Spectrum crispness sensory scale correlation with instrumental acoustic high-sampling rate and mechanical analyses

Journal Pre-proofs Spectrum Crispness Sensory Scale Correlation with Instrumental Acoustic High-Sampling Rate and Mechanical Analyses Pamela Andreani, Jaqueline O. de Moraes, Bernardo H.P. Murta, Jade V. Link, Giustino Tribuzi, João B. Laurindo, Stephan Paul, Bruno A.M. Carciofi PII: DOI: Reference:

S0963-9969(19)30772-0 https://doi.org/10.1016/j.foodres.2019.108886 FRIN 108886

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Food Research International

Received Date: Revised Date: Accepted Date:

5 September 2019 30 November 2019 2 December 2019

Please cite this article as: Andreani, P., de Moraes, J.O., Murta, B.H.P., Link, J.V., Tribuzi, G., Laurindo, J.B., Paul, S., Carciofi, B.A.M., Spectrum Crispness Sensory Scale Correlation with Instrumental Acoustic HighSampling Rate and Mechanical Analyses, Food Research International (2019), doi: https://doi.org/10.1016/ j.foodres.2019.108886

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Spectrum Crispness Sensory Scale Correlation with Instrumental Acoustic High-Sampling Rate and Mechanical Analyses Pamela Andreani1, Jaqueline O. de Moraes1, Bernardo H. P. Murta2, Jade V. Link1, Giustino Tribuzi3, João B. Laurindo1, Stephan Paul2, Bruno A. M. Carciofi*1 1Federal

University of Santa Catarina, Department of Chemical and Food Engineering, Florianópolis, SC, 88040-900, Brazil 2 Federal University of Santa Catarina, Department of Mechanical Engineering, Florianópolis, SC, 88040-900, Brazil 3 Federal University of Santa Catarina, Department of Food science and technology, Florianópolis, SC, 88034-001, Brazil

*Corresponding author: Bruno A. M. Carciofi, Federal University of Santa Catarina, Department of Chemical and Food Engineering, 88040-900, Florianopolis, SC, Brazil. E-mail: [email protected]; tel: (+55 48 37216402) Abstract Mechanical and acoustical instrumental tests can help to predict and compare the sensory crispness of food products. This study proposed a method to correlate crispness sensory analyses to instrumental parameters using the standard products of the Spectrum Crispness Sensory Scale. An acoustic system was developed with a high-sampling rate for characterizing food crispness. Force-displacement and acoustic signals were measured during penetration and compression tests of the products in the Spectrum Scale. A bandpass filter suppressed the texture analyzer's engine and gearbox noise. High correlations were obtained between acoustical and sensory parameters in both tests; however, the penetration test better differentiated. The high-sampling rate acoustical measurement system was capable of discriminate crispy products, although the trained sensory panel was still more sensitive to small differences.

Keywords: Texture; acoustic filter; food quality; sensory analysis; mechanical properties.

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1. Introduction The texture is a complex, multi-parameter, and essential quality attribute of foods, only detectable by human senses. It is critical for consumer acceptance of many products. Texture measurements can be done by sensory tests, such as the well-known Spectrum Descriptive Analysis method, which is based on extensive use of reference points from the collective data of several worldwide panels from different places over numerous replicates. This method proposes a refined scale, that may be over 15 points of discrimination and statistical analysis for the descriptive data (Meilgaard et al., 2007). A set of well-chosen reference points reduces panel variability, allowing for a comparison of data across time and products. However, this method requires a trained panel, which makes it time-consuming and expensive. Then, correlate sensory evaluations of instrumental measurements may increase the efficiency and accuracy of texture measurement (Gilbert et al., 2013). Crispness is a quality texture parameter that contributes to the total sensory experience (Szczesniak, 1988 and Szczesniak and Klein, 1963). Different techniques have been used for overall crispness evaluation, such as sensory tests, instrumental mechanical and sound analyses, as well as microstructural observation (Arimi et al., 2010; Assis et al., 2019; Barreto et al., 2019; Costa et al., 2011; Giacosa et al., 2016; Laurindo and Peleg, 2007; Laurindo and Peleg, 2008; Lima et al., 2016; Link, et al., 2017; Link et al., 2018; Monteiro et al., 2016; Monteiro et al., 2018; Porciuncula et al., 2016; Roudaut et al., 2002; Zotarelli et al., 2012). Mechanical tests are the most common instrumental technique, once they have objective and efficient evaluation procedures, resulting in force-displacement-speed control and measurements. Mechanical properties reveal the structural properties of materials using resistance to compression through a blade/probe and to a tensile fixture that pulls the structure of food material apart by using a universal testing machine or a socalled texture analyzer (Kilcast, 2004). According to Tang et al. (2016), textural complexity does not have a standard instrumental measurement method. Each probe simulates a different kind of application, such as bite, fracture, tactile, among others, and each one could identify different food textural parameters. However, this technique by itself is not able to show all the food texture properties. Once consumers often use sound as an indication of crispy food quality (Lauless and Heymann, 2010), mechanical and acoustical measurements should be coupled when 2

crispness or crunchiness are relevant attributes (Duizer, 2001; Szczesniak, 1988; Vickers, 1987). There are commercial solutions to measure acoustic signals synced to force signals, such as the Acoustic Envelope Detector (AED - Stable Micro Systems Godalming, Surrey, UK), a commercial measurement system attached to texture analyzers from the same manufacturer. The AED system has been used in the majority of studies with simultaneous measurement of force-displacement and sound pressure. However, it is limited to relatively low acquisition data rate (500 Hz). A proper analysis of the high-frequency signals typical of the emitted sound requires much higher sampling rates than the 500 Hz, avoiding aliasing (Chen et al., 2005; Giacosa et al., 2016; Gondek et al., 2013; Piazza et al., 2006). Moreover, when acoustic measurement and mechanical analysis are performed simultaneously, the acoustic data should be filtered to remove the machines’ noise. Many authors report the use of high-pass filters to suppress the noise produced by the texture analyzer. Arimi et al. (2010) described the use of a 3rd order high-pass Butterworth filter with 1.5 kHz corner frequency, while other authors reported the use of high-pass filters with corner frequency at 1 kHz (Gondek et al., 2006; Salvador et al., 2009; Varela et al., 2006) or 3.125 kHz (Costa et al., 2011; Taniwaki and Kohyama, 2012). To the best of our knowledge, correlations between sensory analyses and mechanical-acoustical measurements has been studied always for assessing the crispness of a specific food product, such as Marcona almonds with different degrees of roasting (Varela et al., 2006), biscuits crispness (Arimi et al., 2010; Chen et al., 2005), cassava crackers (Saeleaw and Schleining, 2011), potato chips (Salvador et al., 2009, Taniwaki and Kohyama, 2012), Hazelnut kernels (Giacosa et al., 2016), among others. However, an evaluation correlating sensory analyses with mechanical and acoustical measurements using a standard scale, such as the Spectrum Crispness Sensory Scale, with several food products and covering a broad range of crispness is not reported in the literature. In the present study, the objective was to correlate instrumental measurements and sensory analyses within a wide range of crispness using the food products of the standard Spectrum Crispness Sensory Scale. For that, both compression and penetration tests were performed, and a system was developed with a high sampling rate of 51,200 Hz for the acoustic signals emitted during the fracture of food samples.

