基于相转移技术的劣质食用油快速可视化检测方法

近年来,劣质油流入成品食用油市场引起的地沟油事件,导致了严重的食品安全隐患和社会负面反响.本研究结合可视化和相转移技术,建立了以铜离子和聚二烯丙基二甲基氯化铵（PDDA）修饰的阳离子纳米金为探针的两种简单、准确、灵敏的劣质食用油现场快速检测方法.结果表明,两种方法均可检测到合格食用油中掺入2.8%的劣质油含量,大于5%掺入量可明确测定.将上述方法分别应用于共235个样品的盲测,结果显示,总准确率达95.7%.本工作为劣质食用油的快速现场检测提供了有益的参考方法.     

非正常食用油鉴别新方法(一): 三种辣椒碱残留量的液相色谱-质谱分析

非正常食用油(俗称地沟油)是我国食品安全领域亟须解决的问题。准确可行的地沟油检测方法被分析工作者广泛关注。由于国人的饮食习惯,辣味调料在餐饮业应用广泛,而辣椒碱是引起辣味的主要化学物质,因此可作为鉴别地沟油的潜在指标。本文采用固相萃取-液相色谱-串联质谱技术,建立了食用油中3种微量辣椒碱(辣椒素、二氢辣椒素及壬酸香草酰胺)的检测方法。首先用20 g/L氢氧化钠水溶液提取油样中的辣椒碱,再将提取液用C18小柱富集净化后进行液相色谱-质谱检测。用该法对国家食品安全风险评估中心提供的67个盲样进行了分析,结果表明辣椒碱是一个良好的地沟油特征指示物。凭借这3种辣椒碱指标,阳性样品正确识别率达到75%,阴性样品正确识别率达到100%。目前本方法已经成为国家卫生部最新公布的4种地沟油仪器检测方法之一。     

山东不同产地丹参的HPLC指纹图谱-化学模式识别研究

建立RPHPLC指纹图谱-化学模式识别评价丹参质量的方法.利用RPHPLC法测定不同产地丹参药材的指纹图谱及丹参酮ⅡAt和丹参酮的含量,采用主成分分析、系统聚类分析和逐步判别分析对指纹图谱信息进行化学模式识别研究.在主成分分析的基础上,以前4个主成分为聚类分析的指标进行系统聚类分析,取阈值为8时,所有样品可被分为4类;并建立了相应的判别函数,回判准确率100%;以此为依据,初步建立了丹参化学模式识别的评价方法.     

SPME/GC-MS鉴别地沟油新方法(Ⅱ)

采用固相微萃取/气相色谱-质谱(SPME/GC-MS)方法测定地沟油中的微量杂质成分,通过对各种纯正植物油中的内源性微量成分与不同地区地沟油中的外源性杂质成分的对比分析,以及对地沟油中杂质成分来源的分析,确定了广州、贵州、深圳、北京等地区地沟油的特征成分,并作为鉴别指示成分。该方法灵敏度高、特征性强,在卫生部组织的地沟油检测盲样考核中,对阳性样品判断的准确率高达95.5%。     

全二维气相色谱-飞行时间质谱结合聚类分析与Fisher判别分析对铁观音品质等级的评价研究

采用丙酮超声萃取铁观音样品,以全二维气相色谱-飞行时间质谱分析丙酮提取物。经筛选,在24份不同产季与等级的铁观音中获得68种共有组分,并结合质谱数据库、保留指数与结构谱图等进行了初步鉴定。在此基础上以基于Ward法的聚类分析将所有样品分为3个类别,获得了与感官审评基本相似的结果。通过逐步判别获得5种对分类结果有显著影响的组分,以此为变量通过Fisher判别法建立了4个判别函数,其对样品等级分类的结果与感官审评结果的符合率达到95.8%。证实了通过分析茶叶生化成分进行品质评判的可能。     

