根据硝酸钠自身化学性质较稳定且拉曼特征峰与被测组分山梨酸钾谱峰能完全分离的特点,以硝酸钠为内标物对食品中常用防腐剂山梨酸钾的拉曼光谱进行校正;以质量分数为0.1的硝酸钠在拉曼特征位移1053 cm ^-1处的特征峰作为内标峰,分别计算其与49个样品中相同浓度硝酸钠特征峰强的相对比值,用相对比值分别校正49个样品的山梨酸钾特征峰强,采用一元线性回归分析对山梨酸钾进行定量建模分析。结果表明:校正后,山梨酸钾预测模型校正集和预测集的相关系数显著增大,山梨酸钾在1399 cm ^-1处特征峰强的一元线性回归定量预测模型校正集和预测集相关系数的平方分别为0.9885、0.9865,均方根误差分别为3.0384×10 ^-3、3.7643×10 ^-3;基于最佳预测模型对新配制的18个新样品进行预测,预测值和真实值的相关系数的平方为0.9799,均方根误差为4.8702×10 ^-3,说明用硝酸钠内标法可以有效减小检测仪器、检测环境以及人为因素对山梨酸钾拉曼峰强的影响,提高被测物预测模型的精度。

The chemical properties of sodium nitrate are stable, and the Raman characteristic peaks of sodium nitrate and potassium sorbate can be separated completely. The Raman spectrum of potassium sorbate, a preservative commonly used in food, is calibrated with Raman spectrum of sodium nitrate, whose characteristic peak at Raman shift 1053 cm -1 of sodium nitrate with mass fraction of 0.1 is used as internal standard peak. We calculate the relative ratios of the intensity of internal standard speak and characteristic peak intensities of the same concentration sodium nitrate in 49 samples respectively, use the relative ratios to correct characteristic peak intensities of potassium sorbate in 49 samples, and establish the quantitative prediction model of potassium sorbate by linear regression. The results show that the correlation coefficients of calibration set and prediction set significantly improve after correction. The squares of correlation coefficients of potassium sorbate at 1399 cm -1 characteristic peak are 0.9885 and 0.9865, the root mean square errors are 3.0384×10 -3 and 3.7643×10 -3 for calibration set and prediction set, respectively. Based on the optimal prediction model, we predict 18 new samples. The square of correlation coefficient of the predicted and real values is 0.9799 and root mean square error is 4.8702×10 -3, which indicate that the internal standard method of sodium nitrate can effectively reduce the influences of detection instrument, detection environment, and human factors on the Raman peak intensity of potassium sorbate, and can improve the accuracy of the predicted model.