Abstract
Based on the three-step hybrid strategy of effective variable selection (rough selection, fine selection and optimal selection) in multivariate calibration of spectra, a feature variable selection method combining interval partial least squares (iPLS), interval variable iterative space shrinkage approach (iVISSA) and iteratively retaining informative variables (IRIV) was proposed to select the feature wavelengths of the near-infrared spectra of fresh chicken breast,and established a chicken moisture detection model. The number of modeled wavelengths was reduced by 0.76% after iPLS-iVISSA-IRIV, but the accuracy were stability of the model are gradually improved. The modeling results using the selected 8 characteristic wavelengths were as follows: correlation coefficient of calibration RC=0.907 7, root mean square error of calibration RMSEC=0.516 1, correlation coefficient of prediction RP=0.943 5, root mean square error of prediction RMSEP=0.612 3. The result shows that the iPLS-iVISSA-IRIV method based on the three-step hybrid strategy can effectively select the characteristic wavelengths of chicken moisture detection.
Publication Date
2-18-2023
First Page
72
Last Page
76, 81
DOI
10.13652/j.issn.1003-5788.2020.09.012
Recommended Citation
YUAN, Kai; ZHANG, Zhi-yong; XI, Qian; WU, Ying-rui; GUO, Dong-sheng; and HE, Guo-kang
(2023)
"Research on the application of three-step hybrid variable selection strategy in chicken moisture detection by near infrared,"
Food and Machinery: Vol. 36:
Iss.
9, Article 12.
DOI: 10.13652/j.issn.1003-5788.2020.09.012
Available at:
https://www.ifoodmm.cn/journal/vol36/iss9/12
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