裂解气相色谱-质谱分析火场残留物中助燃剂

通过对火灾现场助燃剂及其燃烧残留物进行分析，开展了基于裂解气相色谱-质谱法（PyGC-MS）的火场助燃剂分析方法。选取了汽油和柴油2种助燃剂以及棉布和聚对苯二甲酸乙二醇酯（PET）塑料2类载体，制备了助燃剂与载体的混合燃烧残留物。利用热分析技术确定样品的特征性温度，并对分析条件进行优化与选择。通过闪蒸分析和裂解分析的分步裂解方法，对样品进行了PyGC-MS分析。实验结果表明，PET载体原样燃烧残留物的裂解产物共有35个组分，而PET载体与汽油混合燃烧残留物和PET载体与柴油混合燃烧残留物的裂解产物只有25个组分，且各裂解产物的种类和含量均不相同。该法可对同一载体的自身燃烧残留物和与助燃剂混合燃烧残留物进行区分，适用于火灾残留物中助燃剂的分析，可对火场中是否存在助燃剂进行判别，为火灾性质的判断和火灾调查工作提供科学依据。     

闪蒸气相色谱法应用于天然纤维与助燃剂混合燃烧残留物的分析

应用闪蒸气相色谱法对2种火场中常见的天然纤维载体(木材和棉布)与汽油(或柴油)的混合燃烧残留物进行分析。在模拟火场条件下,分别制得2种载体与汽油或柴油混合燃烧的残留物。根据燃烧残留物热重分析结果选择闪蒸温度为300℃。分析对象为上述2种载体分别在无助燃剂和有助燃剂条件下共同燃烧得到的残渣,分别在完全燃烧后0,24,72h进行取样。结果表明:闪蒸气相色谱技术可从混合燃烧残留物中检测到助燃剂的特征组分,能够满足助燃剂检测鉴定的要求。随着取样时间的延长,助燃剂发生挥发,残留特征组分减少。棉布载体燃烧残留物比木材载体燃烧残留物对助燃剂特征组分的保留效果更好。柴油燃烧产物特征组分较汽油特征组分保留时间更长。     

典型塑料载体与助燃剂燃烧残留物的闪蒸气相色谱-质谱分析

通过对火场常见塑料载体与助燃剂混合燃烧残留物的分析,发展一种适用此类燃烧残留物的火灾物证鉴定方法,对火场中是否存在助燃剂进行判断,避免漏检情况的发生。应用热分析技术确定合适的闪蒸温度,在此温度下对塑料载体与助燃剂混合燃烧残留物进行闪蒸分析,并从实验条件选择、可行性分析、定性分析三方面对闪蒸技术进行评价。结果表明,闪蒸气相色谱-质谱（Flash GC-MS）技术可以检测到热塑性聚合物塑料载体与助燃剂混合燃烧残留物中残留的助燃剂特征组分,可对火场中是否存在过助燃剂进行辨别。闪蒸气相色谱-质谱技术丰富了现代火灾物证鉴定技术,能进一步辅助火灾物证鉴定工作,使鉴定结论更准确、可靠。     

基于广义回归神经网络的微生物降解效应对助燃剂检测的影响

为探究火场土壤载体中微生物降解效应对助燃剂鉴定的影响，在普通土和培养土两种土样上注射助燃剂，以密封存放时间为变量，通过静态顶空的样品预处理方式对样品内的助燃剂残留物进行气相色谱-质谱法（GC-MS）鉴定。研究发现，微生物降解效应会改变样品内助燃剂组分，不同土样内降解结果有所不同，普通土样的降解效应较培养土样明显，C9~C12直链烷烃和单取代芳香烃更易被降解，多取代芳烃的降解难度随取代基含量的增多而增加。按土样种类采用主成分分析（PCA）的方式进行数据降维后，采用广义回归神经网络（GRNN）对不同土样结果区分，准确率达100%。     