2. Material and Methods 3

2.1 Samples Seven food products were selected from the standard Spectrum Crispness Sensory Scale (Meilgaard et al., 2007) of dry foods found in supermarkets from different countries. The products used in this study were Granola Bar (Quaker Chewy Chunk, USA), Club Cracker (Keebler, USA), Graham Cracker (Honey Maid, USA), Oat Cereal (Cheerios, USA), All-Bran Fruit'n Fibre (Kellogg’s, France), Corn Flakes (Kellogg’s, Brazil) and Toast (Bauducco, Brazil). Samples were kept in its original package at 24 °C. 2.2 Physicochemical Analyses Water activity was determined in a hygrometer (Aqualab Model Series 3, Decagon Devices Inc., Pullman, USA) and moisture content was determined by vacuum drying at 70 ºC until constant weight (AOAC, 2005). These analyses were performed in triplicate for each food sample. 2.3 Sensory Analyses Spectrum Descriptive Analysis method was performed with fifteen panelists, which were screened and participated in a texture training using products described on the standard Spectrum Crispness Sensory Scale for Solid Oral Texture Attributes, according to Meilgaard et al. (2007). The intensities of crispness attributes of each product were scored on a 17 cm unstructured line scale labeled from none (0) to high (17), as shown in Table 1. Panelists were instructed to evaluate crispness altogether by mastication using the molar teeth, regarding the required strength and the sound produced, as proposed by Meilgaard et al. (2007). After several pieces of training and extensive texture discussions, panelists disagreed to the intensities from Intensity Scale Values. Then, the scale was restructured by panelists (Panelists Restructured Crispness Sensory Scale), as shown in Table 1. 2.4 Mechanical texture measurement system A TA-XT plus Texture Analyzer (Stable Micro Systems, Godalming, UK) with a 25 kg load cell, along with a P/2 cylinder (2 mm diameter) was used for penetration/puncture essays and with a P/50 cylinder (50 mm diameter) for compression essays. Samples were penetrated or compressed to a depth of 40% of total thickness with a speed of 3 mm s-1. Penetration essay was performed with one piece of the sample, while 4

compression depended on the sample. The quantity/size of each sample was described in Table 1, except for the Toast sample that was used ¼ of the total sample. A peak threshold value of 0.049 N was used to determine the Number of Force Peaks. The data acquisition rate was 500 Hz for force signals. All tests were performed inside a room with a controlled temperature of 25 ± 1 ºC. 2.5 Acoustic measurement and recording system A semi-anechoic box measuring 800 x 600 x 680 mm³ was built using 25 mm thick high-density MDF panels for the walls, ceiling, and floor. It aimed to reduce the influence of the room's background noise. A PVC window with double glazing was installed to offer access to the equipment and visibility of the experiment. The interior of the semi-anechoic box was lined with 25 mm thick sound-absorbing foam (SONEX, Illtec, São Paulo, Brazil). The texture analyzer (Stable Micro Systems, Godalming, UK) and the microphone were placed inside the acoustic chamber, as sketched in Figure 1. Signal acquisition used a free-field half inch condenser type 1 microphone (G.R.A.S. Model 46AE, sensitivity: 52.27 MV/Pa), its IEPE preamplifier, and a National Instrument 9234AD converter with 24-bit resolution and 102 dB of dynamic range. The sampling rate was set to be 51,200 Hz, allowing investigation of frequencies covering the entire human hearing range (20-20,000 Hz). The microphone’s diaphragm was positioned at 50 mm distance and with an angle of 45º to the probe, that position was optimized in a preliminary test (data not shown). Data were recorded using in-house software in MATLAB® (MathWorks Inc, USA) while all post-processing codes were implemented in a Python package. The signal processing procedure included a step of filtering, followed by sound peaks counting and sound pressure level (SPL) calculation. The system was calibrated with a 1 kHz pure-tone at 94 dB using a B&K 4231 reference acoustic calibrator. 2.6 Background noise filter Background noise was measured with the enclosure door open and closed to characterize semi-anechoic enclosure sound attenuation. It was characterized using acoustic measurements with a wideband Insertion Loss of 18 dB and a minimum at the third-octave band centered in 100 Hz of 9 dB (Murta et al., 2017a; Murta et al., 2017b). The engine and gearbox produce a background noise when switched on. Measurements of the texture analyzer working with no food sample were performed to 5

determine the frequency distribution of the texture analyzer noise. The characteristics of this noise are shown in Figure 2, in which most of the acoustical energy was found at lower frequencies, below 1 kHz. Global SPL was around 74 dB. Time-frequency graphs were performed using the AtemiS SUITE (HEAD acoustics, Germany) software (Murta et al., 2017b). Data from the acoustic measurements were processed with a band-pass filter designed following literature recommendations. The band-pass FIR was set with 60 dB attenuation and inferior stopband frequency of 1 kHz, inferior passband frequency of 3.125 kHz, superior stopband frequency of 22 kHz, and a superior passband frequency of 20 kHz. Figure 3 shows filter's efficacy in evidencing acoustic sound peaks. Also, closer peaks are not correctly detected in a time integration of the filtered signal emulating a lower sampling rate system. 2.7 Mechanical and acoustic data analyses The simultaneous acquisition of both sound and force signals was carried out for 15 s. Force vs. displacement/time and SPL vs. time were plotted synchronized. The parameters extracted from the curves and their units are displayed in Table 2. The force signals measured by the texture analyzer were synchronized to the acoustical signals to assure correlation between measured data and sample fissure. The features extracted from each curve, duration, and peak positions were used to synchronize both time signals. Nine replications of each essay were performed for each food product. Food products selected to this study have different size and shape. The full data set was obtained from penetration/compression distance of 40% of the total sample height (h40%). Thus, data were analyzed using different approaches, as detailed below. Mechanical and acoustical parameters were classified into two groups (see Table 2); i) parameters that present cumulative characteristic (such as the Number of Peaks and Linear Distance) that were normalized by dividing the total amount by the h40% value of each product, and ii) non-cumulative parameters (such as Maximum Force and SPLmax). The non-cumulative parameters dependence on the compression/penetration distance were tested. For that, these parameters were estimated cumulatively at each 1/10 of the h40% distance for each product (data available as supplementary material). Additionally, the data analyses described above were performed considering the same

