Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, <i>Ocimum</i>, and <i>Withania somnifera</i> study

Samreen Khan; Abhishek Kumar Rai; Anjali Singh; Saudan Singh; Basant Kumar Dubey; Raj Kishori Lal; Arvind Singh Negi; Nicholas Birse; Prabodh Kumar Trivedi; Christopher T. Elliott; Ratnasekhar Ch

doi:10.1016/j.jpha.2023.04.018

Volume 13 Issue 9

Sep. 2023

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Article Navigation > Journal of Pharmaceutical Analysis > 2023 > 13(9): 1041-1057

Samreen Khan, Abhishek Kumar Rai, Anjali Singh, Saudan Singh, Basant Kumar Dubey, Raj Kishori Lal, Arvind Singh Negi, Nicholas Birse, Prabodh Kumar Trivedi, Christopher T. Elliott, Ratnasekhar Ch. Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study[J]. Journal of Pharmaceutical Analysis, 2023, 13(9): 1041-1057. doi: 10.1016/j.jpha.2023.04.018

Citation:

Samreen Khan, Abhishek Kumar Rai, Anjali Singh, Saudan Singh, Basant Kumar Dubey, Raj Kishori Lal, Arvind Singh Negi, Nicholas Birse, Prabodh Kumar Trivedi, Christopher T. Elliott, Ratnasekhar Ch. Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study[J]. Journal of Pharmaceutical Analysis, 2023, 13(9): 1041-1057. doi: 10.1016/j.jpha.2023.04.018

Citation:

Samreen Khan, Abhishek Kumar Rai, Anjali Singh, Saudan Singh, Basant Kumar Dubey, Raj Kishori Lal, Arvind Singh Negi, Nicholas Birse, Prabodh Kumar Trivedi, Christopher T. Elliott, Ratnasekhar Ch. Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study[J]. Journal of Pharmaceutical Analysis, 2023, 13(9): 1041-1057. doi: 10.1016/j.jpha.2023.04.018

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Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study

doi: 10.1016/j.jpha.2023.04.018

a. Metabolomics Lab, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India;
b. Department of Crop Production & Protection, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India;
c. Academy of Council of Scientific and Industrial Research (ACSIR), Ghaziabad, 201002, India;
d. Department of Biotechnology, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India;
e. Genetics and Plant Breeding Department, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India;
f. Department of Phytochemistry, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India;
g. School of Biological Sciences, Queen's University, Belfast, BT9 5DL, UK

Funds:

Authors would like to thank Government of India for Department of Science and Technology-SERB-SRG research grant (Grant No.: SRG/2021/000750-G) and Department of Biotechnology for Ramalingaswami grant (Grant No.: BT/RLF/Re-entry/21/2020), and Director, Prabodh Kumar Trivedi, of CSIR-CIMAP for providing infrastructure, facility, and funding support from CSIR, India (Grant Nos.: FC2020-23/NMITLI/TLP0001 & TLP0002). We acknowledge Dr. Ritu Trivedi (CSIR-CDRI Lucknow, India) for support and Dr. Abolie Girme and Dr. Lal Hingorani (Pharmanza herbal Pvt. Ltd, India) for providing Withania somnifera samples. We acknowledge Dr. Neerja Tiwari for FT-NIR access, Ms. Manju Yadav and Ms. Namita Gupta for HPLC access, and Ms. Anju Yadav for GC-MS access. Authors would like to thank Aroma mission HCP-0007, India for funding support. Prof. Christopher T. Elliott would like to thank Bualuang ASEAN Chair Professor Fund, UK and Queen's University Belfast Fund, UK.

Received Date: Dec. 23, 2022
Accepted Date: Apr. 27, 2023
Rev Recd Date: Apr. 02, 2023
Publish Date: Apr. 28, 2023

Abstract

Abstract

Herbal medicines are popular natural medicines that have been used for decades. The use of alternative medicines continues to expand rapidly across the world. The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications, as their therapeutic potential varies between different geographic origins, plant species, and varieties. Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach. Their quality should be considered based on a complete metabolic profile, as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds. A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization. In this study, a rapid and effective quality assessment system for geographical traceability, species, and variety-specific authenticity of the widely used natural medicines turmeric, Ocimum, and Withania somnifera was investigated using Fourier transform near-infrared (FT-NIR) spectroscopy-based metabolic fingerprinting. Four different geographical origins of turmeric, five different Ocimum species, and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches. Extremely good discrimination (R² > 0.98, Q² > 0.97, and accuracy = 1.0) with sensitivity and specificity of 100% was achieved using this metabolic fingerprinting strategy. Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.
- Rapid metabolic fingerprinting,
- Natural medicines,
- FT-NIR,
- Chemometric models

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