气相色谱/质谱检验合成大麻素K3中AKB48

目的建立较为快速准确的合成大麻素K3中AKB48的气相色谱/质谱检验方法。方法对进样口温度、初始柱温、柱流速及质谱采样率等4项色谱及质谱实验参数进行考察优化。结果 GC/MS检验合成大麻素K3中AKB48的优化条件为:进样口温度280℃,柱初始温度80℃,柱流速为2.0ml/min,质谱采样率为2。结论该方法具有快速、准确、灵敏等优点,可用于K3中AKB48的定性检验鉴定。     

保存温度与时间对生物样品中氯胺酮稳定性的影响

目的研究不同温度和时间条件保存时生物样品中氯胺酮的稳定性。方法家兔以氯胺酮150mg/kg灌胃,30min后处死,取其血、肝、肾、脑,分别在室温（18～24℃）和冷冻（-20℃）条件下保存,并用气相色谱-质谱法定性分析、气相色谱-氮磷检测器法测定不同时间各样品中氯胺酮含量。结果血、肝、肾、脑冷冻保存至第30天氯胺酮含量均降低（P〈0.05）;室温条件下各样品中氯胺酮含量自第5天起均升高（P〈0.05）。结论生物样品在冷冻条件下保存时氯胺酮稳定性较好,怀疑氯胺酮中毒或死亡的检材应冷冻保存,尽快检测。     

气相色谱在毒物分析中的应用3例

本文列举了气相色谱在各类案件中的检验方法,进一步表明气相色谱在毒物检验中的作用。１实验部分１．１实验仪器美国产HP－６８９０型气相色谱仪,NPD,FID;色谱柱HP－１（CrosslinkedMethy１Siloxane）毛细管气相色谱柱（３００mm×０．５３mm×１．５um）;HP－５（Crosslinked５％PHMEsiloxane）毛细管气相色谱柱（３００mm×０．３２mm×０．２５um）。１．２仪器条件１．２．１氮磷检测器条件进样口：温度２５０℃,进样口压力２５psi模式,不分流,分流流量６０ml／min,分流时间０．７５min。检测器：温度２８０℃,H２流…     

GC/MS法检测血液及肺组织中二氧化硫1例

1简要案情2005年11月7日,某男子对该厂脱硫塔进行维护时,出现昏迷,送到医院时已死亡。11月16日进行解剖,取血和肺进行毒物分析。2毒物分析检验2.1主要仪器与试剂美国Varian CP-38OO/SATRUN 2000气相色谱-谱联用仪;电热磁力搅拌器;2m l自动进样瓶(美国Supelco公司);浓硫酸。2.2仪器操作条件气相条件石英毛细柱:DB-5,30m×0.25mm×0.25μm;氦气0.8m l/m in,初始柱温:30℃,保持2m in,以30℃/m in升至280℃,保持1m in,进样口温度:260℃。质谱条件电子轰击离子源(EI),传输线温度250℃,阱温:170℃,扫描范围:40~70amu,不分流进样,溶剂延迟:…     

气相色谱-质谱法测定新型催泪喷雾中的3种常见辣椒素

目的 壬酸香草酰胺是新型催泪喷雾的主要刺激成分，旨在建立壬酸香草酰胺的分析方法，为打击相关犯罪活动提供技术支撑。方法 以最为常见的3种辣椒素类物质（天然辣椒素、二氢辣椒素、壬酸香草酰胺）为研究对象，建立以弱极性的DB-5MS色谱柱（30 m×0.25 mm,0.25μm）进行分离，以进样口温度为280℃进行检测的气相色谱-质谱法。结果 实现了3种辣椒素的基线分离，推断了辣椒素类物质的裂解机理，确定了m/z 137为辣椒素类物质的特征离子。将所建立的方法用于实际案件中催泪喷雾的检测，实现了喷雾中刺激成分的鉴定。结论 利用气相色谱-质谱法建立了催泪喷雾中的新型辣椒素壬酸香草酰胺的分析方法，该方法简便、灵敏度高，可用于实际案件的检验。     

