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The freezing point (FP) is an important quality indicator of the superchilled meat. Currently, the potential of hyperspectral imaging (HSI) for predicting beef FP as affected by multiple freeze-thaw (F-T) cycles was explored. Correlation analysis revealed that the FP had a negative correlation with the proportion of bound water (P 21 ) and a positive correlation with the proportion of immobilized water (P 22 ). Moreover, the optimal wavelengths were selected by principal component analysis (PCA). Principal component regression (PCR) and partial least squares regression (PLSR) models were successfully developed based on the optimal wavelengths for predicting FP with determination coefficient in prediction (R P 2 ) of 0.76, 0.76 and root mean square errors in prediction (RMSEP) of 0.12, 0.12, respectively. Additionally, PLSR based on full wavelengths was established for predicting P 21 with R P 2 of 0.80 and RMSEP of 0.67, and PLSR based on the optimal wavelengths was established for predicting P 22 with R P 2 of 0.87 and RMSEP of 0.66. The results show the potential of hyperspectral technology to predict the FP and moisture distribution of meat as a nondestructive method., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.

Effects of food microstructure and processing methods on moisture migration in food during processing are important for improving product quality. This study evaluated the effects of fibre directions and dehydration methods on beef dehydration and rehydration behaviours using near-infrared hyperspectral imaging. Beef slices (transversal cut) and splits (longitudinal cut) were dehydrated by hot air drying (HAD) and microwave vacuum drying (MVD). Results indicated the moisture in the slices evaporated faster than that in the splits during HAD and MVD processed samples exhibited higher rehydration rates than HAD ones. Principal component analysis showed great performance in clustering samples based on fibre directions, processing methods and time. The established simplified RC-SG-PLSR and RC-2nd Der-PLSR models possessed good prediction accuracies for moisture (R 2 p  = 0.994) and rehydration rate (R 2 p  = 0.889), respectively. Chemical distribution maps also confirmed the influence of fibre directions and dehydration methods on moisture migration in samples., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

[Display omitted] • SVC, RF and CNN models were built to distinguish beef of different states. • RFE-RF and FI-RF were used to select the optimal characteristic wavelengths. • Different fusing spectra and texture methods based on CNN were proposed and compared. • Visualisation of beef in different states was achieved. In this study, hyperspectral imaging (HSI) technology combined with a convolutional neural network (CNN) was used to distinguish fresh and frozen-thawed beef samples. After obtaining hyperspectral data of beef with different freezing/thawing cycles, the CNN was used to extract spectral features of all bands to compare with recursive feature elimination based on random forest and feature importance based on random forest. Then, eight characteristic wavelengths extracted by the first derivative-feature importance based on the random forest were used to establish the CNN model with an accuracy of 86.11%. Textural features of beef were used in the CNN model with early feature fusion of spectra and texture and late feature fusion of spectra and texture, and the CNN model using early feature fusion of spectra and texture showed more excellent results with an accuracy of 88.89%. Finally, beef samples in different states were well visualised. The research in the current study should provides a potential detection method for non-destructive and rapid tracing beef of different states. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hyperspectral imaging (HSI) was first time used to study vacuum cooling (VC) process. • Quality attributes of mushroom were dynamically evaluated during (VC) by HSI. • Partial least squares regression models realized good prediction for the attributes. • Pseudocolour maps were developed to visualize the distribution of the attributes. • The study provides a rapid and real-time nondestructive detection strategy. Visible near-infrared (400–1000 nm) hyperspectral imaging technology (HSI) was first time used to evaluate changes in various quality attributes including moisture contents (MC), lightness (L*), yellowness (b*), hardness (H) and chewiness (ch) of Agaricus bisporus as affected by vacuum cooling (VC). Partial least squares regression (PLSR) models based on Savitzky-Golay (SG) smoothing combined with the first derivative (SG-1st der) spectral preprocessing achieved good results for predicting MC with R2 P of 0.823 and RMSEP of 0.341%. Similarly, PLSR models based on full wavelengths achieved good results for predicting L*, b* and H, and ch with R2 P of 0.796, 0.848 and 0.810, and 0.903, with RMSEP of 1.253, 0.808 and 9.254, and 2.717. Finally, pseudocolour maps were developed to visualize the distribution of these attributes during VC. The current study provides a rapid, real-time and non-destructive detection strategy for evaluating the VC process. [ABSTRACT FROM AUTHOR]

• MVD mushroom stipes showed higher moisture contents, ε ' and ε ' ' values than caps. • MVD mushroom caps displayed higher water evaporation and rehydration rates. • The centre area of the MVD mushroom slice had the highest temperature. • The more porous structure in the MVD cap led to its higher mass transfer rates. This research work aimed to investigate the effects of microstructures, dielectric property and temperature distributions on drying feature difference between the mushroom cap and stipe during the microwave-vacuum drying (MVD) process. Near-infrared hyperspectral imaging (NIR HSI) was employed to visualize distribution maps for moisture content (MC), dielectric constant ε ' and dielectric loss factor ε ' ' of mushroom slices during the MVD process. Infrared (IR) thermal imaging was used to evaluate the temperature distribution of the mushroom slices. Results demonstrated higher MC, ε ' and ε ' ' values in MVD mushroom stipes. Nevertheless, the centre area of the mushroom slice showed the highest temperature, while the MVD mushroom cap displayed a more porous structure. The effect of microstructure could encounter effects of dielectric properties and temperature to cause higher water evaporation in the MVD cap. This work highlights the novelty to combine different detection techniques to investigate the effects of microstructures, dielectric property and temperature distributions on drying patterns of mushroom slices. [ABSTRACT FROM AUTHOR]