Chemometric perspective on herbal medicine evaluation: Tools, techniques, and trends

Dekai Banerjee; Neel Parekh; Ginpreet Kaur; Sanjay Sharma; Harpal Singh Buttar; Ritu Chauhan; Damandeep Kaur; Hardeep Singh Tuli; Ammar Abdulrahman Jairoun; Moyad Shahwan

doi:10.7324/JAPS.2025.226539

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Abstract

Herbal systems are difficult to study due to their complex chemical composition, even if spectroscopic and chromatographic methods are used to standardize herbal remedies. To address these issues, many advanced analytical methods have been developed to assess the quality of herbal medications, including spectroscopy (nuclear magnetic resonance, infrared, ultraviolet, and inductively coupled plasma spectroscopy techniques, high-performance liquid chromatography, gas chromatography, capillary electrophoresis and extensive approaches along with hybridized chemometric techniques which are used to extract valuable information through various data processing methodologies which are increasingly used in herbal medication authentication. Numerous phytochemical and pharmacological investigations show that one plant can contain hundreds or thousands of chemical constituents. These components create the intended pharmacological effects through various targets and pathways. As a result, the WHO has developed standards for evaluating herbal therapies. Advanced software tools are used in chemometrics, which combines chemistry and statistics to extract relevant information from large chemical datasets. In conclusion, chemometrics software includes spectral analysis, statistical approaches, machine learning, and data visualization. Researchers can gain insights from large chemical datasets using these software programs, improving our understanding of complex systems. This review article describes chemometrics software tools and their use in data analysis, pattern detection, and model construction which are extremely useful for the futuristic approaches towards evaluating herbal medicines.

Keyword: Chemometrics principal component analysis PLS_ Toolbox unscrambler X SIMCA

Citation:

Banerjee D, Parekh N, Kaur G, Sharma S, Buttar HS, Chauhan R, Kaur D, Tuli HS, Jairoun A, Shahwan M. Chemometric perspective on herbal medicine evaluation: Tools, techniques, and trends. J Appl Pharm Sci. 2025. Online First. http://doi.org/10.7324/JAPS.2025.226539

Copyright: © The Author(s). This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Reference

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1. Rebiai A, Hemmami H, Zeghoud S, Ben Seghir B, Kouadri I, Eddine LS, et al. Current application of chemometrics analysis in authentication of natural products: a review. Comb Chem High Throughput Screen. 2022;25:945-72. https://doi.org/10.2174/1386207324666210309102239

2. Takamura A, Ozawa T. Recent advances of vibrational spectroscopy and chemometrics for forensic biological analysis. Analyst. 2021;146:7431-49. https://doi.org/10.1039/D1AN01637G

3. Sarraguça MC, Lopes JA. Quality control of pharmaceuticals with NIR: from lab to process line. Vib Spectrosc. 2009;49:204-10. https://doi.org/10.1016/j.vibspec.2008.07.013

4. Liu X, Bai B, Rogers KM, Wu D, Qian Q, Qi F, et al. Determining the geographical origin and cultivation methods of Shanghai special rice using NIR and IRMS. Food Chem. 2022;394:133425. https://doi.org/10.1016/j.foodchem.2022.133425

5. Robotti E, Marengo E. Chemometric multivariate tools for candidate biomarker identification: LDA, PLS-DA, SIMCA, ranking-PCA. Methods Mol Biol. 2016;1384:237-67. https://doi.org/10.1007/978-1-4939-3255-9_14

6. Cao X, You G, Li H, Li D, Wang M, Ren X. Comparative investigation for rotten xylem (kuqin) and strip types (tiaoqin) of scutellaria baicalensis georgi based on fingerprinting and chemical pattern recognition. Molecules. 2019;24:2431. https://doi.org/10.3390/molecules24132431

7. Lavine B, Workman J. Chemometrics. Anal Chem. 2008;80:4519- 31. https://doi.org/10.1021/ac800728t

8. Acevska J, Stefkov G, Cvetkovikj I, Petkovska R, Kulevanova S, Cho J, et al. Fingerprinting of morphine using chromatographic purity profiling and multivariate data analysis. J Pharm Biomed Anal. 2015;109:18-27. https://doi.org/10.1016/j.jpba.2015.02.016

