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Predicting the sensory crispness of coated turkey breast by its acoustic signature
Food Research International 40 (2007) 827–834 www.elsevier.com/locate/foodres
Predicting the sensory crispness of coated turkey breast by its acoustic signature Lubov Iliassafov, Eyal Shimoni
*
Faculty of Biotechnology and Food Engineering, Technion—Israel Institute of Technology, Haifa 32000, Israel Received 12 November 2006; accepted 20 January 2007
Abstract Crispness of coated turkey breast after frying, and reheating by microwave or oven, was evaluated by sensory, mechanical tests and a newly developed acoustic signature-classifying model (ASCM). The acoustic signatures were recorded during compression-tests and analyzed by the ASCM. Support vector machines (SVM) and Fuzzy KNN classifiers were used to performed classification of the acoustic signals to frying, and heating by microwave or oven. The sensory crispness scores and the initial slope of the force deformation curve were predicted using frequency domain spectra of the acoustic signals by neural networks (NN). The results showed that a linear classifier (LC) provides satisfying predictions of the sensory crispness grades and the initial slope of the force deformation curve for fried and microwave heated samples. 2007 Elsevier Ltd. All rights reserved. Keywords: Crispness; Acoustic signature; Mechanical properties; Sensory properties; Coated turkey breast
1. Introduction Coated pre-fried turkey breast is a popular product, characterized by crispness of the crust, and a soft and moist interior. Many studies have been carried out to test various techniques to characterize and measure crispness, which is a desirable textural characteristic for most dry foods (Edminster & Vickers, 1985; Katz & Labuza, 1981; Luyten, Plijter, & van Ton, 2000; Pamies, Roudaut, Dacremont, Meste, & Mitchell, 2000; Vickers, 1988) and breaded fried foods (Antonova, Mallikarjuman, & Duncan, 2003). Crispness is a two components phenomenon: oral and aural. Therefore, it has been suggested that crispness may be evaluated by mechanical and acoustical properties (Brennan, Jowitt, & Williams, 1974; Edminster & Vickers, 1985; Mohamed, Jowitt, & Brennan, 1982; Vickers, 1988). Food texture is determined by various tests such as compression, penetration and snap, and the resulting force–dis-
*
Corresponding author. Tel.: +972 4 8292484; fax: +972 4 8293399. E-mail address: [email protected] (E. Shimoni).
0963-9969/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodres.2007.01.013
placement plot. There are no definite criteria for the selection of an apparatus to measure the mechanical properties of foods, and a variety of mechanical tests have been reported for a number of low moisture foods, which are more homogenous than breaded-fried products, such as saltine, potato chip, popcorn, and cereal snacks. Maximum peak force, total energy (Antonova et al., 2003; Seymor, 1985; Vickers & Christensen, 1980), the initial slope of the force–deformation curve (Katz & Labuza, 1981; Vickers, 1987), and Young’s modulus (Brennan et al., 1974; Vickers & Christensen, 1980) were correlated with sensory crispness. They also correlate with average sound energy (Mohamed et al., 1982), logarithm of the number of sound bursts, and the mean amplitude of the bursts in the acoustic signature obtained during the compression test (Edminster & Vickers, 1985; Vickers, 1985). One of the difficulties in the evaluation of crispness by mechanical properties of crispy products is their irregular and irreproducible force–deformation relationships. Thus, it is difficult if not impossible to convert it to stress–strain relationships (Wollny & Peleg, 1994). The degree of jaggedness of the force–deformation curves or the filtered and/or
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compressed (Tesch, Normand, & Peleg, 1995) acoustic signatures can be quantified in terms of an apparent fractal dimension. Out of the various available algorithms to perform fractal analysis, the Kolmogorov and Richardson algorithms have provided a good indication of the jaggedness of the force–deformation curves (Borges & Peleg, 1996; Tesch et al., 1995; Wollny & Peleg, 1994) providing good correlation with crispness. Fast Fourier transform (FFT) analysis is another means to quantify mechanical and acoustical signatures. FFT analysis of the force–displacement plots is not quite adequate to express the degree of jaggedness in numerical terms (Wollny & Peleg, 1994), however, its application in the analysis of sound signals in frequency domain seem to offer a more quantitative assessment (Barret, Normand, Peleg, & Ross, 1992). The mean values of sound pressure, sound pressure level, and sound intensity were found lowest for non-crispy food products with high moisture level (Seymour & Hamann, 1988), and crisp potato chips had a greater amount of high-frequency components (Lee, Deibei, Glembin, & Munday, 1988). The average power of the popping sounds signal during extrusion, analyzed by FFT and artificial neural network (ANN), has allowed giving bulk density and fracturing force prediction of the extruded product (Barton et al., 1998). ANN was also used for predict sensory crispness based on the features of sound signals (Lui & Tan, 1999). High accuracy prediction of crispness grades and moisture content was achieved by back propagation neural network model, based on frequency-domain spectra analysis of acoustic signals (Jindal, 2001). It is important to note that all the above-mentioned crispness evaluation and measurement techniques were applied to low-moisture foods. Studies related to the crispness in high-moisture foods have been very limited. One more recent study by Antonova et al. (2003), evaluated the crispness in breaded fried chicken nuggets, and found high correlation of the ultrasonic velocity with sensory crispness (r = 0.83). Measurements of the acoustic and mechanical properties of a food during crushing (compression-test), provides information about its crispness. Despite a large number of studies, the analysis of acoustic and mechanical properties of multi-domain foods continues to be a difficult task because of the very complex relationship between sensory attributes of crispness and acoustic/mechanical measurements (Rosenthal, 1999; Vickers, 1988). Therefore, the objective of this study was to develop and evaluate a methodology for evaluation of the crispness of pre-fried coated turkey breast, using different heating regimes to form variable crispness. 2. Materials and methods 2.1. Sample preparation Coated turkey breast was prepared using standard predust, batter, and breading (Einat-Bar Ltd., Kibbutz Einat,
Israel). Thirty (30)% ice water at temperature of 4 C was added to the meat, followed by 1% P2O5 and 1% NaCl (based on the weight of meat and water). The materials were weighted and tumbled until the water was absorbed. The meat was cut to pieces with standardized size about of 1 · 5 · 5 cm3, and then coated by pre-dust, batter and breading. It was then deep-fried in soy-oil for 90 s at 185–190 C. After cooling the samples to 72 C, the samples were frozen in a freezing tunnel at 40 C for 40 min, and then stored at 18 C for 9 days. Samples were re-heated in either microwave oven (Chromex, WP700L17, 750 W, 2450 Hz) for 50 s (full power) or in conventional oven for 10 min at 190 C. These times were determined by measuring the temperature in the sample until the geometric center of the product reached 70 C (Digital Thermometer, T-type, Analog Devices). 2.2. Sensory crispness measurement The crispness of the coated pre-fried turkey breast was evaluated by a sensory panel consisting of 24 untrained volunteers (students, 12 females and 12 males, 20–40 years of age), using a 1–9 Hedonic scale (1—not crispy; 9—very crispy). Crispness was defined as the force and noise level, at which a product breaks or fracture (rather than deforms) when chewed with the molar teeth first and second chew. The volunteers were instructed to place sample between the molar teeth, biting at the middle of the sample. They were asked to bite down evenly until the coating breaks, based on the techniques suggested by Meilgard, Civille, and Carr (1999). Evaluations were performed in individual testing booths with red lighting to mask appearance differences of the samples. Samples were coded by 3-digit random numbers in a random order for each panelist. Data was statistically analyzed by ANOVA for significant differences at the 5% level (JMP, Version 4.04. SAS). 2.3. Mechanical properties measurements Mechanical properties were measured by a Texture Analyzer TA500 (Lloyd Instruments LTD., Fareham, Hampshire, UK), using either a Kramer cell or a cylinder probe. Using the Kramer cell, maximum stress (force/area) was recorded in the first bite, during a two cycle-tests up to deflection limit of 9 mm with crosshead speed of 10 mm/ min. Cylinder probe (10 mm diameter) was used in compression-test at crosshead speed 10 and 200 mm/min, and load cell range 50 N to a deflection limit 9 mm. In all tests, data was collected by Nexygen software (Version 4.5, Ametet Lloyd Instruments Ltd., Fareham, Hampshire, UK) to obtain force–deformation, maximum stress and stress– strain curve. Initial slope from force–deformation curve and maximum stress were also collected. During the measurement of the mechanical, acoustical, and sensory properties the temperature was 65–70 C.
