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Review
Texture measurement approaches in fresh and processed foods — A review
Lan Chen a,b, Umezuruike Linus Opara b,⁎a School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, Chinab Postharvest Technology Research Laboratory, South African Research Chair in Postharvest Technology, Stellenbosch University, Stellenbosch 7602, South Africa
Knowledge of textural properties is important for stakeholders in the food value chain including producers,postharvest handlers, processors, marketers and consumers. For fresh foods such as fruit and vegetable,textural properties such as firmness are widely used as indices of readiness to harvest (maturity) to meetrequirements for long term handling, storage and acceptability by the consumer. For processed foods, under-standing texture properties is important for the control of processing operations such as heating, frying anddrying to attain desired quality attributes of the end product. Texture measurement is therefore one of themost common techniques and procedures in food and postharvest research and industrial practice. Variousapproaches have been used to evaluate the sensory attributes of texture in foods. However, the high costand time consumption of organizing panelists and preparing food limit their use, and often, sensory textureevaluation is applied in combination with instrumental measurement. Objective tests using a wide range ofinstruments are the most widely adopted approaches to texture measurement. Texture measurement instru-ments range from simple hand-held devices to the Instron machine and texture analyzer which providetime-series data of product deformation thereby allowing a wide range of texture attributes to be calculatedfrom force–time or force–displacement data. In recent times, the application of novel and emergingnon-invasive technologies such as near-infrared spectroscopy and hyper-spectral imaging to measure tex-ture attributes has increased in both fresh and processed foods. Increasing demand for rapid, cost-effectiveand non-invasive measurement of texture remains a challenge in the food industry. The relationships be-tween sensory evaluation and instrumental measurement of food texture are also discussed, which showsthe importance of multidisciplinary collaboration in this field.
Texture is one of the key quality attributes used in the fresh andprocessed food industry to assess product quality and acceptability.Texture attributes are also used along the food value chain to monitorand control quality, ranging from decision about readiness to harvestto assessing the impacts of postharvest handling and processing opera-tion on product shelf life and consumer preference and acceptability.Postharvest handling and operating conditions such as storage temper-ature usually have distinct effects on food texture properties (Farag,Lyng, Morgan, & Cronin, 2009; Konopacka & Plocharski, 2004; Lana,Tijskens, & van Kooten, 2005). Food formulation practices are alsooften associated with desirable or undesirable changes in texture(Foegeding, Çakır, & Koç, 2010; Morris & Morris, 2012; Pedreschia &Moyano, 2005; Saeleaw & Schleining, 2011a; Sudha, Srivastava,Vetrimani, & Leelavathi, 2007).
Textural quality attributes of food may be evaluated by descriptivesensory (subjective) or instrumental (objective) analyses. The combi-nation of time and high cost associated with sensory analysis has moti-vated the development and widespread use of empirical mechanicaltests which correlate with sensory analysis of food texture. Over theyears, a wide range of instrumental tests has been used in both researchand industry to assess food texture. Often the choice of any particularinstrument and analytical procedure depends on costs and availabilityof expertise within the organization.
Food texture has been defined as “all the rheological and structural(geometric and surface) attributes of the product perceptible bymeans of mechanical, tactile, and where appropriate, visual and audito-ry receptors” (Lawless & Heymann, 1998). Many food scientists, engi-neers and technologists evaluate mechanical properties to understandsubjective texture (Damez & Clerjon, 2008; Saeleaw & Schleining,2011b), while material scientists have been developing rheologicaland fracture mechanics approaches to understand the properties offood material in general (Ross, 2009). More recently, another group ofresearchers has focused on the fundamental understanding of the bio-logical mechanisms involved in mastication, oral processing and oralsensation (Chen, 2009; Chen & Stokes, 2012). Among the latest re-views, some (Awad, Moharram, Shaltout, Asker, & Youssef, 2012;de Wijk, Janssen, & Prinz, 2011; Foegeding et al., 2011; Funami,2011; Guessasma, Chaunier, Della Valle, & Lourdin, 2011; Ross,2009; Saeleaw & Schleining, 2011b; Szczesniak, 2002) have exam-ined specific aspects of food texture including sensory and instru-mental measurement, oral processing, non-invasive techniques(such as application of low and high ultrasound techniques); othershave emphasized specific food products such as meat, fruit, fish anddairy (Damez & Clerjon, 2008; Dowlati, Mohtasebi, & de la Guardia,2012; Drake, 2007; Foegeding et al., 2010; Foucquier et al., 2012;Harker, Redgwell, Hallett, Murray, & Carter, 2010; Mizrach, 2008;Ruiz-Altisent et al., 2010; Sila et al., 2008). The aim of this article isto provide a review of recent technological developments in foodtexture measurement, including subjective and objective methods.Starting with an overview of the approaches and limitations of sensoryevaluation of food texture, the review also discusses the relationshipsbetween sensory and instrumental measurement.
2. Sensory evaluation of texture — an overview
Subjective measurement of texture, often referred to as “sensoryperception” or “sensory evaluation”, encompasses all methods tomeasure, analyze and interpret human responses to the propertiesof foods and materials as perceived by the five senses: taste, smell,touch, sight and hearing (Civille & Ofteda, 2012; de Liz Pocztaruk etal., 2011). Bourne (2002) classified these methods as ‘oral’ and‘nonoral’. In sensory science, which is a ‘tool’ for documenting and un-derstanding human responses to external stimuli (Foegeding et al.,2011), texture measurement is carried out by trained or untrainedtaste panels (Kealy, 2006). The approach used in sensory analysis de-pends on the type of food and specific goals of assessment.
