基于新型变量筛选-偏最小二乘判别分析方法的金银花与山银花近红外无损鉴别

doi:10.3964/j.issn.1000-0593(2025)06-1605-07

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Abstract Both Lonicerae Japonicae Flos and Flos Lonicerae are plants of the Caprifoliaceae family. They are rather similar in appearance. However, there are differences in chemical composition, content, efficacy, and price. To obtain excessive profits, unscrupulous merchants sell the cheaper Flos Lonicerae as Lonicerae Japonicae Flos. It is difficult for consumers to distinguish them with the naked eye. Currently, there is no study on the non-destructive identification of Lonicerae Japonicae Flos and Flos Lonicerae. Rapid and non-destructive analysis of complex samples can be achieved using near-infrared (NIR) spectroscopy. The identification of samples from different sources can be achieved by combining pattern recognition methods, such as partial least squares discriminant analysis (PLS-DA). However, an excessive number of spectral variables may easily lead to the problem of overfitting in the PLS-DA method. In this study, 643 spectra of Lonicerae Japonicae Flos from three production areas and 200 spectra of Flos Lonicerae from the local area were collected using a grating portable NIR spectrometer. Besides, 50 samples of Lonicerae Japonicae Flos from each production area and local Flos Lonicerae were collected one month later as the external validation set. A new pattern recognition method, named randomization test (RT)-PLS-DA, was proposed. This method was compared with principal component analysis (PCA), PLS-DA, and existing variable selection-PLS-DA methods, such as competitive adaptive reweighted sampling (CARS)-PLS-DA and Monte Carlo-uninformative variable elimination (MC-UVE)-PLS-DA. The accuracies of the models were further improved with the spectral pretreatments. The results showed that there were severe interferences, including peak overlapping, baseline drift, and background, in the original spectra. Even with optimized pretreatment methods, the accurate identification of Lonicerae Japonicae Flos and Flos Lonicerae cannot be achieved using the PCA method. Accurate identification results could be obtained using PLS-DA with either first derivative (1st) or continuous wavelet transform (CWT) pretreatment, while the identification rates for the validation and external validation sets were 100% and 98%, respectively. Among the three variable selection-PLS-DA methods, the CARS method selected the fewest variables. The selection of feature variables and achieving satisfactory identification rates can be done simultaneously with the RT method. The 1st-RT-PLS-DA model was the best, and the identification rates for the validation and external validation sets were 100% and 99.50%, respectively. The above results indicate that the accurate identification of Lonicerae Japonicae Flos and Flos Lonicerae can be achieved using a portable NIR spectrometer and a variable selection-PLS-DA method, providing a new approach for the rapid detection of adulteration in traditional Chinese medicinal materials.

Key words：Portable near infrared spectrometer; Lonicerae Japonicae Flos; Flos Lonicerae; Nondestructive identification; Partial least squares discriminant analysis

Received: 2024-07-24 Accepted: 2024-12-30

ZTFLH:

O657.3

Corresponding Authors: DU Guo-rong, LI Pao E-mail: nkchem09@mail.nankai.edu.cn；lipao@mail.nankai.edu.cn

Cite this article:

LIU Wei,TAN Hui-zhen,JIANG Li-wen, et al. Nondestructive Identification of Lonicerae Japonicae Flos and Flos Lonicerae With Near Infrared Spectroscopy and New Variable Selection-Partial Least Squares Discriminant Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(06): 1605-1611.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2025)06-1605-07 OR https://www.gpxygpfx.com/EN/Y2025/V45/I06/1605

[1] LI Ji, JIA Bo(李冀，贾波). Formulology(方剂学). Beijing：China Traditional Chinese Medicine Press(北京：中国中医药出版社), 2014.
[2] LI Yuan, HE Bing-hui, QIN Hua-jun, et al(李源，何丙辉，秦华军，等). Journal of Mountain Agriculture and Biology(山地农业生物学报), 2022, 41(5): 67.
[3] WEI Jing-yue, LUO Shi-wen, FENG Ling-ran, et al(韦晶玥, 罗诗雯, 冯龄燃, 等). Chinese Journal of Experimental Traditional Medical Formulae(中国实验方剂学杂志), 2024, 30(11): 273.
[4] WU Ting, DU Jia-hui, LI Zhen-bin, et al(伍婷, 杜家会, 李振斌, 等). Chinese Journal of Ethnomedicine and Ethnopharmacy(中国民族民间医药), 2022, 31(15): 61.
[5] SONG Meng-ru, LI Dan, ZHU Qing-xia, et al(宋梦如, 李丹, 朱青霞, 等). Journal of Mountain Agriculture and Biology(山地农业生物学报), 2024, 43(6): 15.
[6] López-Fernández J, Moya D, Benaiges M D, et al. Fuel, 2022, 319: 123794.
[7] Chen M J, Yin H L, Liu Y, et al. Analytical Methods, 2022, 14(2): 114.
[8] Hao N, Ping J C, Wang X, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2024, 320: 124590.
[9] Jin Y, Du W J, Liu X S, et al. Infrared Physics and Technology, 2022, 123: 104135.
[10] Zhao J, Cui P D, Liu H, et al. Infrared Physics and Technology, 2019, 104: 103139.
[11] Zhang X X, Li S K, Shan Y, et al. Journal of Food Processing and Preservation, 2022, 46: e16480.
[12] Dong Y Q, Shan Y, Li P, et al. Analytical Letters, 2022, 55(16): 2554.
[13] Yan Z Y, Liu H G, Li T, et al. LWT-Food Science and Technology, 2022, 162: 113490.
[14] Sorochan Armstrong M D, de la Mata A P，Harynuk J J. Frontiers in Analytical Science, 2022, 2: 867938.
[15] Zhang P F, Xu Z P, Ma H M, et al. Infrared Physics and Technology, 2023, 133: 104800.
[16] Wu K, Zhu T Y, Wang Z Q, et al. European Food Research and Technology, 2024, 250: 191.
[17] Tan H Z, Liu Y, Tang H, et al. Foods, 2024, 13(23): 3856.
[18] Li H D, Liang Y Z, Xu Q S, et al. Analytica Chimica Acta, 2009, 648(1): 77.
[19] Yao K S, Sun J, Chen C, et al. Infrared Physics and Technology, 2022, 127: 104414.
[20] Han Q J, Wu H L, Cai C B, et al. Analytica Chimica Acta, 2008, 612(2): 121.
[21] LI Yan-kun, DONG Ru-nan, ZHANG Jin, et al(李艳坤, 董汝南, 张进, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(11): 3331.
[22] Xu H, Liu Z C, Cai W S, et al. Chemometrics and Intelligent Laboratory Systems, 2009, 97(2): 189.