working on �ndingalternatives to durum wheat to be able to use the local grown species, in most cases varietiesof common wheat (e.g. Aalami and Leelavathi, 2008; Fuad and Prabhasankar, 2012; Heneenand Brismar, 2003; Jyotsna et al., 2004). In a recent study, common wheat and emmerwere analysed for its feasibility for the production of pasta (Fuad and Prabhasankar, 2012).The usage of common wheat led to an inferior pasta quality, as it has been reported before(Heneen and Brismar, 2003). The cooking quality and sensory perception of emmer basedpasta was comparable to durum pasta. Spelt wheat (Triticum aestivum ssp. spelta) is especiallyused in the ecological food market and is similar in its properties to durum wheat (Fuadand Prabhasankar, 2010). It is notable, that even for other grains like rice, experimentsare carried out to test additives such as gluten or gum arabic to achieve texture propertiescomparable to pasta (Raina et al., 2005).Another major goal is to increase the nutritional value of the pasta while maintaining

good cooking properties. One possibility is to use �ours with an optimized nutritionalpro�le, which is often derived from other crops. An alternative is to add puri�ed compon-ents such as �bres. In fact, most of the listed examples in Table 2.2 seek to improve thenutritional value by increasing protein content, decreasing the glycaemic response or evenincreasing the content of omega-3-fatty acids (Iafelice et al., 2008). Other approaches, notlisted in the Table, include enrichments using kamut, sprout �nger millet, amaranth seed oreven shrimp meat. It seems, that almost every ingredient could be suitable. An extensivelist of dietary �bre containing ingredients suitable for extruded cereal products includingtheir respective �bre concentration can be found in the review of Robin et al. (2012).Maybe of higher importance in history, egg pasta amounts nowadays for only a small

part of the market, especially for dried pasta. Therefore, studies on the in�uence of eggon the quality of dried egg pasta are rare (Materazzi et al., 2008; Schreurs et al., 1986).Research on fresh egg pasta is more frequent (e.g. Akillioglu and Yalcin (2010); Alampreseet al. (2005, 2009); Fratianni et al. (2012); Nouviaire et al. (2008); Zardetto and Dalla Rosa(2009); Hager et al. (2012a)). Egg has also been unsed as a supplemental for non-glutenpasta based on buckwheat starch (Alamprese et al., 2007) and on oat and te� �our (Hageret al., 2013).

22

Currently, there is not only a striving for pasta with higher nutritional value, but alsoallergens shall be decreased (Krishnan and Prabhasankar, 2012). One approach is to uselactobacilli to ferment pasta dough so that the non-tolerated gliadin fractions got hydro-lysed di Cagno et al. (2005). Other studies evaluate the properties of amaranth, buckwheat,soy, maize and quinoa for the production of gluten-free pasta (Mastromatteo et al., 2011;Schoenlechner et al., 2010). In a recent and comprehensive review for gluten replace-ments in bread-making, further ingredients and technological adjustments such as highhydrostatic pressure are discussed (Zannini et al., 2012). For the case of pasta, Marti andPagani (2013) review di�erent technologies to pre-treat �ours. Petitot et al. (2009a) discussthe implications of modern process modi�cations on starch digestibility and allergenicity.As an example, very high temperature drying induces a higher protein aggregation, whichmay encapsulate starch granules stronger. This would give a lower glucose response duringdigestion.Hager et al. (2012b) screened 33 commercial gluten-free pasta brands from several

European countries. They recommend further product and process improvements, as thetested gluten-free pasta brands showed di�erent colours and higher cooking loss as well ashigher stickiness than durum wheat pasta.Several of the before mentioned ingredients have made its way to the market, at least in

the United States. Pszczola (2010) has written a market review for what he calls "better-for-you" pastas.

23

Table 2.2.: List of selected �ours and other non-traditional ingredients tested for pastaproduction

Category Component Examples for (functional)properties1

References

Wheats Whole meal of

durum wheat

Lower �rmness and higher cooking loss,

however e�ect not so pronounced when

pasta was dried at high temperature

Baiano et al. (2008); Manthey and

Schorno (2002)

Common wheat Similar properties to durum could be

achieved through additives

Fuad and Prabhasankar (2012);

Jyotsna et al. (2004)

Spelt Similar properties to durum Marconi et al. (2002)

Emmer Similar properties to durum Fuad and Prabhasankar (2012)

Barley Darker colour, higher �bre content, higher

cooking loss

El-Faham et al. (2010)

Buckwheat Gluten-free Manthey and Hall (2007); Verardo

et al. (2011)

Other

�ours

Oat Increase nutritional value Chillo et al. (2009); De Pilli et al.

(2013)

Lupin Higher protein + �bre content, slight

changes in cooking properties, stickier at

high lupin amounts

Jayasena and Nasar-Abbas (2012)

Corn Similiar properties (if pregelatinised) Pagani et al. (2007)

Flaxseed Increase alpha-linolenic acid Lee et al. (2003)

Amaranth Increase nutritional value Fiorda et al. (2013)

Split pea, faba bean Minor changes in microstructure,

improved nutritional value

Petitot et al. (2010)

Pea protein Higher protein content Mercier et al. (2011); Zhao et al. (2006)

Soy Lower cooking loss, similar sensory

characteristics

Baiano et al. (2011)

Insoluble

�bres

Wheat bran <15% similar sensory characteristics Aravind et al. (2012a); Kaur et al.

(2012); Shiau et al. (2012)

Germ Higher cooking loss, darker colour, similar

overall acceptability

Tarzi et al. (2012)

Oat bran Lower GI, negative cooking properties,

reduced �rmness at >5%, <5% similar

sensory characteristics

Bustos et al. (2011b,a)

Hemi-/Cellulose From bamboo �bre; decreased �rmness Brennan and Tudorica (2007)

Pea �bre Slightly decreased �rmness, unchanged

stickiness

Brennan and Tudorica (2007); Tudorica

et al. (2002)

Soluble

�bres

Guar gum Lower GI, high water absorption +

cooking loss, softer + stickier, less yellow

Aravind et al. (2012c); Tudorica et al.

(2002)

Inulin Lower GI, higher cooking loss, inulin was

lost to cooking water

Aravind et al. (2012d); Brennan et al.

(2004); Manno et al. (2009)

b-glucan Depending on type of b-glucan rise in

cooking loss and stickiness, softer pasta

Aravind et al. (2012b); Chillo et al.

(2011); Cleary and Brennan (2006)

Carboxymethylcellulose

(CMC)

Lower GI, comparable cooking properties Aravind et al. (2012c); Chillo et al.

(2009)

Hydroxypropyl

cellulose (HPC, E463)

Improved taste, less stickiness, longer

OCT

Majzoobi et al. (2011b)

24

Table 2.3.: List of selected �ours and other non-traditional ingredients tested for pastaproduction (continued)

Category Component Examples for (functional)properties1

References

Starches Resistant starch,

type II and IV

Lower GI, partly improved textural

properties after cooking

Alsa�ar (2011); Bustos et al. (2011a);

Sozer et al. (2008); Vernaza et al.

(2012)

Pregelatinised starch Acted as an structuring agent for

non-durum wheat �ours

Chillo et al. (2009)

Other Egg Improved colour and taste e.g.Fratianni et al. (2012)

ingredients Beef heart Improved nutritional value, higher

�rmness

Dhanasettakorn et al. (2009)

Blue-green algae Green colour, lower cooking loss, higher

�rmness and swelling index, improved

sensory characteristics

Zouari et al. (2011)

Broccoli powder Signi�cant swelling of broccoli particles,

could be controlled with hydrocolloids

Silva et al. (2013)

Cassava Increase nutritional value in gluten-free

pasta

Fiorda et al. (2013)

Fatty acids Similar properties at low concentrations,

at higher concentrations a�ected sensory

characteristics

Iafelice et al. (2008)

Fenugreek seed

powder

Decreased cooking loss and stickiness, at

high concentrations sensory

characteristics a�ected

Jyotsna et al. (2011)

Fermented pasta

dough

Increase in vitamin B2 Capozzi et al. (2011)

Mustard protein

isolate

Higher protein content, improved

�rmness, decreased cooking loss and

stickiness

Alireza Sadeghi and Bhagya (2008)

Peanut Darker colour and higher cooking loss;

e�ect reduced when hydrocolloids were

used or when dried at high temperature

Howard et al. (2011)

Seaweed Increase in fucoxanthin Prabhasankar et al. (2010)

Transglutaminase Reduced polymer solubility, increased

cooking quality of poor durum wheat

varieties

Takács et al. (2007); Aalami and

Leelavathi (2008); Sissons et al. (2010)

Unripe banana �our Higher water absorption and increased

chewiness, darker colour

Agama-Acevedo et al. (2009),Krishnan

and Prabhasankar (2010)

Vegetable puree Decreased cooking loss; sensory

characteristics negatively a�ected

Rekha et al. (2013)

Whey protein

concentrate

White colour, mashy strand quality,

highly increased stickiness, sensory

characteristics could be improved by

using hydrocolloids

Prabhasankar et al. (2007)

1In comparison to durum wheat pasta

.

25

3. Processing

Only a few process steps are necessary to produce pasta. However, it is crucial to executethem in a proper way to achieve a su�cient result. A �ow chart illustrates the processincluding SEM images for some intermediate products (Figure 3.1). Additionally, reportedtested variations for each unit operation are listed in Table 3.1.

Table 3.1.: Unit operations, important parameters and possible technique variations inpasta processing.

Unitoperation

Equipment/Parameter

Reported techniquevariations1

References

Grain

preparation

Cleaning Kill and Turnbull (2001)

Milling Tempering Delcour et al. (2010)

Particle size < 350 mm Rubin (2007)

Mixing Pre-mixing Pagani et al. (2007)

Kneading Single-screw, co-rotating

twin-screw, Conveyor belt

Pagani et al. (2007)

Vacuum quality Carini et al. (2010); Icard-Vernière and

Feillet (1999)

Water temperature Kill and Turnbull (2001)

Time 20 s -15 min Ait Kaddour and Cuq (2011); Rubin (2007)

Extrusion Dough movement Cylinder-plunger, Single-

screw, Double-screw

Kratzer (2007)

Extruder speed Wojtowicz and Moscicki (2011)

Die material Bronze, te�on Lucisano et al. (2008)

Shape size/form Carini et al. (2009)

Re-extrusion Chillo et al. (2010)

or Temperature

Sheeting

Drying Hot air Convection-heat forced, Benali and Kudra (2010)

Vacuum drying, microwave Altan and Maskan (2005); De Pilli et al.

(2009)

Pre-treatment Controlled pressure drop Maache-Rezzoug and Allaf (2005)

Temperature/Time 50-100°C Cubadda et al. (2007); Zweifel et al. (2003)

1Bold text shows the current technological standard

Opposed to pasta, (asian) noodles are often based on common wheat �our instead ofdurum wheat. Certain types of noodles are made of mung bean starch only, combinedwith additives such as salts, fat or gums (Oh et al., 1983). In addition, the processing ofnoodles is more varied. After mixing, the noodles are usually sheeted and cut followed byto be either dried, fried, steamed or directly boiled directly. In the case of steaming, theintermediate product can again be either dried, frozen or fried. The steaming process isespecially used to produce instant noodles (Oh et al., 1983).

26

Figure 3.1.: Flow chart of the processing of pasta. SEM micrographs show (I) semolina,(II) extruded pasta, (III) the surface and (IV) cross section of dried spaghettiat high temperature. Images were assembled by Petitot et al. (2009a).