2.1. Use of sensory panels in food texture measurement
Traditionally, sensory attributes are assessed using a panel consistingof 6 to 10 (Foegeding et al., 2011), 6 to 12 (Drake, 2007), or 8 to 16(Brookfield, Nicoll, Gunson, Harker, & Wohlers, 2011) members whohave been trained in sensory evaluation methodologies. The commonlyaccepted or required number of panelists is about 10, but more variablein fresh foods, ranging from 1 to 23 (Table 1). The use of small sensoryteams (b6 individuals) is often faced with the question as to whetherthe results are statistically and scientifically reasonable. Nevertheless, lit-erature evidence shows that small panels can be used effectively forpostharvest assessments of sensory properties of fruits, such as apples,where they focus on a small number of attributes (Brookfield et al.,2011). Furthermore, the extent of training of panelists varies considerablyand this may affect the sensory results obtained. In one recent study onavocado, the one panelist used had over 15-year experience in avocadopostharvest assessment (Gamble et al., 2010). Chambers, Allison, andChambersiv (2004) found that only limited training may be necessaryto find differences among products for many texture attributes andsome flavor attributes; however, extensive training may be required toreduce variation among panelists and increase the discriminant abilitiesof panelists. Several terminologies have been used to describe thedegrees of training and ability in sensory analysis, ranging from“semi-trained”, “trained”, “highly experienced/trained”, and “expert”(Albert, Varela, Salvador, Hough, & Fiszman, 2011; BArcenas et al.,2007; Çakır et al., 2012; Taniwaki, Takahashi, Sakurai, Takada andNagata, 2009; Whetstine et al., 2007). Overall, evidence from literatureshow that essentially anyone between the ages of 19 and 69 could bepart of a sensory panel and the panel can be made of any numberdepending on experience and the circumstances.
Where information on hedonic liking, preference or purchase inten-tions is required in food analysis, it has been recommended that thesensory panel should be composed of untrained product consumers,and the number of panelists is expected to be higher and at least 70(Civille & Ofteda, 2012). However, many studies have used a smallernumber of untrained panelists (Table 2), especially when the resultsof sensory perception are used in combination with instrumental re-sults. Furthermore, evidence from literature (Brookfield et al., 2011;Heenan, Dufour, Hamid, Harvey, & Delahunty, 2010, Heenan, Hamid,Dufour, Harvey, & Delahunty, 2009; Ross, Chauvin, & Whiting, 2009)
Table 1Characterizations of trained sensory panel used to study fresh and processed food texture.
Number of panelists Age (yrs) Food type (s) Reference
1 – Avocado Gamble et al. (2010)2 – Pear Taniwaki, Hanada, Tohro, et al. (2009b)3 26–50 Apple Brookfield et al. (2011)5 – Apple Arana, Jaren, and Arazuri (2004)6 25–46 Beef rib steak; cream cheese; biscuits; persimmon Caine, Aalhus, Best, Dugan, and Jeremiah (2003), Kealy (2006), Sudha et al.
(2007), Taniwaki, Hanada and Sakurai (2009)7 20–64 Cheddar cheese; snack model and bread model products Rogers et al. (2009), Dijksterhuis et al. (2007)8 19–62 Parma hams; mixed gels; toasted rusk roll; semi-solid
foods; Parma hams; European raw ewes' milk cheese varietiesBenedini et al. (2012), Çakır et al. (2012), Castro-Prada et al. (2009), deWijk, Wulfert, and Prinz (2006), Benedini et al. (2012), Bárcenas et al. (2007)
9 24–48 Crust and crumb; potato chips; custard Polaki, Xasapis, Fasseas, Yanniotis, and Mandala (2010), Salvador et al. (2009),Janssen et al. (2007)
10 22–60 Salvelinus alpines (fish); acidmilk gels; hot served fish nuggets; snack bars; cake; mothbean flour dough; roasted almonds; agarose gels;commercially-available starch-based full-fat vanilla; custarddessert; apple
Ginés, Valdimarsdottir, Sveinsdottir, and Thorarensen(2004), Pereira, Matia-Merinoa, Jones, and Singh (2006), Albert et al.(2011), Greve et al. (2010), Heenan et al. (2010), Bhattacharya (2010),Varela et al. (2008), Barrangou et al. (2006), de Wijk, Polet, Bult, and Prinz(2008), Zdunek, Cybulsk, et al. (2010), Zdunek, Konopacka, et al. (2010)
11 24–55 Tortilla corn chip product; bread, cake and biscuit;crisp; apple
Bruwer, MacGregor, and Bourg (2007), Heenan et al. (2009), Rojo andVincent (2009), Zdunek et al. (2011), Ioannides et al. (2007)
Kim et al. (2012), Saeleaw et al. (2012), Akhtar, Murray, and Dickinson(2006), Çakır et al. (2012), Ross et al. (2009), Costa et al. (2011)
13 23–57 Commercial cornflakes; apple Chaunier et al. (2005), Ioannides et al. (2007, 2009);14 – Jellies; Cheddar cheese Blancher et al. (2007), Whetstine et al. (2007)15 22–31 Young cheese; mayonnaises and dressings; apple Brown, Foegeding, Daubert, Drake, and Gumpertz (2003),
de Wijk, Engelena, and Prinz (2003), Harker et al. (2002)21 22–34 Vanilla custard desserts de Wijk et al. (2003)23 – Apple Costa et al. (2011)25 20–35 Wheat flakes; filled dark chocolate Lenfant, Loret, Pineau, Hartmann, and Martin (2009), Ali, Selamat, Man, and
Suria (2001)30 20–35 Fluid food products Chen et al. (2008)
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shows that in trained sensory studies, the number of female panel-ists is often higher than that of male panelists but the high degreeof training and experience of all panelists reduces gender bias. Inthe untrained panels, the numbers of females and males are almostthe same (de Liz Pocztaruk et al., 2011; Varela, Salvador, &Fiszman, 2008, 2009).