气相色谱-质谱联用法同时测定8种合成大麻素

目的采用气相色谱质谱联用(GC/MS)法检测8种合成大麻素,并对方法进行评价。方法甲醇提取及超声处理样本后,采用GC/MS法检测JWH-203、JWH-250、RCS-4、JWH-018、JWH-019、JWH-122、AM-2201和JWH-210等8种合成大麻素,并对色谱柱、初始温度、升温速率等条件进行了优化。结果使用中等极性的DB-17MS色谱柱(30m×0.32mm×0.25μm),采用起始温度200℃,以20℃/min速率升温至280℃,以10℃/min速率升温至300℃的条件进行检测。8种合成大麻素能够很好的分离,检测限达到20μg/m L;应用该方法对未知样品进行了检测,并对各类合成大麻可能的质谱碎裂途径进行分析。结论本文方法具有分析简便、快速、灵敏的特点,可以用于实际案件的检测。     

海洛因毒品中残留有机溶剂的检测方法与结果分析

目的建立固体海洛因毒品中残留有机溶剂的顶空-气相色谱和顶空-气相色谱-质谱联用检测方法。方法采用干法和湿法处理42份样品,密封后90℃加热振荡20min,抽取顶空气体用气相色谱法（DB-WAX毛细柱,30m×0.25mm,0.25μm）和气相色谱-质谱联用法（HP-5MS毛细柱,30m×0.25mm,0.25μm）检测,以已知17种有机溶剂外标法定性。在样品中加水后检测,根据峰高估算5种共有成分的含量。结果在42份海洛因毒品中检出乙酸、乙醚、乙醇、乙酸乙酯、乙醛、三氯甲烷等12种有机溶剂成分,5种主要共有成分相对含量有差别。结论本研究建立的检测方法快速、简便,定性可靠,可用于固体海洛因毒品的来源与批次分析。     

5F-UR-144的热不稳定性研究

目的利用气相色谱-质谱法(GC-MS)、液相色谱-四级杆-飞行时间质谱(LC-Q-TOF/MS)和核磁共振光谱法(NMR)研究合成大麻素5F-UR-144遇热分解的具体变化情况。方法对照品用无水乙醇定容稀释后,经GC-MS和LC-Q-TOF/MS检测得到对应的色谱图和质谱图;对照品分别于常温和280℃密封加热后用无水乙醇定容稀释,经LC-Q-TOF/MS和NMR检测得到对应的质谱图、¹H和¹³C核磁共振波谱;对照品分别于常温逐步提升至300℃密封加热后用无水乙醇定容稀释,经LC-Q-TOF/MS检测得到对应的色谱图。结果5F-UR-144在高温下会开环产生新的物质;5F-UR-144从130℃开始分解,随着温度升高分解程度提升,240℃时分解率达到98%;随着温度继续升高,超过260℃,分解产物会碳化。结论基于5F-UR-144的热不稳定性,在检测时应考虑若通过烫食方式吸食5F-UR-144,其进入人体的成分会发生变化;气相色谱或气相色谱-质谱法不适合定量检测5F-UR-144。     

