Assessment of acoustic-mechanical measurements for crispness of wafer products

Accepted Manuscript Assessment of acoustic-mechanical measurements for crispness of wafer products Erdem Çarşanba, Klaus Duerrschmid, Gerhard Schleining PII:

S0260-8774(17)30473-9

DOI:

10.1016/j.jfoodeng.2017.11.006

Reference:

JFOE 9066

To appear in:

Journal of Food Engineering

Please cite this article as: Erdem Çarşanba, Klaus Duerrschmid, Gerhard Schleining, Assessment of acoustic-mechanical measurements for crispness of wafer products, Journal of Food Engineering (2017), doi: 10.1016/j.jfoodeng.2017.11.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Assessment of acoustic-mechanical measurements for crispness of wafer products

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Erdem Çarşanbaa* Klaus Duerrschmidb and Gerhard Schleiningb

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a

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University, Hatay/Turkey, [email protected]

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b

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Sciences, Vienna/Austria, [email protected], [email protected],

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*Corresponding author: [email protected]

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Abstract

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The objective of this work was to investigate instrumental tests regarding the capacity to

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differentiate crispy wafer products of different quality and regarding to correlations

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between instrumental parameters and sensory descriptors. Therefore two fracturing

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methods, a 3-point bending and a cutting test with simultaneously recorded sound

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emissions and a descriptive sensory analysis were carried out with nine different brands of

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wafers representing different qualities.

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The results showed that both instrumental methods are capable to differentiate products of

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different quality, but in different ways. Only the maximum sound pressure (r=0.89) and the

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number of force peaks (r=0.83) of both tests correlate. The sensory descriptor “crispness”

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was mainly correlated with the area under sound-displacement curve (r=0.76) and mean

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sound value (r=0.59) of the cutting test, and weakly related to the number of force peaks

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(r=0.42), the maximum sound pressure (r=0.50) and the area under sound-displacement

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curve (r=0.42) of the 3-point bending test.

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Keywords: Acoustic, Crispness, Texture, Wafer.

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1.

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The food attribute “crispness” related to sound emission is commonly referred as quality

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description of food during biting or chewing (Duizer, 2001; Duizer, 2004; Mallikarjunan,

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2004; Vickers, 1983) meaning freshness and wholesomeness and one of the important

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texture characteristics appreciated by customers (Piazza et al., 2007; Saeleaw and Schleining,

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2011; Tunick et al., 2013). Crispy foods are generally appealing and enjoyable (Szczesniak

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and Kahn, 1971), due to the fact that the sounds when biting or eating have positive affect

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on the customer perception (Spence and Shankar, 2010).

Food Technology, Altınözü Vocational School of Agricultural Science, Mustafa Kemal

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Department of Food Science and Technology, BOKU-University of Natural Resources and Life

Introduction

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Wafer is also considered as crispy food and crispness of it is primary textural attribute

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perceived at the first bite during bending (Martínez-Navarrete et al., 2004). Manufacturing

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process, ingredients compositions and keeping conditions can usually affect the crispness,

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crunchiness of wafers furthermore water intake causes the soggy or leathery property

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(Stephen et al., 1994) which leads the poor quality cause the low consumption of final

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product. To understand crispness of wafer different tests such as sensory, mechanical and

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acoustical can be applied. Commonly, most known method to determine crispness of wafer

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is sensory test which has some difficulties such as time consuming, not convenient for

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routine tests, requiring more statistical works and most of all providing participants who

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have good knowledge in texture attributes (Gregersen et al., 2015; Zdunek et al., 2011). To

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overcome these difficulties, acoustic methods were tired to assess wafer samples by using an

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Acoustic Envelope Detector (AED) attached to the Texture Analyser (TA) and force-

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displacement and acoustic signals were simultaneously recorded.

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Earlier researches on determination of crispness from crispy foods started by adapting

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sensory tests techniques and later acoustic detection devices and mechanical methods were

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developed (Christensen and Vickers, 1981; Drake, 1963, 1965; Edmister and Vickers, 1985; Hi

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et al., 1988; Kapur, 1971; Mohamed et al., 1982; Seymour and Ann, 1988; Szczesniak, 1963;

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Vickers and Bourne, 1976; Vickers, 1984, 1985). Those methods, mainly 3-point bending,

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cutting, penetration, compression methods, allowed scientist to predict crispness of snack

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foods (Duizer, 2001) and showed good correlations between acoustic-mechanical and

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sensory parameters. Mohamed et al. (1982) indicated that performing both acoustic and

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mechanical measurements together can allow predicting better crispness than using only this

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test. Later, Chaunier et al. (2005), Chen et al. (2005) and Varela et al. (2006) approved also

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this by testing different kinds of solid crispy foods.

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From these works regarding acoustic parameters, Chen et al. (2005) demonstrated that

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maximum sound pressure (Smax) and acoustic events could be used to range biscuit

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crispness, which had highest Smax and acoustic event values was also highest in crispness.

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According to this assumption, they could differentiate six biscuits samples with the highest

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correlation between acoustic and sensory measurements. Varela et al. (2008) indicated that

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number of sound peaks (NSP) was better to discriminate precooked chicken nuggets and

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directly related to crispness. Primo-Martín et al. (2008); Primo-Martin et al. (2009) worked

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on bread crust and explained that high NSP and force events determined the crust crispness

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better. In another work, Salvador et al. (2009) stated that sensory crispness on potato chips

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was positively related with number of force peaks (NFP), Smax and NSP. Saeleaw et al. (2012)

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showed also mean sound pressure (MS), NSP and NFP could be used to determine the

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crispness of rye-based extrudates and cassava crackers produced in different process

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conditions such as extrusion conditions and frying parameters respectively. Recent years,

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NSP and maximum force peak (Fmax) were also used to characterize crispness of extruded

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cereals (Chanvrier et al., 2014), biscuits (Blonska et al., 2014) and apples (Cybulska et al.,

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2012) whereas Smax, NSP and MS were used for hazelnut kernels (Giacosa et al., 2016) and

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apple as well (Piazza and Giovenzana, 2015). Jakubczyk et al. (2017) studied also on co-

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extruded snacks by using MS, Smax and NSP parameters and explained that milk filling

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extrudates were crispier than jelly filling ones since they had highest values of these acoustic

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parameters. Works on these samples demonstrated that acoustic parameters were well

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correlated within sensory ones and gave them opportunity to use fast and reliable method to

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examine textural properties of crispy-crunchy foods.

