Authentication of Linderae Radix through plant metabolomics coupled with a machine learning-enhanced <i>in situ</i> hyperspectral imaging approach

Yangbin Lv; Hongwei Sun; Qiaoling Ding; Bangxu Chen; Hongwei Ye; Ning Xu; Chu Chu

doi:10.1016/j.jpha.2025.101476

Article Contents

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Yangbin Lv, Hongwei Sun, Qiaoling Ding, Bangxu Chen, Hongwei Ye, Ning Xu, Chu Chu. Authentication of Linderae Radix through plant metabolomics coupled with a machine learning-enhanced in situ hyperspectral imaging approach[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101476

Citation:

Yangbin Lv, Hongwei Sun, Qiaoling Ding, Bangxu Chen, Hongwei Ye, Ning Xu, Chu Chu. Authentication of Linderae Radix through plant metabolomics coupled with a machine learning-enhanced in situ hyperspectral imaging approach[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101476

Citation:

Yangbin Lv, Hongwei Sun, Qiaoling Ding, Bangxu Chen, Hongwei Ye, Ning Xu, Chu Chu. Authentication of Linderae Radix through plant metabolomics coupled with a machine learning-enhanced in situ hyperspectral imaging approach[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101476

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Authentication of Linderae Radix through plant metabolomics coupled with a machine learning-enhanced in situ hyperspectral imaging approach

doi: 10.1016/j.jpha.2025.101476

Yangbin Lv^a,b,
Hongwei Sun^a,
Qiaoling Ding^a,
Bangxu Chen^a,
Hongwei Ye^a,
Ning Xu^{a
,
,},
Chu Chu^{a,b
,
,}

a. College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, 310014, P. R. China;
b. Zhejiang Provincial Key Laboratory of TCM for Innovative R & D and Digital Intelligent Manufacturing of TCM Great Health Products, Hangzhou, 310014, P. R. China

Funds:

The authors gratefully acknowledge the financial support of this study by the National Natural Science Foundation of China (Grant No.: 82374017), and Key R&

D Program of Zhejiang Province (Grant Nos.: 2023C03038, and 2022C02013).

Received Date: May 08, 2025
Accepted Date: Oct. 27, 2025
Rev Recd Date: Oct. 22, 2025
Available Online: Oct. 29, 2025

Abstract

Abstract

Abstract:
- L. aggregata,
- Different root types,
- Plant metabolomics,
- Hyperspectral imaging,
- Machine learning algorithms,
- Visual in situ assessment

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References(35)