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compression/penetration distance in mm to all products, that is the thinnest thickness (hthin) among the food sample evaluated (i.e., the All Bran Fruit'n Fibre). 2.8 Statistical Analyses One-way analysis of variance (ANOVA) was performed on physical properties to evaluate differences among the samples using the software Statistica 10.0 (StatSoft, Tulsa, EUA). Principal component analysis (PCA) and matrix correlations were done to correlate sensory and instrumental parameters. The factor loadings were not rotated. In this analyses, Kaiser normalization was used, and correlations were considered if higher than 0.7. PCA analysis and the matrix correlation were performed using XLSTAT (Addinsoft, Paris, France). 3. Results and discussion

3.1 Physicochemical properties Table 3 presents moisture content and water activity of food products, arranged from left to right in ascending order of the intensity scale values for crispness. Both water activity and moisture content values did not present a direct relationship with the crispness intensity suggested by the panelists. As expected, the different composition, size, shape, porosity, and microstructure among the food products had considerable influence on crispness. 3.2 Coupled force-displacement and acoustic measurements Figure 4 shows the typical force vs. displacement/time curves synchronized to SPL vs. time curves for three products of the Spectrum Crispness Sensory Scale (Granola Bar, Oat Cereal, and Toast) for both compression and penetration tests. SPL data was submitted to the band-pass filter (Figure 4). In general, for both tests, the occurrence of acoustic peaks was more frequent than the occurrence of force peaks, and they did not necessarily correlate. The higher rate sampling for acoustic data may explain the higher number of events for this measurement. Similar results were pointed by Arimi et al. (2010), Chen et al. (2005) and Piazza et al. (2006). However, the absence of correlation between peaks means that force and acoustic signal provide different information. According to Chen et al. (2005) and Varela et al. (2006), the Number of Acoustic Peaks and the Number of Force Peaks do not have to be 7

correlated one-to-one, as the sound emission is the result of a sudden release of energy, while the force curve is a reflection of the energy applied to, or released from the sample. Compression and penetration curves (Figure 4) presented different patterns, as expected. In compression tests, the parameter Maximum Force was around 10 times bigger than the same value in the penetration test of the same food product, except for the flaked cereals. On the other hand, penetration test showed a higher Number of Force Peaks. Compression tests produced a bigger Number of Acoustic Peaks than in penetration test and, as shown in Figure 4a,b,c, after the first contact probe-sample, the recorded force in compression began to increase until a stress build-up, in which a force drop took place along with many acoustic events. Then, the force continues to build-up until another drop, followed by a notable peak in the SPL curve. The major force drop may be the result of a substantial structural breakdown of the sample, and it was followed by a high SPL, clearly visible in the SPL-time curve. Acoustic and mechanical curves from the penetration test are shown in Figure 4d,e,f. They showed a sequence of events with numerous peaks and valleys, that was a consequence of the probe getting through product layers and pores. According to Tang et al. (2016), the penetration test might simulate a simplified chewing process between the molars, and it is related to the material microstructure. 3.3 Acoustical and mechanical parameters The food products in the Spectrum Crispness Sensory Scale have different size and shape, and this is one of the main challenges when developing an instrumental correlation applicable to a broad spectrum of products. Experimental essays were performed until the probe compressed/penetrated 40% of the thickness of each sample (h40%). The analysis considering data from equal penetration/compression distance (in millimeters) to compare different products may lead to a misconception, once this distance in some products is much shorter than the thickness of samples and could not represent the full microstructure of the product. On the other hand, results from displacements over the h40% value may be influenced by particles from the upper layers, which deposit and accumulate within the porous space. The parameters Area (below the curve), Linear Distance, Number of Force Peaks, and Number of Acoustic Peaks may increase as the compressed/penetrated distance increased. These four parameters were analyzed after dividing them by the h40% value, generating the parameters Mean Force (equivalent to the Normalized Area), Normalized Linear 8

Distance, Normalized Number of Force Peaks, and Normalized Number of Acoustic Peaks. Additionally, it was estimated the values of the parameters for each interval of h40%/10 (see data in the supplementary material). This procedure allowed to assess how each interval of the penetration/compression distance contributes to each parameter, and if the parameter is dependent or not of the test distance. It was observed different behaviors based on the parameter increment at each interval, as it follows. The Area below the curve in compression tests depended on the distance, while for penetration tests it presented an initial increasing value followed by a plateau. The Linear Distance in penetration tests was independent of the interval (plateau behavior) and, for the compression tests, it reached a plateau after an initial increase of its values. The Number of Force Peaks in both tests presented a close to a plateau behavior; however, the Number of Acoustic Peaks in both tests presented an increment followed by reaching a plateau. These observations were independent of the food product evaluated, except for the Corn Flakes. Finally, when using the same compression distance in millimeters for all products, data analysis resulted in conservative values for all parameters after 2.0 mm (lowest compression distance value among all replicates for all products) of compression, except for the Area below the curve that is dependent on the compression distance as previously highlighted. On the other hand, for penetration tests, the key parameters Mean Force and Maximum Force did not reach a plateau around the penetration distance for the thinnest sample evaluated (0.6 mm), as well as for the Linear Distance parameter that was pointed out as dependent on the penetration distance. Thus, they require bigger distances for a correct analysis using a representative value. All mechanical properties calculated from the force-displacement curves for penetration and compression tests considering h40% and equal distances are shown in Table 3. In general, mechanical properties values did not follow the sensory intensity described by the Spectrum or panelists scales. For example, the Number of Force Peaks in compression tests was different only for the two products (Granola Bar and Club Cracker) with the lowest values in the intensity scale values for crispness. Acoustical parameters derived using the designed filter are shown in Table 4 for penetration and compression tests considering h40% and equal distances. All peaks of sound pressure above the maximum average value of the background noise SPL were counted (Arimi et al., 2010). Background noise SPL was calculated by analysis of several measurements without food samples. However, background noise from the ambient may 9

