Feasibility of non-destructive evaluation for apple crispness based on portable acoustic signal

In this paper, apple crispness was evaluated by sensory evaluation and compared with non-destructive measurements of portable acoustic signal to discuss the feasibility of non-destructive evaluation for apple crispness based on portable acoustic signal. Acoustic eigenvalues from the acoustic signal were processed by time domain and Hilbert–Huang transform (HHT), followed by analysing the correlations with apple crispness that had been evaluated via sensory evaluation. Multiple linear regression (MLR) and artificial neural network (ANN) were applied to predict apple crispness. The results proved that crispness correlates significantly (P < 0.01) with four acoustic eigenvalues, including waveform index, sound intensity, energy of low frequency and energy of high frequency. The average relative error of apple crispness predicted by ANN was 1.42 ± 1.9%, remarkably lower (P < 0.01) that of MLR (6.79 ± 5.64%), implying that the model predicted by ANN is more accurate than that of MLR.     

Instrumental and sensory evaluation of crispness: I—In friable foods

Using the Constant Loading Rate Texture Testing instrument it was possible to extract from the deformation/time and force/time curves together with the sound emitted from the samples during the test, those variables included in both the definition of crispness and the assumption that in the case of friable foods crispness may be regarded as ‘compressive brittleness’.Significant correlations (P<0·01) were obtained between sensory crispness and the instrumental variables: equivalent sound level L_eq (r = 0·701), ratio of work done during fracture to total work W_F/W_T (r = 0·878) and fracture or collapse rate (r = 0·570).A poor correlation between sensory crispness and both sensory hardness (r = 0·398) and instrumental fracture force (r = 0·018) indicates that a hard friable food material is not necessarily a crisp one, whereas the highly significant correlation between sensory crispness and both sensory sound intensity (r = 0·908) and instrumental L_eq (r = 0·701) indicates the importance of sound in the sensory and instrumental evaluations.The results of regression analysis showed a number of almost equally well fitting equations with the ratio W_F/W_T and L_eq as the dominant variables in predicting sensory crispness in friable foods.     

Understanding potato chips crispy texture by simultaneous fracture and acoustic measurements, and sensory analysis

The fracture and acoustic properties of six commercial potato chips that differ in sensory hardness and sensory crispness were analysed and related in this work. Principal component analysis showed a correlation among the sensory attributes and the instrumental parameters (both mechanical and acoustic). Two components mainly explained the behaviour of the different potato chips. The first component was positively related to the number of force and sound events, to sound pressure level maximum, to the area under the force curve, and to sensory crispness, and negatively related to fat content; and the second component was positively related to the gradient (slope of the first part of the curve), the potato chip thickness, and to sensory hardness and sensory crispness. The behaviour of the different potato chips was explained by either one of the two components or by both components. Results indicate that certain degree of sensory hardness is necessary for higher crispness perception.     

Correlating Objective Measurements of Crispness in Breaded Fried Chicken Nuggets with Sensory Crispness

ABSTRACT: Crispness is one of the most distinctive textural characteristics of dry crisp and fried products. This study was undertaken with the following objectives: to determine ultrasonic parameters and mechanical properties and to investigate the relationships between the instrumental parameters and sensory crispness obtained using a trained panel for chicken nuggets. Significant differences in ultrasonic velocity, transmission loss, peak force, and total energy were observed among the samples cooked by different methods. Ultrasonic velocity had high correlation with sensory crispness. This indicates that the ultrasonic velocity could predict sensory crispness.     

ACOUSTIC TESTING OF SNACK FOOD CRISPNESS USING NEURAL NETWORKS

The crispness of selected snack foods, Pringles potato chips, Paprika extruded snack and Munchy crackers, was evaluated by crushing the snack samples with a pair of pincers, and analyzing the frequency domain spectra of acoustic patterns by the neural networks (NNs). The inputs for training the NNs comprised 102 amplitudes of sound signals in 0–7 kHz frequency range at the intervals of about 69 Hz with moisture content or crispness grades as outputs. Both backpropagation (BPNN) and probabilistic (PNN) models showed good performance in classifying the snack foods into four grades of sensory crispness. The prediction accuracy of PNN models ranged approximately from 96 to 98% and was higher than the accuracy of BPNN models by about 10 to 25%. Results showed that frequency domain spectra of acoustic signals could be successfully analyzed by the NNs for predicting the crispness of snack food products.     

