利用高效液相色谱-蒸发光散射检测器（HPLC-ELSD）法对青海亚麻籽油中的甘三酯（TAG）组分进行了定性、定量研究，并运用中药色谱指纹图谱相似度评价系统建立其TAG标准指纹图谱，利用指纹图谱鉴别掺入5%~50%的大豆油、玉米油、菜籽油、花生油、葵花籽油、芝麻油的掺伪模型。结果表明：亚麻籽油中主要的TAG为OLnLn（29.40%）、LnLnLn（23.71%）、OLnO（15.10%）、OLLn（13.43%）、LLnLn（13.32%）；指纹图谱鉴别结果与真实掺伪量的相对误差表明所建立的指纹图谱可以较好地鉴别掺入5%~50%的大豆油、葵花籽油、芝麻油的掺伪模型，对花生油掺伪量10%的掺伪模型的鉴别相对误差较高（9.15%），未能实现对菜籽油掺伪量5%掺伪模型的鉴别。试验构建的青海亚麻籽油TAG指纹图谱可为青海省亚麻籽油质量监控和掺伪识别提供理论依据。The triglyceride (TAG) components in Qinghai flaxseed oil were determined qualitatively and quantitatively by HPLC-ELSD, and the standard TAG fingerprint was established by the similarity evaluation system of traditional Chinese medicine chromatographic fingerprint. The fingerprint constructed was used to identify the adulteration model of soybean oil, corn oil, rapeseed oil, peanut oil, sunflower seed oil and sesame oil with adulteration amount 5%-50%. The results showed that the main TAGs in Qinghai flaxseed oil were OLnLn (29.40%), LnLnLn (23.71%), OLnO (15.10%), OLLn (13.43%), LLnLn (13.32%).The relative error between the identification result and the true adulteration amount showed that the fingerprint constructed could identify the adulteration models of soybean oil, sunflower seed oil and sesame oil with adulteration amount 5%-50%, and the relative error for the 10% peanut oil adulteration model was relatively high (915%), and the identification of the 5% rapeseed oil adulteration model could not be realized. The TAG fingerprint of Qinghai flaxseed oil constructed provides a theoretical basis for quality control and adulteration identification of Qinghai flaxseed oil.

为了探索基于近红外光谱技术快速无损鉴别掺假油茶籽油的可行性，以赣南茶油为研究对象，通过掺入不同植物油如玉米油、花生油、菜籽油、葵花籽油和大豆油等制备掺假油茶籽油，应用近红外光谱技术采集其光谱特征信息，对比不同预处理方法和主成分数，并结合线性和非线性建模方法建立油茶籽油掺假鉴别模型，以识别准确率(纯油茶籽油样品和掺假油茶籽油样品被正确判别的比例）、灵敏度（纯油茶籽油样品被正确判别为纯油茶籽油的比例）、特异性（掺假油茶籽油样品被正确判别为掺假油茶籽油的比例）作为模型的评价指标，优选出最佳模型。结果表明：二阶微分联合线性判别分析(SD-LDA）模型为最优线性模型，标准正态变量变换联合人工神经网络(SNV-ANN）模型为最优非线性模型，两个模型的识别准确率、灵敏度、特异性分别为 97.58%、100%、97.33%和98.99%、100%、98.88%。SNV-ANN 模型鉴别效果优于SD-LDA模型，说明非线性模型更适于油茶籽油掺假判别，该模型能更准确地鉴别油茶籽油是否掺假。 In order to explore the feasibility of rapid and non-destructive identification of adulterated oil-tea camellia seed oil based on near infrared spectroscopy, Gannan oil-tea camellia seed oil was selected as the research object, and the adulterated oil-tea camellia seed oil was prepared by blending different vegetable oils such as corn oil, peanut oil, rapeseed oil, sunflower seed oil and soybean oil. The spectral characteristics of the adulterated oil-tea camellia seed oil were collected by near infrared spectroscopy, and different pretreatment methods and main components were compared and determined. The identification model of oil-tea camellia seed oil adulteration was established by combining linear and nonlinear modeling methods. The identification accuracy(the percentage of pure and aduterated oil-tea camellia seed oil samples correctly identified, sensitivity (the percentage of pure oil-tea camellia seed oil samples correctly identified as pure oil-tea camellia seed oil) and specificity (the percentage of adulterated oil-tea camellia seed oil samples correctly identified as adulterated camellia seed oil) were used as the evaluation indexes of the model to select the best model. The results showed that the second derivative-linear discriminant analysis (SD-LDA) model was the optimal linear model, and the standard normal variable transformation-artificial neural network (SNV-ANN) model was the optimal nonlinear model, their identification accuracy, sensitivity and specificity were 97.58%, 100%, 97.33% and 98.99%, 100%, 98.88% respectively. The identification effect of SNV-ANN model was better than that of SD-LDA, which indicated that the nonlinear model was more suitable for the identification of oil-tea camellia seed oil adulteration, and the model could more accurately identify whether the oil-tea camellia seed oil was adulterated.

