为了快速、准确测定成品植物油和花生原油中黄曲霉毒素B1（AFB1）的含量，建立了采用多功能净化柱对油样进行前处理，再结合光化学衍生高效液相色谱法测定植物油中AFB1含量的方法，对前处理提取剂、高效液相色谱法的分析条件（色谱柱、进样量）进行优化，再通过与国标中免疫亲和柱法进行比较，对所建立的方法进行评价。结果表明：优化的条件为以乙腈-水溶液（84+16）为提取剂，采用Zorbax Eclipse XDB-C18色谱柱（4.6 mm×150 mm，5 μm），进样量10 μL；建立的方法加标回收率和精密度良好，定量限为0.2 μg/kg，低于GB 2761—2017规定的最低限量值要求，可满足企业生产的监测需求；将建立的方法用于测定浅色植物油中AFB1时，检测结果与免疫亲和柱法相比相对误差为0~18.0%，符合国标要求，但对于深色植物油测定的相对误差超出国标要求。综上，建立的方法可用于快速、准确检测花生油（成品油和原油）及成品玉米油、亚麻籽油、葵花籽油、浅黄色菜籽油、黄色芝麻油中AFB1的含量。 To determine aflatoxin B1(AFB1) in finished vegetable oil and crude peanut oil quickly and accurately,a method was developed for the determination of AFB1 content in vegetable oil using a multifunctional clean-up column for the pretreatment of oil samples combined with photochemical derivatization and high performance liquid chromatography(HPLC). The pretreatment extractant and the HPLC analysis conditions (chromatographic column and injection volume) were optimized,and the method was evaluated by comparing with the national standard of immune- affinity column. The results showed that the optimal conditions were with acetonitrile-water solution (84+16) as the extractant, Zorbax Eclipse XDB-C18 (4.6 mm×150 mm， 5 μm) chromatographic column and injection volume 10 μL. The established method had good spiked recovery and precision,and the quantitation limit was 0.2 μg/kg, lower than the minimum limit specified in GB 2761-2017, which could meet the requirements of actual production. When the established method was used for the determination of AFB1 in light-colored vegetable oils, the relative error of the detection results compared with the immune-affinity column method was 0-18.0%, which was in accordance with the national standard, but the relative error for the determination of dark-colored vegetable oils exceeded the national standard. In summary, the method can detect AFB1 content in light vegetable oil such as peanut oil (finished oil and crude oil) and finished corn oil, flaxseed oil, sunflower seed oil, light yellow rapeseed oil and yellow sesame oil quickly and accurately.

为实现山茶油与3种常见食用植物油(菜籽油、大豆油和玉米油)的区分，制备可视化传感器阵列，采用嗅觉可视化技术对4种不同种类的食用植物油进行分类识别。采用主成分分析(PCA)对4种油样的特征数据进行降维，然后将降维后的数据导入K近邻(KNN)、极限学习机(ELM)、支持向量机(SVM) 3种分类模型中进行模型参数优化，对比了3种分类模型的分类结果。结果表明：建立的SVM分类模型性能最优，当输入主成分向量数为7、c=1.741 1、g=4.549 8时，SVM分类模型的测试集分类识别准确率为95.8%，五折交叉验证准确率为89.6%。制得的可视化传感器阵列可以实现4种食用植物油的分类识别，嗅觉可视化技术用于食用植物油检测是可行的。In order to distinguish oil-tea camellia seed oil from three common edible vegetable oils (rapeseed oil, soybean oil and corn oil), visual sensor array was prepared, and four different edible vegetable oils were classified and identified by olfactory visualization technology. Principal component analysis (PCA) was used to reduce the dimension of the characteristic data of the four oil samples. The data after PCA dimensionality reduction was imported into three classification models namely K-Nearest Neighbor (KNN), Extreme Learning Machine (ELM), and Support Vector Machine (SVM), and the model parameters were optimized, and the classification results of the three classification models were compared. The results showed that the established SVM classification model had the best performance. When the number of input principal component vectors was 7, c=1.741 1, and g=4.549 8, the classification and recognition accuracy of the test set of the SVM classification model was 95.8%, and the 5-fold validation accuracy was 89.6%. The visual sensor array can achieve the classification and recognition of four edible vegetable oils, and the olfactory visualization technology is feasible for the classification and identification of edible vegetable oils.