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    A rapid identification of four medicinal chrysanthemum varieties with near infrared spectroscopy

    Bangxing Han2,4★, Hui Yan3, Cunwu Chen2,4, Houjun Yao2,4, Jun Dai2,4, Naifu Chen2,4★ Corresponding author

    1. 1College of Biological and Pharmaceutical Engineering, West Anhui University, Anhui Province, Lu’an, China.
    2. 2School of Biological and Environmental Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China.
    3. 3Engineering Technology Research Center of Plant Cell Engineering, Anhui Province, Lu’an, People’s Republic of, China.

    CORRESPONDENCE

    Bangxing Han

    School of Biological and Environmental Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China.

    Received: 01-02-2013; Revised: 14-03-2013.

    Volume 10, Issue 39 · pp. 353–358 · PUBLISHED 24 July 2014 · DOI: 10.4103/0973-1296.137378

    View on Pharmacogn. Mag. original site ↗

    ABSTRACT

    Background: For genuine medicinal material in Chinese herbs; the efficient, rapid, and precise identification is the focus and difficulty in the filed studying Chinese herbal medicines. Chrysanthemum morifolium as herbs has a long planting history in China, culturing high quality ones and different varieties. Different chrysanthemum varieties differ in quality, chemical composition, functions, and application. Therefore, chrysanthemum varieties in the market demands precise identification to provide reference for reasonable and correct application as genuine medicinal material. Materials and Methods: A total of 244 batches of chrysanthemum samples were randomly divided into calibration set (160 batches) and prediction set (84 batches). The near infrared diffuses reflectance spectra of chrysanthemum varieties were preprocessed by first order derivative (D1) and autoscaling and was built model with partial least squares (PLS). Results: In this study of four chrysanthemum varieties identification, the accuracy rates in calibration sets of Boju, Chuju, Hangju, and Gongju are respectively 100, 100, 98.65, and 96.67%; while the accuracy rates in prediction sets are 100% except for 99.1% of Hangju. Conclusion: The research results demonstrate that the qualitative analysis can be conducted by machine learning combined with near infrared spectroscopy (NIR), which provides a new method for rapid and noninvasive identification of chrysanthemum varieties.

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      Han, B., Yan, H., Chen, C., Yao, H., Dai, J., & Chen, N. (2014). A rapid identification of four medicinal chrysanthemum varieties with near infrared spectroscopy. Pharmacognosy Magazine, 10(39), 353–358. https://doi.org/10.4103/0973-1296.137378