Sensory and instrumental measurement of food crispness: II — In a high moisture food

Preliminary attempts to compress cylinders of apple tissue under constant loading rate to obtain catastrophic breakdown of the samples were unsuccessful. Puncture and ‘biting’ tests, where the shear component was dominant, yielded some useful results. Highly significant correlations (p < 0·001) were obtained between sensory crispness and sensory hardness and between sensory crispness and most of the physical measurements obtained when using these two tests. The measurements chosen for the regression analysis (equivalent sound level, fracture rate and work done during fracture) were those thought to be of significance in the sensory measurement and included in the definition of crispness.The results of the regression analysis show that both the ‘biting’ and puncture tests produced a number of almost equally-well-fitting equations with the equivalent sound level and the work done during fracture as the dominant variables in predicting sensory crispness in a high moisture food. The regression equations of the ‘biting’ test always tend to give a better prediction of crispness (higher values of R²) than the respective puncture test equations.The values of the maximum sound levels in octave bandwidths over the audible range for the fresh and stored apples show the same broad spectrum, with the stored samples producing, in general, less sound at all frequencies, significantly so for frequencies greater than 3 kHz.     

Relationship of carrot sensory crispness with acoustic signal characteristics

The purpose of this study was to investigate the relationships of carrot sensory crispness with acoustic signal characteristics and microstructure after storage under common shelf conditions. The results showed that carrot sensory crispness declined significantly after the 14th day of storage, and the sensory score reduction was nearly up to 42% after the 28th day of storage. The carrot sensory crispness was positively related to both the waveform index (r = 0.785, P < 0.01) and sound intensity (r = 0.732, P < 0.01) of the acoustic signals in the time domain. Hilbert–Huang transform is a useful method for the observation of frequency changes in the time domain. The overall frequency bands were distributed around 200–2000 Hz through the Hilbert spectra. Rupture did not occur until the 14th day (crispness declined significantly) of storage through the scanning electron microscopy images of microstructure, and the crispness declined gradually with the increase of rupture.     

INSTRUMENTAL MEASURES OF BITE FORCES ASSOCIATED WITH CRISP PRODUCTS

The usefulness of a bite force apparatus for measuring bite forces generated while biting extruded crisp snacks was assessed. Snacks were produced using a twin‐screw extruder, and their textural properties were modified by equilibrating to water activities ranging from 0.11 to 0.44. Maximum force to bite, stress, duration and area under the bite force curve were analyzed and compared to perceived sensory crispness, hardness, brittleness and sound duration evaluated by eight individuals. Maximum bite force and stress were significantly related to hardness, brittleness and crispness showing for the first time, and the relationship between sensory crispness of extruded crisp products and hardness was measured intraorally.     

Consumer perception of crispness and crunchiness in fruits and vegetables

Crispy and crunchy textures are desirable qualities and contribute to our enjoyment of foods, and this is particularly true in the case of fruits and vegetables. However, most of the literature on crispness and crunchiness focuses on dry foods, and little work has been carried out on wet foods such as fruits and vegetables. The study presented here was designed to address two main issues: firstly, to gain a better understanding of consumer terminology relevant to the textural characteristics of fruits and vegetables, with particular emphasis on crispy and crunchy terms; secondly, to investigate the relationship between the vocabularies used by trained panels and consumers. Products were selected to give a wide range of textural characteristics. Free choice profiling was carried out with a small number of naı̈ve consumers, as well as with trained panellists. Both sets of data were analysed using generalised Procrustes analysis (GPA), which allowed comparison of the two sets of vocabularies generated. GPA maps showed that sensory perceptions expressed by a trained panel correlated well with consumer terminology. The study also reinforced the idea that crispness and crunchiness are complex concepts that combine a wide range of perceptions such as fracture characteristics, sound, density and geometry. Results showed a better agreement on the meaning and use of crunchy terms by consumers and panellists alike. On the other hand, crispness was less well understood, showing a much wider conceptual space.     