References

[1]	Q.E. Huang, K.H. Liu, L.P. Qin, et al., Lindera aggregata (Sims) Kosterm: a systematic review of its traditional applications, phytochemical and pharmacological properties, and quality control, Medicinal Plant Biology 2 (2023) 11.
[2]	Y. Tao, Y.L. Deng, P. Wang, Traditional uses, phytochemistry, pharmacology, processing methods and quality control of Lindera aggregata (Sims) Kosterm: A critical review, J Ethnopharmacol. 318 (2024) 116954.
[3]	W.F. Du, X.K. Yue, Y. Wu, et al., Analysis of different forms Linderae Radix based on HPLC and NIRS fingerprints, Zhongguo Zhong Yao Za Zhi. 41 (2016) 3551-3556.
[4]	S.M. Li, C.L. Sun, G. Zhou, et al., Characteristic Spectra and Multi-component Content Determination of Different Root Forms of Lindera aggregate, Zhongguo Zhong Yao Za Zhi. 38 (2021) 2548-2553.
[5]	K.Y. Huang, J.N. Shu, R. OuYang, et al., Quality comparison of different forms Linderae radix, Chin J Mod Drug. 4 (2010) 1-3.
[6]	S. Li, C.W. Jiao, Z.P. Xu, et al., Determination of geographic origins and types of Lindera aggregata samples using a portable short-wave infrared hyperspectral imager, Spectrochim Acta A Mol Biomol Spectrosc. 279 (2022) 121370.
[7]	X. Peng, Y.Y. Luo, J. Wang, et al., Integrated analysis of the transcriptome, metabolome and analgesic effect provide insight into potential applications of different parts of Lindera aggregata, Food Res Int. 138 (2020) 109799.
[8]	Y. Zhang, W.F. Du, Y.Y. Luo, et al., Establishment of HPLC fingerprint of different forms of Linderae Radix, Chinese Traditional Patent Medicine 42 (2020) 2201-2204.
[9]	N. Kumar, J.S. Bharat, A. Kumar, et al., Recent advancement in mass based plant metabolomics: techniques, tools, and analytical approaches, Phytochem Rev (2024).
[10]	Z.Z. Xue, X.B. Zhang, H.S. Peng, et al., Exploration of habitat-related chemomarkers for Magnoliae officinalis cortex applying both global and water-soluble components-based metabolomics method, Phytomedicine 98 (2022) 153957.
[11]	M. Jiang, Y. Sha, Y. Zou, et al., Integration of deep neural network modeling and LC-MS-based pseudo-targeted metabolomics to discriminate easily confused ginseng species, J Pharm Anal. 15 (2025) 101116.
[12]	Y.B. Lv, Y.F. Zou, X.D. Zhang, et al., A review on the chemical constituents and pharmacological efficacies of Lindera aggregata (Sims) Kosterm, Front Nutr. 9 (2022) 1071276.
[13]	Q. Wang, Development status of novel spectral imaging techniques and application to traditional Chinese medicine, J Pharm Anal. 13 (2023) 1269-1280.
[14]	T. Ahmed, N.K. Wijewardane, Y. Lu, et al., Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence, Comput Electron Agr. 220 (2024) 108855.
[15]	M. Seididamyeh, I. Tahmasbian, A.D.T. Phan, et al., Geographical origin discrimination of lemon myrtle (Backhousia citriodora) leaf powder using near-infrared hyperspectral imaging, Food Biosci. 59 (2024) 103946.
[16]	T.T. Shi, Y. Gao, J.Y. Song, et al., Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains, Food Chem. 461 (2024) 140651.
[17]	J.K. Zhao, X.J. Kang, Z.Q. Ying, et al., Comparison of chemical constituents of volatile oil in straight roots and roots of Radix Linderae by GC-MS, China Journal of Traditional Chinese Medicine and Pharmacy 35 (2020) 408-410.
[18]	Y. Cao, Y. Zhang, S. Yu, et al., Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning. Foods 12 (2023) 3154.
[19]	D. Cozzolino, P.J. Williams, L.C. Hoffman, An overview of pre-processing methods available for hyperspectral imaging applications, Microchem J. 193 (2023) 109129.
[20]	K. Wu, T. Zhu, Z. Wang, et al., Identification of varieties of sorghum based on a competitive adaptive reweighted sampling-random forest process, Eur Food Res Technol 250 (2024) 191-201.
[21]	L. Yuan, Y. Liu, F. Gao, et al., Prediction of bayberry SSC by ensemble model with random frog successively selected from the residual vis-NIR spectra, Food Control 178 (2025) 111525.
[22]	D. Schillemans, R. Tauler, M. Haverkorn, et al., Enhancing Chemical Image Analysis: Utilizing the Successive Projection Algorithm for Unmixing, Spectrochim Acta A. 338 (2025) 126201.
[23]	A. Ali, S. Qadri, W.K. Mashwani, et al., Machine learning approach for the classification of corn seed using hybrid features, Int J Food Prop. 23 (2020) 1110-1124.
[24]	S. Sperandei, Understanding logistic regression analysis, Biochem Med (2014) 12-18.
[25]	R. Goyal, P. Singha, S.K. Singh, Spectroscopic food adulteration detection using machine learning: Current challenges and future prospects, Trends Food Sci Tec. 146 (2024) 104377.
[26]	J. Jiang, H. Cen, C. Zhang, et al., Nondestructive quality assessment of chili peppers using near-infrared hyperspectral imaging combined with multivariate analysis, Postharvest Biol Tec. 146 (2018) 147-154.
[27]	J.A.K. Suykens, J. Vandewalle, Least Squares Support Vector Machine Classifiers, Neural Process Lett. 9 (1999) 293-300.
[28]	M. Girmatsion, X. Tang, Q. Zhang, et al., Progress in machine learning-supported electronic nose and hyperspectral imaging technologies for food safety assessment: A review, Food Res Int. 209 (2025) 116285.
[29]	H. Zhou, H. Guan, X. Ma, et al., A method of maize seed variety identification based on near-infrared spectroscopy combined with improved DenseNet model, Microchem J. 206 (2024) 111542.
[30]	M. Greenacre, P.J.F. Groenen, T. Hastie, et al., Principal component analysis, Nat Rev Methods Primers 2 (2022) 1-21.
[31]	J.Y. Chen, Study on untargeted metabolomics and antioxidant activity of Eucommia ulmoides Oliver, Master, Beijing University of Chemical Technology, (2022).
[32]	H.L. Xia, W. Chen, D. Hu, et al., Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion, Food Chem. 440 (2024) 138242.
[33]	N.V. Thieu, S. Mirjalili, H. Garg, et al., MetaPerceptron: A standardized framework for metaheuristic-driven multi-layer perceptron optimization, Comput Stand Inter. 93 (2025) 103977.
[34]	Chinese Pharmacopoeia Commission, Pharmacopoeia of the People’s Republic of China:Volume 1, China Medical Science Press, Beijing, 2020.
[35]	L.H. Chen, Study on Developmental Characteristics of Lindera aggregata Tuberous Root and its Genetical Diversity, Master, Chinese Academy of Forestry, 2005.