9. Huang Q, Yang D, Jiang L, Zhang H, Liu H, Kotani K. A novel unsupervised adaptive learning method for long-term electromyography (EMG) pattern recognition. Sensors. 2017;17:1370. https://doi.org/10.3390/s17061370

10. Sensinger JW, Lock BA, Kuiken TA. Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms. IEEE Trans Neural Syst Rehabil Eng. 2009;17:270-8. https://doi.org/10.1109/TNSRE.2009.2023282

11. Zhang X, Mei X, Wang Z, Wu J, Liu G, Hu H, et al. Chemical fingerprint and quantitative analysis for the quality evaluation of docynia dcne leaves by high-performance liquid chromatography coupled with chemometrics analysis. J Chromatogr Sci. 2018;56:575- 81. https://doi.org/10.1093/chromsci/bmy007

12. Xie J-P, Ni H-G. Chromatographic fingerprint similarity analysis for pollutant source identification. Environ Pollut. 2015;207:341-4. https://doi.org/10.1016/j.envpol.2015.09.049

13. Schulze AE, De Beer D, Mazibuko SE, Muller CJF, Roux C, Willenburg EL, et al. Assessing similarity analysis of chromatographic fingerprints of Cyclopia subternata extracts as potential screening tool for in vitro glucose utilisation. Anal Bioanal Chem. 2016;408:639-49. https://doi.org/10.1007/s00216-015-9147-7

14. Souza M, José Comin J, Moresco R, Maraschin M, Kurtz C, Emílio Lovato P, et al. Exploratory and discriminant analysis of plant phenolic profiles obtained by UV-vis scanning spectroscopy. J Integr Bioinform. 2021;18:20190056. https://doi.org/10.1515/jib-2019-0056

15. Silva EFR, da Silva Santos BR, Minho LAC, Brandão GC, de Jesus Silva M, Silva MVL, et al. Characterization of the chemical composition (mineral, lead and centesimal) in pine nut (Araucaria angustifolia (Bertol.) Kuntze) using exploratory data analysis. Food Chem. 2022;369:130672. https://doi.org/10.1016/j.foodchem.2021.130672

16. G?d?k B. Antioxidant, antimicrobial activities and fatty acid compositions of wild Berberis spp. by different techniques combined with chemometrics (PCA and HCA). Molecules. 2021;26:7448. https://doi.org/10.3390/molecules26247448

17. Al-Dabooni S, Wunsch D. Model order reduction based on agglomerative hierarchical clustering. IEEE Trans Neural Netw Learn Syst. 2019;30:1881-95. https://doi.org/10.1109/TNNLS.2018.2873196

18. McLachlan GJ, Bean RW, Ng S-K. Clustering. Methods Mol Biol. 2008;453:423-39. https://doi.org/10.1007/978-1-60327-429-6_22

19. Sharma A, López Y, Tsunoda T. Divisive hierarchical maximum likelihood clustering. BMC Bioinformatics. 2017;18:546. https://doi.org/10.1186/s12859-017-1965-5

20. Aeria G, Claes P, Vandermeulen D, Clement JG. Targeting specific facial variation for different identification tasks. Forensic Sci Int. 2010;201:118-24. https://doi.org/10.1016/j.forsciint.2010.03.005

21. Oliveira S, Douglas de Sousa Fernandes D, Véras G. Overview of analytical techniques associated with pattern recognition methods in sugarcane spirits samples. Crit Rev Anal Chem. 2019;49:477-87. https://doi.org/10.1080/10408347.2018.1548926

22. Shamsudin S, Selamat J, Sanny M, A R SB, Jambari NN, Khatib A. A comparative characterization of physicochemical and antioxidants properties of processed Heterotrigona itama honey from different origins and classification by chemometrics analysis. Molecules. 2019;24:3898. https://doi.org/10.3390/molecules24213898

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