根据食用植物油4种主要脂肪酸（油酸、亚油酸、硬脂酸和软脂酸）分子结构中拉曼活性较强的基团所对应的谱区以及实测光谱筛选多个特征区间，结合偏最小二乘方法建立46份食用油样本4种脂肪酸的快速拉曼定量分析模型。实验结果表明相较于全谱建模，特征谱区筛选能有效地减少建模所用的变量数，明显提高模型的预测性能。实验中发现食用油颜色的差异会引起拉曼光谱受到不同程度的荧光干扰，因此在上述挑选的特征谱区基础上增加扰动较为明显的了295~325 cm-1波段进行联合建模，显著提高了拉曼定量模型的性能。并与采用数理统计方法进行特征谱区优化的近红外模型相比较，结果表明两类模型性能相当，均可用于食用油脂肪酸的快速、准确检测，其中拉曼模型因其所需样本量少，机理更明确等特点更有优势。

Rapid Raman quantitative analysis models were constructed for four fatty acids (oleic acid, linoleic acid, stearic acid, and palmitic acid) from edible vegetable oil. The regions corresponding to the Raman active groups in the molecular structure of the fatty acids were selected as multiple characteristic regions in the measured spectra, and 46 edible vegetable oil samples were collected as experimental materials to construct the model for these four fatty acids via the partial least squares method. The experimental results indicated that compared with the model built according to the full spectrum, use of the characteristic spectral region reduced the number of variables required for effective modeling and significantly improved the prediction performance of the models. In the experiment, variation in the degree of fluorescence interference was caused by the different colors of edible oils. Therefore, the selected characteristic spectral regions were combined with the spectral bands that showed significant interference (ranging from 295 to 325 cm-1) for joint modeling, thus significantly enhancing the performance of the Raman quantitative models. Furthermore, a mathematical-statistical method was employed to optimize the characteristic spectral regions for the construction of near infrared (NIR) models, which were compared with the established Raman models. The results indicated that the two kinds of models had similar performance and both can be applied for the rapid and accurate detection of the fatty acids in edible oils. However, the Raman-based model has more advantages due to the smaller sample size required and the explicit mechanism.

北京市自然科学基金面上项目 （4132008）；北京市教委重点项目（KZ201310011012）