人工神经网络用于直接化学电离质谱分析食用油品质的研究

无需任何样品预处理,采用表面解吸常压化学电离质谱(DAPCI-MS)技术直接对涂覆在载玻片表面的食用油样品和地沟油样品进行检测,快速获得了不同油类样品的质谱信号;并运用改进的反向传输(BP)人工神经网络对DAPCI-MS所得到的油类样品质谱数据进行有监督的分类识别,建立多分组预测模型。结果表明:DAPCI-MS能够承受食用油中复杂基体的影响,可对油类样品进行直接快速质谱分析;误差反转(BP)神经网络具有良好的分类判别能力,对食用油样品质谱数据识别效果比较理想,能够在对地沟油和非地沟油样品进行有效区分的同时,实现对不同品种的食用油的分离及分类判别。本方法分析速度快,信息提取准确,识别精度高,对快速质谱技术结合神经网络在该领域的应用以及食用油品质的快速鉴定具有重要的借鉴意义。     

基于红外光谱的植物油品质指数及地沟油检测

为探寻地沟油检测中的内源性指标,应用傅里叶变换红外光谱技术对6种不同种类食用植物油加热前后的红外光谱变化情况进行了研究。通过谱学分析,发现了一个对加热处理过程高度敏感且特异性极强的红外光谱区间。通过对这一光谱区间的简单归一化处理,发展了一种用于衡量植物油是否经历过加热处理过程的植物油品质指数。可以将此指数作为内源性指标用于地沟油鉴定,应用此指数,以两种不同煎炸老油作为地沟油模拟物对提出的指标的有效性进行了验证。这一植物油品质指数的建立可用于地沟油的快速检测。     

A Novel Method for the Identification of Gutter Oils Using SPME/GC - MS ( Ⅱ )

采用固相微萃取/气相色谱-质谱(SPME/GC- MS)方法测定地沟油中的微量杂质成分,通过对各种纯正植物油中的内源性微量成分与不同地区地沟油中的外源性杂质成分的对比分析,以及对地沟油中杂质成分来源的分析,确定了广州、贵州、深圳、北京等地区地沟油的特征成分,并作为鉴别指示成分.该方法灵敏度高、特征性强,在卫生部组织的地沟油检测盲样考核中,对阳性样品判断的准确率高达95.5％.     

电喷雾萃取电离质谱法用于掺假白酒的快速分析

采用自行研制的电喷雾萃取电离源(EESI)与LTQ-XL质谱仪耦合,并结合主成分分析(PCA)和聚类分析(CA),建立了一种能在无需样品预处理的条件下直接、快速、准确鉴别茅台等八种掺假白酒(混掺水和工业酒精,且通过酒精计调控掺假酒的酒精度与真酒保持一致)的新方法. 同时,利用串联质谱能够对目标组分进行准确鉴定. 研究结果表明,EESI-LTQ-MS检测单个样品的时间小于1 min,且重现性好,PCA区分正确率高达96.5%. 通过设置未知样分析进一步验证了该方法的可行性. 此外,还结合单光子电离飞行时间质谱法(SPI-TOF-MS)对检测谱图进行对比分析. 阐述了EESI和SPI两种电离技术在挥发性有机物分析上具有各自的优势,且两种检测手段具有互补的特点. 为市场上酒类饮品真假的快速鉴别及品质鉴定建立了一个综合的分析方法,对于快速筛选伪劣酒类产品具有非常重要的应用价值.     

非正常食用油鉴别新方法(二): 特征奇数碳脂肪酸的多维气相色谱-质谱检测

通过对脂肪酸的非靶标/靶标筛查,确立了非正常食用油(俗称地沟油)的内源性特征指示物: 两种奇数碳脂肪酸,包括源自动物油的13-甲基十四烷酸和源自加热植物油的十一烷酸。并借助多维气相色谱-质谱技术,依据不同极性气相色谱柱的保留作用,不仅实现了不同碳数烷酸以及同碳数烷酸异构体之间的有效分离,达到了对13-甲基十四烷酸和十一烷酸准确定量的目的;而且实现了对目标化合物的在线净化、富集。凭借该项检测方法,参加了国家食品安全风险评估中心组织的第四、五批地沟油盲样考核。经过不断完善,该方法阴性样品的正确率提高到100%,阳性样品的正确率分别达到71%和75%。再结合辣椒碱指标,从内、外源指示物两方面全面、准确地对食用油样品进行判定,使得阳性样品的正确率分别提高至89%和100%。目前,该方法已经入选国家卫生部公布的四大地沟油鉴定仪器分析方法,正等待权威部门的协同性验证。     