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2.4. Acoustic measurements Acoustic signature was collected during the measurements of the mechanical properties in Texture Analyzer TA500 (Lloyd Instruments Ltd., Fareham, Hampshire, UK), using the compression-test with a cylinder probe (diameter 10 mm). The crosshead speed was 200 mm/min and load cell range 50 N. Sound was recorded by a microphone (frequency response: 20–16 kHz; sensitivity: 58 ± 3 dB at 1 kHz) using the Adobe Audition 1.5 software (Adobe System Incorporated). 2.5. Acoustic signature classifying model (ASCM) The acoustic model was constructed using the MATLAB software (Version 7.01.24701 (R14) Service Pack 1), due to its capability to handle audio files in PCM format. Classification of the sound signal was performed by the linear (LC) and non-linear classifiers (NLC) of the support vector machines (SVM) (MATLAB support vector machine toolbox) and the fuzzy K-nearest neighbors (KNN) algorithm. SVMs are learning machines that can perform binary classification (pattern recognition). Nonlinear support vector machines map their n-dimensional input space into a high dimensional feature space. In this high dimensional feature space, a linear classifier is constructed. For classification of more than two groups, SVMs are used several times. A block-scheme of the developed ASCM is presented in Fig. 1. As the first step in building the acoustic signatureclassifying model, data was collected simultaneously using the mechanical test. This data included the acoustic signatures and the initial slope of the force–deformation curve (crosshead speed 200 mm/min). In order to obtain sufficient data that varies markedly in its acoustic and mechanical signature, we tested 100 samples heated by frying, or oven, or microwave, and additional 10–30 validation samples for each which not used for building the ASCM. The accuracy of the classification of a sample to one of the three types of heat treatments (frying, oven, and microwave) was determined for each type of classifier (data not shown). Table 1 presented accuracy of the LC SVM, which was found to be more exact than NLC. The additional algo-
rithm used for classifying sound signal to the treatment type (frying, oven-, microreheating) is Fuzzy KNN. It determines the percent of nearest neighbors from each group to the new sample (11 nearest neighbors are taken). The number of neighbors was chosen based on trial and error, and usually a prime number is chosen. In addition to crispness evaluation, we used the ASCM to predict the initial slope of the force–deformation curve by using the neural network (NN). A back propagation feed forward neural network (BPNN) was used, with a special architecture, using the following signal parameters: average amplitude in time domain, zero crossings, center of mass in frequency domain, center of mass in the critical band, average energy, maximum amplitude in time domain, and average amplitude in frequency domain in the critical band. The inputs for the NN were extracted from the frequency domain data of the spectrogram of each acoustic signature. The spectrogram was produced by calculating the frequency spectrum of segments of the signal, followed by FFT analysis. The segment length depends on the desirable resolution in the frequency domain. Segment length values for the different acoustic signals are presented in Table 2. Typical acoustic signal length was about 8 s. However, only about 3.5–5 s of the signal composed the greatest changes of frequencies from 2 to 8 kHz, which contained the information about the reaction of the samples to pressure. Moreover, the acoustic signals contained an extraneous noise; therefore high pass filter (HPF) was used to reduce the noise. Back propagation neural network (BPNN) models with the parameters presented in Table 2 and 107 inputs, three layers of hidden slabs, and three output nodes were developed for each type of treatment (frying, oven, and microwave) for estimating the initial slope. The input of each node corresponded to the recorded sound signal amplitude from the FFT at a selected frequency, and the output node represented the initial slope of the coated turkey breast after different types of treatments. Examples for the response of the various classifiers to the type of treatments Table 1 Accuracy of sample classification by linear SVM for samples used to setup the ASCM Original treatment
Linear classifier
Audio properties analyzer Combination measurements
Frying Microwave Oven
Non Linear classifier
Fuzzy knn
Audio file
Audi oprocessing
Neural Network
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Probability of evaluating the heated sample Oven (%)
Frying (%)
Microwave (%)
14 14 72
84 3 8
2 83 20
Table 2 Neural network parameters and error estimations
Grades
Fig. 1. Scheme of the acoustic signature classification model (ASCM).
Frying Microwave Oven
Neurons no.