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Wafer is also a crispy product and until now there were few studies (Juodeikiene and

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Basinskiene, 2004; Martinez-Navarrete et al., 2004; Mohammed et al., 2014) on the

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definition of wafer texture by using acoustic tests. The aim of this work was to evaluate

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acoustical and mechanical parameters for wafer quality applying different test methods and

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then to relate those parameters with sensory descriptors.

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2.

Materials and methods

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2.1.

Samples

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Wafer samples of nine different brands (Bella, Manner, Sweet Gold, Napoli, Biscoteria, Jadro,

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Fin Carre Normal, Fin Carre Strawberry and Fin Carre Lemon) were analysed. All were of 16

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mm thickness, 51 mm length and 18 mm width with 9 layers. The selection was based on the

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aim to have samples with different qualities, no matter if the different qualities were based

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on different recipes of the filling or different moisture content. Samples were kept in its

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original package at 24ºC. For each experiment, a new package was opened. All samples were

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used within maximum 20 minutes.

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2.2.

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The mechanical and acoustical measurements were carried out with a Texture Analyser

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((TA.XT.plus, Stable Micro Systems (SMS), Surrey, U.K)) using a 5-kg load cell and connected

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with a microphone (Brüel Kjaer, Type 2671 Naerum, Denmark), which includes an “Acoustic

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Texture analysis

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Envelope Detector” (AED) to avoid background sounds. The microphone was calibrated with

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the acoustic calibrator type 4231 (1 Hz, Brüel Kjaer) at 94 and 114 dB sound pressure level

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(SPL). The amplifier was set to level 4. The microphone position was 1 cm distance from the

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sample at 45°. The test speed was set at of 0.5 mm/s used for all measurements. The

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microphone position and test speed were optimized before using nine different positions (0º-

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1 cm, 0º-5 cm, 0º-10 cm, 45º-1 cm, 45º-5 cm, 45º-10 cm, 90º-1 cm, 90º-5 cm and 90º-10 cm)

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and four different speeds (0.1 mm/s, 0.5 mm/s, 1.0 mm/s and 1.5 mm/s) (Alchakra et al.,

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1997; Castro-Prada et al., 2007; Chen et al., 2005).

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Two fracturing methods were used to investigate wafer samples; a 3-point bending (Alvarez

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et al., 2000; Blonska et al., 2014; Castro-Prada et al., 2007; Chen et al., 2005) and cutting test

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(Castro-Prada et al., 2007). For the first test, 3-point bending rig (code: HDP/3PB of SMS) and

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for the second test, craft knife adapter (code: A/CKB of SMS) were used with load cell of 5 kg

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and recording rate of 500 points per second at the compression mode.

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The following parameters; maximum force (Fmax), distance of maximum force (DFmax), area

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under force-displacement curve (AF), number of force peaks (NFP), length of force peaks (LF),

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maximum sound pressure (Smax), distance of maximum sound peak (DSmax), mean sound

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pressure (MS), area under sound-displacement curve (AS), number of sound peaks (NSP) and

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length of sound peaks (LS) were evaluated with the Exponent Software (Stable Micro

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Systems) from the force- and acoustic-displacement curves (Fig.1-A and Fig. 1-B) within a

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range from 0 to 5 mm for the 3-point bending and 10 mm for the cutting test.

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2.3.

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Two descriptive sensory analysis methods; free choice profiling (FCP) and quantitative

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descriptive analysis (QDA) were carried out to assess the sensory characteristics of samples.

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FCP was used to generate a list of important sensory characteristics of wafers. First, the

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procedure of FCP was explained to nine untrained panellists, who were food science and

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technology students between 22 and 28 years old. Plastic containers with different code

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numbers were used to serve the samples to the panellists. Packaging of wafers was opened

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right before the testing and served to participants in same dimensions and in fresh quality.

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The panellists were instructed to bite up to nine samples of the same kind with their incisors

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and then defined sensory characteristics using their individual vocabulary, which were

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evaluated on an unstructured line scale (length of 10 cm). Not more than twelve

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characteristics were found by the FCP participants: “crispness”, “taste”, “consistence”,

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Sensory analysis

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“colour”, “smell”, “volume quantity”, “aroma”, “sweetness”, “hardness”, “mouth feel”,

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“biteable” and “general appearance”.

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The QDA sample characteristics, which have been found in the FCP, have been discussed and

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trained among participants. Products with codes were tasted and most used words to

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describe textural properties of wafer were chosen. After this definition and training phase,

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participants graded the samples by using the selected characteristics. The nine panellists

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agreed on the following five attributes: “crispness”, “volume quantity”, “hardness”, “creamy”,

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and “swallowing behaviour”. Those five descriptors were quantified on an unstructured line

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scale with defined anchor marks.

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2.4.

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The obtained results were subjected to statistical analysis using SPSS and XLSTAT (version free

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2017) programs for Analysis of Variance (ANOVA), multiple range tests, principle component

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analysis (PCA) and correlation analysis. The number of the tested samples was nine and ten

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repetitions were carried out.

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3.

Results and discussion

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3.1.