27

Still, in the next section the description is concentrated on the processes on for dry pastaproduction as dry pasta is by far the most sold type of pasta product in Europe (Serventiand Sabban, 2002). The production process of durum wheat based pasta is reviewed ingreat detail by Kill and Turnbull (2001) and Rubin (2007), but shall be brie�y describedin the next sections.

3.1. Durum wheat processing

The aim of the milling process is to separate the endosperm from the germ and the branof the wheat kernel to get a maximum of semolina at the best quality (Kill and Turnbull,2001). Milling comprises therefore the following steps: Cleaning of the wheat, temper-ing (or debranning) to loosen the bran layers as well as milling and purifying to achievesemolina of the right particle size.The cleaning process is - besides checking the grain at the delivery - a measure to ensure

product integrity and is used to remove impurities such as sticks, stones, metals or strawas well as seeds, other grains and broken or infected kernels. Between a �rst and a secondcleaning, water is added to adjust the kernel to the right hardness, moisture content andto loosen the bran layers before milling (Kill and Turnbull, 2001).The milling process is a combination of grinding, sifting and blending to reduce milling

passages. To grind preprocessed kernels, they are led over several rolls which are categor-ised according to their roughness into breakers, reduction rolls, steel screens and puri�ers(Cubadda et al., 2009). The breakers open the kernels and shall separate the carbohydrate-rich endosperm from bran and germ. The reduction rolls are mainly used to adjust theparticle size whereas a larger number of puri�ers is used to remove remaining bran particles(Delcour et al., 2010).Traditionally, coarse semolina was used for pasta production with a particle size from

200 mm to 630 mm (Kill and Turnbull, 2001). A trend can be seen towards smaller particlesize to reduce hydration time as smaller particles absorb water faster than bigger particles.Due to the hardness of the durum wheat kernels the amount of starch damage is highercompared to other wheats and is increasing with smaller particle size fractions and theash content is increasing as well (Delcour et al., 2010; Feillet et al., 2000). Whether thishas negative e�ects on pasta quality, was disputed in recent years (Sissons, 2008). Olderstudies showed, however, that the amount of starch damage correlated to the amount ofcooking loss and gave poor surface characteristics (referred to by Dexter and Marchylo,2000).

3.2. Pasta production

Pasta is produced by three main steps: Semolina is polymerised during mixing, the ma-terial is compacted and formed by means of sheeting or extruding and this structure isstabilised by drying (Kratzer, 2007). The impact of extrusion and drying on the interme-diate products are visualised by SEM images (Figure 3.1; Petitot et al., 2009a). Semolinashows a compact structure with distinct starch granules and few of them are entrappedinto a protein matrix. After extrusion, starch granules are embedded into a protein matrixand are aligned along the �ow direction. Some granules might be slightly swollen. Afterdrying, starch granules are deeply embedded into the protein matrix in the pasta strandwhile they are associated with the protein �lm at the surface. Some cracks and small holesare visible in the protein �lm at the surface, too, probably due to shrinking and tensionsduring drying.

28

Figure 3.2.: Microstructure of durum pasta dough before and after various types of ex-trusion. (A) unprocessed endosperm, (B) cylinder-plunger system (C) single-screw extruder (D) double-screw extruder. Reproduced from Kratzer (2007)

Light microscopy was also used to show the change in microstructure during extrusion(Kratzer, 2007; Zweifel et al., 2003). The organisation of starch granules into cellularstructures and the cell walls themselves are clearly noticeable in semolina (Figure 3.2A),while the cellular structure is lost after extrusion (Figure 3.2B-D). Depending on the typeof extruder, starch granules are more or less compactly organised and cell walls cannot beseen any more.

Mixing Commonly, semolina particles and water are pre-mixed at high-speed to ensure ahomogeneous particle wetting (Kill and Turnbull, 2001). Afterwards the mixture is kneadedfor some minutes at lower speed to form a dough and especially to form the gluten network.To achieve a homogeneous dough a narrow particle size distribution is necessary to reducethe risk of the formation of non-wetted particles (white spots) (Kill and Turnbull, 2001).A smaller particle size is preferred to reduce the mixing time. Semolina with a particlesize below 250 mm can be mixed in 5 min compared to the coarse semolina which needs 15min to mix (Rubin, 2007). An alternative, integrated mixing system was introduced someyears ago which uses co-rotating twin-screws, leading to intensi�ed mixing and kneading.The mixing time could be reduced to 20 sec (Kill and Turnbull, 2001).Vacuum in the mixing zone prevents oxidation of semolina natural pigments and the

intrusion of air bubbles, which gives a better shine of the pasta product (Pagani et al.,2007). Additionally, temperature should be controlled during dough preparation with anoptimum temperature between 35 and 40°C (Kill and Turnbull, 2001).

Extrusion The formed dough is moved under kneading towards the extrusion zone. Thedough is compacted and pressure is built up. The pressure depends on dough moistureand temperature as well as on die resistance which in turn depends on the extrusion areaand speed (Kill and Turnbull, 2001).Extrusion can increase the water solubility of dietary �bre and solubility increases with

higher speci�c mechanical energies(Robin et al., 2012). However, stickiness increases withhigher energy input during extrusion, too (Kratzer, 2007). Thus, the energy input shouldbe just high enough to form an homogeneous pasta dough (Kratzer, 2007).

29

Figure 3.3.: 3D models of microCT data obtained during spaghetti drying. (A) Torsionalshrinkage in the �rst 40 min of drying (B) Central shrinkage after more than40 min of drying. Reproduced from Zhang et al. (2013).

Temperature should be kept below 50°C to prevent gluten aggregation already in theextruder as this network would be destroyed by the high shear forces at the die. In general,shear forces (e.g. from worn dies) should be kept at a minimum, as they increase damageof starch granules (Petitot et al., 2009a).Commonly two types of dies are available: Bronze and te�on. While te�on dies produce

a smooth texture and even surface, the surface of pasta extruded through bronze diesis rough. The e�ect was quanti�ed by Lucisano et al. (2008). The bronze die extrudedspaghetti had a higher porosity and a reduced breaking strength of the dried spaghetti of20-30%. The type of drying cycle and semolina particle size distribution had an in�uenceas well, but to a minor extent.The type of shaping in�uences the cooking properties (shown for fresh pasta). Laminated

products were more yellow and had a lower cooking loss than extruded products Cariniet al. (2009). The authors assumed that the milder conditions during lamination led to asofter, less extensible material which retained solids in a better way.Sissons (2008) discusses the in�uence of the type of gluten on the extrusion. Strong gluten

wheats lead to less sticky doughs facilitating the extrusion. This is specially important forthin-walled instant pasta. Fresh pasta, however, needs a more extensible dough and thusa weaker gluten quality.The quality of non-durum based pasta can often be increased by using pre-gelatinised

starch. Multiple re-extrusion of the dough can be one way to increase the degree of doughgelatinisation. This was tested for amaranth, quinoa and oat �ours (Chillo et al., 2010).The degree of dough gelatinisation did in fact increase for oat and quinoa dough, but not foramaranth. With increasing re-extrusion number, the breaking strength of the dry doughsamples was increased. The colour of the samples were improved, but overall sensorialcharacteristics of the cooked spaghetti samples remained the same.

Drying Drying shall reduce the moisture content from roughly 30% to below 12.5% tomake pasta a stable product (Pagani et al., 2007). The local water concentration in thedough di�ers during drying, which can induce internal stress, potentially leading to frac-tures and cracks (Migliori et al., 2005). Drying is therefore seen as the critical step duringprocessing. In a very recent study, Zhang et al. (2013) showed that spaghetti shrank fasterin radial than in axial direction during drying in an experimental drying chamber. Addi-tionally, the spaghetti shrank torsional, non-central directed in the beginning and linear,central directed later (Figure 3.3). These �ndings may explain the arising of internal stressand cracks.

30

Figure 3.4.: Temperature-humidity pro�les and drying curves during pasta drying: refer-ence drying at 55°C (A) and high-temperature (HT) drying at 80°C and high(B), intermediate (C), and low moisture (D) conditions, and HT drying at100°C and high (E), intermediate (F), and low moisture (G) conditions, re-spectively. Trend curves describing changes in pasta moisture content duringdrying are superimposed on experimental points. Reproduced from Zweifelet al. (2003).

Drying pro�les including phases of high temperatures shall lead to higher product qual-ity and can generally compensate to some extent raw materials of inferior quality (Zweifelet al., 2003; Petitot et al., 2009a). In addition, high temperature drying can reduce thedrying time (Petitot et al., 2009a). Zweifel et al. (2003) analysed various drying pro�les(Figure 3.4) and concluded that drying at high temperature at a late stage gave the bestproduct quality (compared to low-temperature- and early high-temperature drying). Theprotein network was preserved through promoted protein denaturation to a dense and con-tinuous protein network encapsulating starch granules and thus swelling of starch granuleswas reduced. Compared to early high-temperature drying, late high-temperature dryingstabilised the protein network to such an extent that the network was still visible even inthe external zone of overcooked pasta.High temperature drying can make up for inferior protein composition (Del Nobile et al.,

2003b). However, if the temperature is too high and the polymerisation reaches a point,where the proteins get too rigid to expand, it will result in an inferior pasta, too (Delcouret al., 2010). High temperature drying also increases the risk for heat damages of the driedpasta - mainly o�-colours and o�-�avours and reduced nutritional value of the proteinswith the breakdown of lysine due to the formation of Maillard reaction products (de Noniand Pagani, 2010; Peressini, 2011). It may also a�ect the allergenicity of the proteins(Petitot et al., 2009a).

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3.2.1. In�uence of processing on selected parameters

Grant et al. (1993) studied the in�uence of several processing parameters on pasta cookingquality. Sprouting decreased the �rmness of cooked spaghetti, however, this e�ect couldbe prevented to some extent with high-temperature drying. Regrinding of the semolinaincreased the amount of damaged starch (Grant et al., 1993).The amount of damaged starch is of importance for the cooking properties as it increases

water absorption and is more susceptible to enzyme attack (Sissons, 2008). Damaged starchis in general formed due to mechanical forces during kneading and extruding (Delcouret al., 2010). However, also the semolina can in�uence the amount of damaged starch witha smaller granulation size tending to lead to increased levels (de Noni and Pagani, 2010).Active enzymes can form reducing sugars and a higher amount of damaged starch lead

to the formation of more reducing sugars (de Noni and Pagani, 2010). During high-temperature drying these reducing sugars in turn can be converted together with freeamino groups into products of the Maillard reaction (e.g. furosine), giving an unfavourablebrown product colour (Sissons, 2008). However, the Maillard reaction can be controlled byreducing the content of reducing sugars during processing. This can be achieved by usingsemolina with low alpha-amylase activity, by milling to larger granulation or by controllingthe moisture content during drying (Peressini, 2011; de Noni and Pagani, 2010).The dough should not be overdeveloped during mixing and extrusion. The optimal time

for this is generally found empirically. However, Ait Kaddour and Cuq (2011) studied theopportunity of using NIR as an automated tool to determine the optimal end point ofmixing semolina with water. They showed that raw NIR spectra could be correlated tothe size of the agglomerated particles. Second NIR spectra could be correlated to chemicalchanges during the wetting phase. Finally, through mathematical modelling, a NIR timecould be determined which corresponded to the time de�ned as being necessary to achievea constant particle distribution in the mixer.Bran inclusion modi�es the drying kinetics and the equilibrium moisture is di�erent in

bran-rich than in bran-free semolina during drying (Villeneuve and Gelinas, 2007).Factors important for ready-to-eat pasta meals (such as capacity to hold sauce, wholeness

and weight constancy) are discussed by Kindt et al. (2008).