2.2. Sensory scale
Usually, panelists use some common sensory terms to describe foodtexture. For example, the texture of potato chips is often described interms of crispness, hardness and crunchiness (Salvador, Varela, Sanz, &Fiszman, 2009). Since language richness seems to be an influential factorexplaining differences in sensory characterization among different coun-tries (Blancher et al., 2007), the use of sensory scales is a popular methodadopted by researchers. In most studies, attribute intensities are rated ona continuous, unstructured graphical intensity scale, the left side of thescale usually corresponding to the lowest intensity (value 0 or 1) and
Table 2Characterizations of untrained sensory panel used to study fresh and processed food textur
Number of panelists Age (yrs) Food type (s)
7 25–55 Carrot zucchini, apricot, red radish, and jicam9 24–36 Four different cheese products10 – Four types of commercial biscuits13–17 – Jellies14 – Butter fat-in-water emulsions15 – Cornflakes20 20–60 Cookies; hot served fish nuggets24 19–41 Crispy biscuits30 – A kind of cheese analog; bread40 – Sausages50 19–50 New Zealand King Salmon100 18–60 Roasted almonds; cherry115/102 – Bread, cake and biscuit130 20–65 Roasted almonds and two different types of220 – Beef240 – Beef steak713 – Beef steak
the right side to the highest intensity (value 5, 7, 9, 10, 15, 100, 150, oreven 1000) of the attribute (Arimi, Duggan, O'Sullivan, Lyng, &O'Riordan, 2010b; Benedini, Parolari, Toscani, & Virgili, 2012; Castro-Prada, Primp-Martin, Meinder, Hamer, & Van Vliet, 2009; Costa et al.,2011; Harker et al., 2002; Heenan et al., 2009; Kim et al., 2012; Marzec,Kowalska, & Zadrozna, 2010; Nguyen et al., 2010; Oraguzie et al., 2009;Primo-Martín, Castro-Prada, Meinders, Vereijken, & van Vliet, 2008;Rogers et al., 2009; Rojo & Vincent, 2009; Salvador et al., 2009;Taniwaki, Hanada, Tohro & Sakurai, 2009; Varela et al., 2009; Zdunek,Cybulska, Konopacka, & Rutkowski, 2011). When the larger scoreranges were used, computer programs for automated sensory analy-sis were likely applied (Castro-Prada et al., 2009; Dijksterhuis,Luyten, de Wijk, & Mojet, 2007). However, the choice of the startingpoint in the scale (0 or 1) and the upper limit just depends on the re-searcher. Sensory science has also developed several other method-ologies, such as the projective mapping technique under the name of“Napping®” procedure (Pagès, 2005) and flash profile (Dairou &Siefffermann, 2002). Recently, Varela and Ares (2012) reviewed
e.
Reference
a Nguyen et al. (2010)Dan and Kohyama (2007)Arimi et al. (2010b)Blancher et al. (2007)Akhtar, Stenzel, Murray, and Dickinson (2005)Gregson (2002)Gupta, Bawa, and Abu-Ghannam (2011), Albert et al. (2011)de Liz Pocztaruk et al., 2011Cunha, Dias, and Viotto (2010), Wang et al. (2007)Lee and Kwon (2007)Larsen, Quek, and Eyres (2011)Varela et al. (2008), Ross et al. (2009)Heenan et al. (2009)
extruded snacks Varela et al. (2009)Destefanis et al. (2008)Yancey et al. (2010)Voges et al. (2007)
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the implementation, advantages and disadvantages of novel productprofiling techniques developed in the last ten years.
2.3. Limitations of sensory perception
The sensory response to a food mechanical stimulus is non-linearand can be affected by adaptation and fatigue (Peleg, 2006), and levelof training received by the participants (Bárcenas et al., 2007). Unlikeman-mademachines operating in their designed load range, the senso-ry sensitivity of humans depends on rheological properties of the tis-sues involved (Peleg, 2006), other basic attributes (such as taste andaroma) of the foods (van Vliet, van Aken, de Jongh, & Hamer, 2009),and even the containers of the food (Piqueras-Fiszman & Spence,2012). A trained sensory panel, also like any instrument for measure-ment, requires regular calibration and maintenance (Foegeding et al.,2011). In comparison with instrumental measurement, sensory evalua-tion is time consuming and expensive (Kim et al., 2012; Wang, Zhou, &Isabelle, 2007). Researchers have also shown that sensory perceptionhad limited success in assessing large cultivar collections and breedingmaterials (Costa et al., 2011).
3. Instrumental measurement of food texture
To overcome the limitations of sensory perception of food texture asdiscussed in Section 2.3, objectivemeasurement involving instrumentalapproaches have been developed (Costa et al., 2011), and a great deal ofeffort has been expended in improving the instruments and measure-ment techniques for meaningful estimation of textural properties(Oraguzie et al., 2009; Zdunek, Cybulsk, Konopacka, & Rutkowski,2010, Zdunek, Konopacka, & Jesionkowska, 2010). Since texture per-ceived in the mouth largely depends on the mechanical behavior offood which will determine the dynamics of breakdown during eating(Foegeding et al., 2010), most of the objective measurement researchare based on the mechanical/rheological properties of foods. The typesof experiments can be classified as fundamental, empirical and imitativemethods. Fundamental tests have been developed by scientists and en-gineers interested in the theory and practice of materials of construc-tion, and they may not be very useful in measuring what is sensed inthe mouth when food is masticated (Bourne, 2002). Thus, empiricaland imitative instrumental tests have been commonly used to quan-tify the texture properties of foods (Barrangou, Drake, Daubert, &Foegeding, 2006; Toivonen & Brummell, 2008). A wide range of de-structive and non-destructive methodologies and relevant instrumentshas been used to measure the texture of fresh and processed foods.
3.1. Destructive methods
3.1.1. Three-point bending testDuring the three-point bending test, force is applied to the center
of the sample, such as biscuits (James et al., 2011), potato crisps (Rojo& Vincent, 2009) and cornflakes (Chaunier, Della Valle, & Lourdin,2007), by an anvil until fracture occurs. The crosshead speed rangesfrom 1 mm/min to 120 mm/min (James et al., 2011; Rojo & Vincent,2009). Based on the data of fracture stress and strain, the Young'smodulus of food material can be achieved (Kim et al., 2012).
3.1.2. Single-edge notched bend (SENB) testThe SENB test is a well-established test method, in which, the test
specimens have to satisfy the standard requirements for their geometry.Similar to the three-point bending test, the whole instrumental teststrip is placed across two support anvils. The notch ismade on the under-side, and force is applied from the top to the center of the test strip by athird anvil until fracture occurs. The speed of the third anvil is often at2 mm/s (Brookfield et al., 2011; Harker, White, Gunson, Hallett, & DeSilva, 2006). Fracture toughness (including critical stress intensity factor
and fracture energy) of food, for example biscuits (James et al., 2011) orapples (Brookfield et al., 2011; Harker et al., 2006), can be evaluated.