Evaluation of wafers acoustic amplitude-time and force-time curves

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Fig.1-A and Fig.1-B show typical force-time and acoustic amplitude-time curves of the 3-

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point bending (A) and cutting test (B). In general it can be seen, that the acoustic peaks do

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not necessarily correlate with the force peaks. From this it can be concluded, that the force

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and acoustic signal provide different information.

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-----PLEASE INSERT FIGURE 1 HERE-----

For the 3-point bending test the maximum sound emission Smax is usually obtained within

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the first 5 seconds of the test, when the first layer of wafer sample is cracked. During this

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time, the force continuously increases until fracture occurred and even at the highest level of

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the force values, the sound is lower than in the beginning of bending. These results are in

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agreement with Varela et al. (2008) who obtained the same pattern of curves with high

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sound peaks in the beginning of chicken nuggets deformation.

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In Fig.1-B, showing typical results of a cutting test, it can be seen, that there are five force

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peaks which relate to the five layers of wafer sheets. The results of the cutting test showed

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more force and sound peaks than 3-point bending test, due to more fracturing events.

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Generally, the more peaks in the force and sound signals, quantified as number of peaks NFP

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and NSP, are obtained during the fracturing process the more crispy and crunchy the samples

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are (Varela et al., 2008). Some researchers also associate high values of the maximum

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acoustic peak (Smax) and mean of sound response (MS) with crispness (Giacosa et al., 2016;

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Jakubczyk et al., 2017; Piazza and Giovenzana, 2015; Saeleaw et al., 2012; Salvador et al.,

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2009). Also high levels of the maximum force peak and of area under the force curve were

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often related to evaluate sensory hardness (Saeleaw et al., 2012). According to that, the

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results in Fig.1 with highly jagged curves and numerous acoustic peaks with high amplitude

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values, show a crispy character of the wafer samples.

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3.2.

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Table 1 illustrates significant differences between products regarding all evaluated

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parameters (p<0.05). A PCA was performed also to illustrate relationships between the

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different parameters of the 3-point bending test. The bi-plot of the PCA in Fig. 2 shows that

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80.43% of the variability could be explained by the first two principal components, 64.46 %

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by the first component F1 and 15.98 % by the second component F2. Furthermore Fig. 2

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shows that some parameters were positively correlated with F1 (AF, DFmax, NFP, NSP),

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whereas MS, Fmax, Smax are more correlated to F2.

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From Table 2 and Fig. 2 several linear correlations can be seen between instrumental

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parameters (e.g. Fmax-LF, r=0.939; AF-NSP, r=0.914; DSmax-AS, r=0.936; AS-NSP, r=0.945;

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AS-LS, r=0.948 and NSP-LS, r=0.982). The correlation DSmax-AS sounds reasonable as the

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later the maximum sound peak occurs, the bigger the area under the sound curve is and also

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for AS-NSP-LS, the more sound peaks occur, the longer the curve is and the bigger the area

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under the sound curve, whereas for some of these correlations like Fmax-LF or AF-NSP, there

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are no plausible explanations.

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Other parameters seem to be independent from each other, e.g. NSP does not correlate

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with NFP or Fmax is independent from Smax. That means the acoustic signal gives different

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information than the force signal. Correlation to sensory descriptors will be discussed in 3.6.

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The products Napoli, Sweet Gold, Fin Carre Strawberry and Fin Carre Lemon have high values

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in NSP, AS and LS. These products can be considered as crispier than the products Bella,

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Jadro, Manner Biscoteria and Fin Carre Normal, which form a group in the opposite

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direction. Regarding mean values it can be seen, that highest values in acoustic and textural

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parameters were generally obtained by products Napoli, Sweet Gold, Fin Carre Lemon and

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Fin Carre Strawberry, which form the group of crispy products in the bi-plot of the PCA.

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The results were in agreement with works done by Chen et al. (2005) who used also the 3-

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Acoustic and mechanical characteristics of wafers using the 3-point bending method

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point bending test with parameters Smax and NSP in order to rank crispness of biscuits and

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found good correlation with sensory and instrumental results. It could be also seen in our

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study that most acoustic descripters correlate and can distinguish the wafer products.

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Moreover, smilar results were found by Blonska et al. (2014) regarding the evaluation of

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acoustic properties of short-dough biscuits by using a 3-point bending test. Inulin addition to

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biscuits in terms of replacing fat content increased NSP. The acoustic energy and the sound

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amplitude correlated well with each other, like in our study.

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It can be summarized that most of the instrumental parameters for the3-point bending test;

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especially acoustic parameters correlated with each other and products were dispersed well

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in the bi-plot. According to these results, 3-point bending test was applicable to distunguish

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acoustic-mechanical properties of wafer products.

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-----PLEASE INSERT TABLE 1 HERE-----

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-----PLEASE INSERT TABLE 2 HERE-----

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-----PLEASE INSERT FIGURE 2 HERE-----

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3.3.

Acoustic and mechanical characteristics of wafers using the cutting method

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The results of the cutting test are summarized in Table 1 and Figure 3. Table 1 shows that

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there are also significant differences between the products regarding instrumental

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parameters (p<0.05). High values in most acoustic and textural parameters were obtained by

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products Jadro, Sweet Gold, Napoli and Fin Carre Normal, whereas products Manner and Fin

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Carre Lemon show low values especially in Fmax, DFmax and AF. Manner and Bella are

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clearly lowest in NFP and Bella in NSP. Clearly the hardest product was Sweet Gold, whereas

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Manner was the softest.

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This can also be seen in the bi-plot of the PCA in Figure 3. The two dimensions of the bi-plot

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explain 64.20 % of the variability (38.71 % by F1 and 25.49 % by F2).

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Correlations between the measured textural properties are low in general, only for Fmax-AF

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(r=0.934) as well as for NSP-LS (r=0.980) high correlation coefficients could be found (Table

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2), which seems reasonable.