汽油燃烧残留物标准样品制备及特征组分研究

汽油燃烧残留物的检测往往是纵火案件侦破的关键,对鉴定机构的检验(鉴定)能力有很高的要求。汽油燃烧残留物的鉴定需要已知样品进行比对分析,但目前还没有相应的标准样品可以提供。因此,建立汽油燃烧残留物标准样品有助于对鉴定机构相应的鉴定能力进行培养和考察。本研究以石英砂为燃烧载体,93号汽油为助燃剂,制备汽油燃烧残留物标准样品,对比研究了溶剂法和直接顶空进样法对汽油燃烧残留物的提取效果。气相色谱-质谱法的分析结果表明,汽油在燃烧前后的特征组分存在明显差异,不同的提取方法对检测结果有一定影响,但是特征组分的变化趋势是一致的,取代的芳烃和稠环芳烃等特征组分的鉴别和含量的相对大小可以作为汽油燃烧残留物鉴定的重要判定依据。     

固相微萃取-气相色谱-质谱法测定火场残留物中白酒类助燃剂北大核心CSCD

白酒主要指中国白酒。白酒是含乙醇较高的一种水溶性液体饮料,其主要成分是乙醇、水,极少的成分是酸、酯、醇、醛等有机化合物。白酒的种类繁多,乙醇含量较高的白酒有良好的燃烧性能,可被犯罪分子用来作为放火的助燃剂,因此目前对白酒类助燃剂的正确分析鉴定对于放火案件的侦破具有非常重要的意义。火场残留物中白酒类物质的提取和鉴定向来是火场助燃剂提取鉴定技术的难题。     

固相微萃取-气相色谱-质谱法测定火场残留物中白酒类助燃剂

正白酒主要指中国白酒。白酒是含乙醇较高的一种水溶性液体饮料,其主要成分是乙醇、水,极少的成分是酸、酯、醇、醛等有机化合物。白酒的种类繁多,乙醇含量较高的白酒有良好的燃烧性能,可被犯罪分子用来作为放火的助燃剂,因此目前对白酒类助燃剂的正确分析鉴定对于放火案件的侦破具有非常重要的意义。火场残留物中白酒类物质的提取和鉴定向来是火场助燃剂提取鉴定技术的难题。由于火灾现场通     

不同型号的汽油燃烧残留物的薄层色谱分析

将93、97汽油作为研究对象,模拟火灾现场条件制备了完全燃烧的火场残留物,采用薄层色谱扫描技术对燃烧残留物中两种不同型号汽油的特征进行了实验研究,其研究结果对判断火场中是否有汽油参与燃烧有一定的指导作用,可以为消防部队放火火灾的调查提供一种物证鉴定手段,为放火火灾原因认定提供科学的理论依据,亦能为我国火场可燃液体鉴定标准的建立提供参考。     

裂解气相色谱法分析火场燃烧残留物的研究

采用裂解气相色谱法对四种不同木材原样及分别浸渍汽油和柴油的木材燃烧残留物进行了分析研究，结果表明，无论是不同种类的木材还是含有不同助燃剂的同种木材，其燃烧残留物的裂解色谱图都存在明显的差异，通过对火灾现场燃烧残留物裂解色谱分析，可以确定载体木材的种类及木材中是否浸渍过助燃剂汽油或柴油，从而为火灾原因调查中纵火案件的侦破和诉讼提供科学的依据和证据。     

火场燃烧残留物检验鉴定方法研究进展

火场燃烧残留物的检验鉴定涉及浓缩、前处理及实验室鉴定等多个环节,同时火场高度的破坏性和暴露性,造成燃烧残留物成分分析存在较多干扰.为提高火场燃烧残留物检验鉴定的准确性和实效性,国内外学者围绕检验鉴定方法进行了研究和探索.为此,对相关研究进行了梳理,特别是对现场快速检验、现场浓缩、实验室前处理和实验室分析检验新方法进行了总结归纳,旨在为火场燃烧残留物物证的检验鉴定提供参考.     

