Nondestructive detection of mutton moisture content based on hyperspectral technique

Nondestructive detection of mutton moisture content was studied by hyperspectral imaging system(1000~2500 nm). Spectral information was collected from 108 mutton samples. The original spectra were pretreated by standard normal variate,normalization,detrend correction,Savitzky-Golay,derivative and multiple scattering correction. The partial least squares regression(PLSR)model was established for original and pre-processed spectra in the whole band. The best preprocessing algorithm was the detrend correction method. The original data was pretreated by the detrend correction method,and the characteristic wavelength was selected by the correlation coefficient method. The PLSR and stepwise multiple linear regression(SMLR)model for the mutton moisture content were established. The results showed that the SMLR model had the best effect on the prediction of moisture content. The correlation coefficient(R_c)and standard error(SEC)for calibration set were 0.8597 and 0.0521 respectively,and correlation coefficient(R_p)and standard error(SEP)for predictive set were 0.8654 and 0.0387 respectively. The results showed that it is feasible to detect mutton moisture content by hyperspectral imaging technology.