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-----PLEASE INSERT FIGURE 3 HERE-----

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Fig.3 shows that the products Jadro, all Fin Carre products, Sweet Gold and Napoli have high

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values in crisp-relevant parameters like NSP, NFP and Smax. Products Bella, Biscoteria and

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Manner can be considered as less crispy products due to low values in the selected acoustic

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parameters.

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3.4.

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According to Fig. 1, Fig. 2, Fig. 3 and Table 1, the two test methods differentiate the products

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in different ways. Whereas in the 3-point bending test the product Sweet Gold exhibited

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high values in most mechanical and acoustic parameters, this was not the case in the cutting

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test for some mechanical parameters and not for the acoustic parameters.

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According to Table 2, only Smax (r=0.892) and NFP (r=0.829) correlate and give similar

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information related to acoustic-mechanical properties of wafer. These two parameters were

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also used by other researchers (Chen et al., 2005; Giacosa et al., 2016; Jakubczyk et al., 2017;

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Piazza and Giovenzana, 2015; Saeleaw et al., 2012; Salvador et al., 2009) for the evaluation of

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crispness and crunchiness properties of foods. Therefore, these two instrumental

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parameters can be used for both tests to differentiate crispness of wafers.

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Table 2, Figure 2 and Figure 3 show that the parameters gained in the 3-point bending test

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were more correlated than the parameters of the cutting test. It has also to be mentioned

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that the parameter DFmax of the cutting test shows a very high standard deviation due to

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the fact that the maximum force could appear in different regions of the force-displacement

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curve, whereas the DFmax of the 3-point bending test was generally at the end of test.

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Furthermore the PCA of the acoustic-mechanical parameters of the 3-point bending test

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could explain 80.43% of the variability, whereas the PCA of the acoustic-mechanical

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parameters of the cutting test could explain only 64.20% of the variability.

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3.5.

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The results of the sensory analysis (Tab. 1) show that there are significant differences

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(p<0.05) between wafer samples regarding the selected five textural attributes. Especially

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the attributes “crispness” and “volume quantity” differentiate the samples better than all

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others according to Tukey-b test. The product Sweet Gold was scored highest for crispness

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(86) followed by Fin Carre Normal (74), whereas Biscoteria (11) and Manner (20) were

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lowest in “crispness”. Product Manner was highest in “volume quantity”, “creaminess” and

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“hardness”. Fin Carre Strawberry (79) was the most intense product according to “swallow

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behaviour”, followed by product Fine Carre Lemon (74). Clearly lowest in “swallow

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behaviour” was product Jadro (12).

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Comparison of 3-point bending and cutting test

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Fig. 4 shows these data as a PCA bi-plot, in which the first two dimensions explain 79.22 % of

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variability (52.55 % F1, 26.67 % F2). This bi-plot demonstrates that while products Jadro,

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Bella and Napoli are explained more by sensory “hardness”, Fin Carre group and Manner are

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more characterized by attributes “creamy”, “volume quantity” and “swallow behaviour”.

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Moreover, product Sweet Gold is also characterized more with “crispness”. Finally, product

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Biscoteria is the product with very low values in all selected texture descriptors.

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The results show that the descriptors; “creamy”, “volume quantity” and “swallow

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behaviour” correlated well and describe similar characteristics of wafers. Hardness and

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crispness were also slightly correlated.

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3.6.

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Table 2 shows, that the descriptor “crispness” could be related in case of the 3-point

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bending test with NFP (r=0.418), Smax (r=0.494) and AS (r=0.415). For the cutting test the

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descriptor “crispness” correlates well with Fmax (r=0.573), AF (r=0.416), NFP (r=0.422), LF

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(r=0.422), AS (r=0.755) and MS (r=0.586).

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The descriptor “volume quantity”, which correlates very well with “creamy” and “swallow

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behaviour”, correlates mainly with force characteristics and with MS in the 3-point bending

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test but not very well with the parameters of the cutting test.

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These results are in agreement with findings of several other studies (Giacosa et al., 2016;

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Jakubczyk et al., 2017; Piazza and Giovenzana, 2015; Saeleaw and Schleining, 2010)(Blonska

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et al., 2014; Chanvrier et al., 2014; Chen et al., 2005; Jakubczyk et al., 2017; Piazza and

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Giovenzana, 2015; Primo-Martín et al., 2008; Saeleaw et al., 2012; Salvador et al., 2009),

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which report similar correlations between crispness and crunchiness of food with

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instrumental parameters. Especially, Smax, NSP and MS were used to explain crispness and

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crunchiness of extruded snacks by Saeleaw et al. (2012) who found that crispness was

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positively correlated with Smax, NSP, MS and also NFP of mechanical parameter, but

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correlated negatively with Fmax.

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The sensory descriptor “hardness” shows only relations to MS (r=-0.52) of the 3-point

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bending test and to AS (r=0.54) of the cutting test. It was expected, that the sensory

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“hardness” would correlate with parameters extracted from the force-displacement curves,

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but this was not the case. Obviously the descriptor “hardness” was associated not only with

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mechanical hardness.

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Correlation between results of instrumental and sensory analysis

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Figure 5 and 6 illustrate the results of the 3-point bending and the cutting tests including the

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results of the sensory analysis. The two first dimensions explain 69.54 % of the variance in

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the 3-point bending and 55.09 % of the variance for the cutting test.

