Chemical fingerprinting of petroleum biomarkers using time warping and PCA

A new method for chemical fingerprinting of petroleum biomakers is described. The method consists of GC-MS analysis, preprocessing of GC-MS chromatograms, and principal component analysis (PCA) of selected regions. The preprocessing consists of baseline removal by derivatization, normalization, and alignment using correlation optimized warping. The method was applied to chromatograms of m/z 217 (tricyclic and tetracyclic steranes) of oil spill samples and source oils. Oil spill samples collected from the coastal environment in the weeks after the Baltic Carrier oil spill were clustered in principal components 1 to 4 with oil samples from the tank of the Baltic Carrier (source oil). The discriminative power of PCA was enhanced by deselecting the most uncertain variables or scaling them according to their uncertainty, using a weighted least squares criterion. The four principal components were interpreted as follows: boiling point range (PC1), clay content (PC2), carbon number distribution of sterols in the source rock (PC3), and thermal maturity of the oil (PC4). In summary, the method allows for analyses of chromatograms using a fast and objective procedure and with more comprehensive data usage compared to other fingerprinting methods.     

Forensic fingerprinting of diamondoids for correlation and differentiation of spilled oil and petroleum products

Diamondoids (adamantanes and diamantanes) are rigid, three-dimensionallyfused cyclohexyl-ring alkane compounds that can be found in almost all crude oils and in most petroleum products. Forforensic environmental investigations, the most commonly used biomarkers are high molecular weight (MW) tri- to pentacyclic terpanes and steranes. Most of these high MW biomarkers, however, are removed from the original crude oil feedstocks during the refining processes, while smaller biomarkers including diamondoids are concentrated in petroleum products. Fingerprinting diamondoids could thus provide another diagnostic means for correlation and differentiation of spilled oils and be particularly valuable for light to midrange distillates, such as jet and diesel fuels, the source of which may be difficult to identify using routine biomarker techniques. In this work, a reliable GC-MS analytical method has been developed for characterization and quantitation of diamondoids. The method detection limits for five target diamondoids were determined to be in the range of 0.06-0.14 microg/g oil. Distributions of diamondoids in over 100 different oils and refined products were quantitatively compared. The concentrations of four groups of target biomarkers were found, in general, to decrease in the order of sesquiterpanes > terpanes and steranes > adamantanes > diamantanes in both crude oils and refined products. A number of indices of admantanes and diamantanes have been developed and assessed as source indicators using their diagnostic powers (DP). The effects of evaporative weathering and biodegradation on alteration of diamondoid distributions have been quantitatively investigated. Finally, a spill case study by statistical evaluation of diagnostic ratios using the "two-tailed" Student's tapproach is presented to illustrate the unique utility of diamondoids for correlation and differentiation of unknown spilled diesels.     

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。     

电子舌快速检测食用植物油掺伪

目的应用法国Alpha M.O.S公司生产的传感器型味觉电子舌系统对3组不同程度掺伪的食用植物油进行掺伪检测。方法选取6种不同类型植物油,用20%的乙醇浸泡后超声,静置隔夜,将油脂中的味觉信息提取出来,由电子舌自动进样系统采集原始数据,所得样品数据用主成分分析法、判别因子法进行分析。结果 2种方法均能较好地检测区分不同的食用植物油样品,大部分的指纹分辨指数高于95分。此方法可以区分不同榨取工艺或不同产地的同种类油脂,可鉴别的掺伪检测限为0.1%。结论本实验鉴别精确度远大于常规的油脂检测方法,且具有较高的灵敏度,能够快速有效地鉴别不同种类食用植物油并区分不同掺杂比例的油脂样品。     

Source allocation by least-squares hydrocarbon fingerprint matching

There has been much controversy regarding the origins of the natural polycyclic aromatic hydrocarbon (PAH) and chemical biomarker background in Prince William Sound (PWS), Alaska, site of the 1989 Exxon Valdez oil spill. Different authors have attributed the sources to various proportions of coal, natural seep oil, shales, and stream sediments. The different probable bioavailabilities of hydrocarbons from these various sources can affect environmental damage assessments from the spill. This study compares two different approaches to source apportionment with the same data (136 PAHs and biomarkers) and investigate whether increasing the number of coal source samples from one to six increases coal attributions. The constrained least-squares (CLS) source allocation method that fits concentrations meets geologic and chemical constraints better than partial least-squares (PLS) which predicts variance. The field data set was expanded to include coal samples reported by others, and CLS fits confirm earlier findings of low coal contributions to PWS.     

