The identification of tea variety and producing area using laser-induced breakdown spectroscopy combined with neural network

Zesheng Liu; Xiaohong Ma; Rui Wang; Liuyang Zhan; Taiyu Zhang

doi:10.1117/12.2542895

18 December 2019 The identification of tea variety and producing area using laser-induced breakdown spectroscopy combined with neural network

Zesheng Liu, Xiaohong Ma, Rui Wang, Liuyang Zhan, Taiyu Zhang

Author Affiliations +

Proceedings Volume 11337, AOPC 2019: Optical Spectroscopy and Imaging; 113370K (2019) https://doi.org/10.1117/12.2542895
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

Abstract

The identification of tea varieties and producing areas has an increasingly important market value as the continuous development of the tea industry. A method combining laser induced breakdown spectroscopy (LIBS) and neural network algorithm is proposed in order to identify tea varieties and producing areas rapidly and accurately. In this paper, LIBS spectra of six major tea varieties and eight green tea samples from different producing areas in the range of 200-430 nm are collected, elemental analysis and spectra pre-processing are performed, principal component analysis (PCA) method is applied to select the features, and error back propagation (BP) neural network is used to model the tea classification problem. Key issues such as feature dimensions, network parameters, network structure, and the size of dataset are discussed. The result shows that the best model accuracy reaches 100%, indicating that this method can be used to establish a practicable tea classification model as long as sufficient data are obtained, which is feasible for solving the problems of the identification of tea varieties and producing areas.

Citation Download Citation

Zesheng Liu, Xiaohong Ma, Rui Wang, Liuyang Zhan, and Taiyu Zhang "The identification of tea variety and producing area using laser-induced breakdown spectroscopy combined with neural network", Proc. SPIE 11337, AOPC 2019: Optical Spectroscopy and Imaging, 113370K (18 December 2019); https://doi.org/10.1117/12.2542895

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