3.1.3. Compression and puncture testCompression test and puncture test are the most common methods
to measure food texture properties. The testing foods may be solid orsemi-solid. For instance, the compressing sample foods include gels(Çakır et al., 2012), gram (Sasikala, Ravi, & Narasimha, 2011), applerings (Farris, Gobbi, Torreggiani, & Piergiovanni, 2008), cornflakes(Chaunier et al., 2007), cheese (Dan & Kohyama, 2007), cellular corn-starch extrudates (Agbisit, Alavi, Cheng, Herald, & Trater, 2007), breadcrumb (Le-Bail, Boumali, Jury, Ben-Aissa, & Zuniga, 2009) and carrot(De Roeck, Mols, Duvetter, Van Loey, & Hendrickx, 2010; Sila, Smout,Vu, & Hendrickx, 2004); the puncturing sample foods include apple(Brookfield et al., 2011; Harker et al., 2006; Ioannides et al., 2007,2009), kiwifruit (White, De Silva, Requejo-Tapia, & Harker, 2005), pota-to slices (Troncoso & Pedreschi, 2007) and cereal snacks (Tsukakoshi,Naito, & Ishida, 2007). These types of texture tests can be carried outon ‘whole fruit’ or ‘parts’ (skin, pulp or skin and pulp) depending onthe research purpose (Rolle et al., 2012). Sometimes, the two testshave been employed in one research. Takahashi, Hayakawa, Kumagai,Akiyama, and Kohyama (2009) evaluated eleven solid foods by com-pression and puncture tests. Nguyen et al. (2010) tested severalprocessed vegetables and fruits, and Sirisomboon, Tanaka, and Kojima(2012), Sirisomboon, Tanaka, Kojima, and Williams, Sirisomboon andPornchaloempong (2011) investigated the firmness, hardness, energyabsorption of tomato, pomelo and mango fruit by the two methods.For both compression and puncture tests, the probes are usually cylin-drical in shape, while the diameters of heads are quite different. In com-pression tests, they can be 10 mm (Sasikala et al., 2011), 25 mm (DeRoeck et al., 2010), 80 mm (Farris et al., 2008) and even 150 mm likea plate (Takahashi et al., 2009). In puncture tests, the diameter of thehead (plunger) is often smaller such as 11 mm (Ioannides et al.,2009), 2 mm (Nguyen et al., 2010), or even 1 mm like a needle(Tsukakoshi et al., 2007).With regard to the puncture crosshead testingspeed, it is likely to be several millimeters per second such as 4 mm/s(Ioannides et al., 2009); however, the compression crosshead speedcan range from 10 mm/min to 30 mm/s (Moreira, Chenlo, Chaguri, &Fernandes, 2008; Varela et al., 2008). Similarly, the puncture depth isusually several millimeters mainly depending on the size of sampleswhile deformation of the original height by compressing can be 75%(Farahnaky, Azizi, & Gavahian, 2012), 50% (Jaworska & Bernas, 2010)and 25% (Çakır et al., 2012) depending on the mechanical propertiesof samples as long as the maximum compression force is achieved.Based on literature evidence, the minimum number of samples foreach measurement is five, which has been discussed further by Rolleet al. (2012). In these compression or puncture experiments, theperforming force, deformation percentage or puncture depth and cross-head speed are the concerned parameters. However, the relationshipsbetween the force or other property parameters and the crossheadspeed are seldom discussed (Castro-Prada et al., 2009).
Texture profile analysis (TPA) test, which is based on the imitation ofmastication or chewing process, is performed with double-compressioncycles. For irregular shape testing, the food sample is often cut into cylin-drical shapes. For example, Jaworska and Bernas (2010) cut cylindricalsamples (20 mm in length and diameter) out of mushrooms (caps andstipes, respectively). Through TPA test, a wide range of food texture prop-erties, such as hardness, springiness, cohesiveness, adhesiveness, resilien-cy, fracturability, wateriness, gumminess, sliminess, and chewiness, canbe analyzed (de Huidobro, Miguel, Blázquez, & Onega, 2005; Guiné &Barroca, 2012; Jaworska & Bernas, 2010).
Magness-Taylor puncture test (M-T) is the current industry stan-dard method for fruit flesh firmness analysis, which is based onforce-deformation characteristics of the fruit flesh mimicking the“mouth-feeling” of the consumer (Lu & Tipper, 2009; Qing, Ji, &
Fig. 1. Idealized loading of a food particle supported by cusps (left: before loading,middle:on bending like a beam to a displacement δ, and right: after cracking). The letters F, l, b, tand a are the vertical force, span, breadth, thickness and notch length, respectively. (Lucas,Prinz, Agrawal, & Bruce, 2002).
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Zude, 2008; Ruiz-Altisent, Lleó, & Riquelme, 2006). In this test, themaximum force is recorded as a measure of fruit firmness.
3.1.4. Stress relaxation testStress relaxation test ismainly used to analyze the viscoelastic proper-
ty of semi-solid foods, such as fish (Herrero & Careche, 2005), cheese (DelNobile, Chillo, Mentana, & Baiano, 2007), sausage (Andrés, Zaritzky, &Califano, 2008) and flour dough (Bhattacharya, 2010). During measure-ment, the sample is compressed to an expected strain at a certain speedand the decreasing force is recorded during the relaxation time whichmay range from 1 min to 10 min (Andrés et al., 2008; Del Nobile et al.,2007; Herrero & Careche, 2005). The decaying stress and the appliedstain are related by relaxation modulus, where the relaxation modulusis usually calculated by using a generalized Maxwell model.