基于嗅觉可视化技术的食用植物油分类识别

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。     

Geographical classification of sicilian olive oils in terms of sterols and fatty acids content

The sterols and fatty acids contents of 22 extravirgin olive oil samples produced in the Etna area and in other areas of Sicily were determined. Principal component analysis (PCA) of these data provided a ‘scores’ plot where samples produced in the Etna area were clearly grouped and unknown samples could be classified according to their geographical origin. This classification was confirmed by the residual standard deviation values of all samples fitted into a disjoint principal components model derived from Etna oils only. The above results suggest that the DOC (denominazlone di origine controllata) origin mark for Etna extravirgin olive oils can be attributed on the basis of their sterols and fatty acids contents by means of PCA.     

基于主成分分析法评价两种餐饮用油的煎炸性能

目的 分析两种常见餐饮用油(餐饮调和油A和餐饮调和油B)煎炸性能的差异,并对餐饮用油煎炸品质进行综合评估。方法 在模拟西式快餐实际油炸条件下进行煎炸薯条实验,考察了餐饮用油在油炸过程中十项品质指标,并利用主成分分析法(PCA)对两种餐饮用油在油炸过程中的煎炸性能进行综合评价。结果 餐饮用油品质指标间存在不同程度的相关性,PCA将10个品质指标缩减成2个综合性评价指标,累计贡献率达87.920%,反映了餐饮用油煎炸品质的大部分信息,并进一步提取出评估餐饮用油煎炸性能的5个关键指标,即过氧化值、极性组分、碘价、羰基值以及亚油酸/棕榈酸比值。主成分分析综合得分表明,在油炸过程中,餐饮调和油A得分较高,具有较好的煎炸性能。结论 本研究建立的餐饮用油煎炸性能评价体系为煎炸油的筛选和质量控制提供借鉴。     

基于傅里叶变换红外光谱的大豆原油掺伪定量 分析模型研究

目的应用傅里叶变换红外光谱(FTIR)结合最小偏二乘法(PLS)建立大豆原油-棕榈油二元掺伪体系的定量分析模型。方法以42个大豆原油、21个精炼油、88个掺伪油的FIIR谱图为模型样本,预处理方法选用标准正态变量(SNV),在此基础上应用主成分分析(PCA)提取特征变量,随机选取60个掺伪油样组成校正集,28个掺伪油样组成验证集,以PLS方法建立大豆原油的掺伪定量模型。结果 PCA可将大豆原油及精炼油分成独立的2类。经PCA分析,大豆原油中掺入棕榈油的掺伪检测限为5%。PLS校正模型的判定系数R2为0.9926,校正误差均方根RMSEC为1.8121。预测模型的R2为0.9823,交叉验证误差均方根RMSECV为2.8189。同时得到的预测结果的偏差在1.3909%~3.1019%之间,差异不显著,说明此模型可行。结论 FTIR-PLS模型能够实现大豆原油的掺伪定量分析,分析速度快,能够满足大豆原油入库要求,是一种可行的大豆原油掺伪分析方法。     

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。     

基于脂肪酸和生育酚组成的油茶籽油掺假判别可行性分析

从脂肪酸和生育酚的组成分析以及所得色谱数据的系统聚类分析(HCA)和主成分分析(PCA)方面考察了10种植物油的异同点。气相色谱分析10种植物油的脂肪酸组成,鉴别得到12种脂肪酸。对脂肪酸组成数据的HCA分析结果表明,油茶籽油与橄榄油和菜籽油具有相似的脂肪酸组成,而与其他植物油的脂肪酸组成差异显著;而PCA分析结果可以进一步区分油茶籽油与橄榄油和菜籽油。高效液相色谱分析10种植物油的生育酚组成,测得4种异构体的相对含量。对生育酚组成数据的HCA、PCA分析结果表明,油茶籽油和菜籽油的生育酚组成差异显著。因此,基于脂肪酸和生育酚组成分析可以作为油茶籽油掺假判别的依据。     