3.3. Pasta cooking and post-cooking

During pasta cooking, starch granules absorb water and swell. This increases the volumeand pressure on the protein network (Delcour et al., 2010). With increasing temperature,two endothermic transitions take place. First starch gelatinises and at a higher temperatureamylose-lipid complexes dissociate (Petitot et al., 2010). Starch gelatinisation and glutenpolymerisation occur at the same time and are competitive in regard to the absorbedwater as well as they are controlled by the water penetration inside the pasta (Petitotet al., 2009a). Depending on the amount of starch swelling, amylose can leach out of thegranules and the starch granules disintegrate. This can induce excessive cooking loss andincreased stickiness (Delcour et al., 2010). The later depends on the protein network, witha strong network preventing the leakage and dissolving of starch granules (Zweifel et al.,2003; Bruneel et al., 2010). Honeycomb-like structure at the surface of partly and fullycooked spaghetti could be detected (Heneen and Brismar, 2003; Sung and Stone, 2005). Inany case, the cooking generates a concentrically change from the centre to the surface in themicrostructure (Figure 5.2). This gradient in the change of microstructure and moisturecontent with a �rm core is often referred to as 'al dente'. Delcour et al. (2010) summarisedthe cooking process by arguing that the transformation of starch is a hydration-drivengelatinisation process in the outer layer while it is a heat-induced crystallite melting in the

32

centre of the pasta. This process is characterised by two di�usion coe�cients for the twodi�erent transformations (Cunningham et al., 2007).Heneen and Brismar (2003) studied the microstructure of cooked pasta made from durum

or common wheat. They argue that during cooking of pasta, large voids appear aroundthe swelling starch granules and granules are �attened in the intermediate region. Laterin the process starch granules are fusing. The authors suggest that due to the largersize of common wheat granules, more granules will fuse. This results in an insu�cient,discontinued protein network.Storing cooked and pre-cooked pasta deteriorates their sensorial properties as the uneven

water distribution evens out and thus reduces the �rmness (McCarthy et al., 2002; Wood,2009; Olivera and Salvadori, 2012). The deterioration e�ect can be controlled by the storageprocess. Irie et al. (2004) treated cooked spaghetti samples di�erently after production:samples were dried, frozen, or stored at moderate or chilled conditions. Dried and frozenspaghetti showed a clear moisture gradient from surface to core with a low core moisturecontent of below 15%. Fresh and chilled spaghetti showed a gentle moisture gradientwhereas one week stored spaghetti did barely show any gradient. Mechanical propertiesfollowed the tendencies in the moisture gradient, with higher forces needed to break driedand frozen spaghetti and lower forces for the chilled spaghetti. Faster product freezing canremain better initial quality during frozen storage (Olivera and Salvadori, 2011).Also the choice of raw materials can in�uence the properties after storage. Chickpea-

forti�ed spaghetti remained �rmer than the control (Wood, 2009). Furthermore, spa-ghetti maintained �rmer when seasoned after cooking with sodium chloride or monosodiumglutamate as those salts temporarily absorbed excess water at the surface of the spaghetti(Horigane et al., 2009).

3.3.1. In�uence of cooking conditions

The cooking water composition a�ects texture properties of pasta. Both increased pH andwater hardness result in higher stickiness values (Malcolmson and Matsuo, 1993). Weakacidic pH seems to be optimal. Distilled water has a pH of 6 (as it binds carbon dioxide)and is thus not recommended to compare di�erent pasta samples. It might minimizedi�erences which are more apparent when cooking pasta in tap water (Malcolmson andMatsuo, 1993; Cole, 1991). Instead, arti�cial water with a standardized water compositionhas been recommended, but not often used (Matsuo et al., 1992). Another standard(AFNOR Standard NF-V 03-714) recommends mineral water mixed with 0.7 % sodiumchloride (referred by Delcour et al., 2000a).Salted (sodium chloride) cooking water in�uences pasta cooking properties, too. Higher

salt contents decrease the water absorption of pasta samples, resulting in longer cookingtimes (Majzoobi et al., 2011a; Sozer and Kaya, 2003, 2008; Ogawa and Adachi, 2013).Ogawa and Adachi (2013) assume that the sodium ion positively hydrates and thus bindssome water molecules and due to its larger ion diameter hinders water di�usion. Saltstabilizes also the protein structure by increasing the hydrophobic interactions betweenthe gluten units (Peressini et al., 2000; Sozer and Kaya, 2008). Salt improves the sensorialperception and an increases in salt concentration increase the hardness and adhesivenessof cooked spaghetti, too (Sozer and Kaya, 2008).The amount of absorbed water depends on the water temperature, too (Del Nobile and

Massera, 2002). Below 40°C pasta strands increased in weight to 160% of its dry weight.For temperatures above 40°C (here: 60, 80, 100°C) the weight increased in the stationaryphase to about 300, 400 and above 500%, respectively.Finally, microwave cooking can reduce cooking loss and better retain the yellow colour

than cooking in boiling water while �rmness decreases(Cocci et al., 2008).

33

.

Part II.

METHODS

35

4. Introduction Part II Methods

Food systems are organised at several length scales, which can be analysed and visualizedby a variety of methods. For the example of starch as food component, Conde-Petit (2003)structured several methods into nano-, micro and macroscale. Other used varying termsand introduced an additional mesoscale (Trystram, 2011; van der Sman and van der Goot,2009). Starch granules and gluten are cited as examples for mesoscale structures by van derSman and van der Goot (2009).In the following chapters, several methods are brie�y described, which were or could

be used to analyse the structural elements of pasta and its raw materials over severallength scales. Additionally, suitable methods to observe water and moisture migrationare mentioned. Some of the methods can be used in several ways, for example SEM andTEM can either be used as an imaging or as a spectroscopic tool. Therefore, the followingclassi�cation may not be completely stringent, but lists methods rather in the contextwhere they are most often (potentially) used in carbohydrate research.

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5. Analysing the microstructure

5.1. Microscopy

Microscopy techniques use microscopes to enlarge the image of objects, which could oth-erwise not been seen in the same detail with the eye. Thus it is possible to gain knowledgeabout structural properties. In optical and electron microscopy, electromagnetic radiation(light) or an electron beam is interacting with the specimen, whereas in scanning probemicroscopy the interaction is based on atomic forces of the specimen.In general, microscopical methods can also be distinguished whether they are applied

destructively or non-destructively and whether the surface of the specimen is analysed ora inner layer of it.

5.1.1. Light microscopy (LM)

Light microscopy is one of the oldest analytical method as its working principle is rathersimple. A light beam is sent through a set of lenses and the sample to receive an enlargedimage of the structure of the sample on a detector. The image is formed by several sourcesof contrast such as absorption, scattering and re�ection of the illumination. Di�ractionlimits the image resolution theoretically at about the half of the length of the incident lightbeam - using visible light this gives a limit of about 200 nm. This limit is in practice in themagnitude of mm due to sample preparation and properties. Resolution limits are lowerwhen using electron microscopy, but light microscopy o�ers an useful range for analysingfood products (Autio and Salmenkalliomarttila, 2001).Based on di�erent contrast mechanisms, several optical microscopy set ups have been

developed and frequently used ones are shown in Figure 5.1 (Cisek et al., 2009).

· Bright field (BF) light microscopy Bright light coming from the light source isfocused through the condenser lens on the specimen, from where the light is collectedthrough the microscope objective, which also creates a magni�ed intermediate image.Through the ocular the image is magni�ed further and the light is focused again ona detector.

· Dark field light microscopy excludes the unscattered light ray through anspeci�cally designed annular aperture. It enhances contrast in unstained specimensand can display elements of low light intensity, but which scatter and di�ract thelight.

· Phase contrast can evolve when light rays are travelling through materials ofvarying refractive index or thickness. This results in di�erent light path lengths andthe interference of the di�erent phases of these light paths can form the contrast.To enhance the occurring phase contrast, an angular aperture is used before thecondenser and a special phase plate behind the objective,

· Polarised light microscopy A polarising �lter (polariser) beneath the condenseraligns the light rays into one single plane. A second �lter (analyser) above theobjective is positioned in right angle to the polariser. Thus, only polarised light will

37

Table 5.1.: Commonly used staining agents for carbohydrates in (A) bright-�eld light mi-croscopy and (B) �uorescence microscopy. Adapted from Autio and Laurikainen(1997), Autio and Salmenkalliomarttila (2001) and Aguilera and Stanley (1999),except for the references mentioned in the foot notes.

(A) Bright-fieldlight microscopy

Component Stain Colour

Starch Iodine solution Black,

violet

Amylose Iodine solution Blue

Amylopectin Iodine solution Beige,

brown

Protein Light green Green

Lipid Sudan III / IV Red

Oil Red O Red

Sudan Black B Blue-black

Cellulose Thionin Violet

Lignin Phloroglucinol Red

Pectin Ruthenium red Rose

(B) Fluorescencemicroscopy

Component Stain

Starch Periodic acid Schi�'s (PAS)

Protein ANS, Fuchsin acid, Texas

red, FITC, Thiazine red

Rhodamine reda

Glutenin Tetraethyl- rhodamine Bb

Gliadin/ Glu-

tenin subunits

Immunostainingc

Lipid Nile blue, Nile red

b-glucan Calco�our, Congo red

Immunostaining

Cell wall comp. Congo redd

Lignin Crystal violet/erythrosin B

Arabinoxylans ImmunostainingaKratzer (2007), bLi et al. (2004), cTosi

et al. (2011), dAlminger et al. (2012)

Figure 5.1.: Comparison of several methods in optical microscopy. O, ocular; CO, micro-scope objective; S, sample; CL, condenser lens; LS, light source; AA, annularaperture; PP, phase plate; A, analyser; P, polariser; N, Normaski prism; F1/2,Transmitting �lter. Reproduced from Cisek et al. (2009)

38

Figure 5.2.: Light micrographs of cooked spaghetti and stained with Fast Green and iodine.Starch is coloured violet/brown, while gluten is coloured green. Left image

specimen thickness 2 mm. Right image specimen thickness 9 mm, Scale bar25 mm. Reproduced from Heneen and Brismar (2003) and Zweifel et al. (2003)

be detected which has been rotated when passing through the sample. The rotationis induced by anisotropic structures showing birefringence. This is the case for e.g.ungelatinised starch granules (their appearance under polarised light is called Maltesecross) or for �brils and cell walls of plants.

· Differential interference contrast microscopy (DIC) The polarised lightray is split into two parallel rays (caused by a prism) and travels through the speci-men in such a way, that the split light ray paths are phase shifted. With a secondprism the rays are combined again, giving the image a three-dimensional appearance(Cisek et al., 2009). This method is suitable to visualise di�erences in thickness ororientation in the specimen.

· Fluorescence microscopy uses the e�ect that certain components will - after be-ing illuminated with light of a certain wavelength - emit light of a longer wavelength.The technique is explained in more detail in the next section (see Section 5.1.2).

