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

文献类型： 外文期刊

第一作者： Dong, Kai

作者： Dong, Kai;Wang, Qia;Zeng, Qibing;Huang, Qun;Dong, Kai;Wang, Qia;An, Fengping;Huang, Qun;Guan, Yufang;Huang, Yonghui;Luo, Zhang;Huang, Qun;Huang, Qun;Huang, Qun;Zeng, Qibing;Luo, Zhang;Huang, Qun

作者机构：

关键词： Hyperspectral technique; Yak meat; Freshness; PCR; SVR; PLSR

期刊名称：FOOD CHEMISTRY-X （ 影响因子：6.1； 五年影响因子：6.4 ）

ISSN： 2590-1575

年卷期： 2023 年 17 卷

页码：

收录情况： SCI

摘要： 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 wave-lengths 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.

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