三维荧光光谱法快速检测橄榄油中掺假廉价油

目的 建立三维荧光光谱结合机器学习快速检测橄榄油中掺假廉价油的方法。方法 采集橄榄油及掺入大豆油、玉米油、棕榈油三种不同浓度梯度油的荧光光谱数据,利用标准差标准化（standardscaler）、标准正态变换（standard normal variate,SNV）、归一化（normalize）三种光谱预处理方法,基于K近邻(K-nearest neighbor,KNN)、随机森林(random forest,RF)、支持向量机(support vector machine,SVM)、偏最小二乘法(partial least squares,PLS)和卷积神经网络(convolutional neural network,CNN) 5种机器学习方法,构建5种橄榄油定量掺假模型。结果 在定性模型中,基于PLS算法构建的模型效果最好,对3种掺假橄榄油的准确率为79%~97%,其中,在鉴定掺假大豆油的橄榄油中正确率高达97%。在构建的掺假油定量模型中,Standardscaler预处理结合RF算法,构建的定量模型最优,Rc2、Rp2、RMSEC、RMSEP最高,分别为1.00、0.99、0.01、0.02。结论 构建橄榄油掺假3种油的定性定量模型,并建立一种快速、实时、低成本的橄榄油掺假检测方法,能够准确判断是否掺入廉价油,并量化掺假程度,提供更全面的橄榄油质量评估。     

芝麻油品质检测方法的研究现状与分析

针对近些年来芝麻油掺伪现象日益严重的问题,对目前采用的芝麻油品质检测方法及其研究现状进行了综述分析。主要介绍了常用的显色法、色谱法、光谱法、电子鼻技术以及一些联用的方法在芝麻油掺伪检测中的研究现状,通过检测脂肪酸、甘三酯、芝麻林素、芝麻酚等特征物质来实现芝麻油品质的有效检测。     

基于GC-MS和MassHunter统计方法的芝麻油掺伪识别

目的提出了一种用于芝麻油掺伪识别的新方法。方法为实现芝麻油中掺伪的识别,对掺入不同比例的大豆油、玉米油、棕榈油的芝麻油的40个样品进行近红外分析,并且基于主成分分析对掺入油进行识别;应用固相微萃取-气相色谱/质谱联用仪(SPME-GC/MS)和Mass Hunter软件,对于玉米油掺伪芝麻油的风味质谱数据进行了研究。结果 Mass Hunter解卷积软件和Agilent Mass Profiler Professional数据统计软件更加灵敏地从复杂包埋的目标物剖析分离得到了独特的标记物。倍率变化(FC=5)分析和ANOVA(P=0.05)分析的结果以火山曲线表示,确定区分芝麻油和玉米油的独特特征标记物。基于此独特的标记物,通过主成分分析,可对纯芝麻油和掺伪芝麻油进行分类。结论试验证明通过统计分析风味质谱数据寻找特征标记物可解决芝麻油掺伪的识别问题。     

Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data

This study focuses on the detection and quantification of extra-virgin olive oil adulteration with different edible oils using mid-infrared (IR) spectroscopy with chemometrics. Mid-IR spectra were manipulated with wavelet compression previous to principal component analysis (PCA). Detection limit of adulteration was determined as 5% for corn–sunflower binary mixture, cottonseed and rapeseed oils. For quantification of adulteration, mid-IR spectral data were manipulated with orthogonal signal correction (OSC) and wavelet compression before partial least square (PLS) analysis. The results revealed that models predict the adulterants, corn–sunflower binary mixture, cottonseed and rapeseed oils, in olive oil with error limits of 1.04, 1.4 and 1.32, respectively. Furthermore, the data were analysed with a general PCA model and PLS discriminant analysis (PLS-DA) to observe the efficiency of the model to detect adulteration regardless of the type of adulterant oil. In this case, detection limit for adulteration is determined as 10%.     

