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Issue 25, 2019
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Prediction of chemical component contents of the fruit of Xanthium strumarium L. during processing based on a computer vision system combined with a support vector machine

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Abstract

The fruit of Xanthium strumarium L. (Xanthii Fructus), a traditional Chinese medicine commonly used to treat rhinitis, need to be processed to reduce their toxicity before clinical use. The chemical components and colour features of Xanthii Fructus change during processing, and thus these changes could be used to evaluate the processing degree and quality of Xanthii Fructus. In the present study, high performance liquid chromatography equipped with a diode array detector (HPLC-DAD) was used to quantify the chemical components in Xanthii Fructus. A computer vision system (CVS) was also established to determine three kinds of colour spaces, including red-green-blue (RGB), L*a*b*, and HSV (hue, saturation and value), and the colour values were used to predict the chemical component content using machine learning. A support vector machine (SVM) and an artificial neural network (ANN) were employed to establish regression models between the colour values and the chemical composition contents. The different colour values were chosen as inputs of the model, and the contents of each chemical component were considered as outputs, respectively. The experimental results showed that a CVS combined with an SVM could be a cost-efficient, easy-to-build, and rapid detection system for on-line monitoring of Xanthii Fructus processing compared with those obtained using an ANN.

Graphical abstract: Prediction of chemical component contents of the fruit of Xanthium strumarium L. during processing based on a computer vision system combined with a support vector machine

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Article information


Submitted
27 Mar 2019
Accepted
02 Jun 2019
First published
04 Jun 2019

Anal. Methods, 2019,11, 3260-3268
Article type
Paper

Prediction of chemical component contents of the fruit of Xanthium strumarium L. during processing based on a computer vision system combined with a support vector machine

W. Fan, Q. Xu, L. Wang, L. Li, J. Wang, Z. Wei, L. Fan, D. Zhang, W. Peng and C. Wu, Anal. Methods, 2019, 11, 3260
DOI: 10.1039/C9AY00637K

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