Identification of Gentiana Macrophylla by FTIR Technology and Sparse Linear Discriminant Analysis
LI Si-hai1, YU Xiao-hui2, ZHAO Lei2, JIN Ling2*
1. School of Information Science & Engineering, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, China
2. Key Laboratory of Chemistry and Quality for Traditional Chinese Medicines of the College of Gansu Province, Lanzhou 730000, China
Abstract：Fourier transform infrared(FTIR) spectrum usually includes a large number of wavelength variables and the qualitative analysis of FTIR spectrum needs to establish a stable and interpretable classification model. Sparse linear discriminant analysis (SLDA), a relatively new and effective machine learning algorithm, is commonly used for variable selection and discriminant analysis of high-dimensional settings, in which the number of wavelength variable is very large and the number of observations is limited. By introducing regularization items into linear discriminant analysis, the classifier training and variable selection are performed simultaneously in SLDA, and the sparsity of load coefficients in different discriminant directions increases the interpretability of the model. A total of 94 samples of Gentiana macrophylla, including 30 Gentiana straminea Maxims, 28 Gentiana officinalis and 36 Gentiana macropylla Pall, were collected. FTIR spectrum of all samples was obtained by Fourier transform infrared spectroscopy method. 70 of the samples were selected as the training set, the remaining as the test set. Based on the training set, the SLDA model was established through the grid optimization of the number of non-zero loading coefficients in the two discriminant directions, and the optimal parameter space was obtained. According to the model parameters, the prediction accuracy of the test set was 100%, and thus the rapid and accurate identification of the three kinds of Gentiana macrophylla was realized. The experimental results showed that the SLDA model was superior to PLS-DA method in terms of classification accuracy, sparseness and interpretability. SLDA will be a novel and effective method for spectroscopy qualitative analysis.
李四海，余晓晖，赵 磊，晋 玲. 基于FTIR技术和稀疏线性判别分析的秦艽种类鉴别[J]. 光谱学与光谱分析, 2018, 38(08): 2390-2394.
LI Si-hai, YU Xiao-hui, ZHAO Lei, JIN Ling. Identification of Gentiana Macrophylla by FTIR Technology and Sparse Linear Discriminant Analysis. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2390-2394.
[1] HUANG Lu-lin，YANG Xiao，FENG Xian-hong，et al(黄璐琳，杨 晓，丰先红，等). Modern Chinese Medicine(中国现代中药)，2011，13(5)：40.
[2] XIONG Bo，GUO Shu-peng，HU Lin(熊 波，郭树鹏，胡 林). Chinese Journal of Experimental Traditional Medical Formulae(中国实验方剂学杂志)，2015，21(17)：230.
[3] De Luca M，Terouzi W，Ioele G，et al. Food Chemistry，2011，124(3)：1113.
[4] WU Zhe，ZHANG Ji，JIN Hang，et al(吴 喆，张 霁，金 航，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2017，37(6)：1754.
[5] Shao J，Wang Y，Deng X，et al. The Annals of Statistics，2011，39(2)：1241.
[6] Kong H，Lai Z，Wang X，et al. Neurocomputing，2016，177：198.
[7] Clemmensen L，Hastie T，Witten D，et al. Technometrics，2011，53(4)：406.
[8] Rasmussen M A，Bro R. Chemometrics and Intelligent Laboratory Systems，2012，119：21.
[9] Zou H，Hastie T. Journal of the Royal Statistical Society：Series B (Statistical Methodology)，2005，67(2)：301.
[10] Efron B，Hastie T，Johnstone I，et al. The Annals of Statistics，2004，32(2)：407.
[11] Liu C，Yang S X，Deng L. Expert Systems with Applications，2015，42(22)：8497.