基于电子鼻气味指纹图谱快速鉴别茶籽油

以不同产地、不同类型、不同工艺和不同质量的茶籽油为研究对象，通过电子鼻技术获取茶籽油的风味信息，结合Loading负载分析、主成分分析（?principal component analysis，PCA）、线性判别分析（linear discriminant analysis，LDA）等对电子鼻传感器数据进行解读。通过欧氏距离（?euclidean distance，ED）、马氏距离（?markov distance，MD）和判别函数法(discriminant function method，DFA)对未知样品进行鉴别，以期建立不同茶籽油的快速、准确鉴别方法。结果表明：加工工艺极大地影响了茶籽油的气味差异，电子鼻响应值雷达图及Loading负载分析均表明W1S、W1W、W2S、W2W传感器对茶籽油样品具有较好的响应值，通过ED、MD、DFA可以识别不同类型和不同质量的茶籽油。基于PCA的LDA可以有效的鉴别不同类型的茶籽油，模型准确性高，其确定性大于99%。电子鼻技术结合化学计量学可以快速、有效鉴别不同的茶籽油并为茶籽油掺伪的快速鉴别提供了借鉴。     

电子鼻在芝麻油及芝麻油香精识别中的应用

采用电子鼻时芝麻油、芝麻油香精和其他油脂样品进行了分析.对所获得的数据进行主成分分析(Principal Component Analysis,PCA)、判别因子分析(Discriminant Factor Analysis,DFA)和统计质量控制分析(Statistical Quality Control,SQC),结果表明:电子鼻能够有效识别芝麻油、芝麻油香精和其他油脂,且电子鼻对芝麻油和其他油脂的识别比对芝麻油和芝麻油香精的识别效果更好;2号芝麻油香精和芝麻油样品的香气较为相似,其香气的模拟比较成功;电子鼻能够识别不同储存时间的芝麻油样品,随着储存时间的延长,样品在PCA图中呈现规律性的变化,这可能与芝麻油在储存过程中发生氧化有关.     

Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil

A chemometric approach based on partial least (PLS) square methodology was applied to unfolded differential scanning calorimetry data obtained by 63 samples of different vegetable oils (58 extra virgin olive oils, one olive and one pomace olive oil, three seed oils) to evaluate fatty acid composition (palmitic, stearic, oleic and linoleic acids, saturated (SFA), mono (MUFA) and polysaturated (PUFA) percentages, oleic/linoleic and unsaturated/saturated ratios).     

基于电子鼻对不同植物油油炸薯片的区分

以大豆油,玉米油,葵花籽油,花生油及棕榈油5种植物油油炸的薯片为研究对象,利用电子鼻检测其在贮藏期内(0～60 d)的风味物质变化,根据主成分分析(Principal Component Analysis,PCA)、线性判别分析(Linear Discriminant Analysis,LDA)和Fisher判别分析,考察电子鼻对5种植物油油炸薯片区分效果。结果表明,在整个贮藏期内电子鼻传感器对样品的氮氧化物、硫化物和芳香类物质响应值高;PCA分析和LDA分析都可以代表样品的主要信息,但LDA分析的区分效果较PCA分析更明显;Fisher判别分析除0 d时1个样品被误判,总的正确判别率为96%,其余样品均未被误判,总的正确判别率都为100%。     