Long segment length (s)
Error estimation
6 9 6
0.25 0.25 0.5
0.31429 0.27076 0.25004
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Table 3 Examples for the response of the various classifiers to three different samples Sample
Classifier prediction Fuzzy KNN
Linear
Non-linear
Microwave
Oven
Frying
Fried Microwave reheated Oven reheated
Fried Microwave reheated Oven reheated
Fried Microwave reheated Microwave reheated
Fried Microwave reheated Oven reheated
1.9382 1.2931a 1.3554a
1.9523 1.8208 1.8084
1.9213a 1.8973 1.8051
a
Predicted initial slope
Initial slope predicted according to LC was used throughout the study.
are presented in Table 3. It also presents the results of classification according to Fuzzy KNN. It shows that the predicted crispness of fried and microwave reheated samples, done by the linear classifier, fits the crispness of fried and microwave reheated coated turkey breast, and that the oven reheated is practically similar to the microwave reheated samples. Fuzzy KNN and NLC provided a good prediction as well. Prediction of the sensory score was calculated according to the formula:
Table 4 Crispness score and stress for each method and probe and different type of treatment Method
Frying Microwave Oven
Sensory crispness (mean ± st. dev.) 7.63 ± 1.05a 4.77 ± 1.38b 6.55 ± 1.47c
Stress (N/mm2) Kramer cell (2 cycle)
Cylinder (compress)
0.116 ± 0.004c 0.06 ± 0.003d 0.063 ± 0.01d
0.194 ± 0.079a 0.130 ± 0.03b 0.138 ± 0.249ab
Various letters designate a significant difference (p < 0.05).
A ¼ ðni i þ nj xj þ nq qÞ=ðni þ nj þ nq Þ where A—prediction of the sensory score, i—crispness score for fried samples; ni—number of samples which classified as fried samples; j—crispness score for oven reheated samples; nj—number of samples which classified as oven reheated samples; q—crispness score for microwave reheated samples; nq—number of samples which classified as microwave reheated samples. 2.6. Data and statistical analysis All experiments were performed in 100 replications for the mechanical and acoustic properties evaluation used for ACSM creating. Ten to 30 (10–30) samples were used for ACSM validation, and 36 samples for the sensory evaluation for each treatment type. The results were expressed as the average. In the case of significant variability, the results are presented also with their standard deviation. Statistical analysis was performed by JMP software (JMP, Version 4.04. SAS). 3. Results and discussion 3.1. Sensory crispness of coated turkey breast The first step of this study was to verify that indeed the three treatments being tested and used to evaluate the model do have significantly different sensory properties. Sensory crispness evaluation of fried, microwave, and oven reheated coated turkey breast, were found to fit well to theoretical expectation as shown in Table 4. The sensory panel found that fried samples (7.63) were significantly crispier than oven reheated samples (6.55), and that the crispness of microwave-reheated samples was the lowest (4.77). The average sensory scores appeared to be well correlated with the type of treatment. This relation for coated turkey
breast is in agreement with the study reported by Antonova et al. (2003) on breaded fried chicken nuggets. 3.2. Mechanical properties Mechanical properties are associated with the structural properties of the material derived by means of the resistance to compression by a probe that pulls the structure of the food apart by a universal testing machine. For the mechanical properties measurements, we tested two probes: a 2 cycle-test with a Kramer cell probe and compression test with a cylinder probe, which simulated a chew and compression, respectively. Table 4 shows the maximal stress for each method and probe. It is evident that distinct values of maximal stress, for different types of treatments were observed when using the Kramer cell and the cylinder-probe in the compression test. Although, the cylinder probe technique could not differentiate between microwave and oven reheated samples, and between fried and oven reheated samples, it better simulates a bite. Therefore, and it was the method of choice throughout the study. Moreover, it allows evaluate mechanical properties of the center of the sample, while the Kramer cell compresses the full surface including the edges which might introduce measurement errors. Coated and fried turkey breast is a complex system to test in terms of mechanical properties. During compression, much of the force being recorded is due to the mechanical properties of the meat. Since our aim was to test the mechanical properties of the coating, which are responsible to the sensory crispness, we suggested that the crispness is related only to the initial part of the compression test. This figure of initial slope has been very similar to the results of Katz and Labuza (1981) for snack food products. By analyzing these curves we could calculate the
L. Iliassafov, E. Shimoni / Food Research International 40 (2007) 827–834
Initial slope (mean ± st. dev.)
and Young modulus was very weak. It can be concluded that such test cannot reveal fine differences between mechanical properties of layers in such multi-layered system. Therefore the use of initial slope was favored.
Crosshead speed 10 mm/min
Crosshead speed 200 mm/min
3.3. Acoustic properties
1.825 ± 0.151a 0.824 ± 0.172b 1.213 ± 0.131c
1.899 ± 0.64a 1.029 ± 0.48c 1.774 ± 0.497a
Table 5 Initial slope of force–deformation curve measured under two crosshead speeds Treatment type
Frying Microwave Oven
Various letters designate a significant difference (p < 0.05).
initial slope, which characterized the relation between forces exerted on the material and the ensuing deformation as a function of time. We expected the initial slope to be a satisfactory indicator for crispness. Table 5 shows the initial slope at crosshead speed 10 mm/min (for mechanical properties) and 200 mm/min (for acoustic properties). Evidently, crosshead speed of 10 mm/min provided better differentiation between the different post-storage treatments. A maximal value of the initial slope was measured after frying and the minimal value was found after microwave reheating. Hence, a correlation initial slope and sensory crispness (r = 0.94) was observed. We could demonstrate that increased initial slope correlated with an increase of sensory crispness (Fig. 2). It is important to note that Young Modulus, which also is commonly used indicator for crispness of dry food (chips, snacks) (Brennan et al., 1974; Vickers & Christensen, 1980), was calculated for each one of the treatments from the stress–strain curve. Young modulus was not used however, mainly as previously noted, due to the fact that coated turkey breast is a multi-domain system. Therefore, the influence of the high moisture meat layer on the measured Young modulus may be significant. In addition, there was no difference (p > 0.05) between the values of modulus Young of various samples that showed a difference in the crispness score (p < 0.008) (data are not shown); demonstrating that the correlation between crispness score
2.2
2
Initial slope
1.8 1.6 1.4 1.2
1 0.8 0.6 3
4
831
5
6
7
8
9
Sensory crispness
Fig. 2. Correlation of the force–deformation initial slope (crosshead speed 10 mm/min) with sensory score of coated turkey breast heated by frying, oven, and microwave.
