A novel machine learning scheme for classification of medicinal herbs based on 2D-FTIR fingerprints

文献类型： 外文期刊

第一作者： Yoon, Tiem Leong

作者： Yoon, Tiem Leong;Yeap, Zhao Qin;Yeap, Zhao Qin;Yam, Mun Fei;Tan, Chu Shan;Chen, Ying;Chen, Jingying;Yam, Mun Fei

作者机构：

关键词： Machine learning; Herbal classification; 2D-FTIR fingerprinting

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.831； 五年影响因子：4.073 ）

ISSN： 1386-1425

年卷期： 2022 年 266 卷

页码：

收录情况： SCI

摘要： A proof-of-concept medicinal herbs identification scheme using machine learning classifiers is proposed in the form of an automated computational package. The scheme makes use of two-dimensional correlation Fourier Transformed Infrared (FTIR) fingerprinting maps derived from the FTIR of raw herb spectra as digital input. The prototype package admits a collection of 11 machine learning classifiers to form a voting pool. A common set of oversampled dataset containing 5 different herbal classes is used to train the pool of classifiers on a one-verses-others manner. The collections of trained models, dubbed the voting classifiers, are deployed in a collective manner to cast their votes to support or against a given inference fingerprint whether it belongs to a particular class. By collecting the votes casted by all voting classifiers, a logically designed scoring system will select out the most probable guess of the identity of the inference fingerprint. The same scoring system is also capable of discriminating an inference fingerprint that does not belong to any of the classes the voting classifiers are trained for as the 'others' type. The proposed classification scheme is stress-tested to evaluate its performance and expected consistency. Our experimental runs show that, by and large, a satisfactory performance of the classification scheme of up to 90 % accuracy is achieved, providing a proof-of-concept viability that the proposed scheme is a feasible, practical, and convenient tool for herbal classification. The scheme is implemented in the form of a packaged Python code, dubbed the "Collective Voting" (CV) package, which is easily scalable, maintained and used in practice. (c) 2021 Elsevier B.V. All rights reserved.

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