光谱学与光谱分析, 2017, 37 (2): 461, 网络出版: 2017-06-20  

基于共聚焦拉曼光谱技术检测茶叶中非法添加美术绿的研究

Detection of Lead Chrome Green Illegally Added in Tea Based on Confocal Raman Spectroscopy
作者单位
浙江大学生物系统工程与食品科学学院, 浙江 杭州 310058
摘要
利用共聚焦拉曼光谱技术对茶叶中非法添加的重金属染料——美术绿进行检测研究。 首先通过特定的浓缩方法, 获取了五个浓度水平美术绿茶汤样本的拉曼光谱。 通过比对标准品拉曼光谱, 对混有美术绿的样本光谱进行了定性分析。 并找到了能够用于定性鉴别茶叶中美术绿的4个主要拉曼特征波数, 分别为1 341, 1 451, 1 527和1 593 cm-1。 对原始拉曼光谱进行预处理后, 融合反向间隔偏最小二乘(biPLS)、 竞争性自适应重加权算法(CARS)和连续投影算法(SPA)对拉曼光谱中美术绿的特征波段进行深入挖掘, 最终优选出了14个特征波数。 基于这14个特征波数分别建立了偏最小二乘(PLS)回归模型和最小二乘支持向量机(LS-SVM)模型, 结果表明, 两类模型均具有好的稳健性和很高的预测能力, 模型的建模集、 验证集和预测集的决定系数(R2)均超过了09, 证明了所提取出来的特征波数的有效性。 与偏最小二乘回归模型相比, 基于LS-SVM的非线性定量检测模型的效果更佳, 预测集决定系数(R2)达到0964, 均方根误差(RMSE)为0535。 以上研究结果表明, 共聚焦拉曼技术结合特定的样品处理方法及化学计量学方法, 可以实现茶叶中非法添加美术绿的定量检测。 该研究为茶叶中非法添加美术绿这一食品安全问题的有效监管提供了帮助。
Abstract
In this paper, confocal Raman spectroscopy was applied to detect the contents of lead chrome green as a heavy-metal stain illegally added in tea. Firstly, Raman spectra of five different concentrations of lead chrome green in tea infusion were acquired based on specific concentration method. The qualitative analysis of sample added with lead chrome green was achieved with comparing the Raman spectra of sample and standard substance. Four main Raman characteristic wavenumbers, 1 341, 1 451, 1 527 and 1 593 cm-1, were extracted for the qualitative identification of lead chrome green in tea. After spectral preprocessing of the raw Raman spectra, backward interval PLS (biPLS), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were combined to deeply mine the characteristic wavenumbers of lead chrome green in Raman spectra, and finally 14 characteristic wavenumbers were optimized. Partial least squares (PLS) and least square support vector machine (LS-SVM) were separately used to build the model based on the extracted 14 wavenumbers. As a result, these two models both had good robustness and high ability to predict and all the determination coefficient (R2) of calibration, validation and prediction were higher than 09, which proved the effectiveness of the extracted characteristic wavenumbers. Compared with the PLS model, the nonlinear model built by LS-SVM got a better result, R2 of prediction was 0964 and the root mean square error of prediction (RMSEP) was 0535. This study indicated that it is feasible to detect the contents of lead chrome green illegally added in tea based on confocal Raman spectroscopy combined with specific sample treatment and chemometrics methods. This study helped the valid supervision of food safety problem on lead chrome green illegally added in tea.

李晓丽, 周瑞清, 孙婵骏, 何勇. 基于共聚焦拉曼光谱技术检测茶叶中非法添加美术绿的研究[J]. 光谱学与光谱分析, 2017, 37(2): 461. LI Xiao-li, ZHOU Rui-qing, SUN Chan-jun, HE Yong. Detection of Lead Chrome Green Illegally Added in Tea Based on Confocal Raman Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2017, 37(2): 461.

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