change from a test to another; thus, only sound peaks above 50 dB of SPL and with drops larger than 10 dB were considered in this analysis (Salvador et al., 2009; Costa et al., 2011; Jakubczyk et al., 2017). 3.4 Principal Component Analysis (PCA) The explorative PCA conducted on the physicochemical, mechanical, and acoustical parameters are presented in Figure 5. PCA for compression test (Figure 5a,b) resulted in the two most significant Principal Components (PC), explaining 75.41% and 73.95% of the total data variance for the data with the same distance and normalized data, respectively. Normalized parameters from compression tests presented strong correlation with PC1 in the increasing order: SPLeq (0.955), SPL10 (0.930), SPLmax (0.91), Normalized Number of Acoustic Peaks (0.893), Normalized Number of Force Peaks (0.835), Restructured Panelists Crispness Sensory Scale (0.742), Spectrum Crispness Sensory Scale (0.732), and h40% value (-0.689). PC2 was influenced in increasing order for the aw (-0.859), Normalized Linear Distance (0.748), Mean Force (0.761), and moisture content (-0.710). On the other hand, parameters from data until compression distance of 2.0 mm presented the increasing order of influence on PC1: Number of Acoustic Peaks (0.913), SPLeq (0.874), SPL10 (0.840), SPLmax (0.826), Linear Distance (0.711), Number of Force Peaks (0.685), Spectrum Crispness Sensory Scale (0.623), and Restructured Panelists Crispness Sensory Scale (0.604). PC2 was influenced by aw (0.753), Maximum Stress (-0.732), and Mean Stress (-0.661). Then, one can observe that PC1 describes the Spectrum Crispness Sensory Scale and Restructured Panelists Crispness Sensory Scale better when using the normalized data, once they showed the highest correlations. Figure 5c,d presents results for the penetration test, in which the two first PCs is explaining around 69% of the results. PC1 explains 43.66% and 42.21% of the total variance and PC2 explains 25.81% and 27.51% of the total variance for the data with the same distance and normalized data, respectively. For normalized data, the variables influenced PC1 in the increasing order: SPL10 (0.97), SPLmax (0.961), Panelists Restructured Crispness Sensory Scale (0.894), Spectrum Crispness Sensory Scale (0.894), SPLeq (0.891), Normalized Number of Acoustic Peaks (0.744). For data until 0.6 mm of penetration, the same parameters influenced PC1, but in other order: SPL10 (0.94), SPLmax (0.931), SPLeq (0.918), Panelists Restructured Crispness Sensory Scale (0.815), Spectrum Crispness Sensory Scale (0.766) and Number of Acoustic Peaks 10

(0.741). PC2, for normalized data, was influenced in increasing order by Maximum Force (0.911), Normalized Linear Distance (0.877), and Mean Force (0.845); and, for data until 0.6 mm of penetration, by Mean Force (0.895), Area (0.894) and Maximum Force (0.861). These penetration results corroborate the compression tests. PC1 describes Spectrum Crispness Sensory Scale and Panelists Restructured Crispness Sensory Scale with the highest correlations for normalized data. These results show that performing the traditional penetration/compression tests until 40% of the thickness of the sample and normalizing the measurements by each h40% value was the most effective procedure to correlate the instrumental results to the sensory crispness of products with different sizes and shapes. Biplot graphs for compression (Figure 5a,b) presented most of the food products overlapped, except the Granola Bar and Toast, which are in the extremes of PC1-axis. These two products showed the lowest and the highest values of the representative variables of PC1. For penetration tests, the samples are spread on the biplot space, but still, some clusters were identified. Granola Bar is isolated in the negative PC1-axis position. Club Cracker, Graham Cracker, and Oat Cereal are still in the negative PC1axis position, but close to the middle of the axes. Bran Flakes, Corn Flakes, and Toast are in the positive PC1-axis position. Compression test explained a high percentage of the total data variance with the two first PC; however, the penetration test can better differentiate the samples. Linear correlation matrix (Person) between variables are found in Tables 5 and 6 for compression tests and Tables 7 and 8 for penetration. None of the mechanical properties were highly correlated to the sensory crispness. Ravi et al. (2007) reported a high correlation between sensory firmness of corn balls, puffed rice, and fried chickpea batter and the mechanical properties. However, crumbliness showed no correlation with mechanical properties. Both Spectrum and Panelists Restructured scales showed high correlation (> 0.7) with some acoustic variables. Spectrum and Restructured Scale only presented a linear correlation with the SPLeq in the compression test. The correlation between the Spectrum scale and SPLeq was 0.807 and 0.705 for normalized and hthin data, respectively. For the Restructured scale, these correlations were 0.819 and 0.718, respectively. In penetration tests, the Spectrum and Panelists Restructured scales showed a high linear correlation with other acoustic variables. The Spectrum scale correlation was 0.819 with SPLmax, 0.821 with SPL10, and 0.765 with SPLeq; while Panelists Restructured scale presented a correlation of 0.783 with SPLmax and SPL10. Penetration 11

test using data until 0.6 mm (hthin) did not show a high correlation between the Spectrum scale and any variable. On the other hand, Panelists Restructured scale were highly correlated with SPLmax (0.703) and SPLeq (0.702). Therefore, in general, the crispness of the products at the Panelists Restructured Crispness Sensory Scale could be predicted by SPLeq, especially with the normalized data. Additionally, in the penetration test, acoustic variables were able to predict the panelists' sensory results. 4. Conclusion Mechanical tests and the estimated parameters should be carefully considered when setting up any of these tests aiming to indicate crispness. Acoustic properties, mainly SPLeq, have a high linear correlation with the crispness scale, while none of the mechanical properties presented this high linear correlation to the sensory crispness. Penetration method was better to differentiate the products due to a reduced influence by samples’ size and shape, and normalizing the parameters was the better procedure to correlate sensory crispness and instrumental analysis once it showed a high correlation for the two first components in PCA analysis. The developed high-sampling rate acoustic system is a technique to assess the texture of some crispy products, although it is not able to efficiently detect all the differences among products of Spectrum Crispness Sensory Scale, as the trained sensory panel does. The Spectrum Crispness Sensory Scale is composed of products very different from one another (composition, texture, thickness) perhaps these features together difficult the crispness perception by mechanical and acoustic measurements. However, the enhanced acoustic analyses have been shown as a useful tool for crispness assessment, once it is faster than panel evaluations and more accurate than only force-displacement results from either penetration or compression tests. Future improvements are seen in addressing adaptative filtering to reduce noise and machine learning assets to derive optimal parameters for each food types. 5. Acknowledgments Authors are thankful to Brazilian Agencies National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES) by scholarships and financial support (CNPq Project

