Study on Nonlinear Multivariate Methods Combined with the Visible Near-Infrared Spectroscopy (Vis/NIRS) Technique for Detecting the Protein Content of Cheese

The protein content is an important parameter for the estimation of the quality of cheese products. Nonlinear multivariate methods including kernel partial least squares (KPLS), support vector machine (SVM) and back propagation artificial neural network (BP-ANN) combing with the visible and near-infrared spectroscopy (Vis/NIRS) techniques are introduced for predicting the protein content of cheese. Method of augmented partial residual plot (APaRP) is performed to diagnose the nonlinearity of the raw spectral data. Multiplicative signal correction (MSC), standard normal variate (SNV) with detrending, Savitzky-Golay second-derivative and direct orthogonal signal correction (DOSC) are used to preprocess the spectral data and compared. DOSC algorithm obtains the optimal prediction performance and was chosen as the preprocess method for the following prediction models. Multivariate models including PLS, KPLS, SVM and BP-ANN are trained on the preprocessed cheese data for predicting the protein content. The square of correlation coefficient (R 2), root mean squares error (RMSE) and ratio performance deviation (RPD) are used to evaluate the performance of models. DOSC-KPLS model obtains the highest scores of R 2 and RPD of 0.974 and 5.587, respectively, which indicates better prediction performance than other models. Therefore, DOSC-KPLS model combined with the Vis/NIRS technique is the most promising method for accurately detecting the protein content of cheese.