Acoustic signatures of fried, microwave- and ovenreheated samples were recorded during the mechanical compression, and are presented in Fig. 3. These signals, recorded as amplitude vs. time plots, show different number and magnitude of sound peaks, depending on the way the sample was heated. Acoustic signatures of fried samples, which were considered crispier, showed a different dominant frequency and amplitude when compared with the acoustic signatures of microwave-reheated samples. This frequency is defined as the rate at which the sound vibrates and is measured as the number of cycles of vibration completed per second or as hertz (Hz). Analysis of the sound signal amplitudes vs. the frequency revealed a hump in the amplitude at frequency range of 2–8 kHz (results not shown). To the best of our knowledge, this is the first time that the frequency range of the crispness signal of fried coated foods is reported. Usually, the measured frequency range of crisp products should be at least up to 12 kHz in order to measure the frequencies of crisp low-moisture food products, which are much more homogeneous than coated pre-fried products (Chakra, Allaf, & Jemai, 1996). The frequencies that best characterize crisp products are in the range of 5–12 kHz during mastication of a variety of crisp products (Lee et al., 1988), 3–6 kHz for mastication of potato chips (Dacremont, 1995), and 1.9–3.3 kHz for the compression of potato chips (Seymour & Hamann, 1988). Thus, the frequency range detected in this study falls within the range of the previously reported frequencies of other crispy foods. 3.4. Crispness prediction by the ASCM The possible use of the acoustic signature recorded during compression test of the multi-domain coated pre-fried turkey breast to predict its mechanical and sensory properties was the main goal of the present study. For this purpose, as previously noted, we used three types of samples: fried, heated by oven, and heated by microwave. These yield three different levels of sensory crispness (Table 4). The frequency domain spectra of 300 acoustic signals (100 for each type of samples) and initial slope values of the force deformation curves, obtained during compression tests, were analyzed by the BPNN, LC, NLC and Fuzzy KNN classifiers of the ASCM. These samples were used to build the model and train the system. The performance of the ASCM was verified by new samples of acoustic signatures, recorded during new mechanical tests. Samples were prepared, and the mechanical– acoustical measurements were performed in the same conditions used to prepare the samples for the ASCM setup.
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L. Iliassafov, E. Shimoni / Food Research International 40 (2007) 827–834 Table 6 Accuracy of sample classification by ASCM Heating method
Frying Microwave Oven
Fig. 3. Typical acoustic signatures of fried (a), microwave-reheated (b), and oven-reheated (c) coated turkey breast samples.
The acoustic signature was used as input for the ASCM, and the predictions done by its various components and classifiers are shown in Table 6. These prediction accuracies
Probable heating method predicted by ASCM Microwave (%)
Frying (%)
Oven (%)
10 100 80
20 0 0
70 0 20
were somewhat different from the accuracies estimated during the buildup of the model, as was shown in Table 1. Specifically, the accuracy in evaluating microwave-heated samples as such, was maximal (100%), and exceeded our expectations (83%). However, the classifier failed in separating signals of fried and oven-reheated samples, with only 20% accuracy in both. Looking into the errors it can be seen that the LC underestimates the crispness of all samples, thus, fried samples were assigned as oven reheated (70%), and the oven-reheated samples were assigned as microwave reheated (80%). This may also explain the higher accuracy in assigning the microwave-reheated samples. These observations are particularly interesting when re-examining the mechanical tests shown in Table 5. These results show that when using fast crosshead speed (200 mm/min) the initial slope estimate of crispness could not be used to differentiate fried samples from oven reheated ones, but only to differentiate both from the microwave-reheated samples. It is important to note, that crosshead speed is known to significantly influence the accuracy determination of mechanical properties. This is in agreement with the results obtained by Antonova et al. (2003) measuring the peak force and total energy for breaded fried chicken nuggets with crosshead speed of 100 mm/min. In her work, no significant differences between peak force and total energy of micro and oven reheated samples were found. However, peak force and total energy values of samples, which were reheated by deep-fat fryer, were significantly higher than microwave and oven reheated samples. In addition, the maximal stress values (Table 4), which were determined with Kramer-cell, have similar significant differences between fried samples and reheated samples (microwave and oven). Therefore, it can be concluded that the mechanical properties are significantly different for fried and microwave reheated samples, which are multi-layer systems such as coated turkey breast or breaded fried chicken nuggets. This may explain the accuracy obtained by the LC classifier, since it is based on acoustic tests taken during mechanical compression tests that used this high crosshead speed. The use of the linear classifier based ASCM prediction is not limited to the mere classification of the samples to their heating procedure. This model was developed, as was described in Section 2, also to predict the actual mechanical properties as well as the sensorial score these samples will have if tested. Therefore, for each sample, we collected the ASCM based predicted values of the force–deformation curve initial slope (crosshead speed 200 mm/min),
L. Iliassafov, E. Shimoni / Food Research International 40 (2007) 827–834
a
2.5
a a
Initial slope
2
b
b
b
1.5
b 1
0.5
0 frying
oven
micro
Treatment type
b
10 9
Crispess score
8
a
a a
7
a,b
6
b b
833
that sensory crispness and the initial slope of the force deformation curve were highly correlated (r = 0.94). Coated turkey breast exhibited characteristic acoustic patterns during compression-tests with irregular amplitude– time relationships depending upon treatment type. Classifying the acoustic signal by the ASCM to type of treatment (frying, oven-, and microwave-reheating) was performed by LC of SVM and yield good predictions of the initial slope. The results also show that the LC gives a more accurate classification of treatment type, as well as the initial slope and sensory score predictions. There were no significant differences between predicted and measured results for coated turkey breast after frying or microwave reheating. Moreover, significant difference was found (p < 0.05) between treatment types (frying and microwave reheating). Thus, the developed ASCM provides a reliable means for analyzing the acoustic signal for the purpose of crispness evaluation, and may be used to screen high numbers of samples, prior to human sensory testing.
5
References
4 3 2 1 0
frying
oven
micro
Treatment type
Fig. 4. Measured (white) vs. ASCM predicted (dark) mechanical properties (a) and sensory properties (b) of coated turkey breast samples heated by frying, and oven- or microwave-reheating.
and the sensorial score. These values were compared to the actual values collected during mechanical and sensory testing of samples prepared under similar conditions, and the results are presented in Fig. 4. For fried, oven reheated, and microwave reheated samples, there was no significant difference between measured and ASCM predicted mechanical and sensory values. Significant differences were found between fried and microwave-reheated samples in both mechanical and crispness score values. As for the oven-reheated samples, there was no difference between these samples and microwave-reheated samples. However, the actual mechanical and sensory properties of these samples were significantly low than the fried samples. These results clearly show that the ASCM model, using the linear classifier, can be used for predictions of mechanical and more importantly the sensory score of such products. 4. Concluding remarks This study was aimed at creating and evaluating ASCM for the prediction of the crispness of coated turkey breast after frying, oven- and microwave-reheating based on acoustic signatures. The results were compared to the actual mechanical and sensory properties. It was found
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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, 85–95. Vickers, Z. M. (1987). Sensory acoustical and force–deformation measurements of potato chip crispness. Journal of Food Science, 52, 138–140. Vickers, Z. M. (1988). Instrumental measures of crispness and their correlation with sensory assessment. Journal of Texture Studies, 19, 1–14. Vickers, Z. M., & Christensen, C. M. (1980). Relationship between sensory crispness and other sensory and instrumental parameters. Journal of Texture Studies, 11, 291–307. Wollny, M., & Peleg, M. (1994). Model of moisture-induced plasticization of crunchy snacks based on ferm’s distribution function. Journal of Science Food Agriculture, 64, 467–473.