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454973/2014-4). Also, the authors would like to express gratitude to Weiko do Brasil Co. for kindly supporting the anechoic box construction. 6. References AOAC, Official Methods of Analysis, Association of Official Analytical Chemists, Washington, 2005. Arimi, J. M., Duggan, E., O’sullivan, M., Lyng, J. G., O’riordan, E. D., 2010. Development of an acoustic measurement system for analyzing crispiness during mechanical and sensory testing. Journal of Texture Studies. 41 (3), 320–340. Assis, F. R., Rodrigues, L. G. G., Tribuzi, G., de Souza, P. G., Carciofi, B. A. M., Laurindo, J.B., 2019. Fortified apple (Malus spp., var. Fuji) snacks by vacuum impregnation of calcium lactate and convective drying. LWT-Food Science and Technology. 113, 108298. LWT Barreto, I. M. A., Tribuzi, G., Marsaioli Junior, A., Carciofi, B. A. M., Laurindo, J.B., 2019. Oil–free potato chips produced by microwave multiflash drying. Journal of Food Engineering. 261, 133-139. Chaunier, L., Courcoux, P., Della Valle, G., 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. Costa, F., Cappellin, L., Longhi, S., Guerra, W., Magnago, P., Porro, D., Soukoulis, C., Salvi, S., Velasco, R., Biasioli, F., Gasperi, F., 2011. Assessment of apple (malus_ domestica borkh.) fruit texture by a combined acoustic-mechanical profiling strategy. Postharvest biology and technology. 61 (1), 21–28. Duizer, L., 2001. A review of acoustic research for studying the sensory perception of crisp, crunchy and crackly textures. Trends in Food Science and Technology. 12 (1), 17– 24. 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. Gilbert, L., Savary, G., Grisel, M., Picard, C., 2013. Predicting sensory texture properties of cosmetic emulsions by physical measurements. Chemometrics and Intelligent Laboratory Systems,124, 21-31.

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Murta, B. H., Aguirre, S., Andreani, P., Carciofi, B, A, M., Tribuzi, G., Moraes, J. O., Paul, S., 2017b. Challenges on developing an acoustical measurement system for applications in food engineering. In Proceedings of the 15º Congresso de Engenharia de Áudio da AES Brasil, Florianópolis, Brasil. Porciuncula, B.D.A., Segura, L.A., Laurindo, J.B., 2016. Processes for controlling the structure and texture of dehydrated banana. Drying Technology. 34, 167-176 Piazza, L., Gigli, J. and Benedetti, S., 2006. Study of structure and flavour release relationships in low moisture bakery products by means of the acoustic-mechanical combined technique and the electronic nose. In Proceedings of the 4th International Symposium on Food Rheology and Structure (P. Fisher, P. Erni and E.J. Windhab, eds.). Ravi R., Roopa B.S., Bhattacharya, S., 2007. Texture evaluation by uniaxial compression of some snack foods. Journal of Texture Studies, 38, 135–152. Roudaut, G., Dacremont, C., Vallès-Pàmies, B., Colas, B., Le Meste, M., 2002. Crispness: a critical review on sensory and material science approaches. Trends in food science and technology. 13, 217-227. Saeleaw, M., Schleining, G., 2011. Effect of frying parameters on crispiness and sound emission of cassava crackers. Journal of Food Engineering 103 (3), 229– 236. 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. Szczesniak, A.S., 1988. The meaning of textural characteristics –crispness. Journal of Texture Studies. 19, 51–59. Szczesniak, A., Klein, D., 1963. Consumer awareness of texture and other food attributes. Food Technology. 63, 74–77. Taniwaki, M., Kaoru, K., 2012. Mechanical and acoustic evaluation of potato chip crispness using a versatile texture analyzer. Journal of Food Engineering.112 (4), 268– 273. Tang, J., Larsen, D.S. Ferguson, L.R., James, B.J., 2016. The effect of textural complexity of solid foods on satiation. Physiology & Behavior, 163, 17-24. 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. Int. J. Food Prop. 16, 949-963. Varela, P., Chen, J., Fiszman, S. M., Povey, M., 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. Vickers, Z. M., 1987. Sensory, acoustical, and force deformation measurements of potato chips crispness. Journal of Food Science. 52, 138–140.

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Zotarelli, M. F., Porciuncula, B. D. A., Laurindo, J. B., 2012. A convective multi-flash drying process for producing dehydrated crispy fruits. Journal of Food Engineering. 108, 523-531.

16

Table 1: Food products in the Spectrum Crispness Sensory Scale and its crispness intensity, food products selected for this study and, panelists' crispness intensity, product quantity used in the sensory and compression tests, thickness and shape of the used products. Product (Manufacturer) Granola Bar (Quaker Chewy Chunk) Club Cracker (Keebler) Graham Cracker (Honey Maid) Oat Cereal (Cheerios)

Spectrum Scale Value 3.0 5.0 6.5

Product (Manufacturer, Purchased country) Granola Bar (Quaker Chewy Chunk, USA) Club Cracker (Keebler, USA) Graham Cracker (Honey Maid, USA)

Panelists' Scale Values

Quantity/Size used in the sensory and compression test

Thickness (mm)

Shape

2.0

3.0 cm x 2.5 cm

13.5 ± 0.3

Flat agglomerates of cereals

3.6

3.0 cm x 3.0 cm

5.1 ± 0.3

Flat

5.7

3.0 cm x 2.7 cm

6.7 ± 0.2

Flat

5.5 ± 0.5

Flat cylinders

2.2 ± 0.5

Flaked

3.7 ± 0.9

Flaked

9.6 ± 0.3

Flat

7.0

Oat Cereal (Cheerios, USA)

6.5

Bran Flakes (Kellogg’s)

9.5

All Bran Fruit'n Fibre (Kellogg's, France)

9.5

Corn Flakes (Kellogg’s)

14.0

Corn Flakes (Kellogg’s, Brazil)

13.7

Melba Toast (Devobsheer)

17.0

Toast (Bauducco, Brazil)

15.0

Four units placed in a cylinder of 2 cm of radius Four units placed in a cylinder of 2 cm of radius Four units placed in a cylinder of 2 cm of radius 3.0 cm x 3.0 cm

17

Table 2: Instrumental parameters obtained from the force-displacement/time and SPLtime data. Mechanical Parameters

Description

Unit

Area (Area)

Total area above the force-time curve (cumulative)

Ns

Linear Distance (LinD)

