Non-Destructive Detection of Water Content in Pork Based on NIR Spatially Resolved Spectroscopy

Zhiyong Zhang (), Shuo Wang and Yanqing Zhang
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Zhiyong Zhang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Shuo Wang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Yanqing Zhang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China

Agriculture, 2023, vol. 13, issue 11, 1-12

Abstract: Water is one of the important factors affecting pork quality. In this study, near-infrared (NIR) spatially resolved (SR) spectroscopy was used to detect the water content of pork. The SR spectra of 150 pork samples were collected within the light source–detector (LS-D) distance range of 4–20 mm (distance interval 1 mm). Models were established based on single-point SR spectra of 17 different LS-D distances and combination SR spectra. The results indicated that combination SR spectra achieved better model performance than the single-point SR spectra, and the LS-D distance significantly affected the model accuracy. The optimal LS-D distance combination of 5, 7, 10, and 12 mm provided the best detection model with the calibration determination coefficient ( R 2 C ) of 0.915 and prediction determination coefficient ( R 2 P ) of 0.878. Using the competitive adaptive reweighted sampling (CARS) algorithm, 24 characteristic wavelengths were selected. The model built with the characteristic wavelengths also exhibited good detection accuracy, with a R 2 C of 0.909 and a R 2 P of 0.867, and the number of wavelengths was greatly reduced compared to the full-wavelength model. This study demonstrated that SR spectroscopy combined with the optimized LS-D distances and screened characteristic wavelengths can be a powerful tool for detecting the water content of pork.

Keywords: water content; spatially resolved spectra; pork (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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