Near-Infrared Spectrum of Coal Origin Identification Based on SVM Algorithm

Near infrared spectroscopy is introduced to analyze 243 coal samples of different origins of Australia, Canada, China, Indonesia and Russia, combined with the supportive vector machines (SVM) analysis method. With the pre-processed data from the Principal component analysis (PCA), six supportive vector machines with different kernel functions are employed to discriminate origins of coal samples, namely Linear SVM, Quadratic SVM, Cubic SVM, Fine Gaussian SVM, Medium Gaussian SVM and Coarse Gaussian SVM. Through comparison, Linear SVM has the best performance in prediction accuracy rate while better results are obtained using Medium Gaussian SVM taking accuracy rate and training time into account. It turns out that NIR spectroscopy combined with Medium Gaussian SVM can be used as a good non-destructive method to predict origins of coal, with an accuracy rate of 98.8%, which strengthens the supervision of coal quality.