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Identification of green tea origins by near-infrared (NIR) spectroscopy and different regression tools

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Abstract

In this study, near-infrared (NIR) spectroscopy was applied to efficiently and non-destructively identify Shandong green tea origins coupled with three different regression tools. Analysis results indicated that partial least squares (PLS) had better performance than back propagation artificial neural network (BP-ANN) and support vector machine (SVM). For PLS, the accuracies of identification were up to 100% for both training and testing. The results sufficiently demonstrate that NIR spectroscopy can be efficiently utilized for the non-destructive identification of green tea origins.

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Authors and Affiliations

  1. Advanced Research Center for Optics, Shandong University, Jinan, 250100, China

    XinGang Zhuang, LiLi Wang & JiaXiong Fang

  2. School of Information Science and Engineering, Shandong University, Jinan, 250100, China

    XinGang Zhuang

  3. State Key Laboratories of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China

    XinGang Zhuang, LiLi Wang & JiaXiong Fang

  4. State Key Laboratory of Tea and Agricultural Products Detection (Huangshan), Huangshan, 245000, China

    Qi Chen & XueYuan Wu

Authors
  1. XinGang Zhuang
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  2. LiLi Wang
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  3. Qi Chen
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  4. XueYuan Wu
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  5. JiaXiong Fang
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Correspondence to LiLi Wang.

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Zhuang, X., Wang, L., Chen, Q. et al. Identification of green tea origins by near-infrared (NIR) spectroscopy and different regression tools. Sci. China Technol. Sci. 60, 84–90 (2017). https://doi.org/10.1007/s11431-016-0464-0

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  • DOI: https://doi.org/10.1007/s11431-016-0464-0

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