为准确识别法庭科学领域中微量油脂物证，给涉及微量植物油物证鉴定的相关案件提供技术支持，以遗留在不同载体上并在4、25、38 ℃下分别放置1、3、7、14、30、45、60 d的8种微量植物油（亚麻籽油、油茶籽油、菜籽油、玉米油、花生油、芝麻油、大豆油、葵花籽油）为研究对象，利用气相色谱技术测定其脂肪酸组成，以 5种主要脂肪酸（十六烷酸、硬脂酸、油酸、亚油酸、亚麻酸）作为识别指标，结合化学计量学方法构建Fisher判别分析、卷积神经网络和随机森林3种植物油识别模型。结果表明：Fisher判别分析、卷积神经网络和随机森林3种模型均能实现对8种植物油的准确识别，其中随机森林模型能评估各脂肪酸对分类结果的重要性，且识别准确率最高，达98.2%。综上，随机森林模型参数设置简单，识别准确率高，有效解决了微量植物油种类识别困难的问题。In order to accurately identify trace oil evidence in the field of forensic science, and provide technical support for relevant cases involving the identification of trace amounts of vegetable oil evidence, eight trace vegetable oils (flaxseed oil, oil-tea camellia seed oil, rapeseed oil, corn oil, peanut oil, sesame seed oil, soybean oil, and sunflower seed oil) left on different carriers and stored at 4, 25, 38 ℃ for 1, 3, 7, 14, 30, 45 d, and 60 d respectively were used as research object. The fatty acid composition was determined by gas chromatography, and five main fatty acids (hexadecanolic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid) from eight vegetable oils were selected as identification indicators to construct three vegetable oil recognition models (Fisher discriminant analysis, convolutional neural network, and random forest) using chemometrics methods. The results showed that Fisher discriminant analysis, convolutional neural network, and random forest models could all achieve accurate recognition of eight vegetable oils, among which the random forest model could evaluate the importance of each fatty acid to the classification results, and the recognition accuracy was the highest, reaching 98.2%. In conclusion, the random forest model has simple parameter settings and high recognition accuracy, and can effectively solve the problem of difficult identification types of trace vegetable oils.

为了探索紫外-可见-近红外反射光谱测定油茶籽油掺伪量的方法，按照不同掺伪比例制备了244个油茶籽油掺伪大豆油、菜籽油、花生油、玉米油的样本，以自主搭建的实验平台采集所制备样本在200~1 100 nm范围内的反射光谱。将原始光谱进行Savitzky-Golay(SG)-连续小波变换（CWT）预处理后，利用Kennard-Stone(K-S)算法以2∶ 1的比例将样本划分成校正集和预测集。采用竞争性自适应重加权算法（CARS）、连续投影算法（SPA）、自主软收缩算法（BOSS）、迭代变量子集优化算法（IVSO）进行特征波长选择，分别建立基于支持向量机（SVM）、极限学习机（ELM）、随机森林（RF）的油茶籽油掺伪量快速预测模型，同时对特征波长的特性进行了研究。结果表明：原始光谱经过 SG-CWT（L5）预处理和 BOSS 特征波长筛选后，建立的基于SVM的油茶籽油掺伪量快速预测模型能够鉴别掺伪量为1%及以上的油茶籽油，该模型在十折交叉验证和网格搜索法下得到最佳惩罚因子（c）和核函数（γ）分别为5.278 0和0.108 8，其预测决定系数（R2P）、预测均方根误差（RMSEP）、预测平均绝对误差（MAEP）分别为0.998 5、0.013 4、0.010 2。特征波长聚集程度和陡度对模型预测结果存在一定影响。综上，建立的基于反射光谱的油茶籽油掺伪量快速预测模型预测误差小，预测效果较好。In order to explore the method of UV-Vis-NIR reflection spectroscopy to identify blended oil-tea camellia seed oil(CAO), 244 samples of CAO adulterated with soybean oil, rapeseed oil, peanut oil and corn oil were prepared according to different adulteration amounts, and the reflectance spectra of the prepared samples in the range of 200-1 100 nm were collected by an experimental platform built independently. After pretreating the raw spectra with SG-continuous wavelet transform (CWT), the samples were divided into correction and prediction sets using the Kennard-Stone (K-S) algorithm in a ratio of 2∶ 1. Competitive adapative reweighting sampling(CARS)algorithm, successive projections algorithm (SPA), bootstrapping soft shrinkage(BOSS) algorithm, and iteratively variable subset optimization (IVSO) algorithm were used for characteristic wavelength selection, and rapid identification models based on support vector machine (SVM), extreme learning machine (ELM), and random forest (RF) were established for CAO adulteration amount, respectively, and the characteristics of characteristic wavelength were studied. The results showed that the SVM model established after the SG-CWT (L5) pretreating and BOSS characteristic wavelength screening could discriminate the amount of adulteration 1% and above, and the model obtained the best penalty factor c ( 5.278 0) and kernel function γ (0.108 8) under the ten-fold cross-validation and grid search method, with R2P, RMSEP and MAEP of 0.998 5, 0.013 4 and 0.010 2, respectively. At the same time, the degree of aggregation and steepness of the characteristic wavelength had some influence on the model prediction results. In conclusion, the established rapid prediction model for the adulteration amount of oil-tea camellia seed oil based on reflection spectroscopy has low error and good prediction effect.

