Feasibility Study on Identification of Seeds of Hong Kong Seeds 49, October Red and September Fresh Cabbage Based on Visible/Shortwave Near-Infrared Spectroscopy of Partial Least Squares Discriminant (PLS-DA) and Least Squares Support Vector Machine (LS-SVM)
ZHANG Hai-liang1, NIE Xun1, LIAO Shao-min1, ZHAN Bai-shao1, LUO Wei1, LIU Shu-ling3, LIU Xue-mei2*, XIE Chao-yong1*
1. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013，China
2. School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013，China
3. Jiangxi Institute of Science and Technology Information, Nanchang 330046，China
Abstract：At present, the varieties of cabbage on the market are complex; the quality and germination rate of different cabbage seeds are different, but the appearance of cabbage seeds is not very different, so it has become a big problem to distinguish the types of cabbage seeds. This paper explores the feasibility of analyzing cabbage seed categories based on visible/short-wave near-infrared spectroscopy to achieve rapid differentiation of cabbage seed categories. The experiment purchased three varieties of cabbage seeds of Hong Kong species Sijiu, October red and September fresh from the Nanchang Seed Trading Place. The seeds with good appearance and moderate size were selected, and each kind of cabbage seed was evenly divided into three categories. 30 copies, divided into modeling and prediction sets according to 2∶1, totalling 90 copies of all samples. The near-infrared spectrometer was used to obtain the spectral reflectance of cabbage seeds with a sampling interval of 1 nm, and the wavelength coverage was 325~1 075 nm. The original spectral data were corrected by multivariate scattering (MSC), convolution smoothing (S-G) and standard normal transformation (SNV). )Three preprocessing methods were used for preprocessing. A partial least squares regression (PLSR) model was established for the spectral variables after preprocessing, and SNV was determined to be the best preprocessing method. In addition, principal component analysis (PCA) was used to conduct cluster analysis on cabbage seeds. The scores of the first three principal component factors (PCs) show that the three kinds of cabbage seeds have differences in spectral characteristics. Finally, the original spectral variables, the first three PCs (with a cumulative contribution of 97.15%) and 13 characteristic wavelengths selected based on the random frog (RF) algorithm were used as partial least squares discriminant (PLS-DA) and least squares support vector machines ( The input variables of the LS-SVM) model, from the model results, we can see that among the three input variables when the RF screening characteristic wavelength is used as the model input variable, the model prediction effect is the best, and the model established by PCs is the worst. The characteristic wavelengths screened by RF can better reflect the original spectral information. Judging from the prediction effects of different models, the LS-SVM model has better prediction accuracy than the PLS-DA model. The RF-LS-SVM model is the best prediction model among all models, and the modeling set and prediction set are both 100%. In conclusion, using visible/short-wave near-infrared spectroscopy to study the types of cabbage seeds is feasible. It can achieve a good prediction effect, which provides a theoretical basis for the rapid differentiation of cabbage seeds.
Key words：Cabbage seeds; Principal component analysis; Random frog; Partial least squares discriminant analysis; Least squares-support vector machine
章海亮，聂 训，廖少敏，詹白勺，罗 微，刘书玲，刘雪梅，谢潮勇. 基于PLS-DA和LS-SVM的可见/短波近红外光谱鉴定港种四九、十月红和九月鲜菜心种子的可行性研究[J]. 光谱学与光谱分析, 2024, 44(06): 1718-1723.
ZHANG Hai-liang, NIE Xun, LIAO Shao-min, ZHAN Bai-shao, LUO Wei, LIU Shu-ling, LIU Xue-mei, XIE Chao-yong. Feasibility Study on Identification of Seeds of Hong Kong Seeds 49, October Red and September Fresh Cabbage Based on Visible/Shortwave Near-Infrared Spectroscopy of Partial Least Squares Discriminant (PLS-DA) and Least Squares Support Vector Machine (LS-SVM). SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1718-1723.
[1] GUO Wen-chuan, ZHU De-kuan, ZHANG Qian, et al(郭文川，朱德宽，张 乾, 等). Journal of Agricultural Machinery(农业机械学报), 2020, 51(9): 350.
[2] Ernest T, Charles L Y A, Terry M, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 217：147.
[3] ZHANG Chu, LIU Fei, KONG Wen-wen, et al(张 初，刘 飞，孔汶汶, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(20): 270.
[4] Tyska D, Mallmann A, Gressler L T, et al. Food Additives & Contaminants Part A: Chemistry, Analysis, Control, Exposure & Risk Assessment, 2021, 38(11): 1958.
[5] Platov Y T, Metlenkin D A, Platova R A, et al. Journal of Applied Spectroscopy, 2021, 88(4): 723.
[6] Jingming N, Jingjing S, Shuhuai L, et al. International Journal of Food Properties, 2017, 20: 1515.
[7] Liu P, Wang J, Li Q, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 206：23.
[8] GAO Tong, WU Jing-zhu, MAO Wen-hua, et al(高 彤，吴静珠，毛文华, 等). Journal of Agricultural Machinery(农业机械学报), 2019, 50(S1): 399.
[9] PENG Yan-kun, ZHAO Fang, LI Long, et al(彭彦昆，赵 芳，李 龙, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(5): 159.
[10] Akowuah T O S, Teye E, Hagan J, et al. Journal of Spectroscopy, 2020, 1: 11.
[11] Jiahua W, Yifang W, Jingjing C, et al. LWT, 2018, 96: 90.
[12] GAO Tong, WU Jing-zhu, LIN Long, et al(高 彤，吴静珠，林 珑, 等). Chinese Journal of Cereals and Oils(中国粮油学报), 2019, 34(7): 114.
[13] Jie F, Lingling J, Jialei Z, et al. Journal of Food Science and Technology, 2020, 57(12): 4541.