Sample preparation

In thin biological samples absorption and scattering have generally low e�ects. Therefore,to enhance contrast in optical microscopy, staining agents are often used. An overviewof commonly used agents in staining components in carbohydrate-rich systems is given inTable 5.1. However, some types of microscopy, such as polarised light microscopy, do notneed staining at all.Further sample preparation is dependent on the resolution required. A rule of thumb is

that the thinner the specimen, the better the resolution will be as scattering and absorp-tion of light is reduced. To achieve a specimen thickness of about 1 mm, the structure ofthe sample has to be �xated. Glutaraldehyde and Osmiumtetraoxid are common �xationagents. After the specimen has been embedded in a resin solution and completely poly-merised, it can be sliced using a microtome. As an example, Heneen and Brismar (2003)prepared specimens with a thickness of 2 mm. An alternative to embedding is cryosec-tioning, that is to freeze the specimen and then slice it. Sample thickness of 10 mm canbe obtained (Hug-Iten et al., 1999; Zweifel et al., 2003). A faster option resulting in alarger thickness is to smear the liquid or solid sample onto an object glass (Langton andHermansson, 1989). Examples for the e�ect of specimen thickness on scattering are shownin Figure 5.2)An exception to the rule of thumb is polarised light microscopy, where thicker samples

are useful (at least when analysing pasta). In polarised light microscopy, the intensity ofthe transmitted light is increasing proportional with the sample thickness. Del Nobile et al.

39

Figure 5.3.: Schematic working principle of a CLSM (Anonymous, 2012).

(2003b) used samples with a thickness of about 1 mm (and such samples can be quicklyprepared by cutting uncooked and cooked spaghetti strands with a razor blade).

Application in research

Cisek et al. (2009) list several examples for the use of phase contrast, dark �eld microscopyand DIC in analysing cells. For pasta, however, no examples in the literature could befound. Autio and Salmenkalliomarttila (2001) described bright �eld light microscopy asan useful tool for grain research. It o�ers the opportunity of targeted staining of graincomponents and can visualise structural changes of the material. Within the broader topicof pasta research, light microscopy has been used to compare pasta made of di�erent wheattypes or other �our types (Heneen and Brismar, 2003; Petitot et al., 2010), to study thein�uence of drying on pasta texture (Zweifel, 2001; Zweifel et al., 2003) and to visualisethe change of starch structure during cooking of spaghetti (Cunin et al., 1995). Becauseof the easiness to discriminate amylopectin and amylose when staining with iodine, themethod is also often used to analyse starch phenomena only (Kratzer, 2007; Langton andHermansson, 1989; Srikaeo et al., 2006; Wellner et al., 2011). A common dimension forbright �eld light microscopy images showing pasta structures is at about 20 to 200 mm. Itis possible to display the complete cross section of e.g. a spaghetti, at least when usinglow magni�cations (Del Nobile et al., 2003b). This makes this method suitable to give anoverview over a pasta sample. There are also hot stage microscopes available which havea heatable sampler holder and thus allowing it to follow changes in the microstructure ofthe product at elaborated temperatures (Kratzer, 2007) .

5.1.2. Confocal laser scanning microscopy (CLSM)

CLSM combines in the often used epi-�uorescence mode two concepts - �uorescence andeliminating out-of-focus light rays. Light of a speci�c wavelength is sent to the specimenwhere the �uorophores of the specimen will emit light of a longer wavelength (�uorescence).The emitted light is separated from the excitation beam by a dichroic mirror, which is awavelength �lter and is collected on scanning mirrors. Close to the light source and thedetector pinholes are inserted in conjugated planes to the sample to eliminate the lightrays which are not in focus in the focal plane (Figure 5.31). Image formation is done byscanning a certain area of the specimen. Adjusting the focal plane makes it possible toscan at di�erent depths within a specimen (Cisek et al., 2009). CLSM can be used in thickspecimens and 3D images can be reconstructed.1Source: Danh at the English language Wikipedia under CC-BY-SA 3.0, accessed 06/12

40

Figure 5.4.: Upper Row CLSM of cooked pasta made from (a) durum wheat and (b) splitpea �ours. Fibre fragments in (d) split pea pasta and (e) durum wheat pasta.Al, aleurone, Per pericarp, cell, cellular structure. All samples were stainedwith calco�uor.Lower Row CLSM of cooked pasta in (a) central, (b) intermediate and (c)external region of a spaghetti strand. Proteins were stained with fuchsin acidand appear white. Dark areas can be connected to starch granules. Extractedfrom Petitot et al. (2010) and Petitot et al. (2009a).

The epi-re�ection mode of the CLSM allows to analyse the surface and topography ofsamples, as the re�ected laser light is collected as the signal. To increase contrast thesample can be sputtered with a thin metal �lm (as in SEM; Dürrenberger et al., 2001).The resolution limit is slightly better than light microscopy. Preparation e�ort is de-

pending on the sample and often minimal Aguilera and Stanley (1999). For instance,samples showing auto�uorescence, such as parts of the cell wall as aleurone and pericarpstructures and in general �bres, do not need further preparation. An example is shownin Figure 5.4 (Petitot et al., 2010). The main source for auto�uorescence are polyphen-olic compounds like lignin and ferulic acid (Autio and Salmenkalliomarttila, 2001). Using�uorescents (Table 5.1) or labelled antibodies can increase the spectra of analysable com-ponents, which are otherwise not showing auto�uorescence. It is also possible to bleachsamples to remove auto�uorescence (Zweifel et al., 2003). To analyse cell wall structures,Dornez et al. (2011) tested several immunolabeling techniques. Immunolabeling o�eredthe possibility to stain very speci�c certain cell wall material. However, it is a complextechnique and time-consuming. Dornez et al. (2011) recommend to consider what the pur-pose of the labelling shall be and whether immunolabeling or a common �uorescent shouldbe preferred.The change in the protein structure during processing has been analysed by several

authors (Dürrenberger et al., 2001; Fardet et al., 1998; Zweifel et al., 2003; Petitot et al.,2009b). The protein distribution has been shown in both wheat grain and cooked pasta(Figure 5.4; Petitot et al., 2009a; Tosi et al., 2011). CLSM was also used to show thein�uence of dietary �bres on pasta quality (Petitot et al., 2010; Aravind et al., 2012a,d).

41

Quantum dots (QDs) are a recent development to be used as substitute markers forproteins. QDs are semiconductive nanoparticles, which are �uorescent and their colour aredepending on the particle size. Thus, the colour can easily be controlled by varying thesize. Furthermore, QDs are resistant to photobleaching. At a recent conference, Sozer andKokini (2011) presented their �rst results on using QD to mark proteins in bread.

Fluorescence recovery after photobleaching (FRAP)

A focused laser beam is concentrated on a spot of a specimen for a short time to bleachthe �uorescently labelled molecules in this area. This will decrease the �uorescent signalsigni�cantly. After stopping the laser beam, �uorescent-active molecules will di�use in thisarea, which can be measured by an increased signal. By analysing the drop and the timeof the recovery of the signal, the di�usion coe�cient can be calculated. The working rangeof FRAP is given to be between 0.1 and 100 mm2 s-1 for the di�usion coe�cient (Deschoutet al., 2010).

Photo activation

Photo activation is the vice-versa of FRAP. Instead of bleaching, �uorescent particles areactivated by a concentrated laser beam. Again, the movement of the particles can betracked by e.g. CLSM. This method is especially suitable in aqueous systems.

5.1.3. X-ray computed tomography (XRT)

XRT is another non-invasive technique to create cross-section images of an object, usingX-rays. The cross-sections can later be recalculated to achieve a 3D-model of the object.Besides of XRT, the term m-CT is frankly used, with micro referring to the pixel size andCT standing for computational tomography. The image contrast is based on absorptiondi�erences of the X-rays of the elements of the object (e.g. whether they are solid, liquidor gaseous) and thus, density variations can be determined. Observations are possibleunder environmental conditions without prior sample preparation. The axial and lateralresolution is in size range of some mm (van Dalen et al., 2003). Mohoric et al. (2009) usedthe technique to visualise the porous structure of rice kernels after cooking. The authorsclaim that the technique is especially useful for analysing in 3D porous microstructuresunder low moisture conditions. Another study combined XRT with magnetic resonanceimaging (MRI) to identify structural elements in a food system which cause fast hydration(Weglarz et al., 2008). They baked a probably starch-based food system with crunchyinclusions and determined its structure by XRT in the dry state. Then they analysed thehydration in real-time using a fast spin-echo imaging method and were able to to correlatemoisture content deviations to the type and form of the inclusion.A recent study used XRT to visualise the internal shrinkage in spaghetti during drying

(Zhang et al., 2013). The authors describe the technical prerequisites and calculationsnecessary to visualise the e�ect of drying; among others, they used added aluminiumparticles to increase the contrast in the dense pasta dough.

5.1.4. Scanning electron microscopy (SEM)

The working principle of SEM: A focused, incident beam of electrons hits the sample andby interacting with the atoms of sample, knocks out electrons from it. These knockedout electrons will have a certain energy depending on the atomic shell they were releasedfrom and can be detected using several methods. Electrons can be emitted as X-ray, assecondary electrons, they can be backscattered or they can be transmitted. Each type of

42

Figure 5.5.: Upper Row Scanning electron micrographs of isolated starch granules from(A) durum wheat semolina and (B) split pea �our. Reproduced from Petitotet al. (2010)Lower Row Scanning electron micrographs of transverse sections of (A) un-cooked and (B) cooked spaghetti. P, protein network; S, starch granules; GS-P,gelatinised starch- protein network. Scale 1 and 2 mm, respectively. Repro-duced from Purnima et al. (2012)

electron interaction can be registered with speci�c detectors and the detection of secondaryelectrons (describing re�ected electrons) is most common. The image of the sample iscreated by the collected electrons at the detector and is formed through a raster scan ofthe focused beam over the surface of the sample.The image in a SEM is formed based on di�erences in contrast. Contrast mechanisms

are based on the surface topography, magnetism, atomic number, conductivity and crys-tallographic orientation. SEM in general can be used to analyse surfaces of a specimen.Depending on the detector, the SEM has a high depth of focus, showing the topographyof the specimen in semi 3D.Several factors in�uence the spatial resolution and this includes beam spreading e�ects,

the incident electron beam diameter, signal to noise ratio and instabilities. A higher voltagein the microscope leads to better contrast and higher resolution and thicker samples can beused. However, higher voltage can also lead to sample destruction. Finally, the resolutiondepends on the quality of the sample preparation, too.In general, the specimen has to be conductive or needs to get a conductive layer. This is

often done by sputtering a gold layer onto the specimen which also increases the contrast.Alternatively, low voltage SEM can be used to avoid charging of the material (Heneen andBrismar, 2003; Langton and Hermansson, 1989). Furthermore, water has to be removedbefore SEM analysis. The sample can be dehydrated after a �xation step or solidi�edby freezing. Another limitation is the need of a certain quality of a vacuum to avoidinteractions of the electron beam with molecules in the gas phase. If this is not possible,environmental SEM (ESEM) has been developed. This method gives a lower resolution,

43

but can be used without special treatment in e.g. biological samples. Petitot et al. (2010)used ESEM to visualise the starch granule distribution from various �ours used for pastaproduction (Figure 5.5).The resolution limit of SEM is between 5 an 10 nm and the scanned area is often between

50 and several 100 mm in each dimension.Scanning electron microscopy is widely used to understand phenomena in pasta. The �rst

papers using SEM to analyse the structure of pasta have been published in the 70s and 80s(Dexter et al., 1978; Matsuo et al., 1978; Pagani et al., 1986; Resmini and Pagani, 1983).It is often used when the in�uence of new ingredients on the structure shall be compared,especially on the protein network (Aalami and Leelavathi, 2008; Aravind et al., 2012a,d;Fuad and Prabhasankar, 2012; Majzoobi et al., 2011b; Manno et al., 2009; Prabhasankaret al., 2010; Sung and Stone, 2005). The in�uence of processing on the pasta productwas shown as well using SEM (Cocci et al., 2008; Fardet et al., 1998; Lucisano et al.,2008; Manthey and Schorno, 2002; Zweifel, 2001). Cunningham et al. (2007) used SEM todemonstrate the e�ects of starch swelling on the pasta surface during pasta cooking. Anexample for the change from dried to cooked pasta is given in Figure 5.5 (Purnima et al.,2012) and for the intermediate products during processing in Figure 3.1 (Petitot et al.,2009a).