3.1.5. Warner–Bratzler shear force (WBSF) testTheWBSF test has been used since the 1930s and remains the most
widely used instrumental measure of meat tenderness (Destefanis,Brugiapaglia, Barge, & Dal Molin, 2008; Girard, Bruce, Basarab, Larsen,& Aalhus, 2012). The head or blade can bemounted on different textureanalyzing machines such as the Texture Analyzer (Cai, Chen, Wan, &Zhao, 2011), Instron devices (Yancey, Apple, Meullenet, & Sawyer,2010) or other universal test machines (Lorenzen et al., 2010). Duringtesting, meat sample cores are sheared perpendicular to the musclefiber orientation (Destefanis et al., 2008). A minimum of six 1.27 cmcores from throughout the steak, and an instrument crosshead speedof 200–250 mm/min are required (Derington et al., 2011) followingthe guidelines of the American Meat Science Association (AMSA,1995). Usually, the most considered parameter of the force–distance/time curve is the maximum shear force.
3.1.6. Tests using a combination of mechanical and acoustic methodsTypical characteristic for many hard, crispy and crunchy solid prod-
ucts is their brittle fracture behavior, mostly accompanied by a sharpsound (acoustic emission or vibration) which is closely related to theirtexture attributes (Maruyama, Arce, Ribeiro, & Costa, 2008; Taniwaki,Hanada, & Sakurai, 2006; van Vliet & Primo-Martín, 2011). Thus, re-searchers have combined mechanical tests, such as compression, pene-tration or three-point bending test, with acoustic signal analysis.
For both dry–crispy and wet–crispy foods, the methods of evalua-tion can be divided into two groups, namely mechanical devices com-bined with acoustic emission detector (AED) and those combinedwith piezoelectric sensor. When used in combination with the AEDtest, force–displacement and sound amplitude–time signals were si-multaneously recorded and the results showed that major acoustic sig-nalswere observed togetherwith application of force (Chen, Karlsson, &Povey, 2005; Zdunek & Bednarczyk, 2006). This coincidence wasinterpreted as an energy release in the form of sound as a result of ma-terial fracturing (Zdunek, Cybulsk, et al., 2010, Zdunek, Konopacka, etal., 2010). From the sound data, the maximum sound pressure, numberof sound peaks, sound curve length and area under amplitude–timecurve were obtained (Arimi, Duggan, O'Sullivan, Lyng, & O'Riordan,2010a,b, Arimi et al., 2010b; Salvador et al., 2009). Products perceivedas uncrispy emitted signals with lower average amplitude and higherpeaks, at low frequencies and opposed a high mechanical resistance tocompression. On the other hand, the crispiest flakes emitted soundswith larger average amplitude and fewer high peaks, uniformly distrib-uted in the frequency domain with a moderated mechanical resistance(Chaunier, Courcoux, Della Valle, & Lourdin, 2005). This mechanical–acoustic combining strategy has been successfully applied to measurecrispness of fruits such as apple (Marzec et al., 2010; Zdunek, Cybulsk,et al., 2010, Zdunek, Konopacka, et al., 2010, Zdunek et al., 2011),which was demonstrated to correlate with human sensory perception(Costa et al., 2011). Recently, an increasing number of researchershave adopted the Texture Analyzer (TA-XT plus) (Chen et al., 2005;Costa et al., 2011, 2012; Saeleaw, Dürrschmid, & Schleining, 2012;
Salvador et al., 2009; Sanz, Primo-Martín, & van Vliet, 2007; Taniwaki& Kohyama, 2012; Varela et al., 2009) in texture measurement sincethe AED has become one selective part of the instrument. Other instru-ments combining a mechanical device with a piezoelectric sensor havebeen used to detect the vibration produced by fracture when a probe isinserted into a food product (Taniwaki et al., 2006). The types of foodproducts studied using this method include potato chips, cabbageleaves, pear, persimmon, and grape flesh (Iwatani, Yakushiji, Mitani, &Sakurai, 2011; Taniwaki, Hanada & Sakurai, 2009; Taniwaki, Hanada,Tohro, et al., 2009; Taniwaki, Takahashi, et al., 2009; Taniwaki, Sakura,& Kato, 2010; Taniwaki & Sakurai, 2008).
3.1.7. Imitative methodsDestructive methods which mimic the biting process during eating
follow the motion of the bite by incisors or mastication by molars(Fig. 1) and are often referred to as “tooth methods” (Jiang, Wang, vanSanten, & Chappell, 2008). For example, Varela et al. (2009) usedtooth-like probes to compress snacks, which proved to be as good astraditional penetration tests to assess crispy characteristics. It was alsodemonstrated that results obtained at slow and high tooth-like probespeeds could be complementary, showing the parameters obtained atlower test speeds to be better correlated to human perception and thein-mouth fracture pattern to bemore effectively characterized at highercompression speeds (Varela et al., 2008). Lately, Chung, Degner, andMcClements (2012) developed amethod to characterize the texture at-tributes of semi-solid foods during “instrumental mastication”, whereartificial saliva can be used. Experiments showed that this techniquecan be used to monitor textural changes of starch-based food productsduring oral processing.
3.1.8. Other destructive methodsExcept for the methods discussed above, there still are some other
useful destructive tests, for example, probe tensile separation methodwhich is employed to measure the stickiness of fluid foods (Chen,Feng, Gonzalez, & Pugnaloni, 2008), cutting-shear test which is usedto evaluate the degree of cells being held together and the cuttingforce of fresh food (Emadi, Kosse, & Yarlagadda, 2005), and the trac-tion test, which has been proved to be a valid technique that can beused to measure fundamental mechanical parameters of food duringa certain period (Svanberg, Ahrne, Loren, & Windhab, 2012, 2013).
3.2. Non-destructive methods
Non-destructive testing of texture in fresh and processed foods iscritical for monitoring and controlling product quality. Table 3 sum-marizes some examples of the application of non-destructive tech-niques in food texture measurement.
3.2.1. Mechanical techniquesNon-destructive mechanical techniques used in food texture include
the measurement of quasi-static force-deformation (Ruiz-Altisent et al.,2010), impact response (Herrero-Langreo, Fernández-Ahumada, Roger,
Table 3Non-destructive techniques used in texture measurement of foods.
