本研究旨在利用拉曼光谱学方法鉴别花生油产地，并建立稳健的鉴别模型，进一步筛选出与产地密切相关的特征光谱。对来自不同省份和同一省份不同城市的159个花生油样品进行拉曼光谱测定，获得的数据进行了逐步线性判别分析分析(SLDA)，k-最近邻分析(k-NN)和多因素方差分析等。结果表明，基于全光谱的样本识别率达到90%以上。花生油的产地、品种及其互作对拉曼光谱影响显著，筛选出1400-1500 cm^-1和1600-1700 cm^-1为受品种影响较小的产地特征光谱。结合产地特征光谱建立的SLDA最佳分类模型能够快速、准确地识别花生油的产地。

This study aimed to use Raman spectroscopy to identify the producing areas of peanut oil and build a robust discriminant model to further screen out the characteristic spectra closely related to the origin.  Raman spectra of 159 peanut oil samples from different provinces and different cities of the same province were collected.  The obtained data were analyzed by stepwise linear discriminant analysis (SLDA), k-nearest neighbor analysis (k-NN), support vector machine (SVM) and multi-way analysis of variance.  The results showed that the overall recognition rate of samples based on full spectra was higher than 90%.  The producing origin, variety and their interaction influenced Raman spectra of peanut oil significantly, and 1 400–1 500 cm^–1 and 1 600–1 700 cm^–1 were selected as the characteristic spectra of origin and less affected by variety.  The best classification model established by SLDA combined with characteristic spectra could rapidly and accurately identify peanut oil’s origin.