基于实验室自行搭建的拉曼光谱点扫描系统, 利用表面增强拉曼技术对橙味饮料中山梨酸钾的含量进行了定量快速检测研究。 通过与山梨酸钾标准品拉曼光谱及其水溶液表面增强拉曼光谱等比较分析, 确定了山梨酸钾1 648.4, 1 389.3和1 161.8 cm-1处的表面增强特征拉曼位移。 通过山梨酸钾橙味饮料平行样品的拉曼位移峰强重现性实验并计算其峰强的相对标准偏差证实了该表面增强拉曼方法具有较好的重复性。 采集了山梨酸钾浓度范围为1.706～0.180 7 g·kg-1的33个橙味饮料样品的表面增强拉曼光谱, 所有原始光谱经S-G 5点平滑及Baseline基线去除荧光背景预处理后分别用一元线性回归分析、 多元线性回归分析和偏最小二乘回归分析方法, 建立了山梨酸钾的定量预测模型。 经比较, 选取三个山梨酸钾拉曼特征位移1 161.8, 1 389.3和1 648.4 cm-1所建立的多元线性回归模型校正集的相关系数(R2C)和均方根误差(RMSEC)分别为0.983 7和0.051 7 g·kg-1, 验证集的相关系数(R2P)和均方根误差(RMSEP)分别为和0.969 9和0.052 8 g·kg-1, 比一元线性回归模型和偏最小二乘回归模型误差小、 精度高。 基于表面增强拉曼完全可以实现橙味饮料中山梨酸钾的定量快速预测, 为各类食品中山梨酸钾含量的快速监测奠定了技术基础。

This paper established a rapid quantitative detection method for the content of potassium sorbate in orange flavoreddrink by using surface-enhanced Raman technique, which is based on the self-built laboratory Raman point scanning system. Through the comparative analysis of these Raman spectra and surface-enhanced Raman spectra of potassium sorbate under different states, such as standard potassium sorbate, aqueous solution of potassium sorbate and so on, identified 1 159.3, 1 398.9 and 1 653.0 cm-1 were characteristic Raman peaks of potassium sorbate, 1 648.4, 1 389.3 and 1 161.8 cm-1 were surface-enhanced Raman characteristic peaks of potassium sorbate. Confirmed this method had better repeatability by doing Raman shift peak intensity reproducibility experiment of the parallel samples and calculating the relative standard deviation (RSD) of the peak intensity. The results showed that the peak intensity average relative standard deviation RSD value was 9.88%. Gathered the surface enhanced Raman spectra of 33 orange flavored beverage samples from the concentration range of 0.180 7~1.706 g·kg-1 and all these Raman spectra did the pretreatments of S-G 5 point smoothing and Baseline removal of fluorescence background. Combined with the commonly used quantitative analysis method (Linear regression analysis method, multiple linear regression analysis method, and partial least squares regression analysis method), three prediction models which were based on these three different principles were established for the content prediction of potassium sorbate. By comparison, the multiple linear regression model which used three Raman shifts (1 161.8, 1 389.3 and 1 648.4 cm-1) had the smallest prediction error and highest model precision. In the multiple linear regression model, correlation coefficients of correction set and validation set (R2C and R2P) were 0.983 7 and 0.969 9, root mean square error of correction set and validation set (RMSEC and RMSEP) were 0.051 7 and 0.052 8 g·kg-1. And the relative standard deviation (RSD) of this model was 9.93% and the relative error (RPD) of this model was 5.06. By using the surface enhanced Raman technique combined with multiple linear regression analysis method, a more accurate and rapid prediction of potassium sorbate in orange beverage can be realized, which lays a technical foundation for the rapid monitoring of potassium sorbate in other foods.