ZHUANG Xin-gang1, WANG Li-li2, SHI Xue-shun1, WANG Heng-fei1, CHEN Qi3, FANG Jia-xiong2
（1. The 41st Research Institute of China Electronics Technology Group Corporation, Qingdao 266555, China; 2. Advanced Research Center for Optics, Shandong University, Ji’nan 250100, China; 3. State Key Laboratory of Tea and Agricultural Products Detection（Huangshan), Huangshan, 245000, China）
Abstract： Coupled with partial least squares (PLS), near infrared (NIR) spectroscopy was applied to develop a fast and nondestructive method to identify the production date of Rizhao green tea aiming at the deficiencies of the existing methods. In the modeling process, the raw spectra were first processed by five-point smoothing and first derivative. And then, moving window back propagation artificial neural network (MW-BP-ANN) was applied to select the characteristic spectral variables. After that, the calibration model was built by PLS, and the optimum model was achieved when 9 principal component scores (PCs) were included. The performances of the calibration models were evaluated according to root mean square error of prediction εRMSEP, correlation coefficient (Cp) and residual prediction deviation (σRPD). The optimum results of the calibration model was achieved, and εRMSEP=19.965, Cp=0.943 and σRPD=3.07. The overall results sufficiently demonstrate that NIR spectroscopy combined with PLS can be efficiently applied in the rapid identification of green tea production date.
Key words： near-infrared (NIR) spectroscopy; production date of Rizhao green tea; partial least squares (PLS); five-point smoothing and first derivative
CLD number： O433.4 Document code： A
Article ID： 1674-8042（2018）02-0199-06 doi： 10.3969/j.issn.1674-8042.2018.02.016
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基于近红外光谱分析技术的绿茶生产日期快速鉴别
庄新港1, 王丽丽2, 史学舜1, 王恒飞1, 陈琦3, 方家熊2
（1. 中国电子科技集团公司第四十一研究所， 山东 青岛 266555； 2. 山东大学 光学高等研究中心， 山东 济南 250100； 3. 国家茶叶及农产品检测重点实验室(黄山)， 安徽 黄山 245000）
摘要:针对现有方法检测绿茶生产日期的不足， 采用控制生产日期单一变量的方法， 利用近红外光谱分析技术结合偏最小二乘法对其进行无损伤检测。 首先对原始光谱进行五点平滑和一阶微分预处理， 并利用移动窗口-BP 神经网络算法（MW-BP-ANN）提取特征光谱变量。 然后采用偏最小二乘算法验证方式建立校正模型， 并采用预测均方根误差（εRMSEP）、 相关系数（Cp）和相对分析误差（σRPD）来评价模型质量。 当主成分数为 9 时获得最优模型， 3个评价指标分别为19.965， 0.943和3.07。 研究结果表明， 近红外光谱结合偏最小二乘法可用于对绿茶生产日期的快速无损伤检测。
关键词:近红外光谱； 日照绿茶生产日期； 偏最小二乘； 五点平滑和一阶微分
引用格式： ZHUANG Xin-gang, WANG Li-li, SHI Xue-shun, et al. Rapid determination of production date for green tea by near-infrared spectroscopy. Journal of Measurement Science and Instrumentation, 2018, 9(2)： 199-204. ［doi： 10.3969／j.issn.1674-8042.2018.02.016］
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