Technique Texture property Food product (s) Reference
Quasi-static force–deformation Firmness Fruits and vegetables Tijskens et al. (2009), Ruiz-Altisent et al. (2010)Impact response Firmness, mealiness Apple, kiwifruit, peach Harker et al. (2002), Arana et al. (2004), Molina-Delgado et al. (2009), Ragni,
Berardinelli and Guarnieri (2010), Herrero-Langreo et al. (2012)“Finger” method (compression) Indentation force Catfish filet Jiang et al. (2008)Bioyield detection Firmness Apple Lu and Tipper (2009), Mendoza et al. (2012)Acoustic vibration Texture index Cabbage Taniwaki and Sakurai (2008)Acoustic impulse resonance Flesh firmness Pear, apple Gómez et al. (2005), Zude, Herold, Roger, Bellon-Maurel, and Landahl (2006),
Molina-Delgado et al. (2009), Mendoza et al. (2012)Laser Doppler vibrometer Elastic properties Persimmon, pear Sakurai, Iwatani, Terasaki, and Yamamoto (2005b), Terasaki et al. (2006)Velocity of sound transmitting in samples Firmness Kiwifruit Muramatsu et al. (1997)Video analysis Rigor mortis Sturgeon Oliveira, O'Keefe, and Balaban (2004)Electronic nose Firmness (ripeness) Apple, pear, mandarin Brezmes et al. (2005), Zhang, Wang, and Ye (2008a,b); Gómez et al. (2007)Ultrasonic Hardness, softness,
firmness, mealinessCheese, fruits Benedito, Cárcel, Clemente, et al. (2000), Benedito, Cárcel, Sanjuan, et al. (2000),
Benedito, Gonzáles, et al. (2000), Benedito, Cárcel, Gonzalez and Mulet (2002),Benedito et al. (2006), Mizrach, Flitsanov, Akerman, and Zauberman (2000),Mizrach et al. (2003), Mizrach (2008), Bechar et al. (2005), Kim et al. (2009)
Nuclear magnetic resonance (NMR) Ripeness, mealiness Apple Marigheto, Venturi, and Hills (2008)Magnetic resonance imaging (MRI) Softening/firmness Pear Zhou and Li (2007)Waveguide spectroscopy Firmness Kiwifruit Ragni et al. (2012)Fluorescence Mealiness Apple Moshou, Wahlen, Strasser, Schenk, and Ramon (2003)Visible/near/mid-infrared spectroscopy Mealiness, firmness,
tendernessFruits, cucumber, beef Benedito et al. (2006), Ruiz-Altisent et al. (2006), Kavdir et al. (2007), Nicolaï et al.
(2007), Subedi and Walsh (2009), Valente, Leardi, Self, Luciano, and Pain (2009),Bureau et al. (2009), Yancey et al. (2010), Mendoza et al. (2012),Sirisomboon, Tanaka, Kojima, and Williams (2012)
Hyperspectral scattering technique Firmness Apple and peach Lu (2007), Lu and Peng (2006), Peng and Lu (2008), Huang and Lu (2010),Mendoza et al. (2012), Herrero-Langreo et al. (2012)
Time-resolved (domain)reflectance spectroscopy
Firmness Fruits Valero et al. (2004), Tijskens et al. (2007), Nicolaï et al. (2007), Rizzolo et al. (2009)
Raman spectroscopy Tenderness Beef Beattie, Bell, Farmer, Moss, and Desmond (2004)Light back scattering images Firmness Apple Qing et al. (2008)
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Palagós, & Lleó, 2012; Ragni, Berardinelli, & Guarnieri, 2010), “finger”compression (Jiang et al., 2008), and bioyield detection (Mendoza, Lu, &Cen, 2012). Similar to the destructive measurement, the mechanicalmethods are often combined with the indirect ones. For example, theproduct is excited by means of a small impact, and the vibration (about20–20,000 Hz) is measured using a microphone, piezoelectric sensorsor laser vibrometers (Gómez, Wang, & Pereira, 2005; Ruiz-Altisent et al.,2010).
3.2.2. Ultrasound techniquesAmong the indirect methods of food texture measurement, ultra-
sound technology provides one of the foundations for a non-destructive,fast and reliable technique for correlating specific quality-related indicesand characteristics during growth and maturation, and in the course ofstorage and shelf-life, until readiness for consumption (Mizrach, 2008).Ultrasound techniques are relatively cheap, simple and energy saving,and thus have become an emerging technology for probing food products(Awad et al., 2012). The mechanical structure of the tissue, its physico-chemical quality indices, and each change in the quality attributes of thefruit, affect the energy of the received signal (Bechar, Mizrach, Barreiro,& Landah, 2005). Ultrasound technology is suitable for quality measure-ment in various products such as porous food products (Benedito,Simal, Clemente, & Mulet, 2006), and fruit and vegetables (Mizrach etal., 2003; Saeleaw & Schleining, 2011b). The most important mechanicalproperty of fruit and vegetable that correlates with ultrasound character-istics is firmness and the results are most likely to be compared with de-structive methods such as M-T firmness test (Mizrach, 2008).
3.2.3. Optical techniquesOptical texture measurement techniques have been reported to
hold great promise formealiness detection and classification in fruit be-cause they usually are rapid and non-destructive or non-invasive and,more importantly, they can provide a large amount of informationabout the condition or status of a product (Huang & Lu, 2010). Properdata processing and analysis are critical for achieving superior results
by these techniques (Huang & Lu, 2010; Lu, 2007). Looking at the enor-mous number of literature produced during the last fifteen years, onegroup of optical methods, visible/near/mid-infrared spectroscopy, maybe considered the most researched non-destructive techniques for theassessment of internal food quality (Ragni, Cevoli, Berardinelli, &Silaghi, 2012). Firmness of fruit and vegetables and tenderness ofmeat based on infrared spectroscopy have been investigated by re-searchers (Kavdir, Lu, Ariana, & Ngouajio, 2007; Kojima, Fjita, Tanaka,& Sirisomboon, 2004; Nicolaï et al., 2007; Sirisomboon, Tanaka,Kojima & Williams, 2012; Subedi & Walsh, 2009; Yancey et al., 2010).
Comparisons of several non-destructive methods of texturemeasurement have been made in recent times. These researches(Herrero-Langreo et al., 2012; Mendoza et al., 2012; Molina-Delgadoet al., 2009) demonstrated that the fused systems of measurement pro-vided more complete and complementary information and, thus, weremore effective than individual sensors in food quality prediction.
