Identification of Moxa Grade by Fourier Transform Infrared Spectroscopy Fingerprint

A method for identification of moxa grade by Fourier transform infrared spectroscopy fingerprint was proposed, and the optimal model for identifying moxa grade was obtained by comparison of combination of 8 spectral preprocessing methods denoising, Gaussian filtering, multivariate scattering correction, standard normal transformation, first derivative + Savitzky- Golay (SG) smoothing, second derivative + SG smoothing, first derivative + Norris Gap, and second derivative + Norris Gap and 5 pattern recognition methods back propagation neural network (BP-NN) algorithm, genetic optimization support vector machine (SVM-ga), particle swarm optimization support vector machine (SVM-pso), random forest (RF) algorithm, and K-nearest neighbor (KNN) algorithm. As shown by the results, there were 11 common peaks in maxo fingerprint. Nine principal components were obtained by principal component analysis, and the cumulative variance contribution rate reached 99.67%. The combination of standard normal transformation and SVM-pso algorithm had the best discrimination effect, with the discrimination accuracy of 100% in the training set and 93.3% in the test set.