Discrimination of Tetrastigma hemsleyanum according to geographical origin by near-infrared spectroscopy combined with a deep learning approach.

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Abstract

Recently, deep learning has presented as a powerful approach to overcome the deficiencies of the conventional biochemical approaches. In this study, a method for discriminating medicinal plant Tetrastigma hemsleyanum from different origins was proposed using near-infrared spectroscopy (NIRS) and deep learning models. Support vector machine (SVM), self-adaptive evolutionary extreme learning machine (SAE-ELM), and convolutional neural network (CNN) were used to process the near-infrared spectral data (4000-5600 cm-1). The results indicated that the average recognition accuracy of SVM on the test set samples (n = 60) reached 90%. The average recognition accuracy of SAE-ELM was 98.3%, while CNN correctly discriminated 100% of T. hemsleyanum from different origins. Notably, CNN avoids tedious redundant data preprocessing and is also able to save the trained model for the next call to achieve rapid detection. As above, this study provides an effective deep learning-based method for discriminating the geographical origins of T. hemsleyanum as well as providing a convenient and satisfactory approach to ensure the famous-region of other medicinal plants.
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