A New Feature Extraction Method of Near-Infrared Spectra Based on the Addition of Historical Data

In traditional qualitative analysis of near-infrared (NIR) spectra, the stability of recognition models is decreased when new varieties of samples are added into the model. In order to improve the robustness of the model, a new feature extraction method based on the addition of historical data was put forward. The NIR training samples will be collected first, after that the historical data of the same species is added to constitute a larger and richer dataset. Then, the pretreated data of these training samples is projected to the feature space, which is constructed by feature extraction using partial least squares (PLS) based on the above dataset. Subsequently, orthogonal linear discriminant analysis (OLDA) is employed to extract features of the projected data. 18 varieties of corn seeds were taken as study subject, the comparative experiments with and without historical data are implemented respectively, and then the biomimetic pattern recognition (BPR) method is applied to verify the efficiency of the method proposed. The results suggest that the method adopted can improve the robustness of recognition model more effectively compared with the method without historical data. It maintains the high correct recognition ratios when new varieties are added into the model. Besides that, the recognition effect on test sets of the different days remains the same basically in the condition of same PLS dimensions. Therefore, the dimension of feature extraction can be set to some fixed values in recognition software. In this way, it can keep out of the trouble of manually modifying the optimal PLS parameter in recognition software if new varieties need to be added into the model. The experiment results of the thesis manifested the effectiveness of the proposed method.