自主合成标准物质鉴定缴获物中的新精神活性物质ADB-FUBINACA

目的新精神活性物质在世界范围内蔓延迅速,但相关标准物质的短缺制约着其分析方法的研究和案件检验。本文以我国首次出现的N-(1-氨甲酰基-2,2-二甲基丙基)-1-(4-氟苄基)吲唑-3-甲酰胺(ADB-FUBINACA)制毒案件为例,介绍在无法及时获得商业化标准物质的情况下,不得不通过自主合成制备标准物质解决案件检验难题,建立该新精神活性物质检验的方法。方法建立气相色谱-质谱检验方法,分析条件:色谱柱为Aglient DB-5MS石英毛细管柱(30.00m×0.25mm×0.25μm);初始柱温60℃,按15℃/min升至300℃,保持15min;载气为氦,流速1.0mL/min,分流进样,进样量1.0μL,分流比20∶1;进样口温度280℃;电子轰击(EI)离子源,电子能量70eV,离子源温度230℃,四级杆温度150℃,传输线温度280℃,质量扫描范围m/z 40~500amu,全扫描模式(SCAN)采集总离子流图,溶剂延迟3.0min。案件缴获的送检未知样品经甲醇提取,超声、离心后,取上清液以GC-MS分析;将所得主要质谱特征碎片峰(m/z)通过NIST质谱库、SWGDRUG质谱数据库以及相关文献进行检索,初步确定待测目标物。采用有机合成技术制备ADB-FUBINACA标准物质,合成路线为:吲唑-3-甲酸甲酯与4-氟苄溴发生取代反应,生成1-(4-氟苄基)-1H-吲唑-3-甲酸甲酯;取代产物在碱性条件下经水解反应得到有机酸1-(4-氟苄基)-1H-吲唑-3-甲酸;在催化剂作用下,有机酸与L-叔亮酰胺发生酰化反应,制得化合物ADB-FUBINACA。经气相色谱-质谱(GC-MS)、液相色谱-离子阱-飞行时间质谱(LCMS-IT-TOF)、核磁共振(NMR)等分析,合成化合物的结构得以确证;同时采用超高效液相色谱-二极管阵列检测器(HPLC-PDA)进行分析,对其归一化纯度进行测定。将案件未知样品和合成标准物质分别用甲醇提取,超声、离心后,再行上清液GC-MS分析。结果经GC-MS分析,案件未知样品(RT=19.818min)的质谱特征碎片峰(m/z)信息为109.0(基峰)、253.1、338.1、309.1和145.0,经与合成标准物质的保留时间及质谱图检测比对,证实为N-(1-氨甲酰基-2,2-二甲基丙基)-1-(4-氟苄基)吲唑-3-甲酰胺;通过查阅相关资料,对上述质谱特征碎片峰的产生机制进行了推断,并对1H-NMR、13C-NMR和DEPT-135等一维核磁谱图的信号进行了归属分析。结论本文报道的新精神活性物质ADB-FUBINACA其GC-MS分析方法,可用于实际案件检验鉴定;合成化合物的结构表征方法也可用于固体ADB-FUBINACA的定性分析。基于有机合成技术的新精神活性物质制备与案件检验方法,可缓解有关标准物质短缺制约该类案件检验鉴定的现状。     

裂解气相色谱-质谱法分析鉴别轮胎胎面胶

目的对裂解气相色谱-质谱法分析橡胶的裂解条件进行优化,研究利用橡胶特征裂解产物鉴别轮胎胎面胶。方法采用裂解气相色谱-质谱法,确定对NR、BR、SBR的裂解条件及其特征裂解产物,然后根据特征裂解产物及其相对含量并结合聚类分析,对38种轮胎胎面胶进行区分鉴别。结果确定了裂解温度590℃、裂解时间15s及三种橡胶的特征裂解产物;将38个轮胎胎面胶样品的主体成分归为4类,其中NR类5个、SBR类12个、NR/BR并用类5个、NR/SBR并用类16个,且能够对大部分并用类轮胎胎面胶进行鉴别。结论通过优化裂解条件,裂解产物重复性好,特征裂解产物明显,能够确定轮胎胎面胶的主体成分类别并能对大多数并用类轮胎胎面胶样品进行区分鉴别。     

Chemical fingerprinting of gasoline. 2. Comparison of unevaporated and evaporated automotive gasoline samples

Analysis of the C(0)- to C(2)-naphthalene compounds present in automotive gasoline using gas chromatography-mass spectrometry with selected ion monitoring (GC-MS (SIM)) and principal component analysis (PCA) was used to discriminate between different samples of gasoline. Phase one of this study explored the ability of this method to differentiate gasoline samples at different levels of evaporation. A total of 35 random samples of unevaporated gasoline, covering three different grades (regular unleaded, premium unleaded and lead replacement), were collected in Sydney, Australia and examined. The high-boiling C(0)- to C(2)-naphthalene compounds present in the gasoline were used to chemically fingerprint each sample at different levels of evaporation. Samples of 25, 50, 75 and 90% evaporated gasoline (by weight) were generated from the 35 samples of unevaporated gasoline. Analysis of the data by PCA followed by linear discriminant analysis (LDA) showed that the 35 samples formed 18 unique groups, irrespective of the level of evaporation. Good discrimination between gasoline samples that were collected on the same day was obtained. Phase two of this study examined the change in gasoline samples over time. The C(0)- to C(2)-naphthalene composition in 96 samples of gasoline collected from three service stations over a 16-week period was examined using the method described. In most cases, it was found that the C(0)- to C(2)-naphthalene profile changed from week to week, and from station to station. In a comparison of all 96 samples together it was found that the majority could be differentiated from one another. The application of the method to forensic casework is discussed.     

