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基于激光诱导击穿光谱的茶叶品种快速分类

Fast Classification of Tea Varieties Based on Laser-Induced Breakdown Spectroscopy

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摘要

在提取激光诱导击穿光谱(LIBS)全部特征峰的基础上, 利用支持向量机建立了有效的茶叶分类模型。采集了15种茶叶样品的有效LIBS光谱数据(190~720 nm), 运用窗口平移平滑和峰位漂移函数修正对光谱进行了预处理, 再结合主成分分析降维, 对绿茶、红茶、白茶实现了98.3%的识别率; 对同一种类中不同品种的茶叶也实现了较好的识别。研究结果表明, LIBS在茶叶品种快速识别应用中具有较好的前景。

Abstract

On the basis of extracting all the characteristic peaks of laser-induced breakdown spectroscopy (LIBS), an effective tea classification model is established based on support vector machine. The effective LIBS spectral data (190-720 nm) of fifteen tea samples are collected, and the spectra are preprocessed by window translation smoothing and peak shift function correction. Combined with principal component analysis for dimensionality reduction, the recognition rate of green tea, black tea and white tea is 98.3%. Different varieties of tea in the same species also achieve good recognition. The research results show that LIBS has a good prospect in the rapid identification of tea varieties.

Newport宣传-MKS新实验室计划
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中图分类号:O433.4

DOI:10.3788/cjl201946.0311003

所属栏目:光谱学

基金项目:国家自然科学基金(61574017)、北京理工大学火炸药全链条创新专项(2017CX10007)

收稿日期:2018-09-19

修改稿日期:2018-10-23

网络出版日期:2018-12-12

作者单位    点击查看

徐向君:北京理工大学物理学院, 北京 100081
王宪双:北京理工大学物理学院, 北京 100081
李昂泽:北京理工大学物理学院, 北京 100081
何雅格:北京理工大学物理学院, 北京 100081
柳宇飞:宝瑞激光科技(常州)有限公司, 江苏 常州 213000
何锋:北京理工大学物理学院, 北京 100081
郭伟:北京理工大学物理学院, 北京 100081
刘瑞斌:北京理工大学物理学院, 北京 100081

联系人作者:刘瑞斌(liuruibin8@gmail.com)

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引用该论文

Xu Xiangjun,Wang Xianshuang,Li Angze,He Yage,Liu Yufei,He Feng,Guo Wei,Liu Ruibin. Fast Classification of Tea Varieties Based on Laser-Induced Breakdown Spectroscopy[J]. Chinese Journal of Lasers, 2019, 46(3): 0311003

徐向君,王宪双,李昂泽,何雅格,柳宇飞,何锋,郭伟,刘瑞斌. 基于激光诱导击穿光谱的茶叶品种快速分类[J]. 中国激光, 2019, 46(3): 0311003

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