Rapid determination of TBARS content by hyperspectral imaging for evaluating lipid oxidation in mutton

Chitosan-based packaging films with antibacterial-sterilization integrated continuous activity for extending the shelf life of perishable foods

The current food packaging films can be preservative but lack the function of combining antibacterial and sterilization which lead to films can not maximize prolong shelf life of perishable foods. This study provided a new strategy to realize prolonging shelf life of perishable foods by integrating antibacterial and sterilization which focused on applying photodynamic inactivation to films with continuous activity, where curcumin (CUR) and sodium copper chlorophyll (SCC) were loaded into chitosan (CS) films. Compared to pure CS films, the barrier capacity (oxygen permeability and water vapor permeability) and mechanical properties of composite films were improved by introducing CUR and SCC. In addition, the composite film can effectively against food-borne pathogenic bacteria and significantly prolong the shelf life of cherries and pork. The provided strategy has potential application prospects in food preservation packaging.

A new comprehensive quantitative index for the assessment of essential amino acid quality in beef using Vis-NIR hyperspectral imaging combined with LSTM

The quality of beef is usually predicted by measuring a single index rather than a comprehensive index. To precisely determine the essential amino acid (EAA) contents in 360 beef samples, the feasibility of optimized spectral detection techniques based on the comprehensive EAA index (CEI) and comprehensive weight index (CWI) constructed by factor analysis was explored. Two-dimensional correlation spectroscopy (2D-COS) was used to analyse the mechanisms of spectral peak shifts in complex disturbance systems with CEI and CWI contents, and 15 sensitive feature variables were extracted to establish a quantitative analysis model of a long short-term memory network (LSTM). The results indicated that 2D-COS had good predictive performance in both CEI-LSTM (R2P of 0.9095 and RPD of 2.76) and CWI-LSTM (R2P of 0.8449 and RPD of 2.45), which reduced data information by 88%. This indicates that utilizing 2D-COS can eliminate collinearity and redundant information among variables while achieving data dimensionality reduction and simplification of calibration models. Furthermore, a spatial distribution map of the comprehensive EAA content was generated by combining the optimal prediction model. This study demonstrated that the comprehensive index method furnishes a new approach to rapidly evaluate EAA content.

Evaluating the effects of graphene nanoparticles combined radio-frequency thawing on the physicochemical quality and protein conformation in hairtail (Trichiurus lepturus) dorsal muscle

BACKGROUND

The thawing process is an essential step for a frozen marine fish. The present study aimed to investigate the effects of graphene magnetic nanoparticles combined radio-frequency thawing methods on frozen hairtail (Trichiurus lepturus) dorsal muscle. Seven thawing methods were used: air thawing, 4 °C cold storage thawing, water thawing, radio-frequency thawing (RT), radio frequency thawing combined with graphene nanoparticles (G-RT), radio frequency thawing combined with graphene oxide nanoparticles (GO-RT) and radio-frequency thawing combined with graphene magnetic nanoparticles (GM-RT). The thawing loss and centrifugal loss, electric conductivity, total volatile basic nitrogen, thiobarbituric acid reactive substances and color of thawed hairtail dorsal muscle were determined. The carbonyl content, total sulfhydryl groups, Ca2+ -ATPase activity, raman spectroscopy measurements and Fourier-transform infrared spectrometry measurements were determined using myofibrillar extracted from the dorsal muscle of hairtail. The water distribution was determined using low-field NMR techniques.

RESULTS

The results demonstrated that the RT, G-RT, GO-RT and GM-RT could significantly shorten the thawing time. Moreover, GO-RT and GM-RT efficiently preserved the color of fish dorsal muscle and reduced the impact of thawing on fish quality by reducing lipid and protein oxidation. Meanwhile, the myofibrillar protein structure thawed by GO-RT and GM-RT were more stable and had a more stable secondary structure, which maintained strong systemic stability at the same time as slowing down protein oxidation.

CONCLUSION

The results showed that GO-RT and GM-RT can significantly improve the thawing efficiency at the same time as effectively maintaining and improving the color and texture of thawed fish, slowing down the oxidation of proteins and lipids, and maintaining a good quality of thawed fish meat. © 2023 Society of Chemical Industry.

Slightly acidic electrolyzed water as a novel thawing media combined with ultrasound for improving thawed mutton quality, nutrients and microstructure

The effects of ultrasound-assisted slightly acidic electrolyzed water thawing (UET), air thawing (AT), water thawing (WT) and microwave thawing (MT) on the quality, nutrients and microstructure were investigated. The UET treatment did not affect the lightness (L*) but reduced the redness (a*) and yellowness (b*) of the mutton. The UET treatment could better maintain the textural properties. The UET group had a higher immobilized water and lower free water, which was closer to the state of the control group. The UET treatment not only effectively inhibited the lipid oxidation but also reduced the loss of nutrients, especially minerals. The microstructure of the UET group was smoother and more complete, and the muscle fibers did not show significant breakage. In conclusion, UET treatment could better maintain the quality, nutrients and microstructure of thawed mutton. Therefore, UET could be regarded as a potential thawing method for application in the processing of meat products.