Statistical discrimination of liquid gasoline samples from casework

The intention of this study was to differentiate liquid gasoline samples from casework by utilizing multivariate pattern recognition procedures on data from gas chromatography-mass spectrometry. A supervised learning approach was undertaken to achieve this goal employing the methods of principal component analysis (PCA), canonical variate analysis (CVA), orthogonal canonical variate analysis (OCVA), and linear discriminant analysis. The study revealed that the variability in the sample population was sufficient enough to distinguish all the samples from one another knowing their groups a priori. CVA was able to differentiate all samples in the population using only three dimensions, while OCVA required four dimensions. PCA required 10 dimensions of data in order to predict the correct groupings. These results were all cross-validated using the "jackknife" method to confirm the classification functions and compute estimates of error rates. The results of this initial study have helped to develop procedures for the application of multivariate analysis to fire debris casework.     

Chemical fingerprinting of unevaporated automotive gasoline samples

The comparison of two or more samples of liquid gasoline (petrol) to establish a common origin is a difficult problem in the forensic investigation of arsons and suspicious fires. A total of 35 randomly collected samples of unevaporated gasoline, covering three different grades (regular unleaded, premium unleaded and lead replacement), were examined. The high-boiling fraction of the gasoline was targeted with a view to apply the techniques described herein to evaporated gasoline samples in the future.A novel micro solid phase extraction (SPE) technique using activated alumina was developed to isolate the polar compounds and the polycyclic aromatic hydrocarbons (PAHs) from a 200microl sample of gasoline. Samples were analysed using full-scan gas chromatography-mass spectrometry (GC-MS) and potential target compounds identified. Samples were then re-analysed directly, without prior treatment, using GC-MS in selected ion monitoring (SIM) mode for target compounds that exhibited variation between gasoline samples. Principal component analysis (PCA) was applied to the chromatographic data. The first two principal components (PCs) accounted for 91.5% of the variation in the data. Linear discriminant analysis (LDA) performed on the PCA results showed that the 35 samples tested could be classified into 32 different groups.     

Differentiation of unevaporated gasoline samples according to their brands, by SPME-GC-MS and multivariate statistical analysis

One of the aims of fire investigations is to identify associations among accelerants according to their source. In this study, 50 gasoline samples--representing five brands--were analyzed using solid-phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS). Chemometric procedures, such as principal component analysis (PCA) and discriminant analysis (DA), were applied to a data matrix obtained by the target compound chromatogram method, to discriminate samples according to their brand. PCA was successful in finding a natural grouping of samples according to their brand, suggesting that aromatic compounds were more useful than aliphatics for the purpose of this study. DA, if applied to aromatic compounds, gave both a classification ability and a prediction ability of 100%. The outstanding results obtained by this work provide the basis of a data matrix that could be used in real cases of arson to link a sample of unevaporated gasoline to its brand or refinery.     

Chemical fingerprinting of gasoline. Part 3. Comparison of unevaporated automotive gasoline samples from Australia and New Zealand

A gas chromatography-mass spectrometry with selected ion monitoring (GC-MS (SIM)) method was used to discriminate samples of unevaporated gasoline collected from Auckland, New Zealand and Sydney, Australia. This method was applied to 28 samples of unevaporated gasoline, covering three different grades (regular unleaded, premium unleaded and premium plus unleaded), that were collected from service stations in Auckland, New Zealand in summer (February) and winter (August). The 14 samples of summer gasoline collected in New Zealand could be divided into seven unique groups. The 14 samples of winter gasoline from New Zealand could be divided into 14 unique groups. The 14 samples collected in New Zealand during February 2002 were then compared to 24 samples of unevaporated gasoline collected from service stations in Sydney, Australia during the same month. Most of the samples could be differentiated based on their country of origin.     