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In Fig. 5 can be seen that except “hardness”, all attributes and instrumental parameters are

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positively correlated with the first component F1. High correlations become evident

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between DFmax and “volume quantity” (r=0.845), DFmax and “swallow behaviour” (r=0.918)

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and also DFmax and “creamy” (r=0.717). Considering “crispness”, slight correlations can be

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seen with NFP (r=0.418), Smax (r=0.494) and AS (r=0.415) (Table 2). The sensory attribute

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“hardness” was in two cases weakly negatively correlated with instrumental parameters:

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Fmax (r=-0.333), MS (r=-0.515). This bi-plot illustrates, that products Sweet Gold, Fin Carre

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Strawberry and Fin Carre Normal were described as “creamy”, high in “volume quantity” and

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intense “swallow behaviour”, whereas

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Strawberry have high values in DSmax, LS, AS, NSP and NFP, which parameters can be seen

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as indicators of “crispness”.

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Sweet Gold, Napoli and Fin Carre Lemon and

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In case of the cutting test, the PCA bi-plot of Fig. 6, shows that the attributes “creamy”,

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“volume quantity” and “swallow behaviour” are negatively correlated with or independent

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from the measured instrumental parameters, whereas “crispness” and “hardness” are

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slightly correlated with parameter AS (r=0.755 for crispness and r=0.543 for hardness) (Table

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2). Product Jadro, was highest in Fmax, DFmax NSP and Smax, whereas Bella stands out for

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its high value in DSmax and is also quite high in the sensory attribute “hardness”. Products of

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the Fin Carre group seem to form a cluster, which is characterised by similar intensities of

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the attribute “swallows behaviour”, “volume quantity” and “hardness”. Manner was highest

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in the sensory attributes “creamy”, “hardness” and “volume quantity” also according to the

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bi-plot of cutting test (Fig. 6).

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4.

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The acoustic-mechanical results of both instrumental tests were quite different, however

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there were some correlations between both tests regarding the parameters NFP and Smax.

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The higher these values, the crispier a product is.

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Conclusion

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Among the instrumental parameters, Fmax-LF (r=0.939), AF-NSP (r=0.914), AS-DSmax

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(r=0.936), AS-NSP (r=0.945), AS-LS (r=0.948), NSP-LS (r=0.982) of the 3-point bending and

316

Fmax-AF (r=0.934), NSP-LS (r=0.980) of cutting test showed good correlations.

317

Regarding the correlation of sensory and instrumental parameters; in case of the 3-point

318

bending test, “crispness” can be related more with NFP, Smax and AS, whereas in the case of

319

cutting test it can be related more with Fmax, AF, NFP, LF, MS and AS.

320

It can be concluded that both tests are capable to differentiate wafers of different quality

321

and can be recommended for further investigations of these kinds of foods as fast and

322

objective methods for quality control. But it has to be considered that both tests

323

characterize different textural aspects and therefore differentiate the products in different

324

ways: The cutting test can differentiate products better according “crispy” with parameters

325

extracted from the force signal and parameters characterizing the overall sound emission

326

MS and AS, whereas the 3-piont bending test can differentiate products better according

327

“creamy”, mainly with parameters extracted from the force signal.

328

References

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Alchakra, W., Allaf, K., Ville, J.M., (1997). Acoustical emission technique applied to the characterisation of brittle materials. Applied Acoustics 52(1), 53-69. Alvarez, M.D., Saunders, D.E.J., Vincent, J.F.V., Jeronimidis, G., (2000). An engineering method to evaluate the crisp texture of fruit and vegetables. Journal of Texture Studies 31(4), 457-473. Blonska, A., Marzec, A., Blaszczyk, A., (2014). Instrumental evaluation of acoustic and mechanical texture properties of short-dough biscuits with different content of fat and inulin. Journal of Texture Studies 45(3), 226-234. Castro-Prada, E.M., Luyten, H., Lichtendonk, W.I.M., Hamer, R.J., Van Vliet, T.O.N., (2007). An improved instrumental characterization of mechanical and acoustic properties of crispy cellular solid food. Journal of Texture Studies 38(6), 698-724. Chanvrier, H., Jakubczyk, E., Gondek, E., Gumy, J.C., (2014). Insights into the texture of extruded cereals: Structure and acoustic properties. Innovative Food Science & Emerging Technologies 24, 6168. Chaunier, L., Courcoux, P., Della Valle, G.U.Y., Lourdin, D., (2005). Physical and sensory evaluation of cornflakes crispness. Journal of Texture Studies 36(1), 93-118. Chen, J., Karlsson, C., Povey, M., (2005). Acoustic envelope detector for crispness assessment of biscuits. Journal of Texture Studies 36(2), 139-156. Christensen, C.M., Vickers, Z.M., (1981). Relationships of chewing sounds to judgments of food crispness. Journal of Food Science 46(2), 574-578. Cybulska, J., Pieczywek, P.M., Zdunek, A., (2012). The effect of Ca2+ and cellular structure on apple firmness and acoustic emission. European Food Research and Technology 235(1), 119-128. Drake, B.K., (1963). Food crushing sounds. An introductory study a,b. Journal of Food Science 28(2), 233-241. Drake, B.K., (1965). Food crushing sounds: Comparisons of objective and subjective data. Journal of Food Science 30(3), 556-559.

