Nondestructive Detection for Internal Qualities of Tan-sheep Meat Using NIR Hyperspectral Imaging Technique
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Abstract:
In this paper, protein and fat content, pH value of Tan-sheep meat was nondestructively detected using near infrared hyperspectral imaging technology. Spectral information of 69 samples was collected by hyperspectral image system (900~1700 nm). The partial least squares regression (PLSR) models under full-wave spectrum established by the original spectrum were compared with pretreatment?ones, and the best pretreatment algorithms were selected. In addition, the characteristic wavelengths were selected through β weight coefficient?of PLSR, then the PLSR models of protein and fat content, pH value under the characteristic wavelengths were set up, and the prediction effects of models were analyzed. The results showed that: the best pretreatment algorithms for models of mutton protein and fat content , pH value were Baseline, MSC+ SG and the original spectrum; the determination coefficient (RP2) of models built under characteristic wavelengths were 0.83, 0.86 and 0.72, and the predict root mean square error (RMSEP) were 0.57, 0.09 and 0.12, which could replace the full-wave modeling. Thus, it is feasible for testing internal qualities of mutton quickly and nondestructively using NIR hyperspectral imaging technology.
