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This study evaluated the differences in oral processing and texture perception of breads with varying compositions. The research investigated the dynamic changes in moisture content (MC), reducing sugars (RSs), and chewiness of the bolus formed from white bread (B0) and 50% whole-wheat bread (B50) during oral processing. Hyperspectral imaging (HSI) combined with chemometric methods was used to establish quantitative prediction models for MC, RSs, and chewiness, and to create visual distribution maps of these parameters. The results showed that B0 had a higher moisture content and a faster hydration rate than B50 during the initial stages of oral processing, indicating greater hydrophilicity and ease of saliva wetting. Additionally, the uniformity of moisture distribution in the bolus of B0 was higher than that of B50. B50 exhibited significantly lower RSs content and poorer distribution uniformity compared to B0. The primary differences in chewiness between the two types of bread were observed during the early stages of oral processing, with B50 requiring more chewing effort initially. This study demonstrated that HSI technology can effectively monitor and elucidate the compositional changes in food particles during oral processing, providing new insights into bread texture perception and offering a scientific basis for improving bread processing and texture.

In order to quickly and accurately determine the protein content of corn, a new characteristic wavelength selection algorithm called anchor competitive adaptive reweighted sampling (A-CARS) was proposed in this paper. This method first lets Monte Carlo synergy interval PLS (MC-siPLS) to select the sub-intervals where the characteristic variables exist and then uses CARS to screen the variables further. A-CARS-PLS was compared with 6 methods, including 3 feature variable selection methods (GA-PLS, random frog PLS, and CARS-PLS) and 2 interval partial least squares methods (siPLS and MWPLS). The results showed that A-CARS-PLS was significantly better than other methods with the results: RMSECV = 0.0336, R2c = 0.9951 in the calibration set; RMSEP = 0.0688, R2p = 0.9820 in the prediction set. Furthermore, A-CARS reduced the original 700-dimensional variable to 23 variables. The results showed that A-CARS-PLS was better than some wavelength selection methods, and it has great application potential in the non-destructive detection of protein content in corn.

Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.

S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401–1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R2P) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs.

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

In order to identify tea varieties quickly, efficiently, and nondestructively, an adaptive improved possibilistic c-means (AIPCM) clustering with the fuzzy Mahalanobis distance was proposed to classify the Fourier transform infrared reflectance (FTIR) spectra of tea samples. Three varieties of tea samples were scanned and FTIR spectra were acquired for 96 tea samples of different geographical origins using the FTIR- 7600 infrared spectrometer. Multiple scatter correction was employed to eliminate light scattering of FTIR spectral data. After that, principal component analysis and linear discriminant analysis were applied to reduce the dimensionality of FTIR spectra and extract the discriminant information, respectively. Then, the data were clustered by several fuzzy clustering algorithms. AIPCM had the highest clustering accuracy and its accuracy achieved up to 98.5%. The experimental results showed that FTIR spectroscopy coupled with AIPCM clustering was superior in classification of tea varieties. [ABSTRACT FROM AUTHOR]

In this study, impact of crystal size on salt (NaCl) uptake and water activity (aw) of dry‐cured pork was investigated by hyperspectral imaging. Three grades of NaCl labeled fine flake, medium flake, and large flakes were used during the 3 day salting period. Spectra and spatial information were extracted from three subsurfaces labeled surface‐1, surface‐2, and surface‐3 for each sample and prediction models were established based on partial least squares regression, synergy interval partial least squares, and competitive adaptive reweighted sampling partial least squares. The overall results indicated that processing with NaCl of different crystal sizes influenced NaCl uptake and aw of pork samples. Distribution maps based on spatial analysis further revealed that crystal size impacted NaCl uptake to a large extent. Thus, in the drive to process meat with lower NaCl content, crystal morphology may be considered in further studies in dry‐cured meat processing. Practical applications: Meat preservation with salt (NaCl) is a widely applied traditional technique of safe‐guiding meat from microbial invasion because of its effectiveness and the ease of accessing the commodity. Yet, several studies have associated noncommunicable diseases, including hypertension, cardiovascular disease, and stroke to the high intake of NaCl. As a result, several strategies are being developed for reducing NaCl contents in processed foods in a bid to avert these health‐related concerns. Some of these strategies involved studying the dissolution characteristics of salt crystals in saliva, brining with high‐powered ultrasound, among others. Dry‐cured pork is a worldwide delicacy. Thus, for the purposes of understanding how crystal size influence total NaCl levels in dry‐cured pork, this investigation was carried out. The outcome of this study may be considered in the formulation of future research needs in the drive to achieving sodium reduction in salted meat while maintaining quality and salt intensity. [ABSTRACT FROM AUTHOR]