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Duizer, L., (2001). A review of acoustic research for studying the sensory perception of crisp, crunchy and crackly textures. Trends in Food Science & Technology 12(1), 17-24. Duizer, L.M., (2004). Sound input techniques for measuring texture in: Kilcast, D. (Ed.), Texture in Food. Woodhead Publishing, pp. 146-166. Edmister, J.A., Vickers, Z.M., (1985). Instrumental acoustical measures of crispness in foods. Journal of Texture Studies 16(2), 153-167. Giacosa, S., Belviso, S., Bertolino, M., Dal Bello, B., Gerbi, V., Ghirardello, D., Giordano, M., Zeppa, G., Rolle, L., (2016). Hazelnut kernels (Corylus avellana L.) mechanical and acoustic properties determination: Comparison of test speed, compression or shear axis, roasting, and storage condition effect. Journal of Food Engineering 173, 59-68. Gregersen, S.B., Povey, M.J.W., Kidmose, U., Andersen, M.D., Hammershøj, M., Wiking, L., (2015). Identification of important mechanical and acoustic parameters for the sensory quality of cocoa butter alternatives. Food Research International 76, Part 3, 637-644. Hi, W.E.L., Deibel, A.E., Glembin, C.T., Munday, E.G., (1988). Analysis of food crushing sounds during mastication: Frequency-time studies. Journal of Texture Studies 19(1), 27-38. Jakubczyk, E., Gondek, E., Tryzno, E., (2017). Application of novel acoustic measurement techniques for texture analysis of co-extruded snacks. Lwt-Food Science and Technology 75, 582-589. Juodeikiene, G., Basinskiene, L., (2004). Non-destructive texture analysis of cereal products. Food Research International 37(6), 603-610. Kapur, K.K., (1971). Frequency spectrographic analysis of bone conducted chewing sounds in persons with natural and artificial dentitions. Journal of Texture Studies 2(1), 50-61. Mallikarjunan, P., (2004). Understanding and measuring consumer perceptions of crispness in: Kilcast, D. (Ed.), Texture in Food. Woodhead Publishing, pp. 82-105. Martinez-Navarrete, N., Moraga, G., Talens, P., Chiralt, A., (2004). Water sorption and the plasticization effect in wafers. International Journal of Food Science and Technology 39(5), 555-562. Mohamed, A.A.A., Jowitt, R., Brennan, J.G., (1982). Instrumental and sensory evaluation of crispness: In friable foods. Journal of Food Engineering 1(1), 55-75. Mohammed, I.K., Charalambides, M.N., Williams, J.G., Rasburn, J., (2014). Modelling the microstructural evolution and fracture of a brittle confectionery wafer in compression. Innovative Food Science & Emerging Technologies 24, 48-60. Piazza, L., Gigli, J., Ballabio, D., (2007). On the application of chemometrics for the study of acousticmechanical properties of crispy bakery products. Chemometrics and Intelligent Laboratory Systems 86(1), 52-59. Piazza, L., Giovenzana, V., (2015). Instrumental acoustic-mechanical measures of crispness in apples. Food Research International 69, 209-215. Primo-Martín, C., de Beukelaer, H., Hamer, R.J., Van Vliet, T., (2008). Fracture behaviour of bread crust: Effect of ingredient modification. Journal of Cereal Science 48(3), 604-612. Primo-Martin, C., Sozer, N., Hamer, R.J., Van Vliet, T., (2009). Effect of water activity on fracture and acoustic characteristics of a crust model. Journal of Food Engineering 90(2), 277-284. Saeleaw, M., Dürrschmid, K., Schleining, G., (2012). The effect of extrusion conditions on mechanicalsound and sensory evaluation of rye expanded snack. Journal of Food Engineering 110(4), 532-540. Saeleaw, M., Schleining, G., (2010). Effect of blending cassava starch, rice, waxy rice and wheat flour on physico-chemical properties of flour mixtures and mechanical and sound emission properties of cassava crackers. Journal of Food Engineering 100(1), 12-24. Saeleaw, M., Schleining, G., (2011). A review: Crispness in dry foods and quality measurements based on acoustic-mechanical destructive techniques. Journal of Food Engineering 105(3), 387-399. Salvador, A., Varela, P., Sanz, T., Fiszman, S.M., (2009). Understanding potato chips crispy texture by simultaneous fracture and acoustic measurements, and sensory analysis. LWT - Food Science and Technology 42(3), 763-767. Seymour, S.K., Ann, D.D.H., (1988). Crispness and crunchiness of selected low moisture foods. Journal of Texture Studies 19(1), 79-95. Spence, C., Shankar, M.U., (2010). The influence of auditory cues on the perception of, and responses to, food and drink. Journal of Sensory Studies 25(3), 406-430.

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Stephen, B., Simon, L., Peter, S., (1994). Edible wafers need physics. Physics World 7(9), 49. Szczesniak, A.S., (1963). Classification of textural characteristics. Journal of Food Science 28(4), 385389. Szczesniak, A.S., Kahn, E.L., (1971). Consumer awareness of and attitudes to food texture. Journal of Texture Studies 2(3), 280-295. Tunick, M.H., Onwulata, C.I., Thomas, A.E., Phillips, J.G., Mukhopadhyay, S., Sheen, S., Liu, C.K., Latona, N., Pimentel, M.R., Cooke, P.H., (2013). Critical evaluation of crispy and crunchy textures: A review. International Journal of Food Properties 16(5), 949-963. Varela, P., Chen, J., Fiszman, S., Povey, M.J.W., (2006). Crispness assessment of roasted almonds by an integrated approach to texture description: texture, acoustics, sensory and structure. Journal of Chemometrics 20(6-7), 311-320. Varela, P., Salvador, A., Fiszman, S.M., (2008). Methodological developments in crispness assessment: Effects of cooking method on the crispness of crusted foods. LWT - Food Science and Technology 41(7), 1252-1259. Vickers, Z., Bourne, M.C., (1976). A psychoacoustical theory of crispness. Journal of Food Science 41(5), 1158-1164. Vickers, Z.M., (1983). Pleasantness of food sounds. Journal of Food Science 48(3), 783-786. Vickers, Z.M., (1984). Crispness and crunchiness - a difference in pitch? Journal of Texture Studies 15(2), 157-163. Vickers, Z.M., (1985). The relationships of pitch, loudness and eating technique to judgments of the crispness and crunchiness of food sounds. Journal of Texture Studies 16(1), 85-95. Zdunek, A., Cybulska, J., Konopacka, D., Rutkowski, K., (2011). Evaluation of apple texture with contact acoustic emission detector: A study on performance of calibration models. Journal of Food Engineering 106(1), 80-87.