5.1.5. Transmission electron microscopy (TEM)

The working principle is similar to SEM, however in TEM, the electron beam is sendthrough an ultrathin sample as the resolution is inversely proportional to the sample thick-ness. Instead of topography, the inner structure of the sample is visualised as the imageis formed from the interactions of the electrons with the specimen as the beam passesthrough. Besides the thickness of the specimen, a higher voltage source results in shorterwavelengths which in turn increases resolution. Especially for biological samples, however,it has to be taken into account, that a too high voltage can destroy the sample.TEM o�ers the best resolution compared to LM and SEM, with a limit of down to

some nm, but requires extensive sample preparation. Specimens have to be very thin toavoid multiple scattering and are often below 100 nm in thickness (e.g. Pagani et al.,1986). Resmini and Pagani (1983) presented freeze-fracturing as a preparation techniquefor analysing dry and cooked pasta. TEM was used to show to subunits in gelatinisedstarch granules and lipid inclusions in the protein matrix (Pagani et al., 1986). Besides thebefore-mentioned studies, TEM has not been widely used to analyse the microstructure ofpasta.TEM is mentioned to study the growth rings of hydrolysed starch granules in the review

of Pérez and Bertoft (2010). To name another �eld, TEM o�ers opportunities to showisolated wheat components at great detail. For an isolated crude extract of high molecularweight (HMW) glutenins, a novel nanostructure could be shown (Mackintosh et al., 2009).

5.1.6. Atomic force microscopy (AFM)

AFM uses a tip to scan a surface at the atomistic level. While the tip is moved mechanicallyover the specimen, the tip will interact with the specimen. These movement of tip canbe measured with a laser beam. The deviation of the beam is registered with a detectorand this is used to form an image of the topography of the specimen (Figure 5.6). Theresolution limit is below nm and it can be applied both under environmental conditionsand especially in liquids, too. A drawback of the technique is that there are lots of possibleartefacts and that AFM can be used only for rather smooth surfaces.

44

Figure 5.6.: Left Working principle of Atomic force microscopy (AFM). Right AFMimage of the surface of a dried spaghetti. Maximum roughness was about 300nm. Extracted from Baldwin et al. (1998) and Ogawa et al. (2011)

Albeit being a rather new technique and only being available for a little more thantwenty years, a vast number of studies has been published on analysing biological samplesand there, among others, the starch structure (Paananen, 2007). A recent study analysedthe surface structure of buckwheat starch (Neethirajan et al., 2012). The surface of pastadried at di�erent temperatures was analysed by AFM as well (Zweifel, 2001). Pores werevisible at the surface in the study of Ogawa et al. (2011) (Figure 5.6).

5.1.7. Image analysis

Image analysis summarises techniques to extract meaningful information from and toquantitatively analyse more data in images. The analysis is often based on pattern re-cognition, geometry or signal processing. A good introduction into the topic is o�ered byRuss (2006).To name some examples from recent studies: Using grey level granulometry, the protein

network distribution was studied in dough (Jekle and Becker, 2011) and in pasta (Petitotet al., 2010). Others determined the granule size distribution of wheat starch granules(Wilson et al., 2006) and the pore size in pasta dough (Thorvaldsson et al., 1999). A recentstudy proposed three di�erent techniques to distinguish the surface texture of cooked pastasamples (Fongaro and Kvaal, 2013).

5.2. Other methods

Spectroscopic methods have in common to study the interaction between matter of thesample and radiated energy. The energy source can vary and can be among other light,electrons or X-ray. It is often used for chemical component analysis of a material. Typicalmethods include Light scattering, Near-infrared spectroscopy (NIR or FT-IR), nuclear-magnetic resonance (NMR, Mariette (2009)) and X-ray microanalysis using SEM or TEM.Crystallinity level of starch can be determined with a X-ray di�ractometer (Baiano et al.,

2006). In general, Lopez-Rubio and Gilbert (2009) recommend the increased usage ofneutron scattering for determination of structural elements at the nm scale, e.g. for starchstructures.Finally, some chemical methods shall be brie�y listed: Protein distribution and de-

termination of gluten fractions have been done by size-exclusion high-perfomance liquidchromatography (SE-HPLC, Icard-Vernière and Feillet, 1999; Rao et al., 2001). The extentof protein polymerisation was determined by diluting the sample in sodium dodecyl sulph-ate (SDS) solution, i.e. SDSEP (Lagrain et al., 2008). The amount which is unextractable

45

in the solution corresponds to the degree of protein polymerisation and it has been usedto analyse protein modi�cations during drying and cooking (Bruneel et al., 2010).Petitot et al. (2010) list in their study the current methods to determine the contents of

carbohydrates, protein, lipids, ash and dietary �bre. For the speci�c purpose of analysingwheat grain and �ours, a publication of the Wheat Marketing Center of Kansas StateUniversity o�ers a valuable source (Anonymous, 2008).

46

6. Analysing the macrostructure

6.1. Rheological methods

Rheological measurements are frequently used to describe viscoelastic materials. The mainprinciple is that a force is applied to the material and the response of the material ismeasured. The force can be applied constant (creep), as a sinusoidal force (oscillation) oras a rotational force and it can be applied non-destructive (small amplitude measurement)or destructive (large amplitude measurement). Various material properties can be deductedfrom the di�erent types of measurement. The properties are further depending on the time,temperature and the frequency of the force applied.Commonly three parameters are determined: the storage modulus describing elastic

properties of a material (G'), the loss modulus describing viscous properties (G�) and therelation of G' to G� giving an indication of the viscoelastic behaviour of the material (tand). A thorough introduction into rheology can be found elsewhere (Schramm, 1995).Within the area of pasta research, viscoelastic or mechanical properties can be determ-

ined of components such as gluten or starch, of doughs and of the �nal pasta productand can be correlated to pasta cooking quality. For example, Ca�eri et al. (2010) determ-ined and modelled the elastic modulus and tensile strength during cooking of spaghettistrands, whereas Sozer and Dalgic (2007) used relaxation and creep data to model rhe-ological characteristics for varying spaghetti types. Relaxation and dynamic oscillatorytests were carried out to evaluate changes during storage of frozen lasagne (Olivera andSalvadori, 2011). Other used an Instron Universal testing machine to determine the elasticmodulus of cooked pasta strands during extention (Baiano et al., 2006).

6.1.1. Rheological properties of �ours and doughs

Rheometers were developed to measure rheological properties with great accuracy ando�ering a wide range of available techniques. For example, a dough for fresh pasta wasplaced in a parallel plate geometry and a time sweep as well as a frequency sweep wereapplied using a de�ned oscillating shear force (Peressini et al., 2000). Zhu et al. (2011)summarised the outcome of other studies which used creep data to determine rheologicalproperties of pasta products.Rheometers o�er a great accuracy, but they demand a careful sample preparation and

measurement procedure. Therefore, several devices have been developed to quickly andeasily analyse some empirical rheological properties of �ours and doughs. The AmericanAssociation of Cereal Chemists developed standard methods1 for several of the below-mentioned devices. Some further empirical tools for analysis of all from �ours to pastaare collected in a so called wheat �our book, issued by the North American Export GrainAssociation2.

1http://www.aaccnet.org/approvedmethods/toc.aspx, accessed 03/2012.2http://www.wheat�ourbook.org/doc.aspx?Id=201, accessed 03/2012.

47

Rapid viscoanalyser (RVA)

RVA analyses �our starch properties. A starch sample is mixed with water and the slurryis heated under stirring to 95°C. The resistant to shear is recorded over time (Copelandet al., 2009). From the recorded curve peak viscosity can be determined. Vansteelandtand Delcour (1999) extended the measurement by applying an isothermal step at 95°C andthen cooling the slurry to 50°C. In this way they could also determine the retrogradationbehaviour of the analysed starch.

Falling number

Sprout damages and enzyme activity in �ours can be revealed using the concept of thefalling number. Flour and water are mixed to form a slurry and are placed in a tube witha falling stirrer. The slurry is stirred with a falling stirrer and is heated at 100°C in a waterbath. The time it takes for the stirrer to reach the bottom is recorded as the falling numberin seconds. Low falling numbers indicate high enzyme activity or sprout damages3.

Farinograph and amylograph

The farinograph and the amylograph are devices developed by the company Brabender todetermine �our and resulting dough properties. Flour and water are mixed and the shearresistance over time during kneading at a constant temperature is recorded. Generally, ahockey-stick shaped form of the curve can be seen and can be related to the gluten strengthof the �our. Some empirical parameters can be measured such as developing time (timeto reach maximum resistance), stability (time duration of constant resistance), mixingtolerance index (MTI, describing the di�erence in resistance at the peak value and 5 minlater) and the level of water absorption (Gazza et al., 2011).

Alveograph

Doughs can be analysed with the alveograph. A dough sample is deformed under pressureto an increasing thin bubble until it breaks. Through recorded alveograms tenacity andelasticity of the dough and the relation of these two parameters can be determined (Sissons,2008).

6.1.2. Rheological properties of gluten

An arbitrary, but often used method to determine the gluten strength is the gluten index,which categorises the gluten into qualities from inadequate to excellent (Sissons, 2008).According to a standardised method4, gluten is isolated by washing it from the semolinausing an automatic washing apparatus. Then the wet gluten is placed on a special sieve ina centrifuge. After centrifugation, gluten passed through and gluten remaining on the sieveis weighed and set into relation. The more gluten is remaining on the sieve, the strongerthe gluten is regarded (Rao et al., 2001).Through drying the wet gluten and weighing the remaining, the dry gluten content can

be determined as a percentage of the wet gluten content. The di�erence between theweights of dry and wet gluten is referred to as water-binding capacity.

3http://www.wheat�ourbook.org/p.aspx?tabid=67, accessed 03/2012.4http://www.icc.or.at/standard_methods/158, accessed 03/2012.

48

Figure 6.1.: DSC of milled, non- and partly cooked spaghetti for varying times (cookingtime in min) showing apparent heat capacity by unit mass of dry matter.Extracted from Riva et al. (2000)

6.1.3. Texture analysis

Texture analysis can be used to analyse mechanical properties of pasta products. Forexample, Gonzalez et al. (2000) used a texture analyser to determine the initial slope ofthe resistance force and peak force during cutting. Both values were negatively correlatedwith cooking time. However, they also decreased during holding of the cooked pastasample. Both observations can be correlated to a more uniform moisture distribution.In general, texture analysis is used to determine texture parameters such �rmness, elasti-

city or stickiness (Sissons, 2008; Cubadda et al., 2007). How this is done is described inmore detail in Section 8.2.

6.2. Thermal analysis

6.2.1. Di�erential scanning calorimetry (DSC)

DSC is a thermoanalytical method used to determine physical transformations, especiallyphase transitions. The working principle: A small amount of the milled sample is placedin excess water in a small pan and is sealed. A second pan is �lled with water only. Bothlids are then heated at linear increasing temperature (constant heat rate) and the heattransfer of both lids is recorded. During phase transitions a di�erence will occur and thisdi�erence is referenced to the temperature (see the peak in Figure 6.1). A limitation ofthe method is that the results are dependent on the heating rate.Within pasta analysis it is mostly used to determine the level of starch gelatinisation.

