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Issue 25, 2018
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A novel method for geographical origin identification of Tetrastigma hemsleyanum (Sanyeqing) by near-infrared spectroscopy

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Abstract

Tetrastigma hemsleyanum Diels et Gilg (T. hemsleyanum), also known as Sanyeqing in Chinese, is a rare medicinal herb. The quality of its crude drug highly depends on the geographical origin of the plants. The traditional supervised discrimination methods based on near-infrared spectroscopy (NIRS) construct a classifier to discriminate samples with previously trained geographical origins. Those methods have limited power since a lot of real samples are unknown and untrained by the classifier. It is necessary to develop a discrimination method that takes untrained geographical origins into consideration. In this study, a novel identification method is developed using an improved naive Bayesian classifier combined with a clustering algorithm by fast search and find of density peaks (INBC-CFSFDP). In detail, the INBC is proposed to distinguish samples with new geographical origins that are not given in the training database and the CFSFDP is used to further identify the interior categories of those samples with new geographical origins. The experimental results show that regardless of whether the geographical origins are indexed in the training database or not, they can be accurately identified using an INBC-CFSFDP, and they demonstrate that the proposed method is easy, effective, fast, and feasible to discriminate the geographical origin of T. hemsleyanum.

Graphical abstract: A novel method for geographical origin identification of Tetrastigma hemsleyanum (Sanyeqing) by near-infrared spectroscopy

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Publication details

The article was received on 14 May 2018, accepted on 24 May 2018 and first published on 28 May 2018


Article type: Paper
DOI: 10.1039/C8AY01087K
Citation: Anal. Methods, 2018,10, 2980-2988

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    A novel method for geographical origin identification of Tetrastigma hemsleyanum (Sanyeqing) by near-infrared spectroscopy

    W. Lin, Q. Chai, W. Wang, Y. Li, B. Qiu, X. Peng, T. Lai, J. Huang and Z. Lin, Anal. Methods, 2018, 10, 2980
    DOI: 10.1039/C8AY01087K

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