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Wafer products were examined by two fracturing tests along with sensory analysis. Sound emissions were recorded simultaneously with tests. Wafers’ crispness could be differentiated by the parameters of the cutting test. Wafers’ creamy was related with mechanical parameters of the 3-point bending test. Force peaks number and maximum sound pressure showed correlation on both tests.

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Fig.4. Bi-plot of PCA regarding sensory analysis data set for wafer products Fig.5. Bi-plot of PCA regarding 3-point bending and sensory parameters data set for wafer products

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Fig.6. Bi-plot of PCA regarding cutting test and sensory parameters data set for wafer products

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Table 2 Matrix of correlation coefficients between investigated variables, determined on the basis of PCA ((3-point bending (3PB) and cutting test (CT))

ACCEPTED MANUSCRIPT PRODUCTS Biscoteria

Fin Carre Normal

Fin Carre Strawberry

Fin Carre Lemon

Jadro

Napoli

Sweet Gold

Manner

Fmax (N)

17.3±2.5b

24.4±2.1c

25.4±3.1cd

29.2±3.3d

18.3±4.2b

13.2±1.2a

24.4±3.6c

28.3±4.8cd

19.8±2.2b

DFmax (mm)

3.4±0.7ab

3.1±0.7a

3.9±0.6ab

4.4±0.6b

4.2±0.6b

3.1±0.4a

4.0±1.0ab

4.3±0.5b

4.1±0.5b

AF (N.mm)

40.3±7.8ab

46.0±18.7ab

53.3±8.4b

74.1±10.9c

52.4±17.5b

30.6±5.2a

78.8±10.1c

87.7±12.2c

53.6±9.0b

NFP

4±1a

6±3ab

7±2ab

8±3b

9±4b

6±2ab

6±1ab

9±4b

5±1a

LF (N.mm)

67.4±10.3ab

107.9±26.3cde

105.5±45.1bcde

142.2±40.1e

97.1±33.2abcd

64.2±16.8a

114.4±12.9cde

126.3±35.4de

80.2±13.2abc

Smax (dB)

79.7±2.5ab

77.9±3.3a

83.2±2.7c

82.2±2.8bc

82.2±2.2bc

84.3±1.6c

81.1±1.1abc

79.0±3.0ab

77.8±3,5a

DSmax (mm)

3.6±1.0abc

2.8±1.3a

4.0±0.6abcd

4.3±0.9bcd

5.1±1.0de

3.1±0.8ab

5.7±1.5e

4.6±0.8cde

4.2±0.7abcd

MS (dB)

48.2±0.7b

49.4±0.3cd

51.1±0.6e

50.2±0.5d

48.3±0.6b

47.2±0.5a

49.1±0.7c

49.8±0.8d

48.2±0.3b

AS (dB.mm)

199.6±33.2ab

187.3±47.3a

208.4±21.3ab

237.0±29.6abc

239.2±49.8abc

187.4±32.6a

265.5±55.1c

246.8±26.7bc

204.8±19.33ab

NSP

175±37a

188±53abc

185±22abc

227±30cd

223±32bcd

178±29ab

244±21d

246±43d

195±14abc

LS (dB.mm)

5025±962a

5375±1449ab

5427±616ab

6492±756bc

6665±1269bc

5410±1266ab

7041±864c

6894±1186c

5508±326ab

Fmax (N)

11.9±1.3bcd

11.4±0.8bc

13.7±1.5d

12.0±1.5bcd

14.0±2.2d

12.7±1.0cd

17.7±2.3e

9.9±1.3ab

5.6±2.7a

6.5±1.4a

5.0±2.3a

5.1±0.6a

AF (N.mm)

56.2±6.4b

59.5±5.5bc

67.8±12.4c

55.3±5.6b

NFP

10±2ab

14±2bc

17±5c

19±3c

LF (N.mm)

154.1±13.2ab

172.0±13.5b

246.2±50.6c

234.2±50.6c

Smax (dB)

70.9±2.6a

71.8±4.8ab

75.4±2.7bc

76.4±3.4c

Dsmax (mm)

6.9±2.2b

0.9±0.5a

0.7±0.3a

MS (dB)

47.9±0.3e

45.4±0.3ab

45.9±0.5bc

AS (dB.mm)

443.4±27.6c

420.4±7.0a

431.3±8.1ab

133±31a

229±32bc

235±40bcd

4446±818a

7175±776bcd

7461±863bcd

Crispness

72.7±21.8bc

11.0±4.2a

73.6±15.7bc

Volume Quantity

42.8±20.2a

43.9±14.1a

Hardness

68.8±25.3c

8.0±2.5a

Creamy

51.7±18.0bc

45.0±19.2ab

Swallow Behaviour

26.6±10.2ab

27.0±5.0ab

SC

6.3±1.7a

6.6±1.5a

4.9±1.6a

6.0±1.9a

4.1±1.0a

59.8±3.5bc

68.3±3.8c

100.7±9.8d

52.2±3.5b

16±7c

16±2c

16±2c

17±5c

9±2a

190.8±45.7b

181.5±11.0b

181.8±7.8b

190.8±34.7b

134.7±13.1a

76.7±2.3c

81.7±1.8d

72.2±2.0ab

69.9±3.5a

70.4±3.1a

0.7±0.3a

1.6±0.8a

1.4±0.3a

1.2±0.5a

0.9±0.3a

1.0±0.3a

45.9±0.4bc

46.0±0.4c

46.7±0.6d

45.6±0.2abc

46.1±0.7d

45.2±0.3a

432.7±9.1ab

431.8±13.4ab

441.6±13.0c

425.4±4.6ab

440.4±12.8c

426.0±6.0ab

268±40cd

318±42e

275±26d

200±31b

204±18b

7962±512cd

254±20cd

8409±1196d

10582±1345e

8321±551d

6981±1597bc

6250±636b

51.2±14.8b

67.1±26.1bc

60.1±26.3bc

57.3±18.6bc

86.0±7.8d

19.9±7.1a

80.9±14.4b

76.0±14.7b

71.3±14.3b

31.4±14.0a

47.0±21.9a

79.9±13.9b

87.4±13.8b

27.8±10.7ab

41.4±12.7bc

35.2±11.2ab

71.3±26.4c

67.8±24.9c

69.6±22.4c

72.6±15.7c

75.4±14.9bc

57.8±25.6bc

57.3±21.0bc

21.8±10.3a

49.6±19.6abc

69.9±21.2bc

79.4±15.9c

53.9±19.3bcd

79.4±17.6d

73.9±20.6cd

12.0±3.0a

44.5±20.8bc

54.7±8.1bcd

56.6±15.2bcd

EP

NSP LS (dB.mm)