As one of the most important indexes of internal quality testing of fruit, soluble solids content (SSC) is significant for its rapid and efficient nondestructive testing by using near infrared reflectance spectroscopy (NIRS). In this article, 126 cherry tomatoes were selected as the research object. Reflectance spectra data of 228 bands in cherry tomatoes were acquired by the near infrared spectrometer and SSC was measured by the hand‐held refractometer. Savitzky–Golay (SG) combined with multiplicative scatter correction (MSC) was used to preprocess the spectral data to reduce the effects of light scattering and other noise. Then, the dimensions of spectral data were reduced by iteratively retaining informative variables (IRIV) algorithm and 10 characteristic wavelengths were obtained, which were 1,080.37, 1,113.62, 1,117.3, 1,297.57, 1,301.02, 1,538.32, 1,540.40, 1,590.72, 1,615.94, and 1,636.89 nm, respectively. Subsequently, support vector regression (SVR) and its two optimization models, PSO‐SVR and CS‐SVR, were respectively used to establish SSC prediction models based on full spectra and characteristic spectra. The experimental results showed the IRIV‐CS‐SVR model for SSC prediction achieved the accuracy with RP2 of 0.9718 and RC2 of 0.9845. Thus, it is feasible to use NIRS with IRIV‐CS‐SVR to make a rapid and efficient nondestructive SSC prediction of cherry tomatoes. Practical applications: As one of the important testing standards of fruit internal quality, SSC is of great significance for the rapid and efficient nondestructive testing. In this article, an iteratively retaining information variables (IRIV) algorithm is proposed to extract the characteristic wavelengths, and a regression model CS‐SVR is established by combining the optimization algorithm cuckoo search (CS). This study shows that the model IRIV‐CS‐SVR has a certain effect on SSC prediction of cherry tomatoes. [ABSTRACT FROM AUTHOR]

In order to effectively implement the rapid and nondestructive testing of pesticide residues in lettuce leaves, polarization spectral detection technology was used in this article. There were 90 pieces of lettuce leaves from five different groups, as well as a total of 450 samples of lettuce were used to collect polarization spectral information. CARS, IRIV, and SPA were used to obtain optimal wavelengths. BP neural network, KNN, and SVM were used to establish classification models. The results showed that the significance of these parameters from primary factor to secondary factor were incident zenith angle A, detector polarization angle D, detecting zenith angle B, and sample stage azimuth E, respectively. Besides, optimal level of A, B, D, and E were 60°, 45°, 30°, and 270°, respectively. Furthermore, the best classification model different kinds of pesticide residues in lettuce leaves was CARS‐SVM model, with calibration identification rate of 100%, prediction identification rate of 97.78%. It confirms that polarization spectral detection technology is a feasible and effective method for classifying different pesticide residues in lettuce leaves. Practical applications: It is of great significance to understand the effects of pesticide residues on the biological structure and to reveal new biological functions and mechanisms of action. In order to effectively implement the rapid and nondestructive testing of pesticide residues in lettuce leaves, polarization spectral detection technology was used in this article. In addition, the order and superior level of polarization spectral factor were obtained through the calculation of the range using orthogonal test. It confirms that the polarization spectra is a feasible and effective method for discriminating different pesticide residues in lettuce leaves. [ABSTRACT FROM AUTHOR]