The length of an imaginary line joining all data points of

mm-1

(short symbol)

force in the penetration/compression test (cumulative) Normalized Linear Distance

The length of an imaginary line joining all points in the

(NormLindD)

selected region divided by the penetration/compression

Dimensionless

distance (non-cumulative) Maximum Force (MaxF)

Maximum force value recorded during the penetration or

N

compression test (non-cumulative) Mean Force (MeanF)

Average value of the force measured during the

N

mechanical tests, that is equivalent to the Normalized Area, i.e., Area divided by the compression time/distance (non-cumulative) Maximum Stress (MaxS)

Maximum force divided by the sample superficial area in

N mm-2

the compression test (non-cumulative) Mean Stress (MeanS)

Average value of the strain estimated in the compression

N mm-2

test, that is equivalent to the Mean Force divided by the sample superficial area (non-cumulative) Number of Force Peaks (NumFP)

Number of peaks observed in a force-displacement curve

peaks

(cumulative) Normalized Number of Force

Number of force peaks divided by the penetration or

peaks mm-1

Peaks (NormNumFP)

compression distance (non-cumulative)

Acoustical Parameters

Description

Unit

Number of Acoustic Peaks

Number of peaks observed in the SPL-time curve

peaks

(NumAP)

(cumulative)

Normalized Number of Acoustic

Number of peaks in the SPL-time plot divided by the

Peaks (NormNumAP)

penetration/compression distance (non-cumulative)

SPLmax

Maximum value in sound pressure level (non-cumulative)

dB

SPL10

Equivalent SPL of the 10 higher peaks (non-cumulative)

dB

SPLeq

Average level of all observed peaks (non-cumulative)

dB

(short symbol)

peaks mm-1

18

Table 3: Physical and mechanical parameters for products of Spectrum Crispness Scale obtained by compression and penetration tests. Values are presented as average ± standard deviation. Food products are arranged from the left to the right increasing the value of the Intensity Scale Values for Crispness. Parameter

Granola Bar

Club Cracker

Graham Cracker

Physical Properties Water activity 0.442 ± 0.003abc 0.347 ± 0.005cd 0.254 ± 0.009d Moisture content (%) 5.52 ± 0.21b 5.64 ± 0.07ab 3.67 ± 0.06d Compression test (distance h40% of each sample) NormNumFP (peaks mm-1) 0.26 ± 0.17 3.01 ± 1.02 4.26 ± 1.08 MeanF (N) 54.58 ± 16.78 40.03 ± 7.74 74.49 ± 19.90 NormLindD (mm-1) 22.62 ± 6.10 53.16 ± 10.87 83.51 ± 16.67 h40% 5.95 ± 0.06 2.20 ± 0.11 2.88 ± 0.12 Compression test (equal distance of 2.0 mm for all samples, hthin) NumFP (peaks) 0.22 ± 0.44 5.44 ± 2.00 8.77 ± 3.34 MaxS (N mm-2) 3.64 ± 1.40 9.85 ± 1.98 16.03 ± 3.77 MeanS (N mm-2) 1.35 ± 0.57 4.09 ± 1.08 5.29 ± 2.54 LinD 27.32 ± 10.28 109.20 ± 22.62 162.64 ± 41.42 Penetration test (distance h40% of each sample) NormNumFP (peaks mm-1) 1.19 ± 0.54 4.24 ± 0.96 5.88 ± 0.73 MaxF (N) 8.28 ± 1.58 5.62 ± 1.08 7.13 ± 0.42 MeanF (N) 4.64 ± 1.17 3.05 ± 0.44 3.71 ± 0.67 NormLinD (mm-1) 3.82 ± 0.92 7.87 ± 1.07 11.50 ± 0.85 h40% 5.40 ± 0.14 2.04 ± 0.10 2.69 ± 0.07 Penetration test (equal distance of 0.6 mm for all samples, hthin) NumFP (peaks) 0.55 ± 1.01 1.88 ± 1.05 2.00 ± 1.32 MaxF (N) 2.48 ± 1.64 4.36 ± 1.13 6.14 ± 0.76 MeanF (N) 1.21 ± 0.76 2.00 ± 0.72 2.74 ± 0.48 Area (N s) 0.24 ± 0.15 0.39 ± 0.14 0.54 ± 0.09 Data in each row with different letters are significantly different (p < 0.05).

Oat Cereal

All Bran Fruit'n and Fibre

Corn Flakes

Toast

0.236 ± 0.029d 3.93 ± 0.15cd

0.429 ± 0.106ab 4.18 ± 0.38c

0.522 ± 0.027a 6.15 ± 0.04a

0.386 ± 0.011bc 3.97 ± 0.12cd

4.57 ± 1.65 44.84 ± 38.57 69.67 ± 45.41 2.99 ± 0.71

5.43 ± 0.51 4.07 ± 1.40 23.14 ± 7.83 2.69 ± 0.35

4.67 ± 1.43 2.30 ± 1.50 14.03 ± 7.77 3.44 ± 0.29

5.50 ± 1.60 104.83 ± 48.81 127.39 ± 29.75 4.01 ± 0.29

8.44 ± 3.90 6.76 ± 6.37 2.77 ± 2.71 127.14 ± 100.11

9.77 ± 1.78 0.77 ± 0.33 0.22 ± 0.09 37.61 ± 14.68

8.11 ± 3.01 0.32 ± 0.11 0.11 ± 0.05 20.71 ± 10.50

8.66 ± 2.87 15.92 ± 5.91 7.46 ± 3.42 217.75 ± 48.45

5.75 ± 1.00 8.35 ± 1.59 3.74 ± 0.76 14.93 ± 2.50 2.15 ± 0.21

5.22 ± 2.36 13.57 ± 10.07 4.92 ± 4.05 40.18 ± 5.22 0.89 ± 0.19

3.76 ± 2.36 4.48 ± 2.97 1.33 ± 1.23 7.09 ± 3.76 1.49 ± 0.34

6.67 ± 0.54 9.15 ± 4.80 2.77 ± 2.13 7.61 ± 6.67 3.85 ± 0.12

1.66 ± 1.00 5.00 ± 2.42 2.14 ± 1.18 0.42 ± 0.23

2.77 ± 0.66 12.25 ± 10.36 4.44 ± 3.87 0.88 ± 0.77

1.77 ± 1.48 3.58 ± 1.77 1.41 ± 0.62 0.28 ± 0.12

2.62 ± 0.74 4.89 ± 4.12 1.81 ± 1.74 0.36 ± 0.34

19

Table 4: Acoustical parameters for products of Spectrum Crispness Scale obtained by compression and penetration tests. Values are presented as average ± standard deviation. Graham Cracker