CRISPNESS AND CRUNCHINESS JUDGMENT OF APPLES BASED ON CONTACT ACOUSTIC EMISSION

Contact acoustic emission (AE) measurement, together with a puncture test, were used to evaluate the crispness and crunchiness of three apple cultivars. A storage program was used to induce changes in apple crispness and crunchiness. The instrumental measurements were compared with the results of sensory evaluation.
It was found that crispness and crunchiness could be evaluated instrumentally by measuring firmness, AE events, mean AE amplitude or the product of AE events and mean AE amplitude. For one cultivar, however, firmness was not correlated with any sensory attribute. Generally, the best correlation with sensory attributes was found for the exponential value of AE events. Contact AE allows the measurement of sensory crispness or crunchiness for a given apple cultivar or independently for any apple cultivar. Contact AE can be considered as a useful method for the evaluation of sound properties of wet-crisp foods.

PRACTICAL APPLICATIONS

Contact acoustic emission method can be used for instrumental evaluation of crispness and crunchiness of apples. The method allows more accurate evaluation of these attributes than firmness measurement. There is a chance to extend the application to other wet-crisp foods and to use the method together with other destructive tests, like texture profile analysis or single edge notched bend.     

ANALYSIS OF FOOD CRUSHING SOUNDS DURING MASTICATION: TOTAL SOUND LEVEL STUDIES 1

The total sound level of airborne noise was measured continuously during consecutive chews of fresh and stale samples of potato chips and tortilla chips. Sensory estimations of crispness were also made. Samples were evaluated using both an open mouth and a closed mouth procedure. Analysis showed that the total sound level decreased as chewing progressed. Significant differences were observed between open mouth and closed mouth chewing, with the former displaying higher sound levels. However, there were no statistical differences in the sound levels between fresh and stale samples. Sensory assessments ofcrispness showed decreasing crispness as chewing progressed. Statistical differences were observed in the sensory evaluations between fresh and stale samples. Openmouth sensory evaluations were significantly different from closed-mouth evaluations for potato chips but not for tortilla chips. Good correlations were observed between the acoustic measurements and the sensory estimations, in agreement with the work of other investigators.     

Sensory, Acoustical, and Force-Deformation Measurements of Potato Chip Crispness

The auditory (sound only) and oral (normal biting and chewing) crispness of five different potato chip products, each adjusted to two moisture contents, were measured. Instrumental acoustical and force-deformation measurements were made on these same samples and compared with the sensory crispness measurements. Oral crispness judgments appeared to be made on the basis of both auditory and oral tactile sensations, since auditory crispness was not highly correlated to oral crispness. A combination of acoustical and force-deformation measurements provided an excellent measure of oral chip crispness.     

Prediction of consumer liking from trained sensory panel information: Evaluation of neural networks

This study was set out to establish artificial neural networks (ANN) as an alternative to regression methods (multiple linear, principal component and partial least squares regression) to predict consumer liking from trained sensory panel data. The sensory profile and acceptability of 10 market samples of beef bouillon products were measured. The products were distinct as evaluated by the trained sensory panel. A total of 100 regular beef bouillon product users from Manila measured overall liking, flavour, aftertaste and mouthfeel of the products. Curve fitting method was applied to identify sensory drivers of consumer liking. The sensory drivers of consumer liking were used as explanatory variables in artificial neural networks and regression methods. To overcome the limitations of regression methods we have used artificial neural network techniques to model consumer liking score as a function of trained sensory panel scores and achieved quite encouraging results. Our simulation experiments show that though the regression methods such as multiple linear regression (MLR), principal component regression (PCR) and partial least square (PLS) give an accurate prediction of consumer liking scores, this approach is not robust enough to handle the variations normally encountered in trained sensory panel data. ANNs were trained using the sensory panel raw data and transformed data. The networks trained with sensory panel raw data achieved 98% correct learning, the testing was in a range of 28–35%. Suitable transformation method was applied to reduce the variations in trained sensory panel raw data. The networks trained with transformed sensory panel data achieved about 80–90% correct learning and 80–95% correct testing. It is shown that due to its excellent noise tolerance property and ability to predict more than one type of consumer liking using a single model, the ANN approach promises to be an effective modelling tool.     