9.3±2.3a

38.8±7.1a

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Cutting Test

3-Point Bending Test

Tests

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3PB-Fmax

1

3PB-DFmax

0,579

1

3PB-AF

0,817

0,780

3PBAF

3PBNFP

3PB-LF

3PBSmax

3PBDSmax

3PB-MS

3PB-AS

3PB-NSP

3PB-LS

CTFmax

CTDFmax

CT-AF

CT-NFP

CT-LF

CT-Smax

CTDSmax

CT-MS

CT-AS

CT-NSP

CT-LS

Crispness

Volume Quantity

Hardness

Creamy

Swallow Behavio ur

1

3PB-NFP

0,519

0,671

0,611

1

3PB-LF

0,939

0,632

0,833

0,727

1

3PB-Smax

-0,231

-0,020

-0,205

0,339

-0,053

1

3PB-DSmax

0,308

0,763

0,707

0,500

0,418

0,088

1

3PB-MS

0,876

0,427

0,545

0,416

0,760

0,002

0,151

1

3PB-AS

0,517

0,764

0,863

0,656

0,648

0,065

0,936

0,285

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3PBDFmax

1

SC

3PBFmax

3PB-NSP

0,601

0,742

0,914

0,733

0,744

-0,073

0,808

0,280

0,945

1

3PB-LS

0,497

0,710

0,837

0,781

0,687

0,083

0,833

0,211

0,948

0,982

1

CT-Fmax

0,356

0,069

0,416

0,265

0,273

0,118

-0,015

0,339

0,182

0,265

0,221

1

CT-DFmax

-0,290

-0,513

-0,331

0,222

-0,134

0,212

-0,409

-0,271

-0,247

-0,113

-0,036

0,219

CT-AF

0,518

0,187

0,591

0,226

0,384

-0,188

0,107

0,425

0,293

0,398

0,309

0,934

CT-NFP

0,498

0,334

0,435

0,829

0,678

0,633

0,262

0,526

0,458

0,494

0,568

0,427

CT-LF

0,541

0,347

0,304

0,648

0,613

0,647

0,151

0,739

0,277

0,225

0,277

0,313 -0,060

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1

0,049

1

0,297

0,283

1

0,031

0,168

0,876

0,393

1

-0,406

-0,225

-0,435

0,246

-0,185

0,892

-0,193

-0,241

-0,201

-0,234

-0,073

-0,378

0,507

0,442

1

-0,445

-0,356

-0,379

-0,542

-0,540

-0,157

-0,171

-0,368

-0,242

-0,398

-0,422

-0,142

0,076

-0,190

-0,563

-0,420

-0,241

1

CT-MS

-0,457

-0,358

-0,381

-0,280

-0,485

0,250

-0,236

-0,316

-0,234

-0,371

-0,346

0,196

0,328

-0,009

-0,177

-0,093

0,141

0,856

1

CT-AS

-0,331

-0,051

-0,140

0,039

-0,323

0,366

-0,113

-0,262

-0,073

-0,132

-0,106

0,494

0,298

0,258

0,059

0,053

0,258

0,521

0,841

0,336

-0,018

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CT-Smax CT-DSmax

1

-0,138

-0,047

-0,045

0,396

0,113

0,667

0,134

-0,154

0,156

0,194

0,272

-0,177

0,599

0,354

0,775

-0,653

-0,377

-0,194

1

CT-LS

-0,172

-0,083

-0,066

0,421

0,074

0,699

0,059

-0,182

0,107

0,168

0,310

0,127

0,382

-0,069

0,631

0,362

0,813

-0,619

-0,269

-0,032

0,980

1

Crispness

0,032

0,310

0,261

0,418

0,065

0,494

0,387

0,161

0,415

0,274

0,324

0,573

0,164

0,416

0,422

0,422

0,164

0,267

0,586

0,755

-0,065

0,043

1

Volume Quantity

0,521

0,845

0,505

0,450

0,456

-0,174

0,395

0,511

-0,008

-0,571

0,132

0,120

0,291

-0,289

-0,408

-0,460

-0,161

-0,219

-0,246

0,081

1

Hardness

-0,333

0,134

0,138

-0,251

-0,365

-0,028

0,288

-0,515

Creamy

0,514

0,717

0,472

0,173

0,342

-0,394

0,380

0,549

Swallow Behaviour

0,553

0,918

0,602

0,694

0,650

0,032

0,625

0,455

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EP

CT-NSP

0,339

0,356

0,296

0,208

0,148

0,129

0,292

-0,284

0,269

-0,343

-0,461

-0,118

0,312

0,349

0,543

-0,122

-0,058

0,331

-0,068

1

0,283

0,245

0,148

-0,020

-0,686

0,197

-0,132

0,120

-0,583

-0,171

-0,364

-0,221

-0,502

-0,549

0,055

0,912

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1

0,623

0,603

0,593

-0,217

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-0,121

0,366

0,435

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-0,372

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0,011

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0,126

0,825

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0,659

1