Gelatinisation is an endothermic reaction resulting in a peak in the DSC diagram as seen inFigure 6.1. The longer pasta is cooked, the more starch is already gelatinised and thus smal-ler gelatinisation peaks will be recorded in the DSC analysis (Figure 6.1;Riva et al., 2000).Amylose, amylopectin and starch have di�erent gelatinisation temperatures and thereforeDSC can be used to determine the concentration of these components (Vansteelandt andDelcour, 1999).

49

6.2.2. Dynamic mechanical analysis (DMA)

DMA is a technique to determine the viscoelastic properties of a solid material. The sampleis placed between two grips and then an oscillating torsional or axial force is applied. Mostoften, temperature or frequency sweeps can be carried out.The temperature sweep was used to determine the glass transition temperature in pasta

in dependency of the water content (Takhar et al., 2006). The glass transition is determinedas the temperature region where the storage modulus is sharply decreasing. The sameauthors predict that DMA can help to predict the e�ect of drying and storage temperatures.In another study, DMA was used to analyse the in�uence of the water content on thestructural changes in pasta dough during heating (Thorvaldsson et al., 1999).

6.3. Other methods

Pore size distribution has been measured by mercury intrusion porosimetry (e.g. Ogawaet al., 2011; Petitot et al., 2010). By applying a pressure to a pasta sample, mercury ispressed into it. Based on the weight increase, the volume taken in by mercury can becalculated.

50

7. Analysing water transport and mobility

7.1. Water transport and its modelling

Water is a major part of all foods and its distribution steers food properties. In the contextof pasta products, especially the water absorption and desorption processes are of extra in-terest. During the life-time of a pasta product water migration occur during mixing of thedough, drying and cooking of the pasta and �nally during storage of the cooked pasta. Alot of work has been carried out to model these sorption processes. The modelling is com-plicated by the fact that the sorption is happening at the same time as swelling/shrinkingand physical modi�cations of the structural elements, i.e. gelatinisation of starch and de-naturation of gluten (Del Nobile et al., 2003a). To ease the mathematical description ofthe phenomena, water migration is most often described in one dimension only. That isalso why spaghetti and lasagne plates are used so abundant, as the radial pro�le of thespaghetti and the large surface compared to the thickness of lasagne, respectively, leads toa water migration in predominantly just one dimension.

7.1.1. Water transport mechanisms

Figure 7.1.: Schematic illustration of three approaches to describe water motions. Repro-duced from Schmidt (2007)

The qualitative movement of water and its local distribution and availability can beanalysed at various length scales and with various conceptual approaches. Schmidt (2007)classi�ed three types of water movements (Figure 7.1). At the macromolecular level, theconcepts of water activity and sorption isotherms have been developed and are used widelyespecially as both parameters are useful to describe spoilage or deterioration of foodstu�

51

of medium and high moisture content during storage (Labuza and Atunakar, 2007). How-ever, for dried pasta these parameters are of minor importance as dry pasta has a ratherlow water activity and is a microbiological stable food. Still, a study was published ondetermining the sorption isotherms for macaroni at varying humidity levels at ambienttemperatures (Arslan and Togrul, 2005).For pasta, water transfer mechanisms at the microstructure level are more interesting to

study. Some general concepts for transport mechanisms in porous media are discussed byde Oliveira Romera et al. (2012) (Figure 7.2). Pasta is seen as a porous medium wherewater transport is led by liquid di�usion during drying (Cubadda, 1993). However, it isa medium of low porosity where the large majority of pores is in the size of 2-50 nm andonly a minor fraction in the size of 0.05-2 mm (Petitot et al., 2010).Possible transport mechanisms during hydration for foodstu� in porous media can com-

prise in general di�usion and capillary �ow (Saguy et al., 2011). Others developed a modelbased on the assumption that the hydration is happening as a �uid transport in a porousmedium that can swell (Zhu et al., 2011). Ogawa et al. (2011) combined sorption kineticsexperiments with pore analysis and concluded that the rate of hydration was governed bythe di�usion through the pores of the spaghetti as the hydration rate was assumed to behigher than the di�usion rate of water.Studies which relate water transport to structural elements (such as starch, gluten, air

or bran) could not be found.The other two approaches shown in Figure 7.1 - molecular water mobility and polymer

science approach - are discussed in Section 7.2 and 2.1.3, respectively.

Figure 7.2.: Moisture transfer mechanisms in porous media. Reproduced from de OliveiraRomera et al. (2012)

7.1.2. Theoretical kinetic models of water transport

The driving force of a stationary, continuous di�usion is the chemical potential or vapourpressure di�erence between two regions and is described by Fick's �rst law (Case I Fickiandi�usion, Labuza and Atunakar, 2007; Horigane et al., 2006):

52

J = −D ∂c∂x

J Flux [mol s-1 m-2]; or for water can be written as [gH20 s-1 m-2]

D Di�usion coe�cient [m2 s-1]

∂c∂x Concentration gradient [mol m-4]

c Concentration; or moisture content [mol m-3]

x Distance of di�usion [m]

The di�usion coe�cient is material dependent. If the concentrations are varying over time,Fick's second law (case II Fickian di�usion) can be applied, which is valid for in-stationarydi�usion:

∂C∂t = D ∂²C

∂x²

Another concept introduced the term water demand (WD), which is de�ned as thedi�erence between the existing and the maximum moisture content (Fukuoka et al., 2000).

7.1.3. Kinetic models applied to pasta

In general, most models applied to pasta are based on experimental analysis. This is mostoften done by either direct methods such as gravimetrical measurements (Ca�eri et al.,2008, 2010; Cunningham et al., 2007; Del Nobile et al., 2003a) or indirect methods suchas studying the moisture pro�le by NMR/MRI (Carini et al., 2012; Gonzalez et al., 2000;Hills et al., 1996, 1997; Horigane et al., 2006; Abd Karim et al., 2000; McCarthy et al.,2002; Xing et al., 2007) or NIR (De Temmerman et al., 2007; Ait Kaddour and Cuq, 2011;Zardetto and Dalla Rosa, 2006). Most models have been developed for the process ofdrying (De Temmerman et al., 2008; Hills et al., 1997; Migliori et al., 2005; Ponsart et al.,2003) and cooking (among others Ca�eri et al., 2008; Del Nobile et al., 2003a; Fasano et al.,2011; Ogawa et al., 2011; Zhu et al., 2011), respectively.

Mixing and drying

The reactivities during mixing were described by Oulahna et al. (2011). They describedmixing as a two step process: The �rst step is the spreading-wetting of the solid surfaceand the second the liquid di�usion within the grain. The authors modelled the reactivitiesusing sorption isotherms (in dependence of relative humidity).Drying is a simultaneous process of mass and heat transfer (Migliori et al., 2005). Ac-

cording to Hills et al. (1997), the drying process is a non-�ckian process, as there is a sharpmoisture gradient and the outer layer acts as a barrier to moisture loss. Others, however,could use Fickian di�usion to predict average moisture contents during drying and theycould �t the model to experimental data (De Temmerman et al., 2008). Thus the masstransfer could be expressed by a di�usion coe�cient and the heat transfer by desorptionisotherms.

Rehydration process

The rehydration process occurring during cooking and overcooking depends on four mainphenomena according to Del Nobile et al. (2003b): 1. melting kinetics of crystalline starchdomain, 2. water di�usion, 3. relaxation of the macromolecular matrix (swelling) and 4.residual deformation release.

53

Figure 7.3.: Water concentration pro�les during hydration of a pasta product. l is thedistance from the centre. Reproduced from Ca�eri et al. (2008)

The phenomena are described and explained in the following two paragraph accordingto the model of the same reference (Del Nobile et al., 2003b). Water di�usion is controllingwater uptake at the beginning of the rehydration process. The di�usion occurs throughtwo phenomena at the same time: A. Molecular di�usion (related to Brownian movement)is driven by the concentration gradient of water (low in the core of the pasta and highat the surface). B. Macromolecular matrix relaxation driven by the disequilibrium of thelocal system. The second e�ect means that when water hits the macromolecular matrix(that is starch and protein), the matrix does not take up all the water needed to reach itsequilibrium immediately. The matrix swells over time and the kinetics of the swelling isdependent on the level of disequilibrium.Melting of starch crystals requires a minimum temperature, but is then a fast process

compared to the processes mentioned before. The fourth phenomena (release of residualdeformation) takes into account that the continuous protein phase is during drying �frozen�into a state which is not in equilibrium. During hydration and melting of the starch crystalsthe reformation can occur which reduces the macromolecular matrix. However the fourthphenomena is outweighed by the amount of water taken up, which leads to an increase inspaghetti size during the rehydration process.Several models predicted a moisture pro�le in e.g. spaghetti as shown in Figure 7.3.

The rapid increase at low moisture contents indicate that �rst during starch melting thematrix takes up signi�cant amount of water (Del Nobile et al., 2003a). Furthermore, it isof importance for the models that, as temperature uniformity is reached within seconds,the temperature within a solid pasta product can be considered to be uniform and to beequal to the surrounding boiling temperature (Fasano et al., 2011).Fickian di�usion is only valid, when no other e�ects occur at the same time (e.g. it might

not be valid near the glass transition; Villeneuve and Gelinas, 2007). When water migrationin pasta shall be described, the other before mentioned e�ects have either to be ignoredor to be taken into account to some extent. Several authors based there modelling onFick's second law, but adapted it to various degrees. McCarthy et al. (2002) and Horiganeet al. (2006) assumed for the whole cooking process a constant di�usion coe�cient whereasCa�eri et al. (2008) separated it into two coe�cient constants; one for the undercookedregion and one for the fully gelatinised region.In a recent paper, Zhu et al. (2011) stated that in earlier studies it was often tried to

model water absorption in swelling foodstu�s by adding empirical factors to the classicalmodels. The authors used the empirical data of Ca�eri et al. (2008) and developed a �nite

54

Table 7.1.: Reported di�usion coe�cients for pasta productsProduct Parameter D [mm2 s-1] Reference

Spaghetti, gelatinised parts Boiled 10 min 550 Horigane et al. (2006)Boiled 12 min 490Boiled 10 min,hold 60 min

24

Wheat noodles Boiled 6 min 654 Maeda et al. (2009)Boiled 10 min 569Boiled 15 min,hold 30-120 min

21

Lasagne Boiled 11 min 290 McCarthy et al. (2002)Boiled 15 min 310Boiled 11 min,during holding

16

Spaghetti, gelatinised Boiled 11 min 670 Zweifel (2001)Spaghetti, fully gelatinised Boiled 20 min 960Spaghetti, ungelatinised Model

parameter~ 600 Del Nobile et al. (2003b)

Spaghetti, fully gelatinised Modelparameter

~ 3000

Spaghetti, fully gelatinisedparts

Modelparameter

630 Ca�eri et al. (2008)

Wheat starch dough,kneaded manually

2000 Fukuoka et al. (2000)

element analysis taking into account the simultaneous e�ects of water absorption andviscoelastic deformation. The porosity was also considered, as according to the authors,dried pasta is a dehydrated system and pores can have been created during dehydration.Thus, the food system is unsaturated from the beginning. The moisture transport duringthe hydration process is the sum of di�usion and viscoelastic e�ects and thus, the watertransport is dominated by non-Fickian di�usion (Zhu et al., 2011).The model of Ca�eri et al. (2008) is a simpli�ed version of Del Nobile et al. (2003a) who

tried to describe all the occurring processes separately. Recently, a rather complex modelwith moving boundaries was presented (Fasano et al., 2011). They de�ned three bound-aries: The outer surface, the gelatinisation and the water front and modelled parametersfor every zone.It was shown in another study, that the di�usion coe�cient is also dependent on the

temperature and the dependency can be adequately described with the Arrhenius equa-tion (Cunningham et al., 2007; Ogawa et al., 2011). A selected list of reported di�usioncoe�cients for some pasta products during cooking as well as during warm holding is givenin Table 7.1.All the models so far concentrated mostly on the geometrical aspect of the hydration

process. The direct in�uence of certain raw materials on the pasta hydration process,however, has not been modelled yet. Del Nobile et al. (2003b) used lab-manufacturedspaghetti using di�erent wheat types, but could not relate any di�erence to the propertiesof the raw materials used.It seems, that for so far the in�uence of raw materials on the pasta hydration process is

described only qualitatively and based solely on empirical research.