基于可见—近红外光谱的花生油二元掺伪体系鉴别研究

为了建立一种简便有效的花生油掺伪的定性和定量鉴别方法,采集花生油中分别掺伪0~90%大豆油、棕榈油和棉籽油样品的可见—近红外光谱图,结合主成分分析、判别分析、改进偏最小二乘法,建立花生油掺伪的定性鉴别和定量预测模型。结果表明,在定性鉴别中,对花生油中分别掺入大豆油、棕榈油和棉籽油的整体正确判别率分别达到了100%、96.1%和85.3%。在定量分析中,对MPLS法建立的花生油二元掺伪定标模型进行验证,结果表明,掺入大豆油、棉籽油和棕榈油的预测相关系数R_p~2分别为0.998、0.997和0.995,相对标准差RSD分别为2.33%、3.04%和3.83%,相对分析误差RPD分别为3.542、2.642和2.581,说明这三种掺假花生油所建立的最优定标模型的预测精度高,其中花生油中掺入大豆油的预测精度最高,检测花生油中掺入棉籽油与棕榈油的最低掺假量为3%。为花生油二元掺伪模式提供了一种简便、快速、有效的分析方法。     

Detection of fresh palm oil adulteration with recycled cooking oil using fatty acid composition and FTIR spectral analysis

There is a growing concern over the food safety issue related to increased incidence of cooking oil adulteration with recycled cooking oil (RCO). The objective of this study was to detect fresh palm olein (FPO) adulteration with RCO using fatty acid composition (FAC) and Fourier-transform infrared spectroscopy (FTIR) spectral analyses combined with chemometrics. RCO prepared in the laboratory was mixed with FPO in the proportion ranged from 1% to 50% (v/v) to obtain the adulterated oil samples (AO). FACs for FPO, RCO, and AO were determined using gas chromatography equipped with a flame ionization detector (GC-FID). The compositions of most fatty acids in RCO lied within the normal ranges of Codex standard, except for C8:0, C10:0, C11:0, C15:0, trans C18:1, and polyunsaturated fatty acids (PUFAs), C20:5. PUFAs showed a consistent decreasing trend with increasing magnitude of change with respect to increasing adulteration level and thus might be a good indicator for detecting FPO adulteration with RCO. The evaluation parameters (coefficient of determination, root mean standard error) of the FTIR-partial least square (PLS) model of palm oil adulteration with recycled oil are R² = 0.995 and 3.25, respectively. For FTIR spectral analysis, the distinct variations in spectral regions and aberrations in characteristic bands between FPO and RCO were observed. The optimized PLS calibration model developed from normal spectral of the combined region at 3602–3398, 3016–2642, and 1845–650 cm^?1 overpredict the adulteration level. On the other hand, the discriminant analysis classification model was able to classify the FPO and AO into two distinct groups. Improvement of the principles of combined techniques in authenticating AO from fresh oil is beneficial as a guideline to detect adulteration in cooking oil.     

基于指纹图谱和化学计量分析的鳕鱼肝油软胶囊掺假植物油鉴定

目的 建立一种基于脂肪酸气相色谱(gas chromatography, GC) 指纹图谱技术结合化学计量分析的鳕鱼肝油软胶囊中掺假植物油的鉴定方法。方法 采用GC法测定不同来源鳕鱼肝油软胶囊脂肪酸指纹图谱, 经拟合后构建对照指纹图谱, 并进行样品相似度评价。模拟制备鳕鱼肝油软胶囊中掺入不同种类、不同比例植物油的掺假样品, 以其中14个共有脂肪酸峰的相对峰面积作为数据源, 输入SIMCA-P数据分析软件进行主成分分析(principal component analysis, PCA)及掺假模型建立。结果 获得鳕鱼肝油软胶囊GC对照指纹图谱及三维掺假识别模型, 44批样品经相似度评价发现有2批拟似掺假植物油, 将拟似样品色谱数据标准化后导入SIMCA-P软件, 显示2批样品均掺了大豆油, 掺假比例约为15%和35%。结论 脂肪酸GC指纹图谱结合化学计量分析为鳕鱼肝油软胶囊中掺假植物油的鉴定提供一种可靠准确的检测手段, 可快速有效识别鳕鱼肝油掺假行为。     