Nondestructive discrimination of analogous density foreign matter inside soy protein meat semi-finished products based on transmission hyperspectral imaging

Analogous density foreign matter (ADFM) embedded in soy protein meat semi-finished (SFSPM) is hidden by SFSPM and has similar acoustic impedance features to SFSPM, which makes non-destructive testing techniques such as computer vision (CV), reflectance spectroscopy and ultrasound imaging inappropriate for ADFM, which not only seriously affects the quality of soy protein meat (SPM) products but also increases the safety risk to consumers. In this study, to locate and separate ADFM by using transmission hyperspectral imaging (T-HSI) technique which is sensitive to chemical composition and highlight internal contours. The optimal discrimination model SVM + PCA + MSC + SPA was constructed using transmission spectral information with an accuracy of 95.00 %. The visualization results based on the optimal model showed clearer localization results than CV and ultrasound imaging. The study demonstrated that the advantages of T-HSI technology in detecting and locating ADFM inside SFSPM, which provides a basis for improving the production quality and safety of SPM.

Rapid screening of benzyl dodecyl dimethyl ammonium bromide co-metabolic degrading bacteria based on NIR hyperspectral imaging technology

As a typical disinfectant, the use of benzyl dodecyl dimethyl ammonium bromide (BDAB) has dramatically increased since the emergence of SARS-CoV-2, posing a threat to environmental balance and human health. Screening BDAB co-metabolic degrading bacteria is required for efficient microbial degradation. Conventional methods for screening co-metabolic degrading bacteria are laborious and time-consuming, especially when the number of strains is large. This study aimed to develop a novel method for the rapid screening of BDAB co-metabolic degrading bacteria from the cultured solid medium using near-infrared hyperspectral imaging (NIR-HSI) technology. Based on NIR spectra, the concentration of BDAB in the solid medium can be well predicted by partial least squares regression (PLSR) models, non-destructively and rapidly, with R_c² > 0.872 and R_cv² > 0.870. The results show that the predicted BDAB concentrations decrease after degrading bacteria utilization, comparing with the regions where no degrading bacteria grew. The proposed method was applied to directly identify the BDAB co-metabolic degrading bacteria cultured on the solid medium, and two kinds of co-metabolic degrading bacteria RQR-1 and BDAB-1 were correctly identified. This method provides a high-efficiency method for screening BDAB co-metabolic degrading bacteria from a large number of bacteria.

Multi-task convolutional neural network for simultaneous monitoring of lipid and protein oxidative damage in frozen-thawed pork using hyperspectral imaging

Lipid and protein oxidation are the main causes of meat deterioration during freezing. Traditional methods using hyperspectral imaging (HSI) need to train multiple independent models to predict multiple attributes, which is complex and time-consuming. In this study, a multi-task convolutional neural network (CNN) model was developed for visible near-infrared HSI data (400-1002 nm) of 240 pork samples treated with different freeze-thaw cycles (0-9 cycles) to evaluate the feasibility of simultaneously monitoring lipid oxidation (thiobarbituric acid reactive substance content) and protein oxidation (carbonyl content) in pork. The performance of the commonly used partial least squares regression (PLSR) model based on the spectra after pre-processing (Standard normal variate, Savitzky-Golay derivative, and Savitzky-Golay smoothing) and feature selection (Regression coefficients) and single-output CNN model was compared. The results showed that the multi-task CNN model achieved the optimal prediction accuracies for lipid oxidation (R²p = 0.9724, RMSEP = 0.0227, and RPD = 5.2579) and protein oxidation (R²p = 0.9602, RMSEP = 0.0702, and RPD = 4.6668). In final, the changes of lipid and protein oxidation of pork in different freeze-thaw cycles were successfully visualized. In conclusion, the combination of HSI and multi-task CNN method shows the potential of end-to-end prediction of pork oxidative damage. This study provides a new, convenient and automated technique for meat quality detection in the food industry.