55

7.2. Nuclear magnetic resonance and magnetic resonanceimaging

Nuclear magnetic resonance spectroscopy (NMR) allows to determine physical and chem-ical properties of molecules and related properties such as mass transport or compoundconcentrations in a sample (e.g. moisture content). Magnetic resonance imaging (MRI) isan imaging application of NMR; it adds a spatial information to NMR signals, thus imagescan be established (Falcone et al., 2006).Both techniques are non-invasive and need often only little sample preparation. The

techniques use magnetic nuclei (such as charged protons) which absorb and re-emit elec-tromagnetic radiation in a magnetic �eld. The hydrogen proton (1H) is frequently used(hydrogen is abundant in organic compounds and in water), but charged protons of vari-ous elements can be analysed. The protons are aligned to the same spin orientation ina a strong magnetic �eld (Falcone et al., 2006). They are excited and their orientationis shifted in steps of 90º by applying for a short time a pulse frequency (consisting of asuperimposed magnetic �eld gradient). When the pulse frequency stops, the protons willrelax (turn back to original orientation) and during this relaxation emit a response signal,which depends on their physiochemical environment (Cabrer et al., 2005). By varyingthe strength of the superimposed magnetic �eld, spatial localisation of the recorded signalis possible. Signal intensity depends on proton density as well as relaxation times (T1,T2) and the intensity can be modulated by the experimental parameters repetition time(TR), angle of the excitation pulse and echo time (TE; Cabrer et al., 2005). A detaileddescription of the technique can be found elsewhere (e.g. Schmidt, 2007).The relaxation time can be de�ned as the speci�c time for the protons to rotate back

to the equilibrium. Two types of proton relaxation time, longitudinal (T1) and transverse(T2), can be measured and can be related to the molecular water mobility. Varying relax-ation time constants can be correlated to more mobile and more bound water fractions,with longer relaxation times being associated with more mobile water or higher moisturecontent, respectively (Figure 7.4; Wang et al., 2004; Hills et al., 1996). T1 and T2 arefurthermore in�uenced by the temperature (Mohoric et al., 2004). Through variation ofthe experimental parameters of repetition time and echo time, the peak intensity can bemodulated. Longer echo times reduce generally signal amplitude (Ruan and Chen, 1998).MRI is the preferred method to study water migration. Signal intensity is directly

correlated to proton density which in turn gives the correlation to water content. Ruanand Chen (1998) made the analogy to conventional microscopy, that while in microscopystaining agents are giving the contrast, in MRI protons determine the contrast.

Figure 7.4.: The example illustrates association of relaxation times and moisture content.Numbers indicate moisture content in a soft wheat pasta sample. Reproducedfrom Hills et al. (1996)

56

(A) (B)

Figure 7.5.: Calibration curves for relaxation time (T2) of known moisture contents inpasta samples. Reproduced from Kratzer (2007) and Horigane et al. (2006).

Figure 7.6.: T2 map and moisture pro�les of cooked spaghetti samples. (A) fresh, (B) dried,(C) frozen. Scale bar 1mm. Extracted from Irie et al. (2004)

Three steps are necessary to develop moisture content maps from NMR measurements.At �rst, a scan of the sample is carried out (with a resolution of a certain number of voxels[a 3D-pixel]) and the signal intensity for each voxel at various echo times is recorded. ViaFourier transformation of the intensity values of the sequential images, T2 maps can becalculated. The relation between moisture content and T2 values is then established by acalibration curve. The calibration is carried out by determining T2 values for samples withknown moisture content, using the same NMR equipment as for the latter experiments.In the case of pasta, the resulting calibration curves showed both linear and non-linearrelationships, depending on the moisture content (see Figure 7.5, Horigane et al. (2006);Kratzer (2007)). An intermediate T2 map and a resulting moisture pro�le is shown inFigure 7.6 and for comparison a moisture content map in Figure 7.7 (Irie et al., 2004;Horigane et al., 2006).The voxel size determines the image resolution. In many applications, the spatial res-

olution is much higher than the longitudinal resolution making it di�cult to correlateMRI-results to speci�c e�ects of the microstructure of a sample. In the example of Hori-gane et al. (2006), spatial resolution was 78 Ö 78 mm2, but the longitudinal resolution

57

Figure 7.7.: Moisture content maps of spaghetti (A) cooked for 2-14 min and (B) cookedfor 12 min and hold for 0.5-15 hours. Reproduced from Horigane et al. (2006)

1,000 mm. The resolution of an image can be improved by increasing the number of scans.However, the limiting factor is the time to record the scans (in the above-mentioned ex-ample scan time was at around 2 min). The use of MRI is further limited at low moisturecontents, as the signal to noise ratio becomes problematic (Ruan and Chen, 1998).MRI has been used to study changes in moisture pro�les in spaghetti during drying

(Hills et al., 1997; Xing et al., 2007) and during cooking and post-cooking (Hills et al.,1996; Horigane et al., 2006, 2009; Irie et al., 2004; McCarthy et al., 2002; Bonomi et al.,2012; Sekiyama et al., 2012). Kratzer (2007) and Kratzer et al. (2008) used prepared wheatendosperms of a well-de�ned cylinder shape to determine the moisture pro�les in excess ofwater and during limited availability of water at various temperatures.The MRI method has been expanded to observe water migration in real time using the

example of rice kernels (Figure 7.8; Mohoric et al., 2004). Convection in the water wouldin�uence the NMR measurement. Therefore, they built a set-up where they could raisethe water temperature to 90ºC through a heated air �ow. After reaching the temperature,the heater was turned o� to minimise convection. The images series showed that neitherthe microstructure nor the water di�usion were uniform. Finally, the authors developedbased on the data a 3D model of water di�usion in rice.Within pasta analysis, MRI measurements seem to dominate over NMR measurements.

Nevertheless, a recent study analysed non-conventional pasta sample ingredients usingNMR(Carini et al., 2012). They added soy and carrot ingredients to a standard freshpasta dough formulation and measured how the T1 and T2 relaxation times were changed.The reference pasta showed two peaks which were attributed to protons of the gluten (0.1-1ms) and starch domain (2-80 ms), respectively. The gluten peak disappeared for carrotand soy �our formulations and the authors discussed the possibility that this could becorrelated to an improper formation of the gluten network.

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Figure 7.8.: (A) Set up to measure water migration in real time. The rice kernel is em-bedded in glass wool to �x its position. The tube is �lled with excess water.(B) Time series of the central slice of the rice kernel during cooking. Num-bers indicate time in minutes since the start of the cooking. Reproduced fromMohoric et al. (2004)

7.3. Near-infrared re�ectance spectroscopy (NIR)

NIR spectroscopy uses the near-infrared region of the electromagnetic spectrum. Throughthe radiation, covalent molecules are excited and an absorption spectrum can be measured.Water is strongly absorbing in the NIR spectrum and therefore, the method can be usedto determine moisture contents, but can also describe physical and chemical changes dur-ing wheat product processing (Ait Kaddour and Cuq, 2011). Thorvaldsson et al. (1999)measured the local moisture content in a pasta dough during heating. Other developed anin-line NIR technique to determine the average moisture content of freshly extruded pastaand suggested to use the technique as a process control. Advantages of NIR are that itis a non-invasive and fast technique (De Temmerman et al., 2007). Also others want toimplement NIR for process control and could distinguish di�erences in the dough structure(Zardetto and Dalla Rosa, 2006). Ait Kaddour and Cuq (2011) reviewed the usage of NIRin a broader context of wheat product processing.

7.4. Hyperspectral imaging

Hyperspectral imaging has similarities with NIR, but uses a broader range of the elec-tromagnetic spectrum. Several sensors detect the information from di�erent parts of thisrange in various images and the images are then combined. A proof of concept has beenshown to determine contact-less the cooking front in potatoes during cooking (Figure 7.9,Nguyen Do Trong et al. (2011)) and could potentially be used as a rapid monitoring tool.

Figure 7.9.: Progress of water migration into potato during cooking analysed with hyper-spectral imaging. Extracted from Nguyen Do Trong et al. (2011)

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Figure 7.10.: Gravimetrical determined water uptake over time. Derived from Del Nobileand Massera (2000)

7.5. Gravimetrical analysis

By comparing the weight of dry samples with the weight of cooked samples for varyingtimes, hydration kinetics can be established. A similar approach can be used to describedrying kinetics by recording the weight of samples dried for varying times.Several groups analysed the water uptake of pasta samples during cooking (Ca�eri et al.,

2010; Petitot and Micard, 2010; Del Nobile et al., 2003b) and Figure 7.10 is showing atypical curve. Measurable parameters besides the weight increase include the determinationof the increase in length and in radial size.

7.6. Other methods

The starch in a pasta gelatinises during cooking and looses its crystallinity. This canbe visualised using cross-polarised light and hence the position of the water front duringcooking can be shown (Del Nobile et al., 2003b).Gelatinised starch has di�erent texture properties compared to native starch. Again,

this can be used to determine the position of the water front or the area gelatinised starchduring cooking of pasta. Puncture tests could theoretically be used to determine within asample the point of the fastest change in measured resistance force which would mark theposition of the water front.

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8. Analysing quality parameters

The term quality is not a well-de�ned term and thus it can describe several concepts. In anattempt to de�ne pasta quality, Sissons (2008) collected several factors grouped into threecategories of raw material, recipes and processing. For the case of durum wheat, Troccoliet al. (2000) showed that quality parameters can vary dramatically between interestedgroups along the value chain (Figure 8.1). This chapter concentrates on quality parameterswhich are important for consumers as well as how these parameters can be determined.In general, the major quality parameters for the perception of a food are appearance,

texture and �avour. For pasta they are appearance and textural properties (Cole, 1991).Pagani et al. (2007) list requirements from Italian consumers especially concerning the

appearance and the cooking quality of pasta: Pasta shall have a typical yellow colour andhave an absence of black specks, white spots and fractures or �ssures. Concerning thecooking quality, the pasta shall have an optimum consistence as well as being non-stickyand non-bulky. A �rm and elastic structure of the cooked pasta is often referred to as �aldente� quality, however this attribute is not an universal preference. The optimal cookingtime preferred by consumers can vary dramatically between countries (Kill and Turnbull,2001) and in some countries such as Brazil in general softer textures are preferred as therepasta is often produced using farina (Marchylo et al., 2004). In such countries, 'al dente'would be experienced as an undercooked state.In a broader context also the taste and the tolerance to overcooking are included as

quality parameters important for consumers (Martinez et al., 2007).There is no explicit de�nition for the cooking performance of pasta and hence, in general

only a random selection of the below-mentioned parameters is used to describe cookingperformance in the various studies. Bruneel et al. (2010) stated in a summary that cookedpasta of good quality shows high levels of water absorption, low cooking loss and a goodtexture comprising high �rmness and low stickiness.The properties of the cooked pasta depend strongly on the cooking time (and the time

until the assessment). A reference for the cooking time was established by using the optimal

Figure 8.1.: Principal aspects of quality for interested groups working with durum. Repro-duced from Troccoli et al. (2000)

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cooking time (OCT). The American Association of Cereal Chemists de�ned OCT as thetime for a pasta strand to loose its white uncooked core (AACC, 2000). This is determinedby pressing a small strand between two glass plates and record the time when the whitespot in the core just disappears.