Predicting the sensory crispness of coated turkey breast by its acoustic signature Lubov Iliassafov, Eyal Shimoni
*
Faculty of Biotechnology and Food Engineering, Technion—Israel Institute of Technology, Haifa 32000, Israel Received 12 November 2006; accepted 20 January 2007
Abstract Crispness of coated turkey breast after frying, and reheating by microwave or oven, was evaluated by sensory, mechanical tests and a newly developed acoustic signature-classifying model (ASCM). The acoustic signatures were recorded during compression-tests and analyzed by the ASCM. Support vector machines (SVM) and Fuzzy KNN classifiers were used to performed classification of the acoustic signals to frying, and heating by microwave or oven. The sensory crispness scores and the initial slope of the force deformation curve were predicted using frequency domain spectra of the acoustic signals by neural networks (NN). The results showed that a linear classifier (LC) provides satisfying predictions of the sensory crispness grades and the initial slope of the force deformation curve for fried and microwave heated samples. 2007 Elsevier Ltd. All rights reserved. Keywords: Crispness; Acoustic signature; Mechanical properties; Sensory properties; Coated turkey breast
1. Introduction Coated pre-fried turkey breast is a popular product, characterized by crispness of the crust, and a soft and moist interior. Many studies have been carried out to test various techniques to characterize and measure crispness, which is a desirable textural characteristic for most dry foods (Edminster & Vickers, 1985; Katz & Labuza, 1981; Luyten, Plijter, & van Ton, 2000; Pamies, Roudaut, Dacremont, Meste, & Mitchell, 2000; Vickers, 1988) and breaded fried foods (Antonova, Mallikarjuman, & Duncan, 2003). Crispness is a two components phenomenon: oral and aural. Therefore, it has been suggested that crispness may be evaluated by mechanical and acoustical properties (Brennan, Jowitt, & Williams, 1974; Edminster & Vickers, 1985; Mohamed, Jowitt, & Brennan, 1982; Vickers, 1988). Food texture is determined by various tests such as compression, penetration and snap, and the resulting force–dis-
*
Corresponding author. Tel.: +972 4 8292484; fax: +972 4 8293399. E-mail address: [email protected] (E. Shimoni).
0963-9969/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodres.2007.01.013
placement plot. There are no definite criteria for the selection of an apparatus to measure the mechanical properties of foods, and a variety of mechanical tests have been reported for a number of low moisture foods, which are more homogenous than breaded-fried products, such as saltine, potato chip, popcorn, and cereal snacks. Maximum peak force, total energy (Antonova et al., 2003; Seymor, 1985; Vickers & Christensen, 1980), the initial slope of the force–deformation curve (Katz & Labuza, 1981; Vickers, 1987), and Young’s modulus (Brennan et al., 1974; Vickers & Christensen, 1980) were correlated with sensory crispness. They also correlate with average sound energy (Mohamed et al., 1982), logarithm of the number of sound bursts, and the mean amplitude of the bursts in the acoustic signature obtained during the compression test (Edminster & Vickers, 1985; Vickers, 1985). One of the difficulties in the evaluation of crispness by mechanical properties of crispy products is their irregular and irreproducible force–deformation relationships. Thus, it is difficult if not impossible to convert it to stress–strain relationships (Wollny & Peleg, 1994). The degree of jaggedness of the force–deformation curves or the filtered and/or
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compressed (Tesch, Normand, & Peleg, 1995) acoustic signatures can be quantified in terms of an apparent fractal dimension. Out of the various available algorithms to perform fractal analysis, the Kolmogorov and Richardson algorithms have provided a good indication of the jaggedness of the force–deformation curves (Borges & Peleg, 1996; Tesch et al., 1995; Wollny & Peleg, 1994) providing good correlation with crispness. Fast Fourier transform (FFT) analysis is another means to quantify mechanical and acoustical signatures. FFT analysis of the force–displacement plots is not quite adequate to express the degree of jaggedness in numerical terms (Wollny & Peleg, 1994), however, its application in the analysis of sound signals in frequency domain seem to offer a more quantitative assessment (Barret, Normand, Peleg, & Ross, 1992). The mean values of sound pressure, sound pressure level, and sound intensity were found lowest for non-crispy food products with high moisture level (Seymour & Hamann, 1988), and crisp potato chips had a greater amount of high-frequency components (Lee, Deibei, Glembin, & Munday, 1988). The average power of the popping sounds signal during extrusion, analyzed by FFT and artificial neural network (ANN), has allowed giving bulk density and fracturing force prediction of the extruded product (Barton et al., 1998). ANN was also used for predict sensory crispness based on the features of sound signals (Lui & Tan, 1999). High accuracy prediction of crispness grades and moisture content was achieved by back propagation neural network model, based on frequency-domain spectra analysis of acoustic signals (Jindal, 2001). It is important to note that all the above-mentioned crispness evaluation and measurement techniques were applied to low-moisture foods. Studies related to the crispness in high-moisture foods have been very limited. One more recent study by Antonova et al. (2003), evaluated the crispness in breaded fried chicken nuggets, and found high correlation of the ultrasonic velocity with sensory crispness (r = 0.83). Measurements of the acoustic and mechanical properties of a food during crushing (compression-test), provides information about its crispness. Despite a large number of studies, the analysis of acoustic and mechanical properties of multi-domain foods continues to be a difficult task because of the very complex relationship between sensory attributes of crispness and acoustic/mechanical measurements (Rosenthal, 1999; Vickers, 1988). Therefore, the objective of this study was to develop and evaluate a methodology for evaluation of the crispness of pre-fried coated turkey breast, using different heating regimes to form variable crispness. 2. Materials and methods 2.1. Sample preparation Coated turkey breast was prepared using standard predust, batter, and breading (Einat-Bar Ltd., Kibbutz Einat,
