Quick Discrimination of Rice Storage Period Based on Manifold Dimensionality Reduction Methods and Near Infrared...

This paper proposed a method for rapid identification of rice storage period based on manifold dimensionality reduction algorithms and near infrared spectroscopy (NIRS) technique. The reflection spectrum curve of old rice and new rice were obtained with a field spectroradiometer and the acquired spectral data was preprocessed with direct orthogonal signal correction method (DOSC) to filter the independent signal from the spectral data which is irrelevant with the dependent variable Y array and eliminate the influence and interference of the irrelevant information in the following chemometric analysis. The Durbin-Watson test and Run test methods were utilized to detect the nonlinearity which exists in the spectral data structure. The enhanced partial residual plot analysis method (Augmented partial residual plot) was employed to quantitative analysis of the degree of nonlinearity of the spectral data. Popular linear manifold dimensionality reduction methods including principal component analysis (PCA) method and multidimensional scaling analysis (MDS) method and popular nonlinear manifold dimensionality reduction methods including Isometries mapping method (ISOMAP), locally linear embedding (LLE) method and Laplacian Eigenmap method (LE) were used to extract the real variable from the preprocessed spectral data. Then, the intrinsic variable was taken as the input of the kernel partial least squares method (KPLS) to establish the relationship between the intrinsic variables and the storage time of rice samples. The number of experiment samples of the new rice and the old rice were 200 respectively and randomly separated into the training set with 300 samples and the test set with 100 samples. Through comparing the prediction results of the regression models which were established with different manifold reduction methods, the experiment results show that the prediction effects of the nonlinear-based models are superior to the linear-based models. Finally, the KPLS model established with 40 true variables extracted with ISOMAP approach achieved the optimal prediction effect. The prediction correlation coefficient (R2p), RMSEP (RMSEP) and relative prediction error value (RPD) were 0.917, 0.187 and 2.698, respectively. It was concluded that NIRS combined with ISOMAP-KPLS method can be successfully used to determine the storage period of rice accurately and quickly. The study provides a scientific means for rapid non-destructive detecting for rice storage period research in the future.