Using Mid-Infrared Spectroscopic Fingerprinting, Multivariate Analysis And Machine Learning To Differentiate Traditional Herbal Medicine

Traditional herbal medicine is an important part of the global health system with persistent issues like adulteration and misidentification. While current global standards employ chromatographic identification to combat this issue, there are some disadvantages with such methods. This study looked at...

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Main Author:	Yeap, Zhao Qin
Format:	Thesis
Language:	English
Published:	2022
Subjects:	RS1-441 Pharmacy and materia medica
Online Access:	http://eprints.usm.my/60131/1/24%20Pages%20from%20YEAP%20ZHAO%20QIN.pdf
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id	my-usm-ep.60131
record_format	uketd_dc
spelling	my-usm-ep.601312024-03-12T02:01:39Z Using Mid-Infrared Spectroscopic Fingerprinting, Multivariate Analysis And Machine Learning To Differentiate Traditional Herbal Medicine 2022-11 Yeap, Zhao Qin RS1-441 Pharmacy and materia medica Traditional herbal medicine is an important part of the global health system with persistent issues like adulteration and misidentification. While current global standards employ chromatographic identification to combat this issue, there are some disadvantages with such methods. This study looked at five traditional herbal medicine, namely Anoectochilus roxburghii, Aristolochia manshuriensis, Dioscorea hamiltonii, Gelsemium elegans and Alisma orientalis, and ways to classify them. Infrared spectra of the herbs were collected from a total of 200 samples from 20 °C to 120 °C at 10 °C intervals. Infrared signals of functional groups on the main chemical constituents for every herb were present in the infrared spectra collected. Principal component analysis of these spectra found a limitation where the success of the analysis might require less types of herbs included. Computational combination of thermally perturbed spectra was performed to obtain two-dimensional chemical fingerprints for every sample. Machine Learning Classifier were trained to generate models. The model classified the herbs with an accuracy of 87.9 % when all the herbs were included in training. When Alisma orientalis was excluded from training to measure the robustness of the model, 91.3 % of the samples were classified correctly. 2022-11 Thesis http://eprints.usm.my/60131/ http://eprints.usm.my/60131/1/24%20Pages%20from%20YEAP%20ZHAO%20QIN.pdf application/pdf en public masters Perpustakaan Hamzah Sendut Pusat Pengajian Sains Farmasi
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	RS1-441 Pharmacy and materia medica
spellingShingle	RS1-441 Pharmacy and materia medica Yeap, Zhao Qin Using Mid-Infrared Spectroscopic Fingerprinting, Multivariate Analysis And Machine Learning To Differentiate Traditional Herbal Medicine
description	Traditional herbal medicine is an important part of the global health system with persistent issues like adulteration and misidentification. While current global standards employ chromatographic identification to combat this issue, there are some disadvantages with such methods. This study looked at five traditional herbal medicine, namely Anoectochilus roxburghii, Aristolochia manshuriensis, Dioscorea hamiltonii, Gelsemium elegans and Alisma orientalis, and ways to classify them. Infrared spectra of the herbs were collected from a total of 200 samples from 20 °C to 120 °C at 10 °C intervals. Infrared signals of functional groups on the main chemical constituents for every herb were present in the infrared spectra collected. Principal component analysis of these spectra found a limitation where the success of the analysis might require less types of herbs included. Computational combination of thermally perturbed spectra was performed to obtain two-dimensional chemical fingerprints for every sample. Machine Learning Classifier were trained to generate models. The model classified the herbs with an accuracy of 87.9 % when all the herbs were included in training. When Alisma orientalis was excluded from training to measure the robustness of the model, 91.3 % of the samples were classified correctly.
format	Thesis
qualification_level	Master's degree
author	Yeap, Zhao Qin
author_facet	Yeap, Zhao Qin
author_sort	Yeap, Zhao Qin
title	Using Mid-Infrared Spectroscopic Fingerprinting, Multivariate Analysis And Machine Learning To Differentiate Traditional Herbal Medicine
title_short	Using Mid-Infrared Spectroscopic Fingerprinting, Multivariate Analysis And Machine Learning To Differentiate Traditional Herbal Medicine
title_full	Using Mid-Infrared Spectroscopic Fingerprinting, Multivariate Analysis And Machine Learning To Differentiate Traditional Herbal Medicine
title_fullStr	Using Mid-Infrared Spectroscopic Fingerprinting, Multivariate Analysis And Machine Learning To Differentiate Traditional Herbal Medicine
title_full_unstemmed	Using Mid-Infrared Spectroscopic Fingerprinting, Multivariate Analysis And Machine Learning To Differentiate Traditional Herbal Medicine
title_sort	using mid-infrared spectroscopic fingerprinting, multivariate analysis and machine learning to differentiate traditional herbal medicine
granting_institution	Perpustakaan Hamzah Sendut
granting_department	Pusat Pengajian Sains Farmasi
publishDate	2022
url	http://eprints.usm.my/60131/1/24%20Pages%20from%20YEAP%20ZHAO%20QIN.pdf
_version_	1794024094775115776

Using Mid-Infrared Spectroscopic Fingerprinting, Multivariate Analysis And Machine Learning To Differentiate Traditional Herbal Medicine

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