Israel). Thirty (30)% ice water at temperature of 4 C was added to the meat, followed by 1% P2O5 and 1% NaCl (based on the weight of meat and water). The materials were weighted and tumbled until the water was absorbed. The meat was cut to pieces with standardized size about of 1 · 5 · 5 cm3, and then coated by pre-dust, batter and breading. It was then deep-fried in soy-oil for 90 s at 185–190 C. After cooling the samples to 72 C, the samples were frozen in a freezing tunnel at 40 C for 40 min, and then stored at 18 C for 9 days. Samples were re-heated in either microwave oven (Chromex, WP700L17, 750 W, 2450 Hz) for 50 s (full power) or in conventional oven for 10 min at 190 C. These times were determined by measuring the temperature in the sample until the geometric center of the product reached 70 C (Digital Thermometer, T-type, Analog Devices). 2.2. Sensory crispness measurement The crispness of the coated pre-fried turkey breast was evaluated by a sensory panel consisting of 24 untrained volunteers (students, 12 females and 12 males, 20–40 years of age), using a 1–9 Hedonic scale (1—not crispy; 9—very crispy). Crispness was defined as the force and noise level, at which a product breaks or fracture (rather than deforms) when chewed with the molar teeth first and second chew. The volunteers were instructed to place sample between the molar teeth, biting at the middle of the sample. They were asked to bite down evenly until the coating breaks, based on the techniques suggested by Meilgard, Civille, and Carr (1999). Evaluations were performed in individual testing booths with red lighting to mask appearance differences of the samples. Samples were coded by 3-digit random numbers in a random order for each panelist. Data was statistically analyzed by ANOVA for significant differences at the 5% level (JMP, Version 4.04. SAS). 2.3. Mechanical properties measurements Mechanical properties were measured by a Texture Analyzer TA500 (Lloyd Instruments LTD., Fareham, Hampshire, UK), using either a Kramer cell or a cylinder probe. Using the Kramer cell, maximum stress (force/area) was recorded in the first bite, during a two cycle-tests up to deflection limit of 9 mm with crosshead speed of 10 mm/ min. Cylinder probe (10 mm diameter) was used in compression-test at crosshead speed 10 and 200 mm/min, and load cell range 50 N to a deflection limit 9 mm. In all tests, data was collected by Nexygen software (Version 4.5, Ametet Lloyd Instruments Ltd., Fareham, Hampshire, UK) to obtain force–deformation, maximum stress and stress– strain curve. Initial slope from force–deformation curve and maximum stress were also collected. During the measurement of the mechanical, acoustical, and sensory properties the temperature was 65–70 C.
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2.4. Acoustic measurements Acoustic signature was collected during the measurements of the mechanical properties in Texture Analyzer TA500 (Lloyd Instruments Ltd., Fareham, Hampshire, UK), using the compression-test with a cylinder probe (diameter 10 mm). The crosshead speed was 200 mm/min and load cell range 50 N. Sound was recorded by a microphone (frequency response: 20–16 kHz; sensitivity: 58 ± 3 dB at 1 kHz) using the Adobe Audition 1.5 software (Adobe System Incorporated). 2.5. Acoustic signature classifying model (ASCM) The acoustic model was constructed using the MATLAB software (Version 7.01.24701 (R14) Service Pack 1), due to its capability to handle audio files in PCM format. Classification of the sound signal was performed by the linear (LC) and non-linear classifiers (NLC) of the support vector machines (SVM) (MATLAB support vector machine toolbox) and the fuzzy K-nearest neighbors (KNN) algorithm. SVMs are learning machines that can perform binary classification (pattern recognition). Nonlinear support vector machines map their n-dimensional input space into a high dimensional feature space. In this high dimensional feature space, a linear classifier is constructed. For classification of more than two groups, SVMs are used several times. A block-scheme of the developed ASCM is presented in Fig. 1. As the first step in building the acoustic signatureclassifying model, data was collected simultaneously using the mechanical test. This data included the acoustic signatures and the initial slope of the force–deformation curve (crosshead speed 200 mm/min). In order to obtain sufficient data that varies markedly in its acoustic and mechanical signature, we tested 100 samples heated by frying, or oven, or microwave, and additional 10–30 validation samples for each which not used for building the ASCM. The accuracy of the classification of a sample to one of the three types of heat treatments (frying, oven, and microwave) was determined for each type of classifier (data not shown). Table 1 presented accuracy of the LC SVM, which was found to be more exact than NLC. The additional algo-
rithm used for classifying sound signal to the treatment type (frying, oven-, microreheating) is Fuzzy KNN. It determines the percent of nearest neighbors from each group to the new sample (11 nearest neighbors are taken). The number of neighbors was chosen based on trial and error, and usually a prime number is chosen. In addition to crispness evaluation, we used the ASCM to predict the initial slope of the force–deformation curve by using the neural network (NN). A back propagation feed forward neural network (BPNN) was used, with a special architecture, using the following signal parameters: average amplitude in time domain, zero crossings, center of mass in frequency domain, center of mass in the critical band, average energy, maximum amplitude in time domain, and average amplitude in frequency domain in the critical band. The inputs for the NN were extracted from the frequency domain data of the spectrogram of each acoustic signature. The spectrogram was produced by calculating the frequency spectrum of segments of the signal, followed by FFT analysis. The segment length depends on the desirable resolution in the frequency domain. Segment length values for the different acoustic signals are presented in Table 2. Typical acoustic signal length was about 8 s. However, only about 3.5–5 s of the signal composed the greatest changes of frequencies from 2 to 8 kHz, which contained the information about the reaction of the samples to pressure. Moreover, the acoustic signals contained an extraneous noise; therefore high pass filter (HPF) was used to reduce the noise. Back propagation neural network (BPNN) models with the parameters presented in Table 2 and 107 inputs, three layers of hidden slabs, and three output nodes were developed for each type of treatment (frying, oven, and microwave) for estimating the initial slope. The input of each node corresponded to the recorded sound signal amplitude from the FFT at a selected frequency, and the output node represented the initial slope of the coated turkey breast after different types of treatments. Examples for the response of the various classifiers to the type of treatments Table 1 Accuracy of sample classification by linear SVM for samples used to setup the ASCM Original treatment
Linear classifier
Audio properties analyzer Combination measurements
Frying Microwave Oven
Non Linear classifier
Fuzzy knn
Audio file
Audi oprocessing
Neural Network
829
Probability of evaluating the heated sample Oven (%)
Frying (%)
Microwave (%)
14 14 72
84 3 8
2 83 20
Table 2 Neural network parameters and error estimations
Grades
Fig. 1. Scheme of the acoustic signature classification model (ASCM).
Frying Microwave Oven
Neurons no.