GC和LF-NMR结合化学计量学方法检测掺假油茶籽油

为了对油茶籽油品质控制及评价提供支撑,以纯油茶籽油和掺假油茶籽油(分别掺入菜籽油、花生油、棕榈油和高油酸花生油)为试验材料,采用气相色谱法(GC)分析其脂肪酸组成,采用低场核磁共振技术(LF-NMR)测定其横向弛豫特性数据,结合主成分分析(PCA)、偏最小二乘判别分析(PLS-DA)和偏最小二乘分析(PLS)等化学计量学方法建立油茶籽油掺假的定性和定量分析模型。结果表明：5种植物油的脂肪酸组成和LF-NMR横向弛豫特性数据存在显著区别;油茶籽油和其他4种植物油在PCA得分图上可清晰区分;PLS-DA模型可有效区分油茶籽油和掺假油茶籽油,判别正确率均可达100%;建立的油茶籽油中掺入菜籽油、花生油、棕榈油、高油酸花生油的PLS定量预测模型,真实值与预测值的相关系数(R²)分别为0.994 1、0.998 6、0.997 6、0.978 1。综上,GC和LF-NMR结合PCA、PLS-DA以及PLS等化学计量学方法可用于油茶籽油掺假类别判定及掺假量分析。     

核磁共振氢谱结合化学计量学快速检测掺假茶油

摘 要：以纯茶油和掺假茶油（掺入大豆油、玉米油）作为核磁共振氢谱检测对象,结合化学计量学方法分析处理核磁数据,建立一种能快速预测茶油掺假的方法。结果表明：纯茶油和掺假茶油在主成分分析得分图上有较好地区分,且掺假样品随掺假比例在图中呈规律性分布,但少部分低体积分数的掺假油与纯茶油重叠。而采用偏最小二乘判别（partial least squares discriminant analysis,PLS-DA）法可以得到更好的分离效果,在该模型中,纯茶油的判别准确率为100%。进一步采用PLS可实现对茶油掺假水平的准确定量测定。该方法可简单、快速地用于茶油的掺假鉴别,在茶油品质控制及评价方面具有很大的应用潜力。     

Direct infusion electrospray ionisation mass spectrometry applied in the detection of adulteration of coconut oil with palm kernel oil

ABSTRACT

Coconut oil has properties that are beneficial to human health. It assists in reducing total cholesterol, triacylglycerol (TAG), phospholipids, low-density lipoprotein (LDL) cholesterol, and very low-density lipoprotein (VLDL) cholesterol in serum and tissues. So its production, and consequently consumption, have increased in recent years. However, it has been a target for intentional adulteration with lower priced oils and fats, such as soybean oil and palm kernel oil (PKO). Coconut oil (CO) and PKO have similar chemical and physical characteristics that make it difficult to verify adulteration of CO with PKO. This study demonstrates a simple, sensitive, and fast technique that uses direct infusion electrospray ionisation mass spectrometry (ESI-MS) in conjunction with principal component analysis (PCA), in order to detect CO adulterated with PKO. Among the seven commercial coconut oil samples analysed, three were adulterated with PKO. Therefore, the suggested direct infusion ESI-MS method can be used in routine analysis to guarantee the quality of coconut oil.     

Quantification of soybean oil adulteration in extra virgin olive oil using portable raman spectroscopy

Extra virgin olive oil is produced through either a cold press procedure or a centrifugation with no thermal and chemical treatments and it is considered as the best quality oil under the category of olive oils. The superior properties of olive oil due to its rich in phenolic and antioxidant content and its contribution to prevent several health problems has increased the demand for olive oil over the years. Consequently, it is nowadays sold at remarkably higher price than regular vegetable oils in the market. Unfortunately, extra virgin olive oil (EVOO) has been adulterated with other cheap oils due to potential high commercial profit. Even though, there are methods available to detect the adulteration in EVOO (such as chromatographic methods and PCR), alternative simpler and faster methods are being studied. In this study, performance of portable Raman spectroscopy to quantify soybean oil (SO) adulteration [up to 25?% (w/w)] in EVOO has been evaluated. Partial Least Square Regression (PLSR) calibration models were developed and both internally (using cross-validation, leave-one-out approach) and externally (using an independent sample set) validated. The model gave standard error of prediction (SEP) of 1.34?% (w/w) SO in EVOO and correlation coefficient of prediction (rPred) of 0.99. Additionally, the residual predictive deviation (RPD) value calculated for the model was found to be 5.71, indicating that the model was considered as “good” and could be used for routine analysis and quality control applications.     