费谢尔判别法鉴别原油/燃料油

以多环芳烃作为变量,建立了原油、燃料油属性鉴别的费谢尔判别法。分别测定了来自不同国家和地区的26个原油样品和25个燃料油样品中8种多环芳烃的含量,并将它们作为判别变量。借助SPSS 16.0进行费谢尔判别分析,建立费谢尔判别函数。将未知样品的判别变量值代入后,可以快速地得知样品的类别。结果表明,以多环芳烃作为判别变量进行原油、燃料油费谢尔判别快速而准确。     

Determination of the Polar Compounds in Vegetable Oil by Ultra-Performance Liquid Chromatography–Quadrupole-Time-of-Flight-Mass Spectrometry with Chemometrics

High-throughput ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry was combined with chemometric tools for the rapid determination of polar components in camellia oil, rapeseed oil, and waste cooking oil. The results were analyzed by two unsupervised methods: principal component analysis (one-way ANOVA, p<.05) and volcano plot analysis (p<.05, fold change ≥2) and supervised method: partial least squares discriminant analysis. The results showed that the oils were correctly classified based on their polar components. The first three principal components reflected most detail with a cumulative contribution rate of 84.67% using principal component analysis. The prediction accuracy was close to 100% using partial least squares discriminant analysis. Nineteen components were screened by principal component analysis; twelve were preliminary identified as palmitamide, phytosphingosine, eicosasphinganine, 1-monopalmitin, glyceryl monooleate, glyceryl monostearate, 1α-hydroxyvitamin D2, 1-linoleoyl glycerol, oleamide, sphinganine, stearamide, and linoleic acid. The proposed method may be applied to effectively and accurately authenticate edible oils.     

Acrylate ester-based monolithic columns for capillary electrochromatography separation of triacylglycerols in vegetable oils

A simple and reliable method for the evaluation of triacylglycerols (TAGs) in vegetable oils by capillary electrochromatography (CEC) with UV-Vis detection, using octadecyl acrylate (ODA) ester-based monolithic columns, has been developed. The percentages of the porogenic solvents in the polymerization mixture, and the mobile phase composition, were optimized. The optimum monolith was obtained at the following ratios: 40:60% (wt/wt) monomers/porogens, 60:40% (wt/wt) ODA/1,3-butanediol diacrylate and 23:77% (wt/wt) 1,4-butanediol/1-propanol (14 wt% 1,4-butanediol in the polymerization mixture). A satisfactory resolution between TAGs was achieved in less than 12 min with a 65:35 (v/v) acetonitrile/2-propanol mixture containing 5 mM ammonium acetate. The method was applied to the analysis of TAGs of vegetable oil samples. Using linear discriminant analysis of the CEC TAG profiles, the vegetable oils belonging to six different botanical origins (corn, extra virgin olive, hazelnut, peanut, soybean and sunflower) were correctly classified with an excellent resolution among all the categories.     

LF-NMR结合化学模式识别鉴别油脂种类及餐饮废弃油脂

应用主成分分析(Principal component analysis,PCA)和聚类分析法(Cluster analysis,CA)对9种(27个)常见食用植物油及100个餐饮废油的低场核磁共振(Low-field nuclear magnetic resonance,LF-NMR)(T2)弛豫特性数据进行分析。结果表明:在正常食用油种类区分方面,主成分分析的效果较优,9种食用油在主成分分布图上按种类正确分组,边界清晰。而在正常食用油与餐饮废油的区分方面,聚类分析效果较优,引入30个待测样本后,聚类分析(127个样品,欧式距离=5)的正确率为94.49%,分析误判率为5.51%,分组效果良好。LF-NMR结合化学模式识别可实现对油脂种类及餐饮废弃油脂的鉴别。     

基于石油烃特征比值的多元统计方法进行原油鉴别

基于原油中有生源意义的生物标志物及原油特征多环芳烃的特征比值,采用主成分分析和聚类分析法,对渤海3个不同区块的4种原油,以及其中1种原油风化7d、15d和30d的风化油样和陆地油田1和陆地油田2各1种原油进行鉴别。结果显示:利用主成分分析和聚类分析可以实现大量油样的快速分类鉴别,不仅可以对差异较大的原油进行区分,还可以对原始原油及其风化原油进行很好分辨,但对差异较小的原油,两种分析方法的分辨能力仍有一定局限性。     

Systemic analysis of structures and contents of nitrogen-containing compounds and other non-hydrocarbons in crude oils in conjunction with chemometric resolution technique.