Long segment length (s)
Error estimation
6 9 6
0.25 0.25 0.5
0.31429 0.27076 0.25004
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Table 3 Examples for the response of the various classifiers to three different samples Sample
Classifier prediction Fuzzy KNN
Linear
Non-linear
Microwave
Oven
Frying
Fried Microwave reheated Oven reheated
Fried Microwave reheated Oven reheated
Fried Microwave reheated Microwave reheated
Fried Microwave reheated Oven reheated
1.9382 1.2931a 1.3554a
1.9523 1.8208 1.8084
1.9213a 1.8973 1.8051
a
Predicted initial slope
Initial slope predicted according to LC was used throughout the study.
are presented in Table 3. It also presents the results of classification according to Fuzzy KNN. It shows that the predicted crispness of fried and microwave reheated samples, done by the linear classifier, fits the crispness of fried and microwave reheated coated turkey breast, and that the oven reheated is practically similar to the microwave reheated samples. Fuzzy KNN and NLC provided a good prediction as well. Prediction of the sensory score was calculated according to the formula:
Table 4 Crispness score and stress for each method and probe and different type of treatment Method
Frying Microwave Oven
Sensory crispness (mean ± st. dev.) 7.63 ± 1.05a 4.77 ± 1.38b 6.55 ± 1.47c
Stress (N/mm2) Kramer cell (2 cycle)
Cylinder (compress)
0.116 ± 0.004c 0.06 ± 0.003d 0.063 ± 0.01d
0.194 ± 0.079a 0.130 ± 0.03b 0.138 ± 0.249ab
Various letters designate a significant difference (p < 0.05).
A ¼ ðni i þ nj xj þ nq qÞ=ðni þ nj þ nq Þ where A—prediction of the sensory score, i—crispness score for fried samples; ni—number of samples which classified as fried samples; j—crispness score for oven reheated samples; nj—number of samples which classified as oven reheated samples; q—crispness score for microwave reheated samples; nq—number of samples which classified as microwave reheated samples. 2.6. Data and statistical analysis All experiments were performed in 100 replications for the mechanical and acoustic properties evaluation used for ACSM creating. Ten to 30 (10–30) samples were used for ACSM validation, and 36 samples for the sensory evaluation for each treatment type. The results were expressed as the average. In the case of significant variability, the results are presented also with their standard deviation. Statistical analysis was performed by JMP software (JMP, Version 4.04. SAS). 3. Results and discussion 3.1. Sensory crispness of coated turkey breast The first step of this study was to verify that indeed the three treatments being tested and used to evaluate the model do have significantly different sensory properties. Sensory crispness evaluation of fried, microwave, and oven reheated coated turkey breast, were found to fit well to theoretical expectation as shown in Table 4. The sensory panel found that fried samples (7.63) were significantly crispier than oven reheated samples (6.55), and that the crispness of microwave-reheated samples was the lowest (4.77). The average sensory scores appeared to be well correlated with the type of treatment. This relation for coated turkey
breast is in agreement with the study reported by Antonova et al. (2003) on breaded fried chicken nuggets. 3.2. Mechanical properties Mechanical properties are associated with the structural properties of the material derived by means of the resistance to compression by a probe that pulls the structure of the food apart by a universal testing machine. For the mechanical properties measurements, we tested two probes: a 2 cycle-test with a Kramer cell probe and compression test with a cylinder probe, which simulated a chew and compression, respectively. Table 4 shows the maximal stress for each method and probe. It is evident that distinct values of maximal stress, for different types of treatments were observed when using the Kramer cell and the cylinder-probe in the compression test. Although, the cylinder probe technique could not differentiate between microwave and oven reheated samples, and between fried and oven reheated samples, it better simulates a bite. Therefore, and it was the method of choice throughout the study. Moreover, it allows evaluate mechanical properties of the center of the sample, while the Kramer cell compresses the full surface including the edges which might introduce measurement errors. Coated and fried turkey breast is a complex system to test in terms of mechanical properties. During compression, much of the force being recorded is due to the mechanical properties of the meat. Since our aim was to test the mechanical properties of the coating, which are responsible to the sensory crispness, we suggested that the crispness is related only to the initial part of the compression test. This figure of initial slope has been very similar to the results of Katz and Labuza (1981) for snack food products. By analyzing these curves we could calculate the
L. Iliassafov, E. Shimoni / Food Research International 40 (2007) 827–834
Initial slope (mean ± st. dev.)
and Young modulus was very weak. It can be concluded that such test cannot reveal fine differences between mechanical properties of layers in such multi-layered system. Therefore the use of initial slope was favored.
Crosshead speed 10 mm/min
Crosshead speed 200 mm/min
3.3. Acoustic properties
1.825 ± 0.151a 0.824 ± 0.172b 1.213 ± 0.131c
1.899 ± 0.64a 1.029 ± 0.48c 1.774 ± 0.497a
Table 5 Initial slope of force–deformation curve measured under two crosshead speeds Treatment type
Frying Microwave Oven
Various letters designate a significant difference (p < 0.05).
initial slope, which characterized the relation between forces exerted on the material and the ensuing deformation as a function of time. We expected the initial slope to be a satisfactory indicator for crispness. Table 5 shows the initial slope at crosshead speed 10 mm/min (for mechanical properties) and 200 mm/min (for acoustic properties). Evidently, crosshead speed of 10 mm/min provided better differentiation between the different post-storage treatments. A maximal value of the initial slope was measured after frying and the minimal value was found after microwave reheating. Hence, a correlation initial slope and sensory crispness (r = 0.94) was observed. We could demonstrate that increased initial slope correlated with an increase of sensory crispness (Fig. 2). It is important to note that Young Modulus, which also is commonly used indicator for crispness of dry food (chips, snacks) (Brennan et al., 1974; Vickers & Christensen, 1980), was calculated for each one of the treatments from the stress–strain curve. Young modulus was not used however, mainly as previously noted, due to the fact that coated turkey breast is a multi-domain system. Therefore, the influence of the high moisture meat layer on the measured Young modulus may be significant. In addition, there was no difference (p > 0.05) between the values of modulus Young of various samples that showed a difference in the crispness score (p < 0.008) (data are not shown); demonstrating that the correlation between crispness score
2.2
2
Initial slope
1.8 1.6 1.4 1.2
1 0.8 0.6 3
4
831
5
6
7
8
9
Sensory crispness
Fig. 2. Correlation of the force–deformation initial slope (crosshead speed 10 mm/min) with sensory score of coated turkey breast heated by frying, oven, and microwave.