电子鼻鉴别核桃油中掺入大豆油、菜籽油及玉米油研究

为了快速简便地鉴别核桃油掺伪,利用电子鼻技术鉴别核桃油中掺入大豆油、菜籽油及玉米油,并采用主成分分析(PCA)和线性判别式分析(LDA)对结果进行分析,研究表明:采用PCA方法可以鉴别核桃油掺入大于20%大豆油、7%菜籽油和7%玉米油;采用LDA方法可以鉴别核桃油中掺入大于1%大豆油、1%菜籽油和7%玉米油,LDA方法比PCA方法能更加有效地鉴别核桃油中掺入大豆油、菜籽油和玉米油的现象。电子鼻技术可以作为鉴别核桃油掺假的一种快速简便的检测技术。     

食用油掺伪鉴别技术及模型建立的研究进展

随着食用油掺伪现象频繁出现,对食用油的质量控制显得尤为重要,建立有效的食用油掺伪鉴别体系是保证食用油品质的重要手段。由于食用油掺伪物质复杂多样,基于传统的理化检测已无法准确实现食用油复杂掺假组分的分析。因此有必要利用现代仪器分析技术手段结合相关数据计量学处理方法建立快速、精准、高效的食用油掺伪鉴别方法。本文综述了色谱分析技术、光谱分析技术及新型分析技术手段对食用油掺伪的检测,同时分析了借助化学计量学方法基于食用油组成成分种类及含量差异性而建立的掺伪模型的原理及对模型的国内外应用进展进行了概述,在此基础上对目前食用油掺伪模型的建立方法提出展望,以期为食用油品质掺伪鉴别模型的建立提供理论参考。     

电子鼻对芝麻油掺假的检测

使用电子鼻系统PEN3 对芝麻油中掺入大豆油、玉米油、葵花籽油进行检测分析,分别对芝麻油中不同量的掺假进行辨别,用主成分分析(PCA)和线性判别式分析(LDA)两种方法分析。结果表明：电子鼻能够较好的识别芝麻油掺假不同比例的大豆油、玉米油和葵花籽油,而且LDA 方法比PCA 方法的效果好。PCA 方法对掺入大豆油、玉米油超过50% 和葵花籽油超过70% 的芝麻油能明显区分,而LDA 方法对芝麻油中掺入不同量的大豆油、玉米油和葵花籽油均能明显区分。     

A contribution to the study of the enantiomeric composition of a chiral constituent in hazelnut oil used in the detection of adulterated olive oil

 A method is proposed for the determination of the adulteration of olive oil with hazelnut oil which is based on the study of the enantiomeric makeup of (E)-5-methylhept-2-en-4-one (filbertone). The possibilities of either using a sample preparation technique or performing direct injection of the samples (i.e., without any kind of sample pretreatment) are considered and both simultaneous distillation extraction followed by gas chromatographic analysis and direct injection in on-line coupled reverse-phase liquid chromatography to gas chromatography are used. In both cases, a chiral stationary phase is employed in the gas chromatography step and the determination of the enantiomeric composition of filbertone is recommended for an accurate evaluation and quick control for detecting the adulteration of olive oil with hazelnut oil. Received: 9 October 1998 / Revised version: 22 March 1999     