Understanding and predicting sensory crispness of deep‐fried battered and breaded coatings

Crusted crispness refers to coatings with a dry and brittle surface contrasting a high‐moisture core; it is desirable for the enjoyment and quality of deep‐fried goods. This study aims to investigate instrumental measurements and sensory measurements of crispness. Deep‐fried breadcrumb coatings of eight sizes were investigated: 4.0 mm, 2.8 mm, 2.0 mm, 1.4 mm, 1.0 mm, 710 μm, 500 μm, and 355 μm. Sensory profiling was carried out to develop a tailored lexicon for deep‐fried battered and breaded shrimp. Principal component analysis highlights that large breadcrumb sizes correlate with crispness, hardness, particle size, surface color, color uniformity, surface irregularity, total porosity, maximum force, area, drop in force, number of sound peaks, and sound pressure level. Agglomerative hierarchical clustering was used to confirm clustering of samples according to breadcrumb size. Multiple factor analysis confirmed overall correlation between sensory measurements and instrumental measurements (RV = 0.810). Partial least squares regression was used to develop a predictive model for crispness from instrumental measurements (R² = .854). The use of texture analysis and Acoustics provide information of the structures strength and deformation behavior, while X‐ray microCT provides a high resolution and noninvasive method that acquires information on the internal morphology. These instrumental methods collectively demonstrate the relationship between microstructure to sensory. This study investigates how a change in the microstructure of deep‐fried battered and breaded coatings affect crispness perception. These changes were investigated analytically and by using a sensory panel, this is important from a manufacturing perspective in order to understand what the major contributors are to a crisp texture. The key highlights of this study include both instrumental measurements and sensory measurements can be used to measure crispness as both types of testing are correlated. Changes in the size of breadcrumbs affect both instrumental measurements and sensory measurements. A predictive model can be re‐simulated to allow prediction of crispness in deep‐fried battered and breaded coatings.     

Image texture features as indicators of beef tenderness

Image processing techniques were developed to predict cooked-beef tenderness from fresh-beef image characteristics. Cattle from different finishing treatments were processed in a commercial plant. Two short loin steaks were sampled from each carcass; one used for sensory evaluation and the other for imaging. The samples varied significantly in both US quality grades and sensory tenderness scores. Color, marbling and texture features were extracted from the beef images. Statistical and neural network analyses were performed to relate the image features to sensory tenderness scores. Image texture features were found to be useful indicators of beef tenderness. Partial least squares and neural network models were able to predict beef tenderness from color, marbling and image texture features to R(2)-values up to 0.70.     

An application of the ultrasonic pulse echo technique to the measurement of crispness of biscuits

The possibility of developing an objective method of measuring the crispness of biscuits using the ultrasonic pulse echo technique was investigated. An encouraging relationship between the crispness (from sensory measurement) and the velocity of longitudinal sound was found. A similar, but less convincing relationship was found between crispness and ultra sonic attenuation.
The ultrasonic velocity shows a better correlation with crispness than does the ultrasonically derived Young's modulus.     

Predicting the sensory crispness of coated turkey breast by its acoustic signature

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.     

Evaluation of pork color by using computer vision

The objective of this study was to determine the potential of computer vision technology for evaluating fresh pork loin color. Software was developed to segment pork loin images into background, muscle and fat. Color image features were then extracted from segmented images. Features used in this study included mean and standard deviation of red, green, and blue bands of the segmented muscle area. Sensory scores were obtained for the color characteristics of the lean meat from a trained panel using a 5-point color scale. The scores were based on visual perception and ranged from 1 to 5. Both statistical and neural network models were employed to predict the color scores by using the image features as inputs. The statistical model used partial least squares technique to derive latent variables. The latent variables were subsequently used in a multiple linear regression. The neural network used a back-propagation learning algorithm. Correlation coefficients between predicted and original sensory scores were 0.75 and 0.52 for neural network and statistical models, respectively. Prediction error was the difference between average sensory score and the predicted color score. An error of 0.6 or lower was considered negligible from a practical viewpoint. For 93.2% of the 44 pork loin samples, prediction error was lower than 0.6 in neural network modeling. In addition, 84.1% of the samples gave an error lower than 0.6 in the statistical predictions. Results of this study showed that an image processing system in conjunction with a neural network is an effective tool for evaluating fresh pork color.     

Texture of Pecans Measured by Sensory and Instrumental Means

The texture of pecans (Carya illinoensis) from four cuhivars (Barton, Mahan, Western and Wichita) was analyzed using sensory and instrumental methods. Sensory hardness, flexibility and crispness were rated by trained panelists. Ranking of hardness and crispness was also carried out. A Texture Analyser TAXT2 was used for 50% compression, texture profile analysis (TPA), puncture and bending. Puncture and 50% compression gave best reproducibility, least variability and agreement with sensory data. The most relevant TPA parameters were cohesiveness, elasticity and fracturabihty. Parameters from the bending test did not indicate texture of the pecans suitably.