Acoustic signatures of fried, microwave- and ovenreheated samples were recorded during the mechanical compression, and are presented in Fig. 3. These signals, recorded as amplitude vs. time plots, show different number and magnitude of sound peaks, depending on the way the sample was heated. Acoustic signatures of fried samples, which were considered crispier, showed a different dominant frequency and amplitude when compared with the acoustic signatures of microwave-reheated samples. This frequency is defined as the rate at which the sound vibrates and is measured as the number of cycles of vibration completed per second or as hertz (Hz). Analysis of the sound signal amplitudes vs. the frequency revealed a hump in the amplitude at frequency range of 2–8 kHz (results not shown). To the best of our knowledge, this is the first time that the frequency range of the crispness signal of fried coated foods is reported. Usually, the measured frequency range of crisp products should be at least up to 12 kHz in order to measure the frequencies of crisp low-moisture food products, which are much more homogeneous than coated pre-fried products (Chakra, Allaf, & Jemai, 1996). The frequencies that best characterize crisp products are in the range of 5–12 kHz during mastication of a variety of crisp products (Lee et al., 1988), 3–6 kHz for mastication of potato chips (Dacremont, 1995), and 1.9–3.3 kHz for the compression of potato chips (Seymour & Hamann, 1988). Thus, the frequency range detected in this study falls within the range of the previously reported frequencies of other crispy foods. 3.4. Crispness prediction by the ASCM The possible use of the acoustic signature recorded during compression test of the multi-domain coated pre-fried turkey breast to predict its mechanical and sensory properties was the main goal of the present study. For this purpose, as previously noted, we used three types of samples: fried, heated by oven, and heated by microwave. These yield three different levels of sensory crispness (Table 4). The frequency domain spectra of 300 acoustic signals (100 for each type of samples) and initial slope values of the force deformation curves, obtained during compression tests, were analyzed by the BPNN, LC, NLC and Fuzzy KNN classifiers of the ASCM. These samples were used to build the model and train the system. The performance of the ASCM was verified by new samples of acoustic signatures, recorded during new mechanical tests. Samples were prepared, and the mechanical– acoustical measurements were performed in the same conditions used to prepare the samples for the ASCM setup.
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L. Iliassafov, E. Shimoni / Food Research International 40 (2007) 827–834 Table 6 Accuracy of sample classification by ASCM Heating method
Frying Microwave Oven
Fig. 3. Typical acoustic signatures of fried (a), microwave-reheated (b), and oven-reheated (c) coated turkey breast samples.
The acoustic signature was used as input for the ASCM, and the predictions done by its various components and classifiers are shown in Table 6. These prediction accuracies
Probable heating method predicted by ASCM Microwave (%)
Frying (%)
Oven (%)
10 100 80
20 0 0
70 0 20
were somewhat different from the accuracies estimated during the buildup of the model, as was shown in Table 1. Specifically, the accuracy in evaluating microwave-heated samples as such, was maximal (100%), and exceeded our expectations (83%). However, the classifier failed in separating signals of fried and oven-reheated samples, with only 20% accuracy in both. Looking into the errors it can be seen that the LC underestimates the crispness of all samples, thus, fried samples were assigned as oven reheated (70%), and the oven-reheated samples were assigned as microwave reheated (80%). This may also explain the higher accuracy in assigning the microwave-reheated samples. These observations are particularly interesting when re-examining the mechanical tests shown in Table 5. These results show that when using fast crosshead speed (200 mm/min) the initial slope estimate of crispness could not be used to differentiate fried samples from oven reheated ones, but only to differentiate both from the microwave-reheated samples. It is important to note, that crosshead speed is known to significantly influence the accuracy determination of mechanical properties. This is in agreement with the results obtained by Antonova et al. (2003) measuring the peak force and total energy for breaded fried chicken nuggets with crosshead speed of 100 mm/min. In her work, no significant differences between peak force and total energy of micro and oven reheated samples were found. However, peak force and total energy values of samples, which were reheated by deep-fat fryer, were significantly higher than microwave and oven reheated samples. In addition, the maximal stress values (Table 4), which were determined with Kramer-cell, have similar significant differences between fried samples and reheated samples (microwave and oven). Therefore, it can be concluded that the mechanical properties are significantly different for fried and microwave reheated samples, which are multi-layer systems such as coated turkey breast or breaded fried chicken nuggets. This may explain the accuracy obtained by the LC classifier, since it is based on acoustic tests taken during mechanical compression tests that used this high crosshead speed. The use of the linear classifier based ASCM prediction is not limited to the mere classification of the samples to their heating procedure. This model was developed, as was described in Section 2, also to predict the actual mechanical properties as well as the sensorial score these samples will have if tested. Therefore, for each sample, we collected the ASCM based predicted values of the force–deformation curve initial slope (crosshead speed 200 mm/min),
L. Iliassafov, E. Shimoni / Food Research International 40 (2007) 827–834
a
2.5
a a
Initial slope
2
b
b
b
1.5
b 1
0.5
0 frying
oven
micro
Treatment type
b
10 9
Crispess score
8
a
a a
7
a,b
6
b b
833
that sensory crispness and the initial slope of the force deformation curve were highly correlated (r = 0.94). Coated turkey breast exhibited characteristic acoustic patterns during compression-tests with irregular amplitude– time relationships depending upon treatment type. Classifying the acoustic signal by the ASCM to type of treatment (frying, oven-, and microwave-reheating) was performed by LC of SVM and yield good predictions of the initial slope. The results also show that the LC gives a more accurate classification of treatment type, as well as the initial slope and sensory score predictions. There were no significant differences between predicted and measured results for coated turkey breast after frying or microwave reheating. Moreover, significant difference was found (p < 0.05) between treatment types (frying and microwave reheating). Thus, the developed ASCM provides a reliable means for analyzing the acoustic signal for the purpose of crispness evaluation, and may be used to screen high numbers of samples, prior to human sensory testing.
5
References
4 3 2 1 0
frying
oven
micro
Treatment type
Fig. 4. Measured (white) vs. ASCM predicted (dark) mechanical properties (a) and sensory properties (b) of coated turkey breast samples heated by frying, and oven- or microwave-reheating.
and the sensorial score. These values were compared to the actual values collected during mechanical and sensory testing of samples prepared under similar conditions, and the results are presented in Fig. 4. For fried, oven reheated, and microwave reheated samples, there was no significant difference between measured and ASCM predicted mechanical and sensory values. Significant differences were found between fried and microwave-reheated samples in both mechanical and crispness score values. As for the oven-reheated samples, there was no difference between these samples and microwave-reheated samples. However, the actual mechanical and sensory properties of these samples were significantly low than the fried samples. These results clearly show that the ASCM model, using the linear classifier, can be used for predictions of mechanical and more importantly the sensory score of such products. 4. Concluding remarks This study was aimed at creating and evaluating ASCM for the prediction of the crispness of coated turkey breast after frying, oven- and microwave-reheating based on acoustic signatures. The results were compared to the actual mechanical and sensory properties. It was found
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