A method is described for the systemic identification and quantitative analysis of nitrogen-containing compounds and other non-hydrocarbons in crude oils. The pre-fractionation of a crude oil sample into 7 fractions was performed by di-adsorption column chromatography using neutral aluminum oxide and silica gel. A subsequent high-resolution separation of individual components was achieved by using capillary column gas chromatography, and compound types were detected by a mass spectrometer. In conjunction with a chemometric method, the compounds in the fractions were further resolved or separated, which made it possible to identify some nitrogen-containing compounds and other non-hydrocarbons in crude oils. To a certain extent, this method could relieve the difficulty of classical analysis in identifying those species with very low contents or incompletely separation, particularly in the cases where authentic standards were not available for addition into the unknown samples in order to reveal what indeed existed in them. The structures and contents of 168 nitrogen-containing compounds in one crude sample and 60 non-nitrogen-containing compounds in one of non-hydrocarbon fractions of this oil sample were determined, and the addition-recovery examination of some standard compounds showed that the analytical veracity was satisfactory.     

13C nuclear magnetic resonance spectroscopy to determine olive oil grades

¹³C nuclear magnetic resonance spectroscopy was used in a first attempt to differentiate olive oil samples by grades. High resolution ¹³C NMR Distortionless Enhancement by Polarization Transfer (DEPT) spectra of 137 olive oil samples from the four grades, extra virgin olive oils, olive oils, olive pomace oils and lampante olive oils, were measured. The data relative to the resonance intensities (variables) of the unsaturated carbons of oleate (C-9 and C-10) and linoleate (L-9, L-10 and L-12) chains attached at the 1,3- and 2-positions of triacylglycerols were analyzed by linear discriminant analysis. The 1,3- and 2- carbons of the glycerol moiety of triacylglycerols along with the C-2, C-16 and C-18 resonance intensities of saturated, oleate and linoleate chains were also analyzed by linear discriminant analysis. The three discriminanting functions, which were calculated by using a stepwise variable selection algorithm, classified in the true group by cross-validation procedure, respectively, 76.9, 70.0, 94.4 and 100% of the extra virgin, olive oil, olive pomace oil and lampante olive oil grades.     

基于近红外光谱技术与化学计量学的绿茶无损鉴别方法研究

该文利用近红外光谱技术结合化学计量学方法开发了不同品种绿茶的无损鉴别方法。通过近红外光谱技术得到了8个品种绿茶样品的近红外光谱,比较了单一以及优化组合光谱预处理方法对光谱的影响,利用无监督的主成分分析(PCA)与有监督的线性判别分析方法(LDA)分别构建了茶叶品种鉴别模型。结果表明：对比单一预处理方法,优化组合预处理具有更优的鉴别准确性。标准正态变量变换预处理消除了茶叶样品大小不均造成的光谱散射影响,一阶导数预处理实现了变动背景的消除,减少了基线漂移的影响,突出了图谱中的有效信息,采用二者相结合的预处理方式并结合无监督的主成分分析法可实现较为准确的绿茶样品种类鉴别分析,准确率达75.0%。此外,采用有监督的线性判别分析方法处理原始光谱数据,可达到100%的鉴别准确率,但该方法需提供类别的先验知识。因此,采用近红外光谱技术和化学计量学相结合的手段可实现不同品种绿茶的快速无损鉴别。     

Characterization of the alcoholic fraction of vegetable oils by derivatization with diphenic anhydride followed by high-performance liquid chromatography with spectrophotometric and mass spectrometric detection

Aliphatic and triterpene alcohols present in vegetable oils have been identified and determined by HPLC using UV–vis and MS detection after previous derivatization with diphenic anhydride. The alcoholic fraction was obtained by saponification, extraction and TLC (according to the European Union official procedure). Derivatization was performed in tetrahydrofuran in the presence of suspended grinded urea, which increases the reaction rate and yield. Derivatized extracts were chromatographed on a C8 column using gradient elution with acetonitrile/water mixtures containing 0.1% acetic acid, with UV–vis followed by negative-ion mode MS detection. Using linear discriminant analysis of the HPLC-MS data (extracted ion chromatograms), oil samples belonging to seven botanical origins (hazelnut, sunflower, corn, extra virgin olive, soybean, peanut and grapeseed) were correctly classified with excellent resolution among all the categories.