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
发布时间：2018-11-11 17:29
【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 593cm~(-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(R~2)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(R~2)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]:The confocal Raman spectroscopy was used to detect the heavy metal dyestuff in tea. Firstly, the Raman spectra of five artistic green tea soup samples were obtained by a specific concentration method. By comparing the Raman spectra of standard samples, the spectrum of samples mixed with fine arts green was qualitatively analyzed. The four main Raman characteristic wave numbers which can be used for qualitative identification of fine arts green in tea are 1 341 ~ (-1) C ~ (-1) and 1 593 cm ~ (-1), respectively. After pretreatment of the original Raman spectrum, the feature bands of the fine arts green in the Raman spectrum are deeply mined by combining the reverse interval partial least square (biPLS), competitive adaptive reweighting algorithm (CARS) and the continuous projection algorithm (SPA). Finally, 14 characteristic wavenumber were selected. Based on the 14 characteristic wavenumber, the partial least squares (PLS) regression model and the least squares support vector machine (LS-SVM) model are established, respectively. The results show that both models have good robustness and high predictive ability. The determinant coefficients (R _ (2) of both the verification set and the prediction set are higher than 0.9, which proves the validity of the extracted characteristic wavenumber. Compared with the partial least square regression model, the nonlinear quantitative detection model based on LS-SVM is more effective. The prediction set determination coefficient (RG-2) is 0.964, and the root mean square error (RMSE) is 0.535. The results show that confocal Raman technique combined with specific sample treatment and chemometrics can be used to detect the illegal addition of fine arts green in tea leaves. The study helps to regulate the food safety problem of illegally adding art green to tea.
【作者单位】： 浙江大学生物系统工程与食品科学学院;
【基金】：国家自然科学基金项目(61201073,31471417) 浙江省教育厅科研项目(Y201225966) 浙江大学基本科研业务费专项资金项目(2015QNA6005)资助
【分类号】：O657.37;TS272.7
本文编号：2325612
[Abstract]:The confocal Raman spectroscopy was used to detect the heavy metal dyestuff in tea. Firstly, the Raman spectra of five artistic green tea soup samples were obtained by a specific concentration method. By comparing the Raman spectra of standard samples, the spectrum of samples mixed with fine arts green was qualitatively analyzed. The four main Raman characteristic wave numbers which can be used for qualitative identification of fine arts green in tea are 1 341 ~ (-1) C ~ (-1) and 1 593 cm ~ (-1), respectively. After pretreatment of the original Raman spectrum, the feature bands of the fine arts green in the Raman spectrum are deeply mined by combining the reverse interval partial least square (biPLS), competitive adaptive reweighting algorithm (CARS) and the continuous projection algorithm (SPA). Finally, 14 characteristic wavenumber were selected. Based on the 14 characteristic wavenumber, the partial least squares (PLS) regression model and the least squares support vector machine (LS-SVM) model are established, respectively. The results show that both models have good robustness and high predictive ability. The determinant coefficients (R _ (2) of both the verification set and the prediction set are higher than 0.9, which proves the validity of the extracted characteristic wavenumber. Compared with the partial least square regression model, the nonlinear quantitative detection model based on LS-SVM is more effective. The prediction set determination coefficient (RG-2) is 0.964, and the root mean square error (RMSE) is 0.535. The results show that confocal Raman technique combined with specific sample treatment and chemometrics can be used to detect the illegal addition of fine arts green in tea leaves. The study helps to regulate the food safety problem of illegally adding art green to tea.
【作者单位】： 浙江大学生物系统工程与食品科学学院;
【基金】：国家自然科学基金项目(61201073,31471417) 浙江省教育厅科研项目(Y201225966) 浙江大学基本科研业务费专项资金项目(2015QNA6005)资助
【分类号】：O657.37;TS272.7
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