Oat Cereal

All Bran Fruit'n and Fibre

Corn Flakes

Toast

2645 ± 194

2460 ± 766

2078 ± 253

1959 ± 403

2769 ± 145

67.94 ± 1.36 98.33 ± 5.19 92.68 ± 3.35

67.65 ± 4.09 100.65 ± 5.66 96.09 ± 5.13

71.12 ± 2.20 102.78 ± 4.28 99.08 ± 2.93

74.34 ± 2.05 107.98 ± 2.40 104.89 ± 2.73

75.30 ± 1.09 99.92 ± 3.05 97.22 ± 2.62

Compression test (equal distance of 2.0 mm for all samples, hthin) NumAP (peaks) 53 ± 22 4964 ± 645 5084 ± 546 SPLeq (dB) 54.31 ± 0.87 67.36 ± 1.19 66.99 ± 1.81 SPLmax (dB) 63.83 ± 3.04 101.48 ± 4.39 97.58 ± 5.39 SPL10 (dB) 59.62 ± 2.21 96.65 ± 3.26 92.46 ± 3.41

4794 ± 1892 66.42 ± 5.24 96.08 ± 13.82 91.20 ± 13.26

3794 ± 660 69.37 ± 2.48 100.37 ± 4.19 96.84 ± 3.12

2965 ± 997 71.71 ± 3.40 103.94 ± 5.86 99.80 ± 4.90

5170 ± 500 71.99 ± 1.77 98.21 ± 4.72 94.89 ± 3.68

1192 ± 194

1038 ± 305

1443 ± 344

1298 ± 604

1421 ± 179

58.75 ± 1.15 85.39 ± 3.87 82.49 ± 3.06

58.18 ± 1.72 82.82 ± 5.68 78.46 ± 5.05

67.73 ± 3.11 96.56 ± 7.39 91.58 ± 6.95

73.84 ± 2.62 109.24 ± 6.97 104.45 ±5.52

61.61 ± 1.87 97.70 ± 6.61 92.30 ± 4.34

342.55 ± 330.58 56.57 ± 3.06 71.48 ± 10.02 67.49 ± 9.66

811.44 ± 225.15 69.12 ± 3.46 96.35 ± 7.49 91.36 ± 7.03

790.78 ± 388.73 75.14 ± 6.62 105.42 ± 10.03 100.56 ± 9.21

366.00 ± 290.40 62.98 ± 5.10 85.18 ± 12.76 79.60 ± 11.99

Parameter

Granola Bar

Club Cracker

Compression test (distance h40% of each sample) NormNumAP 66.5 ± 40.7 2463 ± 253 (peaks mm-1) SPLeq (dB) 54.10 ± 0.79 67.35 ± 1.13 SPLmax (dB) 67.38 ± 2.89 101.84 ± 3.69 SPL10 (dB) 64.24 ± 2.67 96.67 ± 3.45

Penetration test (distance h40% of each sample) NormNumAP 15.24 ± 29.13 1201 ± 201 (peaks mm-1) SPLeq (dB) 53.77 ± 1.10 58.42 ± 1.20 SPLmax (dB) 61.26 ± 3.82 83.70 ± 5.68 SPL10 (dB) 57.92 ± 3.89 79.62 ± 4.53

Penetration test (equal distance of 0.6 mm for all samples, hthin) NumAP (peaks) 6.11 ± 7.16 373.11 ± 400.49 323.66 ± 332.71 SPLeq (dB) 52.90 ± 1.86 57.60 ± 3.79 59.17 ± 3.22 SPLmax (dB) 55.33 ± 4.56 73.90 ± 12.57 78.08 ± 8.25 SPL10 (dB) 53.41 ± 3.03 70.08 ± 11.34 73.33 ± 8.31

20

Table 5: Linear correlation between variables (Person matrix) for compression tests (distance h40% of each sample). Variables

aw

Moisture

aw Moisture Spectrum Restructured NormLindD MeanF NormNumFP h40% SPLmax SPL10 SPLeq NormNumAP

1 0.701 0.371 0.398 -0.502 -0.382 -0.099 0.370 -0.079 -0.028 0.075 -0.443

1 -0.112 -0.105 -0.582 -0.410 -0.418 0.229 -0.151 -0.135 -0.216 -0.470

Spectrum Restructured NormLindD

1 0.991 0.327 0.100 0.615 -0.093 0.548 0.605 0.807 0.441

1 0.245 0.008 0.642 -0.125 0.583 0.640 0.819 0.427

1 0.878 0.166 -0.178 0.171 0.180 0.379 0.599

MeanF

NormNumFP

h40%

1 -0.215 0.161 -0.214 -0.207 0.006 0.225

1 -0.490 0.726 0.744 0.755 0.694

1 -0.782 -0.766 -0.599 -0.765

SPLmax SPL10 SPLeq

1 0.989 0.863 0.801

1 0.902 0.804

1 0.780

NormNu mAP

1

21

Table 6: Linear correlation between variables (Person matrix) for compression tests (equal distance of 2.0 mm for all samples, hthin). Variables Aw Moisture Spectrum Restructured LinD MaxS MeanS NumFP SPLmax SPL10 SPLeq NumAP

aw 1 0.701 0.371 0.398 -0.548 -0.551 -0.434 -0.182 -0.109 -0.081 0.029 -0.521

Moisture

Spectrum

Restructured

LinD

MaxS

MeanS

NumFP

SPLmax

SPL10

SPLeq

NumAP

1 -0.112 -0.105 -0.566 -0.484 -0.415 -0.423 -0.161 -0.163 -0.191 -0.493

1 0.991 0.262 0.096 0.197 0.462 0.485 0.520 0.705 0.307

1 0.185 0.009 0.107 0.497 0.508 0.544 0.718 0.284

1 0.931 0.931 0.141 0.290 0.301 0.382 0.700

1 0.951 -0.012 0.144 0.150 0.191 0.558

1 -0.034 0.131 0.143 0.227 0.539

1 0.680 0.684 0.664 0.623

1 0.992 0.896 0.790

1 0.921 0.792

1 0.749

1

22

Table 7: Person matrix (linear correlation between variables) for data normalized for penetration test (distance h40% of each sample)

