Stearic acid content is an important factor affecting mutton odor. To obtain the distribution and content of stearic acid (C18:0) in lamb meat fast and nondestructively, a method integrating spectra...

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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.

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.

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.