3.3. Advantages and disadvantages of instrumental measurementapproaches
For a certain food product, such as apple, usually several instrumen-tal approaches could be chosen for texturemeasurement. Fig. 2 shows awide range of destructive and non-destructive, as well as acoustic opti-cal instrumental methods of texture measurement used for solid andsemi-solid foods such as apples. Choice of any method depends on thepurpose of measurement and specific conditions required. Table 4 sum-marizes the characteristics of typical texture measurement approaches,including their advantages and disadvantages. Fundamental measure-ment methods, such as the single-edge notched bend (SENB) test arelinked with microstructural and molecular mechanisms, where mate-rials under test must be homogeneous and isotropic, and geometric inshape so that stresses and strains can be precisely calculated. This re-quirement clearly limits their applicability to a wide range of foodtypes (Foegeding, Brown, Drake, & Daubert, 2003). Such tests aregenerally slow to perform and do not correlate as well with sensory
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evaluation as do empirical tests (Bourne, 2002) in comparison withcompression and puncture tests which are easier to perform with in-strumentation that are generally less expensive to purchase and oper-ate (Claus, 1995).
Empirical measurements such as compression and puncture testshave problems of poor definition of what is being measured, the arbi-trariness of the test and frequently no available absolute standard(Bourne, 2002). Recently, non-destructive methods such as ultra-sound and optical have emerged as common tools for the measure-ment and analysis of the texture in semi-solid food such as fruit. Incomparisonwith fundamental and empiricalmeasurement approaches,non-destructive measurements have the advantages of being quick,easily installed online and allow continuous evaluation of texture prop-erties on different parts of the same itemwithout producing waste andsubsequent losses (Molina-Delgado et al., 2009). However, the highprice and operating cost of non-destructive texture measurementequipment are prohibitive and often hinder their widespread applica-tion in food research.
Fig. 2. Different instrumental measurement approaches to apple texture properties: (a) penemethod; (d) ultrasonic method; (e) magnetic resonance imaging (MRI); (f) visible and shsystem (Harker, Maindonald, & Jacson, 1996; Harker et al., 2006; Kim, Lee, Kim, & Cho,Group in red are destructive measurements; group in purple are non-destructive measurem
3.4. Mechanical instruments used to measure food texture
The use of mechanical instruments still holds the dominant posi-tion in food texture measurement. Literature evidence shows thatthere are two main instruments used in texture research of solidand semi-solid foods: the Texture Analyzer (TA) (Stable Micro Sys-tems Ltd.) (Al-Said, Opara, & Al-Yahyai, 2009; Farahnaky et al.,2012; Greve, Lee, Meullenet, & Kunz, 2010; Li et al., 2012; de LizPocztaruk et al., 2011), and the Instron testing machine (Instron Ltd.)(Benedini et al., 2012; Brookfield et al., 2011; Çakır et al., 2012; Faraget al., 2009; Svanberg et al., 2012). Among the models of Texture Ana-lyzer, TA-XT2i, TA-XT2 and TA.XT plus appear to be the most popularin food texture research. Compared with the Instron, TA focuses moreon food texture measurement and is convenient for both academicand industrial use. However, Instron is a kind of general and profession-al instrument for studyingmechanical properties of different materials.Nowadays, most researchers combine each of these instruments withother measuring techniques for obtaining more information during
tration test; (b) SENB test; (c) combination of puncture test and acoustic emission (AE)ortwave near-infrared (Vis–SWNIR) spectroscopy; (g) online hyperspectral scattering2009; Létal et al., 2003; Mendoza, Lu and Cen, 2012; Zdunek, Cybulsk, et al., 2010).ents; group in green are acoustic methods; group in blue are optical methods.
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experiments. For instance, researchers have combined TA with anacoustic envelope device (Costa et al., 2012; Sanz et al., 2007), whileothers have combined Instron with I-Scan system to obtain two-dimensional stress distribution maps during compression (Dan, Azuma,& Kohyama, 2007).
In addition to the equipment described above, there are also someother popular texturemeasuring instruments used in food texturemea-surement and analysis. Magness-Taylor tester (Lehman-Salada, 1996),manual Effegi penetrometer (Ragni et al., 2012) and digital penetrome-ter (Ragni et al., 2010) which are based on the M-T puncture test, areoften used in texture analysis of solid and semi-solid fresh food prod-ucts (Harker, Kupferman, Marin, Gunson, & Triggs, 2008; Ioannides etal., 2007, 2009; Iwatani et al., 2011; Oraguzie et al., 2009). The rheome-ter and viscoanalyzer are widely applied to test liquid or semi-solidsamples (Chaunier et al., 2007; Janssen, Terpstra, De Wijk, & Prinz,2007; Kealy, 2006). Many brands and models of universal testingmachines are employed in food texture research as well (Schouten,Natalini, Tijskens, Woltering, & van Kooten, 2010; Tijskens et al., 2009;Zdunek, Konopacka, et al., 2010).
Several researchers have also reported the development and appli-cation of novel texture measurement devices which are not commer-cially available. A twister for measuring in-situ firmness of fleshy foodproducts based on crushing strength was reported (Opara, Studman,& Banks, 1997; Studman & Yuwana, 1992). Japanese researchers havereported the design and application of an acoustic measurement deviceto investigate fruit and vegetable firmness (Sakurai, Iwatani, Terasaki, &Yamamoto, 2005a; Taniwaki et al., 2006). Lu and Tipper (2009)designed a non-destructive portable bioyield device for fruit firmnessmeasurement.
4. Relationships between sensory and instrumental measurement
In comparisonwith instrumentalmethods, sensory evaluations pro-vide a more immediate measure of human perception (Ross, 2009).However, instrumental measurements are objective and, to some ex-tent, considered to be more accurate than sensory analysis (Oraguzieet al., 2009; Zdunek, Cybulsk, et al., 2010; Zdunek, Konopacka, et al.,2010). Therefore, the importance of understanding the relationship be-tween subjective and objective measurements has gained increasingpopularity among researchers and industry.
