Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for prediction of norfloxacin residues in mutton.

Norfloxacin is an antibacterial compound that belongs to the fluoroquinolone family. Currently, hyperspectral imaging (HSI) for the detection of antibiotic residues focuses mostly on individual systems. Attempts to integrate different HSI systems with complementary spectral ranges are still lacking. This study investigates the feasibility of applying data fusion strategies with two HSI techniques (Visible near-infrared and near-infrared) in combination to predict norfloxacin residue levels in mutton. Spectral data from the two spectral techniques were analyzed using partial least squares regression (PLSR), support vector regression (SVR) and stochastic configuration networks (SCN), respectively, and the two data fusion strategies were fused at the data level (low-level fusion) and feature level (middle-level fusion, mid-level fusion). The results indicated that the modeling performance of the two fused datasets was better than that of the individual systems. Mid-level fusion data achieved the best model based on uninformative variable elimination (UVE) combined with SCN, in which the determination coefficient of prediction set (R2p) of 0.9312, (root mean square error of prediction set) RMSEP of 0.3316 and residual prediction deviation (RPD) of 2.7434, in comparison with all others. Therefore, two HSI systems with complementary spectral ranges, combined with data fusion strategies and feature selection, could be used synergistically to improve the detection of norfloxacin residues. This study may provide a valuable reference for the non-destructive detection of antibiotic residues in meat.

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.

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 (RP2) of 0.8813, which improved 2.12%, 1.11% and 2.77% over the RP2 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.

Identification of Near Geographical Origin of Wolfberries by a Combination of Hyperspectral Imaging and Multi-Task Residual Fully Convolutional Network.

Ningxia wolfberry is the only wolfberry product with medicinal value in China. However, the nutritional elements, active ingredients, and economic value of the wolfberry vary considerably among different origins in Ningxia. It is difficult to determine the origin of wolfberry by traditional methods due to the same variety, similar origins, and external characteristics. In the study, we have for the first time used a multi-task residual fully convolutional network (MRes-FCN) under Bayesian optimized architecture for imaging from visible-near-infrared (Vis-NIR, 400-1000 nm) and near-infrared (NIR-1700 nm) hyperspectral imaging (HSI) technology to establish a classification model for near geographic origin of Ningxia wolfberries (Zhongning, Guyuan, Tongxin, and Huinong). The denoising auto-encoder (DAE) was used to generate augmented data, then principal component analysis (PCA) was combined with gray level co-occurrence matrix (GLCM) to extract the texture features. Finally, three datasets (HSI, DAE, and texture) were added to the multi-task model. The reshaped data were up-sampled using transposed convolution. After data-sparse processing, the backbone network was imported to train the model. The results showed that the MRes-FCN model exhibited excellent performance, with the accuracies of the full spectrum and optimum characteristic spectrum of 95.54% and 96.43%, respectively. This study has demonstrated that the MRes-FCN model based on Bayesian optimization and DAE data augmentation strategy may be used to identify the near geographical origin of wolfberries.

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 (R2C of 0.8141, R2P 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.

High freezing rate improves flavor fidelity effect of hand grab mutton after short-term frozen storage.

Taking the eutectic point as the final freezing temperature, the differences of flavor substances of in hand grab mutton (HGM) frozen at three rates of 0. 26 cm/h (-18°C), 0.56 cm/h (-40°C) and 2.00 cm/h (-80°C) were determined and analyzed. The results showed that the flavor of HGM decreased significantly after freezing. With the increase of freezing rate, the contents of aldehydes, alcohols, ketones, acids, esters, others, free amino acids and 5'-nucleotides were higher, and the content of specific substances was also generally increased. All samples from unfrozen and frozen HGM could be divided into four groups using an electronic nose based on different flavor characteristics. Seven common key aroma components were determined by relative odor activity value (ROAV), including hexanal, heptanal, octanal, nonanal, (E)-oct-2-enal, (2E,4E)-deca-2,4-dienal and oct-1-en-3-ol. The higher the freezing rate, the greater the ROAVs. Taste activity values calculated by all taste substances were far <1, and the direct contribution of the substances to the taste of HGM was not significant. The equivalent umami concentration of HGM frozen at -80°C was the highest. These findings indicated that higher freezing rate was more conducive to the retention of flavor substances in HGM, and the flavor fidelity effect of freezing at -80°C was particularly remarkable.

Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat.

Stearic acid content is an important factor affecting mutton odor. To determine the distribution and content of stearic acid (C18:0) in lamb meat fast and nondestructively, a method integrating spectral and textural data of hyperspectral imaging (900-1700 nm) was proposed in this paper. Firstly, spectral information was obtained and preprocessed. Then, the spectral features were extracted by variable combination population analysis-genetic algorithm (VCPA-GA) and interval variable iterative space shrinking analysis (IVISSA). Subsequently, the prediction models of partial least squares regression (PLSR) and least-squares support vector machines (LSSVMs) were established and compared. The model constructed with SNVD-VCPA-GA-PLSR achieved better performance. To improve the prediction results of the models, the textural features were extracted using a gray-level co-occurrence matrix (GLCM) and fused with spectral features. The optimized model achieved good results, with Rc of 0.8716, RMSEC of 0.0793 g/100 g, RPDc of 2.398, and Rp of 0.8121 with RMSEP of 0.1481 g/100 g and RPDp of 1.756. Finally, the spatial distribution of the C18:0 content in lamb meat was visualized using an optimal model. The result indicated that it was feasible to predict and visualize the C18:0 content in lamb meat, providing a way for real-time detection of volatile fatty acid compounds in meat.

Antioxidant potential of a unique LAB culture isolated from Harbin dry sausage: In vitro and in a sausage model.

The lactic acid bacteria Pediococcus pentosaceus, Lactobacillus curvatus, Lactobacillus brevis, and Lactobacillus fermentum isolated from Harbin dry sausage were evaluated for their potential antioxidant activity. The in vitro results showed that P. pentosaceus had the strongest H2O2 resistance, radical scavenging activity, reducing power, and inhibition of lipid peroxidation (P<0.05). Additionally, superoxide dismutase and glutathione peroxidase activities in P. pentosaceus were higher than those observed in three other strains (P<0.05). The inoculation of P. pentosaceus in dry sausage significantly decreased the quantities of thiobarbituric acid-reactive substance and carbonyl formation, while it also reduced the sulfhydryl loss in sausages (P<0.05). Furthermore, the lower content of volatile compounds that are related to lipid-oxidation, such as aldehydes, ketones and hydrocarbons, was observed in the P. pentosaceus inoculated sausage (P<0.05). These results demonstrate that P. pentosaceus has the potential to be employed as an antioxidant starter culture in fermented meat products.