A qualitative recognition method based on Karhunen–Loeve decomposition for near-infrared (NIR) and mid infrared (MIR) spectroscopy analysis
Qualitative recognition is an important research area of NIR and MIR spectroscopy analyses. Feature extraction from the original spectra is the most important step of qualitative analysis for NIR and MIR spectroscopy. In this work, a classification feature extraction method is proposed based on Karhunen–Loeve (K–L) decomposition and the entropy of classification information. Combining the supervised learning method and the extracted features, a qualitative recognition method of NIR and MIR spectroscopy analyses has been proposed. The proposed method has been applied to identify the samples of bamboo pulp fiber, cotton fiber and hemp fiber and also to classify the samples of edible oil. Compared with soft independent modeling of class analogy (SIMCA) based on principal component analysis (PCA) and least squares support vector machines (LS-SVMs) the proposed new method can get better classification results. In the fiber classification test based on NIR spectroscopy analysis, as a linear classification method, SIMCA shows difficulty to classify the overlapping features and only achieves a correct rate of 84.6% (11/13). The nonlinear modeling method LS-SVM improves the accuracy rate to 92.3% (12/13). Our proposed method achieves a 100% (13/13) accuracy rate. In the edible oil classification test based on MIR spectroscopy analysis, in comparison to SIMCA, the proposed method improves the accuracy rate from 59.1% to 77.3%. In both tests, the proposed method demonstrates a better classification and recognition ability.