The sensory evaluation of food texture generally involves terms de-termined at initial contact (usually by hands), first bite, after chewingand after swallowing (Foegeding, 2007). Based on oral processing con-siderations, the first two having a minimal contribution from saliva andsensory perception of texture should be reasonably predicted by me-chanical test (Foegeding et al., 2010), that has been testified by differentresearches: hand-evaluated sensory texture terms correlated very wellwith fundamental rheological properties of agarose gels (Barrangou etal., 2006); sensory crispiness or hardness often has good correlationwith puncture (penetration) force or AE (Brookfield et al., 2011;Chaunier et al., 2005; Ioannides et al., 2007); high correlations ofdried apple were observed during 3-point bending test (Marzec et al.,2010), etc. It was found that the general affirmation that in instrumentaltests “the closer the test speed to themastication speed, the better”, wasnot always true. This is particularly related to the current limitations ofthe standard available equipment and the limited force data samplingrate of the texture analyzer which make the measurements at higherspeed less accurate (Varela et al., 2008). Provided the correct instru-mental conditions are set, a valuable improvement in the prediction ofsensory attributes by instrumental variables can be achieved (Greve etal., 2010).
Texture is a multimodal sensory property and one mechanical testwill most likely not cover all of the nuances of food texture that ahuman experiences when eating (Foegeding et al., 2011). Texture test-ing instruments can detect and quantify only certain physical parame-ters which then must be interpreted in terms of sensory perception
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(Szczesniak, 2002), while a mismatch in vocabulary between sensoryscientists and instrument operators would be another common prob-lem (James et al., 2011). Combining instrumental techniques with sen-sory evaluations may increase the efficiency and accuracy of foodtexture measurement.
5. Standardization of texture measurement
A wide range of test methods and related instrumentation has beenapplied in food texture measurement and analysis. While the availabil-ity of this array of equipment with different levels of sophistication andcost has enabled researchers and industry to develop better under-standing of food texture for product development, quality managementand process control, comparability of texture evaluation results is oftendifficult. Descriptive sensory analysis of texture is usually consideredthe “gold standard” for the objective and analytical measurements offood texture properties (Foegeding et al., 2011; Ross, 2009). For sensorytexture measurement and analysis, the selection of panelists, scales andscale usage, and training are critical (Foegeding et al., 2003). Someguidelines have been developed and widely applied for testing textureproperties of specific types of food. For instance, Patience et al. (2009)screened and selected panelists to evaluate pork quality following pro-cedures of the American Meat Science Association (AMSA, 1995). Intheir sensory analysis of strawberry, Gunness, Kravchuk, Nottingham,D'Arcy, and Gidley (2009) adopted the ISO sensory standards (ISOStandard 8586-1, 1993) for training of panelists and taste procedure.Sasaki et al. (2010) characterized the texture properties of three beefmuscles cooked to four end-point temperatures using ISO5492:1992texture terms for evaluating beef texture. While these industry-based and global test guidelines provide some level of uniformityin texture measurement analysis, they often do not address impor-tant issues such as standardization of food sample preparation andhandling before and during analysis. For example, it is well knownthat cooking methods and cooking conditions (heating rate andend-point temperature at the thermal center) affect the texture ofprepared food (Petracci & Baéza, 2009). Therefore, future effortsshould be made to standardize these aspects of food texture mea-surement and analysis.
There are several guides widely used in instrumental measurementof food texture. For instance, guidelines for instrumental analysis ofcheese texture have been reported in Bulletin 268 of the InternationalDairy Federation (1991), in which, four rheological parameters (firm-ness, fracturability, elasticity and cohesiveness) were precisely defined.Meat tenderness is usually assessed by the WBSF test, for which theAmericanMeat Science Association (AMSA, 1995) provides recommen-dations on instrument selection, sample preparation and testing proce-dure. In horticultural foods such as fruit, flesh firmness measurement istraditionally carried out following theMagness-Taylor procedure, usingeither a texture analyzer or hand-held penetrometer to measure maxi-mum penetration force and other related parameters (Molina-Delgadoet al., 2009). Overall, these guidelines or traditional methods of testingtexture in foods are very useful, especially for industrial applications.Nevertheless, the vast diversity of food products creates a challenge inidentifying and selecting the appropriate instrumentalmethod for char-acterizing textural properties, and in addition, information provided byinstruments still may ormay not relate towhat is perceived by a personactually chewing the product (Hollender & Kropf, 1994). This createsfurther challenges and new opportunities for multi-disciplinary ap-proaches and collaboration among scientists and engineers working infood scientific research.
6. Concluding remarks
Food texture is one of the most widely measured quality attributesduring postharvest handling, processing, and consumption. Given thesubjectivity in human perception of food texture, texture measurement
remains a complex exercise and thus presents both a charm and chal-lenge for researchers and industry practitioners. A survey of the“ScienceDirect” data base showed that the number of publicationswith “food texture” has increased by more than 200% during the pastdecade (2002 to 2012).
The final perception of texture should be based on human sensoryevaluation, while, instrumentalmeasurement of food texture, which in-cludes destructive and non-destructive methods, is also widely used inresearch and industry. Recent advances in information and communica-tion technology offer the potential for innovative technologies fornon-destructive food texture measurement based on acoustic and opti-cal approaches that provide ‘real-time’ or ‘on-line’ texture measure-ment for fresh and processed foods. However, there is still currently alack of international standards for food texture measurement, whichoften makes it difficult to trace and compare research results even inthe same food product and using the same instrument. Since textureis a combined measure of subjective feel to the objective measure, therelationships between physical properties of food and human sensa-tions deserve the attention of researchers. At the same time, sensoryperception of food texture is too complicated to be described by onlyone or two physical properties. Multidisciplinary collaboration amongfood engineers and technologists, consumer scientists and other profes-sionals is necessary for the handling and design of food products highlysought after by the consumer.
Acknowledgments
Thiswork is based upon research supported by Senior Visiting ScholarProgramof the Shanghai Educational Committee, China and the South Af-ricanResearch Chairs Initiative of theDepartment of Science andTechnol-ogy and National Research Foundation. Prof. Opara acknowledges theSouth Africa/Flanders Research Cooperation Programme (Project UID:73936) and the South African Perishable Products Export Control Board(PPECB) for financial support.
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