Classification of high-speed gas chromatography-mass spectrometry data by principal component analysis coupled with piecewise alignment and feature selection

A useful methodology is introduced for the analysis of data obtained via gas chromatography with mass spectrometry (GC-MS) utilizing a complete mass spectrum at each retention time interval in which a mass spectrum was collected. Principal component analysis (PCA) with preprocessing by both piecewise retention time alignment and analysis of variance (ANOVA) feature selection is applied to all mass channels collected. The methodology involves concatenating all concurrently measured individual m/z chromatograms from m/z 20 to 120 for each GC-MS separation into a row vector. All of the sample row vectors are incorporated into a matrix where each row is a sample vector. This matrix is piecewise aligned and reduced by ANOVA feature selection. Application of the preprocessing steps (retention time alignment and feature selection) to all mass channels collected during the chromatographic separation allows considerably more selective chemical information to be incorporated in the PCA classification, and is the primary novelty of the report. This methodology is objective and requires no knowledge of the specific analytes of interest, as in selective ion monitoring (SIM), and does not restrict the mass spectral data used, as in both SIM and total ion current (TIC) methods. Significantly, the methodology allows for the classification of data with low resolution in the chromatographic dimension because of the added selectivity from the complete mass spectral dimension. This allows for the successful classification of data over significantly decreased chromatographic separation times, since high-speed separations can be employed. The methodology is demonstrated through the analysis of a set of four differing gasoline samples that serve as model complex samples. For comparison, the gasoline samples are analyzed by GC-MS over both 10-min and 10-s separation times. The successfully classified 10-min GC-MS TIC data served as the benchmark analysis to compare to the 10-s data. When only alignment and feature selection was applied to the 10-s gasoline separations using GC-MS TIC data, PCA failed. PCA was successful for 10-s gasoline separations when the methodology was applied with all the m/z information. With ANOVA feature selection, chromatographic regions with Fisher ratios greater than 1500 were retained in a new matrix and subjected to PCA yielding successful classification for the 10-s separations.     

Towards the identification of plant and animal binders on Australian stone knives

There is limited information regarding the nature of plant and animal residues used as adhesives, fixatives and pigments found on Australian Aboriginal artefacts. This paper reports the use of FTIR in combination with the chemometric tools principal component analysis (PCA) and hierarchical clustering (HC) for the analysis and identification of Australian plant and animal fixatives on Australian stone artefacts. Ten different plant and animal residues were able to be discriminated from each other at a species level by combining FTIR spectroscopy with the chemometric data analysis methods, principal component analysis (PCA) and hierarchical clustering (HC). Application of this method to residues from three broken stone knives from the collections of the South Australian Museum indicated that two of the handles of knives were likely to have contained beeswax as the fixative whilst Spinifex resin was the probable binder on the third.     

Chemometrics‐enhanced laser‐induced thermal emission detection of PETN and other explosives on various substrates

Infrared emissions (IREs) of samples of pentaerythritol tetranitrate (PETN) deposited as contamination residues on various substrates were measured to generate models for the detection and discrimination of the important nitrate ester from the emissions of the substrates. Mid‐infrared emissions were generated by heating the samples remotely using laser‐induced thermal emission (LITE). Chemometrics multivariate analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares‐discriminant analysis (PLS‐DA), support vector machines (SVMs), and neural network (NN) were employed to generate the models for the classification and discrimination of PETN IREs from substrate thermal emissions. PCA exhibited less variability for the LITE spectra of PETN/substrates. SIMCA was able to predict only 44.7% of all samples, while SVM proved to be the most effective statistical analysis routine, with a discrimination performance of 95%. PLS‐DA and NN achieved prediction accuracies of 94% and 88%, respectively. High sensitivity and specificity values were achieved for five of the seven substrates investigated. Copyright © 2015 John Wiley & Sons, Ltd.     

Rapid discrimination of fatty acid composition in fats and oils by electrospray ionization mass spectrometry.

