Abstract:In order to realize fast nondestructive testing of moisture content fresh mutton in southern Xinjiang, the spectroscopic data of the 134 samples of same variety in fresh hind legs meat of sheep in the range of 780~1700 nm wavelength were collected in this study. The original spectrums were processed by noise reduction such as median smoothing filtering, multiple scatter correction, a derivative, standardization, centralized transformation, smoothing retreatment methods, and S-G smoothing. The samples were divided into training set and test set by a ratio of 13:1. PLSR was adopted to establish the prediction model to forecast the moisture content of the fresh mutton. The results showed that the prediction correlation coefficient of training set was 0.94, its MSEC was 0.04, and prediction success rate was 97.6%, while the prediction correlation coefficient of the test set was 0.89, its standard deviation MSEV was 0.89, and the success rate of prediction was 96.4%. Experimental results confirmed that the established PLSR model combined with preprocessing such as median smoothing filtering, multiple scatter correction, a derivative, standardization, centralized transformation, and the S-G smooth could provide accurately fast nondestructive evaluation for the moisture content of fresh mutton, and provide theoretical reference application for rapid nondestructive testing of the moisture content in fresh mutton.