气相离子迁移谱对山茶油掺假的检测

以掺假山茶油样为气相离子迁移谱（gas chromatography-ion mobility spectrometry,GC-IMS）检测对象,利用多维主成分分析（multi-way principal component analysis,MPCA）法和偏最小二乘（partial least squares,PLS）回归分析处理二维谱图数据,探索并建立一种山茶油纯度检测方法。对配制的不同比例3 种食用植物油的掺假油样进行GC-IMS检测,采用MPCA压缩并提取矩阵中的得分矩阵进行主成分分析,将提取的得分矩阵进行PLS分析,建立掺假量的定量预测模型。结果表明,MPCA处理后的主成分图可以明显区分山茶油样和掺入不同种类食用油的掺假山茶油样,且不同掺入比例组有其明显的归属区域;采用PLS对MPCA的得分矩阵进行回归分析,可实现对山茶油掺假比例的准确定量测定。该方法具有快速、准确、无损的特点,可应用推广到其他联用仪器的数据分析处理中,在食用油品质控制与评价方法中具有很大的应用前景。     

低场核磁技术检测芝麻油掺假

为评价低场核磁检测油脂掺假的能力,先用低场核磁结合主成分分方法区分大豆油和3 种芝麻油（分别为精炼、冷榨和热榨工艺）样品,然后用偏最小二乘法分析不同掺兑比例的模拟掺假样品数据。结果表明,大豆油和芝麻油样品的特征信号区域在0～900 ms弛豫时间段,低场核磁能够较好地区分芝麻油和大豆油样品;低场芝麻油中掺入大豆油的最低检测比例为体积分数5%～10%,精炼芝麻油中掺入冷榨或热榨芝麻油的最低检测比例为体积分数10%～20%。因此低场核磁技术可以作为油脂掺假的快速初筛检测方法之一。     

茶油掺伪的数学回归模型研究

以纯茶油中掺伪不同比例大豆油、花生油和菜籽油中任意两种植物油脂来建立茶油掺伪模型,通过气质联用技术测得各样品脂肪酸的含量,并通过相关性、显著性分析来筛选出合适的标志性脂肪酸建立茶油掺伪的多元回归方程模型。结果表明:某些脂肪酸含量在茶油掺伪模型中变化显著,且与茶油掺伪模型间具有高的相关性,选取该部分脂肪酸建立的相应回归模型方程的相关系数(R~2值)均较高,说明本实验获得的回归方程模型真实、可靠。研究结果可以为茶油的产品质量监控和掺假检测提供借鉴。     

基于电子鼻山茶油芝麻油掺假的检测研究

本文采用电子鼻系统对山茶油、芝麻油的掺假（大豆油）作了检测.通过对传感器信号进行方差分析可知,三种油脂的传感器响应有显著差异.主成分分析（PCA）对山茶油与大豆油及其混合物检测效果较差,对芝麻油、大豆油及两者混合物取得了较好的检测效果;而线性判别式分析（LDA）对山茶油和芝麻油的掺假都有较好的检测效果,并优于PCA方法.运用BP神经网络拟对混合油脂进行定量预测,对山茶油掺假的定量预测效果较差,对芝麻油掺假的预测效果略好于山茶油,但最大绝对误差已达0.134,还不能取得较为准确的结果.     

Differential scanning calorimeter application to the detectionof refined hazelnut oil in extra virgin olive oil

The potential application of differential scanning calorimetry (DSC) to verify adulteration of extra virgin olive oil with refined hazelnut oil was evaluated. Extra virgin olive oil and hazelnut oil were characterised by significantly different cooling and heating DSC thermal profiles. Addition of hazelnut oil significantly enhanced crystallisation enthalpy (at hazelnut oil ?20%) and shifted the transition towards lower temperatures (at hazelnut oil ?5%). Lineshape of heating thermograms of extra virgin olive oil was significantly altered by hazelnut oil addition: a characteristic exothermic event originated at −27 °C in extra virgin olive oil and progressively disappeared with increasing hazelnut oil content, while the major endothermic peak at −3.5 °C broadened (at hazelnut oil ?40%) and the minor endothermic peak at 8 °C shifted toward lower temperatures (at hazelnut oil ?5%). The preliminary results presented in this study suggest that DSC analysis may be a useful tool for detecting adulteration of extra virgin olive oil with refined hazelnut oil.