Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy

Zheng, Xiaochun; Chen, Li; Li, Xin; Zhang, Dequan

doi:10.3390/foods12020300

Open AccessArticle

Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy

Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences, Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing 100193, China

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Foods 2023, 12(2), 300; https://doi.org/10.3390/foods12020300

Submission received: 6 December 2022 / Revised: 6 January 2023 / Accepted: 6 January 2023 / Published: 8 January 2023

(This article belongs to the Special Issue Preservation and Green Processing of Meat Products)

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Abstract

The potential of four dimension reduction methods for near-infrared spectroscopy was investigated, in terms of predicting the protein, fat, and moisture contents in lamb meat. With visible/near-infrared spectroscopy at 400–1050 nm and 900–1700 nm, respectively, calibration models using partial least squares regression (PLSR) or multiple linear regression (MLR) between spectra and quality parameters were established and compared. The MLR prediction models for all three quality parameters based on the wavelengths selected by stepwise regression achieved the best results in the spectral region of 400–1050 nm. As for the spectral region of 900–1700 nm, the PLSR prediction model based on the raw spectra or high-correlation spectra achieved better results. The results of this study indicate that sampling interval shortening and of peak-to-trough jump features are worthy of further study, due to their great potential in explaining the quality parameters.

Keywords: non-destructive detection; meat quality; dimension reduction; near-infrared spectroscopy

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MDPI and ACS Style

Zheng, X.; Chen, L.; Li, X.; Zhang, D. Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy. Foods 2023, 12, 300. https://doi.org/10.3390/foods12020300

AMA Style

Zheng X, Chen L, Li X, Zhang D. Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy. Foods. 2023; 12(2):300. https://doi.org/10.3390/foods12020300

Chicago/Turabian Style

Zheng, Xiaochun, Li Chen, Xin Li, and Dequan Zhang. 2023. "Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy" Foods 12, no. 2: 300. https://doi.org/10.3390/foods12020300

APA Style

Zheng, X., Chen, L., Li, X., & Zhang, D. (2023). Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy. Foods, 12(2), 300. https://doi.org/10.3390/foods12020300

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Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy

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