Nondestructive determination of water content in beef using visible/near-infrared spectroscopy

The water content of fresh beef has an important influence on the processing, storage, trade and quality of beef. In order to improve the economic value of beef and eating quality, we should research nondestructive testing technology on water content in beef. A laboratory visible/near-infrared spectroscopy system using visible/near-infrared spectroscopy was build to collect 86 beef samples'reflectance spectra in a rang of 400-1170 nm. The samples are from Inner Mongolia cattle's and Luxi cattle's longissimus dorsi in different carcasses for the study, 75% of the samples are used as a calibration set, 25% of the samples are used as a validation set. The diffuse reflectance spectra in the fresh cut of beef were collected, and the water contents of the samples were measured with the national standard. The diffuse reflectance spectra of samples were performed with different pretreatments, such as multiplicative scatter correction (MSC), standard normalized variate (SNV) and direct orthogonal signal correction (DOSC). The prediction model of multiple linear regression (MLR), principal component regression (PCR) and partial least squares regression (PLSR) were constructed for prediction of water content in beef with full-spectrum. Correlation coefficient and standard error between prediction water content and real water content of the samples are taken as evaluation criterions for the prediction modal. In general, the higher correlation coefficient of calibration set with validation set and lower standard error of calibration set with validation set mean higher precision of prediction model. Result shows that multiplicative scatter correction is the best pretreatment, and the performance of models established with PLSR is better than others, its correlation coefficient and standard deviation are 0.92 and 0.0047, respectively. The correlation coefficient and standard deviation of external validation set in PLSR model is 0.85 and 0.0054, respectively. Direct orthogonal signal correction combining with principal component regression and partial least squares regression has a high correlation coefficient in calibration set, but a low correlation coefficient in validation set, because of overfitting. This study demonstrated that the PLSR model built by using visible/near-infrared spectroscopy with multiplicative scatter correction pretreatment can nondestructively and rapidly determine the water content in beef. This research can provide a basis for further developing device of nondestructive and rapid determination of water content in beef.