Classification of premium and regular gasoline by gas chromatography/mass spectrometry,principal component analysis and artificial neural networks

Detection and correct classification of gasoline is important for both arson and fuel spill investigation. Principal component analysis (PCA) was used to classify premium and regular gasolines from gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Depending upon the dataset used for training and tests, around 80-93% of the samples were correctly classified as either premium or regular gasoline using the Mahalanobis distances calculated from the principal components scores. Only 48-62% of the samples were correctly classified when the premium and regular gasoline samples were divided further into their winter/summer sub-groups. Artificial neural networks (ANNs) were trained to recognise premium and regular gasolines from the same GC-MS data. The best-performing ANN correctly identified all samples as either a premium or regular grade. Approximately 97% of the premium and regular samples were correctly classified according to their winter or summer sub-group.     

Association of Ignitable Liquid Residues to Neat Ignitable Liquids in the Presence of Matrix Interferences Using Chemometric Procedures*,†

Abstract: In fire debris analysis, weathering of ignitable liquids and matrix interferences can make the identification of ignitable liquid residues (ILRs) difficult. An objective method was developed to associate ILRs with the corresponding neat liquid with discrimination from matrix interferences using principal components analysis (PCA) and Pearson product moment correlation (PPMC) coefficients. Six ignitable liquids (gasoline, diesel, ultra pure paraffin lamp oil, adhesive remover, torch fuel, paint thinner) were spiked onto carpet, which was burned, then extracted using passive headspace extraction, and analyzed by gas chromatography‐mass spectrometry. Both light and heavy burn conditions were investigated. In the PCA scores plot, ignitable liquids were discriminated based on alkane and aromatic content. All ILRs were successfully associated with the corresponding neat liquid using both PCA and PPMC coefficients, regardless of the extent of burning. The method developed in this research may make the association of ILRs with corresponding neat liquids more objective.     

Likelihood ratio methods for forensic comparison of evaporated gasoline residues

In the investigation of arson, evidence connecting a suspect to the fire scene may be obtained by comparing the composition of ignitable liquid residues found at the crime scene to ignitable liquids found in possession of the suspect. Interpreting the result of such a comparison is hampered by processes at the crime scene that result in evaporation, matrix interference, and microbial degradation of the ignitable liquid.Most commonly, gasoline is used as a fire accelerant in arson. In the current scientific literature on gasoline comparison, classification studies are reported for unevaporated and evaporated gasoline residues. In these studies the goal is to discriminate between samples of several sources of gasoline, based on a chemical analysis. While in classification studies the focus is on discrimination of gasolines, for forensic purposes a likelihood ratio approach is more relevant.In this work, a first step is made towards the ultimate goal of obtaining numerical values for the strength of evidence for the inference of identity of source in gasoline comparisons. Three likelihood ratio methods are presented for the comparison of evaporated gasoline residues (up to 75% weight loss under laboratory conditions). Two methods based on distance functions and one multivariate method were developed. The performance of the three methods is characterized by rates of misleading evidence, an analysis of the calibration and an information theoretical analysis.The three methods show strong improvement of discrimination as compared with a completely uninformative method. The two distance functions perform better than the multivariate method, in terms of discrimination and rates of misleading evidence.     

GC/MS/MS时间编程在火灾现场助燃剂检测中的应用

运用Varian SATURN2000气质联用仪通过GC/MS、GC/MS/MS、时间编程 GC/MS/MS对汽油样品进行分析比对,发现利用时间编程GC/MS/MS,对检测火灾现场残留汽油效果很好,可彻底排除样品中基体的背景干扰,大大提高检测灵敏度;同时对混合助燃剂（汽油+煤油、汽油+柴油）进行了实验探索。     

Neural networks in forensic science.

Neural networks were developed to study and mimic the functioning of the human brain. Humans are good at pattern recognition; the question is how good neural networks are at it, particularly with problems of forensic science interest. Simulation experiments with a type of neural network known as a Hopfield net indicate that it may have value for the storage of toolmark patterns (including bullet striation patterns) and for the subsequent retrieval of the matching pattern using another mark by the same tool for input. Another type of neural network, the back-propagation network (BPN), is useful for applications similar to those for which standard statistical methods of pattern classification can be used. This would be an appropriate approach to the matching of general component patterns, such as gas chromatograms of gasoline, or pyrolysis patterns from materials of forensic science interest, such as paint. The BPN may provide better results than statistical methods, but it is currently necessary to try both to determine which would be best for any given situation.