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Non-destructive prediction of yak meat freshness indicator by hyperspectral techniques in the oxidation process.
- Source :
-
Food chemistry: X [Food Chem X] 2022 Dec 15; Vol. 17, pp. 100541. Date of Electronic Publication: 2022 Dec 15 (Print Publication: 2023). - Publication Year :
- 2022
-
Abstract
- 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.<br />Competing Interests: 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.<br /> (© 2022 The Author(s).)
Details
- Language :
- English
- ISSN :
- 2590-1575
- Volume :
- 17
- Database :
- MEDLINE
- Journal :
- Food chemistry: X
- Publication Type :
- Academic Journal
- Accession number :
- 36845518
- Full Text :
- https://doi.org/10.1016/j.fochx.2022.100541