Variables Aw Moisture Spectrum Restructured MaxF MeanF Area NormLinD NormNumFP h40% SPLmax SPL10 SPLeq NormNumAP

aw Moisture Spectrum Restructured MaxF MeanF Area NormLinD NormNumFP h40% SPLmax SPL10 SPLeq NormNumAP 1 0.706 1 0.377 -0.092 1 0.402 -0.088 0.991 1 -0.017 -0.313 0.018 0.037 1 -0.159 -0.242 -0.324 -0.313 0.887 1 -0.158 -0.242 -0.323 -0.312 0.888 1.000 1 0.067 -0.299 0.179 0.224 0.877 0.706 0.708 1 -0.444 -0.628 0.427 0.417 0.206 0.062 0.063 0.305 1 0.006 0.052 -0.252 -0.336 -0.085 0.083 0.082 -0.399 -0.410 1 0.307 0.008 0.783 0.819 -0.085 -0.375 -0.374 0.204 0.385 -0.642 1 0.301 0.008 0.783 0.821 -0.081 -0.371 -0.370 0.215 0.416 -0.658 0.992 1 0.578 0.234 0.698 0.765 0.006 -0.278 -0.277 0.287 0.157 -0.633 0.897 0.910 1 -0.071 -0.266 0.547 0.562 0.068 -0.110 -0.109 0.278 0.799 -0.677 0.652 0.680 0.507 1

23

Table 8: Linear correlation between variables (Person matrix) for penetration tests (equal distance of 0.6 mm for all samples, hthin).

Variables aw Moisture Spectrum Restructured MaxF MeanF Area NumFP SPLmax SPL10 SPLeq NumAP

aw 1 0.706 0.377 0.402 -0.028 -0.089 -0.088 -0.038 0.390 0.416 0.540 0.277

Moisture 1 -0.092 -0.088 -0.294 -0.288 -0.287 -0.272 0.064 0.095 0.193 0.056

Spectrum Retructured

1 0.991 0.072 0.001 0.002 0.346 0.649 0.638 0.641 0.384

1 0.111 0.036 0.037 0.351 0.703 0.694 0.702 0.441

MaxF

1 0.977 0.976 0.180 0.210 0.227 0.230 0.269

MeanF

1 1.000 0.186 0.170 0.190 0.180 0.252

Area

1 0.187 0.170 0.191 0.181 0.251

NumFP

1 0.366 0.352 0.241 0.409

SPLmax

1 0.994 0.948 0.694

SPL10 SPLeq NumAP

1 0.961 0.720

1 0.609

1

24

Graphical abstract

25

26

Highlights

   

Products from Spectrum Crispness Scale characterized by instrumental measurements; SPLeq showed the highest linear correlation with the Spectrum Crispness Scale; Penetration test was better than compression test to differentiate the products; Poor correlation between sensory crispness and mechanical parameters were found;

27

Figure 1: Schematic view of the semi-anechoic box (adapted from Murta et al., 2017a) 4 foam panels

Texture Analyzer

Thickness = 25 mm

Microphone

5 PVC panels Thickness = 25 mm 8 vibration isolators

PVC window with double glazing

28

Figure 2: Comparison between spectrograms generated from raw and filtered data. NFFT: 2048, overlap 50%, Hanning window.

29

Figure 3. Filter's efficacy in evidencing acoustic sound peaks. Comparative SPL for raw and filtered data.

30

Figure 4. Data of force and SPL over time during the compression of (a) Granola Bar, (b) Oat Cereal and (c) Toast and penetration test (d) Granola Bar, (e) Oat Cereal and (f) Toast.

31

Figure 5. PCA biplot graph of a) Compression test for equal distance of 2.0 mm for all samples; b) Compression test for distance of h40% of each sample, c) Penetration test for equal distance of 0.6 mm for all samples, and d) Penetration test for distance of h40% of each sample. Samples are identified as Granola Bar, Club Cracker, Graham Cracker, Oat Cereal, All Bran Fruit’n Fibre, Corn Flakes, and Toast. Parameters are identified as Moisture (moisture content), aw (water activity), Spectrum (Spectrum Crispness Sensory Scale), Restructured (Panelists Restructured Crispness Sensory Scale), NormLindD (Normalized Linear Distance), MeanF (Mean Force), NormNumFP (Normalized Number of Force Peaks), h40% (Penetration/Compression distance), SPLmax, SPL10, SPLeq, and NormNumAP (Normalized Number of Acoustic Peaks), Area, LinD (Linear Distance), MaxF (Maximum Force), MaxS (Maximum Strain), MeanS (Mean Strain), NumFP (Number of Force Peaks), NumAP (Number of Acoustic Peaks).

32

(C)

(A) 5

8

4

MeanF Area MaxF

6 3

aw

1

4

PC2 (25.81 %)

PC2 (27.82 %)

Restructured Spectrum NumFP spleq spl10 splmax

Moisture

2

0 -1

NumAP

-3

NumFP NumAP 0

LinD MeanS MaxS

-2

2

Spectrum

-2

-6

-5

-4

-3

-2

-1

0

1

2

3

-4

4

-4

-3

-2

-1

0

PC1 (47.58 %)

(B)

6

1

2

3

4

5

PC1 (43.66 %)

MeanF

(D)

NormLindD

10

8

4

NormNumAP

PC2 (27,51 %)

0

NormNumFP spleq splmax spl10 Spectrum Restructured

h40% -2

-4

NormNumFP NormNumAP

2

Restructured Spectrum spl10 spleq splmax

0

-2

aw

h40%

-4

aw

NormLindD

4

Moisture -6

MaxF

MeanF Area

6

2

PC2 (23.73 %)

aw

Moisture

-4

spl10 splmax spleq Restructured

Moisture

-6

-8 -6

-4

-2

0

PC1 (50.22 %)

2

4

6

-6

-4

-2

0

2

4

6

PC1 (42.21 %)

33

Author contributions P. Andreani: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing - Original Draft. J.O. de Moraes: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing - Original Draft, Writing - Review & Editing, Funding acquisition. B.H.P. Murta: Methodology, Software, Formal analysis, Writing - Original Draft. J.V. Link: Validation, Investigation. G. Tribuzi: Formal analysis, Validation, Writing - Original Draft. J.B. Laurindo: Methodology, Resources. S. Paul: Methodology, Software, Writing - Original Draft. B.A.M. Carciofi: Conceptualization, Methodology, Resources, Writing - Original Draft, Writing - Review & Editing, Supervision.

34

Conflict of Interest form We declare that we have no conflicts of interest to disclosure.

Pamela Andreani, Jaqueline O. de Moraes, Bernardo H. P. Murta, Jade V. Link, Giustino Tribuzi, João B. Laurindo, Stephan Paul, Bruno A. M. Carciofi

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