旨在更全面、深入地了解橄榄油，对2010—2021年世界橄榄油的产量和消费量以及2015—2019年中国橄榄油的进口量进行了统计分析，测定不同品牌和不同等级市售橄榄油（特级初榨橄榄油、混合橄榄油和混合油橄榄果渣油）与9种其他植物油的脂肪酸组成和微量营养成分，并分析比较其差异。结果显示：世界橄榄油的产量和消费量常年维持在300万t左右，其中欧盟占比最大，分别为58.71%~76.68%和4873%~62.15%；2015—2019年中国橄榄油产量仅在0.5万~0.7万t，而消费量和进口量分别达到了3.9万~5.75万t和3.86万~5.37万t；不同品牌和不同等级市售橄榄油的脂肪酸组成整体差异不大，但微量营养成分存在明显差异，其中特级初榨橄榄油的角鲨烯、多酚含量明显高于混合橄榄油和混合油橄榄果渣油，而混合油橄榄果渣油的甾醇和总生育酚含量最高；与一级菜籽油、一级玉米油、一级葵花籽油、一级大豆油和亚麻籽油等其他植物油比较，特级初榨橄榄油的油酸、角鲨烯和多酚含量最高，而甾醇和总生育酚含量偏低。Aiming to gain a more comprehensive and in-depth understanding of olive oil, a statistical analysis was conducted on the production and consumption of world olive oil from 2010 to 2021, as well as the olive oil import volume of China from 2015 to 2019. The fatty acid composition and micronutrient of commercial olive oils of different brands and grades (extra virgin olive oil, blended olive oil and blended olive-pomace oil) and 9 other vegetable oils were measured, and their differences were compared and analyzed. The results showed that the production and consumption of olive oil in the world remained around 3 million tons annually, with the EU accounting for the largest proportion, ranging from 58.71% to 76.68% and 48.73% to 62.15%, respectively. The total production of olive oil in China is only 5 000-7 000 t from 2015 to 2019, with consumption and import reaching 39 000-57 500 t and 38 600-53 700 t respectively. The overall difference in fatty acid composition among commercial olive oils of different brands and grades was not significant, but there were significant differences in micronutrient. Among them, the squalene and polyphenol contents of extra virgin olive oil was significantly higher than that of blended olive oil and blended olive-pomace oil, while the sterol and total tocopherols contents of blended olive-pomace oil were the highest. Compared with first grade rapeseed oil, first grade corn oil, first grade sunflower seed oil, first grade soybean oil and flaxseed oil, extra virgin olive oil had the highest contents of oleic acid, squalene and polyphenols, lower contents of sterol and total tocopherols.

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.