Virgin olive oil differentiation in relation to extraction methodologies

Sensory green attribute intensities, the total amount of complex phenolic compounds and the composition of volatile compounds from lipoxygenase pathways were measured in oils obtained from both industrial centrifugation plants and different kinds of laboratory mill. These data were evaluated by hierarchical cluster analysis, principal component analysis and linear discriminant analysis with the aim of distinguishing oil samples in relation to the extraction methodology. The statistical tests showed that the mentioned variables were able to differentiate oils according to the method adopted for their extraction from olive pastes. Oils from laboratory mills were clearly different from samples extracted industrially. This implies that results derived from investigations carried out using oils obtained from laboratory mills cannot be immediately transferred to oils from industrial plants. Oils from dual‐phase decanters in industrial plants were more similar to samples extracted in conventional tri‐phase industrial plants, even if they were all well differentiated according to the oil extraction methodology. © 2000 Society of Chemical Industry     

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。     

基于酚类化合物组成差异的7 类植物油脂识别

采用超高效液相色谱-串联质谱联用技术,对葵花籽油、菜籽油、花生油、胡麻油、橄榄油、玉米油和芝麻油中的酚类化合物进行检测,采用主成分分析、线性判别分析和层次聚类分析3?种方法识别植物油。结果表明：植物油中酚类化合物组成和含量存在明显差异;主成分分析中,提取4?个主成分可以反映原变量89.42%的信息,花生油、橄榄油、葵花籽油和芝麻油分布在不同象限,区分良好;线性判别分析结果显示,在84.4%程度上可以对7?种植物油实现良好区分;层次聚类分析中,菜籽油、橄榄油、芝麻油可以同其他植物油明显区分。     

Two years after the Hebei Spirit oil spill: residual crude-derived hydrocarbons and potential AhR-mediated activities in coastal sediments

The Hebei Spirit oil spill occurred in December 2007 approximately 10 km off the coast of Taean, South Korea, on the Yellow Sea. However, the exposure and potential effects remain largely unknown. A total of 50 surface and subsurface sediment samples were collected from 22 sampling locations at the spill site in order to determine the concentration, distribution, composition of residual crudes, and to evaluate the potential ecological risk after two years of oil exposure. Samples were extracted and analyzed for 16 polycyclic aromatic hydrocarbons (PAHs), 20 alkyl-PAHs, 15 aliphatic hydrocarbons, and total petroleum hydrocarbons using GC-MSD. AhR-mediated activity associated with organic sediment extracts was screened using the H4IIE-luc cell bioassay. The response of the benthic invertebrate community was assessed by mapping the macrobenthic fauna. Elevated concentrations of residual crudes from the oil spill were primarily found in muddy bottoms, particularly in subsurface layers. In general, the bioassay results were consistent with the chemistry data in a dose-dependent manner, although the mass-balance was incomplete. More weathered samples containing greater fractions of alkylated PAHs exhibited greater AhR activity, due to the occurrence of recalcitrant AhR agonists present in residual oils. The macrobenthic population distribution exhibits signs of species-specific tolerances and/or recolonization of certain species such as Batillaria during weathering periods. Although the Hebei Spirit oil spill was a severe oil exposure, it appears the site is recovering two years later.     

Characterization, weathering, and application of sesquiterpanes to source identification of spilled lighter petroleum products

Biomarkers have become increasingly important for identifying the source of spilled oil, due to their specificity and high resistance to biodegradation. The biomarkers most commonly used in forensic investigations are the high molecular weight (MW) tri- and pentacyclic terpanes and steranes. For lighter petroleum products such as jet fuels and diesels, the refining processes remove most high MW biomarkers from the original crude oil feedstock. The smaller bicyclic sesquiterpanes, however, are concentrated in these products. Sesquiterpanes are ubiquitous components of crude oils and ancient sediments. Examination of GC-MS chromatograms of these bicyclic biomarkers using their characteristic fragment ions (m/z 123, 179, 193, and 207) provides a highly diagnostic means for identifying spilled oil, particularly for lighter refined product samples that are difficult to identify by current techniques. In this work, sesquiterpanes in crude oils and petroleum products are identified and characterized, distributions of sesquiterpanes in oils and refined products are compared, the effects of evaporative weathering on sesquiterpane distributions are examined, and a methodology using diagnostic indices of sesquiterpanes is developed for oil correlation and differentiation. Finally, two case studies are presented to illustrate the unique utility of sesquiterpanes for fingerprinting and identifying unknown diesel spills.