Fatty acids in 42 types of saponified vegetable and animal oils were analyzed by electrospray ionization mass spectrometry (ESI-MS) for the development of their rapid discrimination. The compositions were compared with those analyzed by gas chromatography-mass spectrometry (GC-MS), a more conventional method used in the discrimination of fats and oils. Fatty acids extracted with 2-propanol were-detected as deprotonated molecular ions ([M-H]-) in the ESI-MS spectra of the negative-ion mode. The composition obtained by ESI-MS corresponded to the data of the total ion chromatograms by GC-MS. The ESI-MS analysis discriminated the fats and oils within only one minute after starting the measurement. The detection limit for the analysis was approximately 10(-10) g as a sample amount analyzed for one minute. This result showed that the ESI-MS analysis discriminated the fats and oils much more rapidly and sensitively than the GC-MS analysis, which requires several tens of minutes and approximately 10(-9) g. Accordingly, the ESI-MS analysis was found to be suitable for a screening procedure for the discrimination of fats and oils.     

Discrimination of Chinese traditional soy sauces based on their physico-chemical properties

This work aimed to classify the categories (produced by different processes) and brands (obtained from different geographical origins) of Chinese soy sauces. Nine variables of physico-chemical properties (density, pH, dry matter, ashes, electric conductivity, amino nitrogen, salt, viscosity and total acidity) of 53 soy sauce samples were measured. The measured data was submitted to such pattern recognition as cluster analysis (CA), principal component analysis (PCA), discrimination partial least squares (DPLS), linear discrimination analysis (LDA) and K-nearest neighbor (KNN) to evaluate the data patterns and the possibility of differentiating Chinese soy sauces between different categories and brands. Two clusters corresponding to the two categories were obtained, and each cluster was divided into three subsets corresponding to three brands by the CA method. The variables for LDA and KNN were selected by the Fisher F-ratio approach. The prediction ability of all classifiers was evaluated by cross-validation. For the three supervised discrimination analyses, LDA and KNN gave 100% predications according to the sample category and brand.     

Study of acidified ignitable liquid residues in fire debris by solid‐phase microextraction with gas chromatography and mass spectrometry

The detection and identification of ignitable liquid residues in fire debris can be meaningful in fire investigations. However, background pyrolysis products and weathering hinder the identification and classification steps. In addition to those processes, the acidification of the ignitable liquids before the combustion process could make those tasks even more difficult. Nevertheless, there are no systematic studies assessing the extraction, analysis, and composition of acidified ignitable liquid residues obtained from fire debris. In this work, a method for the study of acidified ignitable liquid residues in fire debris by solid‐phase microextraction with gas chromatography and mass spectrometry is proposed. This methodology has been evaluated, first with simulated solutions (gasoline/sulfuric acid mixtures set on fire under controlled conditions), and then with analysis of samples from real fire debris obtained from 18 chemical ignition Molotov cocktails made with sulfuric acid and three different ignitable liquids (two types of gasoline and diesel fuel). In addition, the extensive modifications observed in chromatograms of acidified ignitable liquid residues regarding neat and weathered samples were studied. These alterations were produced by the combustion and acidification processes. As a consequence, tert‐butylated compounds are proposed as diagnostic indicators for the identification of acidified gasoline in fire debris, even in strongly weathered samples.     

分析测试新成果(230~237)2种常见燃油添加剂对汽油燃烧烟尘的影响

采用气相色谱-质谱(GC-MS)联用技术,根据汽油原样中的特征成分,对未加入和分别加入汽油燃油精、海龙燃油宝的汽油燃烧烟尘进行对比分析.结果表明,汽油燃油精的加入会使汽油燃烧烟尘中各类特征成分的百分含量有所变化,但对谱图和特征成分的影响较小;而海龙燃油宝的加入对汽油燃烧烟尘的谱图、特征成分及其个数、各类特征成分的百分含量均产生较大影响.结果为火灾物证鉴定提供一定的参考依据.     

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。