针对市场在售的调和油、玉米油、大豆油、花生油和菜籽油等食用植物油,随机购买20种共290份食用植物油样品。采用液相色谱法测定AFB1、AFB2、AFG1、AFG2以及OTA、OTB真菌毒素的含量，对食用植物油中黄曲霉毒素和赭曲霉毒素的污染水平和分布特征进行分析。结果表明：20种290份食用植物油样品中，有16种共67份样品存在不同程度的真菌毒素污染，总污染率达到23.1%，不同种类食用植物油污染呈现“多种类、共分布”的特点，其中AFG1污染率（14.8%）最高，其次为OTA（13.4%）。绝大多数阳性样本受1～4种真菌毒素污染，仅有少数阳性样本受真菌毒素污染数量达到5种。总体上食用植物油样品受到多种真菌毒素的混合污染情况比较严重，应引起一定的重视。 A total of 290 samples from 20 kinds of edible vegetable oils such as blended oil, corn oil, soybean oil, peanut oil, and rapeseed oil on the market were randomly purchased. Then, the contents of AFB1, AFB2, AFG1, AFG2, OTA and OTB were accurately determined by HPLC to investigate the pollution levels and distribution characteristics of aflatoxins and ochratoxins in edible vegetable oils. The results showed that among the 20 kinds of 290 edible vegetable oil samples, 16 kinds of 67 samples suffered from different degrees of mycotoxin contamination, and the total pollution rate reached 23.1%. The pollution of different kinds of edible vegetable oils presented the characteristics of multi-species and co-distribution. AFG1 had the highest pollution rate (14.8%), followed by OTA (13.4%). Besides, the vast majority of positive samples were contaminated by 1-4 kinds of mycotoxins, only a few positive samples were contaminated with 5 kinds of mycotoxins. Overall, the problem that the edible vegetable oil samples are polluted by multiple mycotoxins is serious and should be paid attention to.

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.

旨在为植物油煎炸品质的综合评价提供参考，对市场上常见的6种植物油（大豆油、菜籽油、棕榈油、玉米油、葵花籽油和棉籽油）进行煎炸实验，考察煎炸过程中植物油的总极性组分(TPC)含量、酸值（AV）、过氧化值（POV）、p-茴香胺值（p-AV）、脂肪酸和碘值等常规理化指标，（E,E）-2,4-癸二烯醛含量，黏度和色泽以及维生素E（VE）和植物甾醇含量的变化，分析各指标的两两相关性，并对11项检测指标进行主成分分析（PCA）。结果表明：6种植物油煎炸过程中11项指标的变化存在一定差异，随着煎炸时间的延长，煎炸油的TPC含量、AV、POV、p-AV、红值和黏度不断增加，C18∶ 2与C16∶ 0比值、碘值、VE和植物甾醇含量不断降低，（E,E）-2,4-癸二烯醛含量先增加后降低；不同指标之间存在一定的相关性，其中TPC、AV和黏度两两之间呈极显著正相关，C18∶ 2与C16∶ 0比值、碘值与VE含量两两间存在极显著的正相关性,（E,E）-2,4-癸二烯醛含量与p-AV呈极显著正相关;PCA得到的3个主成分的累积贡献率为86.225%，通过计算3个主成分的加权得分建立了煎炸油的综合评价模型，并通过计算得出6种植物油中棕榈油的煎炸稳定性最好。综上，所建立的煎炸油综合评价模型可以对6种植物油煎炸品质进行评价，棕榈油的煎炸品质最好。Aiming to provide a reference for the comprehensive evaluation of the frying quality of vegetable oils, frying experiments were carried out on six common vegetable oils available in the market (soybean oil, rapeseed oil, palm oil, corn oil, sunflower seed oil, and cottonseed oil) to investigate the total polar component (TPC) content, acid value (AV), peroxide value (POV), p-anisidine value (p-AV), fatty acid and iodine value conventional physicochemical indexes, the content of (E,E)-2,4-decadienal, viscosity and colour, and the contents of vitamin E (VE) and phytosterols of vegetable oils during the frying process were detected to analyse the two-by-two correlation of the indexes, and the 11 tested indexes were subjected to the principal component analysis (PCA).The results showed that there were some differences in the changes of 11 indexes during the frying process of six vegetable oils. With the prolongation of frying time, the TPC content, AV, POV, p-AV, red value and viscosity of the frying oils increased, the ratio of C18∶ 2 to C16∶ 0, iodine value and contents of VE and phytosterol decreased, and the content of (E,E)-2,4-decadienal first increased and then decreased. There were some correlations between them, including highly significant positive correlations between TPC, AV and viscosity, and highly significant positive correlations between the ratio of C18∶ 2 to C16∶ 0, iodine value and the content of VE. There were highly significant positive correlation between the (E,E)-2,4-decadienal content and p-AV. The cumulative contribution rate of the three principal components obtained was 86.225%, and a comprehensive evaluation model of frying oils was established by calculating the weighted scores of the three principal components. The frying stability of palm oil was the best among the six vegetable oils. In conclusion, the frying quality can be evaluated by the established comprehensive evaluation model of frying oil, and the frying quality of palm oil is the best.