Combination of hyperspectral imaging and entropy weight method for the comprehensive assessment of antioxidant enzyme activity in Tan mutton

The antioxidant enzymes play the crucial role in inhibiting mutton spoilage. In this study, visible near-infrared (Vis-NIR) hyperspectral imaging (HSI) combined with entropy weight method (EWM) was developed for the first time to evaluate the antioxidant properties of Tan mutton. The comprehensive index of antioxidant enzymes (AECI) consisting of peroxidase (49.34%), catalase (37.97%) and superoxidase (12.69%) was constructed by the EWM. Partial least squares regression, least squares support vector machine and artificial neural networks (ANN) were developed based on characteristic wavelengths extracted by successful projections algorithm, uninformative variable selection, iteratively retains informative variables (IRIV), regression coefficient and competitive adaptive reweighted sampling (CARS). The textural features (TF) were extracted by the gray level co-occurrence matrix and fused with the spectral data to establish models. Visualization of the changes in antioxidant enzyme activity was constructed from the optimal model. In addition, two-dimensional correlation spectra (2D-COS) with AECI as a perturbation variable was used to identify spectral features, revealing chemical bond changes order under the characteristic peaks at 612-799-473-708-559 nm. The results showed that the IRIV-CARS-TF-ANN model performed the best, with prediction set coefficient of determination (R_P²) of 0.8813, which improved 2.12%, 1.11% and 2.77% over the R_P² of full band, IRIV and IRIV-CARS, respectively. It was suggested that fusion data of HSI may effectively predict the activity of antioxidant enzymes in Tan mutton.

Bayesian Fusion Model Enhanced Codfish Classification Using Near Infrared and Raman Spectrum

In this study, a Bayesian-based decision fusion technique was developed for the first time to quickly and non-destructively identify codfish using near infrared (NIRS) and Raman spectroscopy (RS). NIRS and RS spectra from 320 codfish samples were collected, and separate partial least squares discriminant analysis (PLS-DA) models were developed to establish the relationship between the raw data and cod identity for each spectral technique. Three decision fusion methods: decision fusion, data layer or feature layer, were tested and compared. The decision fusion model based on the Bayesian algorithm (NIRS-RS-B) was developed on the optimal discrimination features of NIRS and RS data (NIRS-RS) extracted by the PLS-DA method whereas the other fusion models followed conventional, non-Bayesian approaches. The Bayesian model showed enhanced classification metrics (92% sensitivity, 98% specificity, 98% accuracy) that were significantly superior to those demonstrated by any of other two spectroscopic methods (NIRS, RS) and the two data fusion methods (data layer fused, NIRS-RS-D, or feature layer fused, NIRS-RS-F). This novel proposed approach can provide an alternative classification for codfish and potentially other food speciation cases.

Non-destructive prediction of yak meat freshness indicator by hyperspectral techniques in the oxidation process

This study examined the potential of hyperspectral techniques for the rapid detection of characteristic indicators of yak meat freshness during the oxidation of yak meat. TVB-N values were determined by significance analysis as the characteristic index of yak meat freshness. Reflectance spectral information of yak meat samples (400-1000 nm) was collected by hyperspectral technology. The raw spectral information was processed by 5 methods and then principal component regression (PCR), support vector machine regression (SVR) and partial least squares regression (PLSR) were used to build regression models. The results indicated that the full-wavelength based on PCR, SVR, and PLSR models were shown greater performance in the prediction of TVB-N content. In order to improve the computational efficiency of the model, 9 and 11 characteristic wavelengths were selected from 128 wavelengths by successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. The CARS-PLSR model exhibited excellent predictive power and model stability.

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near-infrared hyperspectral imaging (NIR-HSI) combined with two-dimensional correlation spectroscopy (2D-COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D-COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH₂ in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136-1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D-COS analysis, and provided excellent results (R²_C of 0.8141, R²_P of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D-COS combined with NIR-HSI could be used as an effective method to monitor Ala content in beef.

Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications

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.

Effect of Traditional Stir-Frying on the Characteristics and Quality of Mutton Sao Zi

The effects of stir-frying stage and time on the formation of Maillard reaction products (MRP) and potentially hazardous substances with time in stir-fried mutton sao zi were investigated. Furosine, fluorescence intensity, Nε-(1-carboxymethyl)-L-lysine (CML), Nε-(1-carboxyethyl)-L-lysine (CEL), polyaromatic hydrocarbons PAHs), heterocyclic aromatic amines (HAAs), and acrylamides (AA) mainly presented were of stir-fried mutton sao zi. The furosine decreased after mixed stir-frying (MSF) 160 s due to its degradation as the Maillard reaction (MR) progressed. The fluorescent compound gradually increased with time during the stir-frying process. The CML and CEL peaked in MSF at 200 s. AA reached its maximum at MSF 120 s and then decreased. All the 5 HAAs were detected after MSF 200 s, suggesting that stir-frying mutton sao zi was at its best before MSF for 200 s. When stir-frying exceeded the optimal processing time of (MSF 160 s) 200 s, the benzo[a]pyrene peaked at 0.82 μg/kg, far lower than the maximum permissible value specified by the Commission of the European Communities. Extended stir-frying promoted MRP and some hazardous substances, but the content of potentially hazardous substances was still within the safety range for food.