8.1. Colour

Colour attributes are commonly measured with the help of a colorimeter and de�ned in theL,a,b colour space (also CIELAB). A sample is enlightened with a de�ned illuminant andthe same instrument records light absorbance intensities in comparison to a white spot.Brightness, yellowness and redness are represented by the values L, a and b, respectively,and the values are ranging from 0 to 100 (L) and -1 to +1 (a and b; Tang et al., 1999).Colour measurements are especially used to instrumentally determine the yellowness

of raw and cooked pasta samples (Zweifel, 2001; Majzoobi et al., 2011a; Debbouz andDoetkott, 1996). The yellowness depends on the natural carotenoid content of the semolina,the degradation of the carotenoids by LOX and processing conditions such as storagetime of the grain, type of extrusion and drying temperatures (Fu et al., 2011). Fu et al.(2011) showed in their study that pasta yellowness can easily be predicted by analysingthe yellowness of the dough.The undesirable brown component in pasta can be measured as the extent of brownness

(Feillet et al., 2000). Brownness is seen to be the opposite to brightness (100-L) and isdue to inherent brownness of the semolina as well as the result of Maillard reactions whichoccur at higher rates during drying at high temperatures (Feillet et al., 2000).

8.2. Texture

The below-mentioned parameters can be determined with a sensory panel or by instru-mental techniques and both require standardised methods (Marchylo et al., 2004). E.g.one often standardised parameter is to cook pasta in boiling water in a ratio of 1:10 (Mar-chylo et al., 2004).Three of the most often used texture parameters for cooked pasta are described by Kill

and Turnbull (2001) using a sensorial de�nition:

· Firmness is the initial resistance to penetration o�ered by the pasta as it is bitten.

· Elasticity describes how pasta breaks down in the mouth on further chewing.

· Stickiness is the overall mouth feel of the pasta together with any residual starchleft in the mouth after swallowing.

Further parameters can be used for sensory analysis Sozer and Kaya (2003):

· Adhesiveness can be evaluated by pressing spaghetti to the palate and determinethe force needed to remove it with the tongue.

· Bulkiness is the extent to which strands of pasta stick together.

· Chewiness is the energy required to chew to the state ready for swallowing

· Cohesiveness is the degree to which the sample holds together after chewing

· Hardness is the resistance of cooked pasta to the compression by teeth

· Resilience is the rate of recovery after deformation.

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Sensory analysis is carried out as a panel test and the panel often uses a scale of 1 to 9with higher numbers indicating a higher quality for respective parameter (Baiano et al.,2006; Sozer and Kaya, 2003). In one study a total score of the sum of several parameters(in this case: stickiness, bulkiness and �rmness) was created to describe a total score ofcooking quality (Marconi et al., 2000). Besides the mentioned texture parameters otherparameters such as aroma are rarer measured. In fact, aroma seems to be of importanceonly when wholegrain �our or other cereals are incorporated into the pasta dough (Baianoet al., 2008; Feillet et al., 2000; Aravind et al., 2012a).Manthey and Dick (2012) concluded that sensory tests are useful for internal quality

control and to monitor quality changes with time. However, the results may vary betweenindividual testers. The wish to develop more objective tests was therefore one main drivingforce to develop instrument tests, according to Manthey and Dick (2012). In several studiesmethods were developed to measure individual quality parameters such as stickiness or�rmness (e.g. Dexter et al., 1983; Oh et al., 1983; Sissons et al., 2008b) and a standardmethod exists to determine �rmness (AACC, 2000).One important example for an instrumental test is the texture analyser, which is an

instrument that moves forms of well de�ned geometries (such as shear blades, plates, ballsor needles) at constant speed into and out of a sample and measures continuously theappearing resistance force to record the force over distance. In a texture pro�le analysis(TPA) several texture parameters can be calculated from this measurement Sozer and Kaya(2003). Five spaghetti strands are aligned side by side and the geometry used is often ashear blade or a plastic tooth (Anonymous, 2008; Martinez et al., 2007; Epstein et al., 2002).Martinez et al. (2007) used this set up to determine the parameters �rmness, stickiness andwith a slightly changed set up hardness, adhesiveness, springiness, cohesiveness, resilienceas well chewiness as as follows:

Figure 8.2.: Illustration of a texture pro�le analysis. Reproduced from Epstein et al. (2002)

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· Firmness was determined as the maximum peak force during the single compressionof the spaghetti at low speed until 70% strain (the higher the value, the �rmer thespaghetti).

· Stickiness was determined by compressing the samples in a similar way until acertain force was reached, hold for 3s and retracted. Stickiness were then de�nedas the maximum peak force to separate the probe from the samples (higher valuesindicated stickier samples).

· To determine the following parameters, the samples were compressed twice at higherspeed with a time delay of 2 seconds. The �rst compression was carried out untilmaximum force was reached and the second compression to 70% of the distance ofthe �rst compression (see for comparison Figure 8.2; the �gure illustrates a similartexture pro�le analysis, which di�ers in how far the compressions shall work (Epsteinet al., 2002)).

· Hardness was de�ned as the peak force during the �rst compression and

· Adhesiveness as the negative area during the �rst probe retraction.

· Springiness was de�ned as to which extent the spaghetti strand moved back to itsundeformed state when the force was removed. It was measured as the ratio betweenthe distance of the �rst peak and the second peak.

· Cohesiveness was calculated as the ratio of the areas under the �rst and secondpeak, respectively.

· Resilience was the ratio of the area under the second half of the �rst peak to the�rst half of the same peak.

· Chewiness was the product of hardness, cohesiveness and springiness (Martinezet al., 2007; Tang et al., 1999).

In contrast to the before mentioned parameters, elasticity can be measured using a tensiontest (Tudorica et al., 2002).It should be noted that Sissons et al. (2008a) question the repeatability of the aforemen-

tioned AACC method to measure �rmness. In the study of Sissons et al. (2008a) �rmnesswas measured with several instruments in di�erent laboratories and results showed thatthere was in general a good agreement between laboratories as well as instruments, butin detail some rank variations for various measured samples occurred. In another studythe authors analysed various test parameters (such as cooking time, water temperature,cooling time, rest time, number and position of strands, crosshead speed) and suggesteda new standardised method (Sissons et al., 2008b). The improvement can be seen in testparameter optimisation and especially in the fact that spaghetti strands were not alignedin contact, but were spaced 1 cm apart. In another work, several probe types were testedto make the AACC method also suitable for short pasta goods such as macaroni (Mantheyand Dick, 2012).The AACC method to determine �rmness seems not to be widespread. It rather seems,

that researcher use their own methods adapted to the equipment available for them andthis is especially true for other parameter such as stickiness or hardness (e.g. Lee et al.,2002; Cubadda et al., 2007; Dexter et al., 1983). However, this makes the comparison ofthe results more di�cult.Very recently, Fongaro and Kvaal (2013) presented a di�erent approach to evaluate the

surface texture of pasta. They used a standard document scanner to aquire images from

64

cooked pasta samples. They extracted then some image parameters (such as grey level) andcould use multivariate analysis to correlate some of the parameters with texture properties.The authors conclude that this set up might be used as an instrument for pasta qualityassessment.

8.3. Composition analysis

In the AACC method 66-50 there are some methods described to analyse quantitativelychanges in the composition of pasta (AACC, 2000). Very common is to determine theamount of residue in the cooking water, the so called cooking loss (Sissons, 2008).

Cooking loss [%] =Weight of drained residue in cooking waterWeight of dried spaghetti ∗ 100

A higher cooking loss is generally considered to correlate with a lower pasta cookingquality (Tarzi et al., 2012). Cooking loss is in�uenced by the water composition used(Malcolmson and Matsuo, 1993), does not correlate necessarily with stickiness (Tudor-ica et al., 2002; Malcolmson and Matsuo, 1993) and maybe misleading when comparingbran-rich and bran-free pasta samples as soluble components can originate from the bran(Edwards et al., 1995).An alternative to the cooking loss is to determine total organic material (TOM), which

is de�ned as the material released from a pasta surface during exhaustive rinsing (D'Egidioet al., 1982; Cubadda et al., 2007). The higher the amount of TOM, the lower is the cookingquality. TOM describes especially the cooking quality of pasta dried at low temperatures,as cooking quality is dominated by surface characteristics (D'Egidio et al., 1993).Determining the amount of water absorption or in general the cooked weight (as described

in Section 7.5) is another quality parameter as well as the degree of changes in size duringcooking (Baiano et al., 2006).

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9. Concluding remarks

Pasta quality is in�uenced by numerous interdependent factors and their implications onpasta production are far from being fully understood. Pasta is a product which has takena steady development. For a long time, most of the development happened in Italy aspasta played a special role in Italian diet even centuries ago. One indicator for this was theformation of guilds of pasta makers in the sixteenth century in some Italian cities. Thisled in turn to a professionalised production. Around this time, for example, the wine pressknown since ancient times was adapted to be able to extrude pasta dough (Serventi andSabban, 2002). From this starting point more and more equipment was developed andnowadays the once artisanal pasta production is dominated by fully automated industrialproduction processes (Serventi and Sabban, 2002; Pagani et al., 2007). The developmentis still ongoing, as it for example has been shown recently by Bühler who presented a newdrying line. In this line starch and gluten shall be kept in a rubbery state through mostof the drying to reduce the induced stress1.These developments and new demands led to new research questions. How can new

ingredients and alternatives to durum wheat be incorporated into a pasta product whilekeeping a high quality? How can the process be adapted to achieve products which keep ahigh quality even if they are not consumed directly after cooking? Which internal structureis necessary and how is the microstructure in�uenced by the various process steps?Several analytical tools are available to study the microstructure of pasta and there

is an ongoing adaption of the techniques to achieve better analysis capabilities. As anexample, MRI is a very suitable non-destructive method to study the water distributionand movement in pasta, but until now the resolution is not at a level to correlate the localwater distribution to components such as single starch granules.The same is true for modelling. There is a wish to model the processes during man-

ufacturing and cooking to both better understand the underlying mechanism as well asto be able to reduce experiments. However, for so long most models are based on empir-ical research and the transition to physically-based models (allowing for more transferableknow-how) is only happening slowly (Saguy et al., 2011).Even in testing the �nished products, there is a drive to improve the instrumental tech-

niques in order to have an automated alternative to sensory panels.In conclusion, even after centuries of empirical improvements and research, the �eld of

carbohydrate-rich foods such as pasta still o�ers plenty of questions yet to be solved.

1http://www.buhlergroup.com/global/en/products/ecothermatik-long-goods-dryer.htm, accessed 06/2012

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