Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis

A fast and objective chemometric classification method is developed and applied to the analysis of gas chromatography (GC) data from five commercial gasoline samples. The gasoline samples serve as model mixtures, whereas the focus is on the development and demonstration of the classification method. The method is based on objective retention time alignment (referred to as piecewise alignment) coupled with analysis of variance (ANOVA) feature selection prior to classification by principal component analysis (PCA) using optimal parameters. The degree-of-class-separation is used as a metric to objectively optimize the alignment and feature selection parameters using a suitable training set thereby reducing user subjectivity, as well as to indicate the success of the PCA clustering and classification. The degree-of-class-separation is calculated using Euclidean distances between the PCA scores of a subset of the replicate runs from two of the five fuel types, i.e., the training set. The unaligned training set that was directly submitted to PCA had a low degree-of-class-separation (0.4), and the PCA scores plot for the raw training set combined with the raw test set failed to correctly cluster the five sample types. After submitting the training set to piecewise alignment, the degree-of-class-separation increased (1.2), but when the same alignment parameters were applied to the training set combined with the test set, the scores plot clustering still did not yield five distinct groups. Applying feature selection to the unaligned training set increased the degree-of-class-separation (4.8), but chemical variations were still obscured by retention time variation and when the same feature selection conditions were used for the training set combined with the test set, only one of the five fuels was clustered correctly. However, piecewise alignment coupled with feature selection yielded a reasonably optimal degree-of-class-separation for the training set (9.2), and when the same alignment and ANOVA parameters were applied to the training set combined with the test set, the PCA scores plot correctly classified the gasoline fingerprints into five distinct clusters.     

Classification of weathered petroleum oils by multi-way analysis of gas chromatography-mass spectrometry data using PARAFAC2 parallel factor analysis

The application of multi-way parallel factor analysis (PARAFAC2) is described for the classification of different kinds of petroleum oils using GC-MS. Oils were subjected to controlled weathering for 2, 7 and 15 days and PARAFAC2 was applied to the three-way GC-MS data set (MSxGCxsample). The classification patterns visualized in scores plots and it was shown that fitting multi-way PARAFAC2 model to the natural three-way structure of GC-MS data can lead to the successful classification of weathered oils. The shift of chromatographic peaks was tackled using the specific structure of the PARAFAC2 model. A new preprocessing of spectra followed by a novel use of analysis of variance (ANOVA)-least significant difference (LSD) variable selection method were proposed as a supervised pattern recognition tool to improve classification among the highly similar diesel oils. This lead to the identification of diagnostic compounds in the studied diesel oil samples.     

Study of the interdependency of the data sampling ratio with retention time alignment and principal component analysis for gas chromatography

An in-depth study is presented to better understand how data reduction via averaging impacts retention alignment and the subsequent chemometric analysis of data obtained using gas chromatography (GC). We specifically study the use of signal averaging to reduce GC data, retention time alignment to correct run-to-run retention shifting, and principal component analysis (PCA) to classify chromatographic separations of diesel samples by sample class. Diesel samples were selected because they provide sufficient complexity to study the impact of data reduction on the data analysis strategies. The data reduction process reduces the data sampling ratio, S(R), which is defined as the number of data points across a given chromatographic peak width (i.e., the four standard deviation peak width). Ultimately, sufficient data reduction causes the chromatographic resolution to decrease, however with minimal loss of chemical information via the PCA. Using PCA, the degree of class separation (DCS) is used as a quantitative metric. Three "Paths" of analysis (denoted A-C) are compared to each other in the context of a "benchmark" method to study the impact of the data sampling ratio on preserving chemical information, which is defined by the DCS quantitative metric. The benchmark method is simply aligning data and applying PCA, without data reduction. Path A applies data alignment to collected data, then data reduction, and finally PCA. Path B applies data reduction to collected data, and then data alignment, and finally PCA. The optimized path, namely Path C, is created from Paths A and B, whereby collected data are initially reduced to fewer data points (smaller S(R)), then aligned, and then further reduced to even fewer points and finally analyzed with PCA to provide the DCS metric. Overall, following Path C, one can successfully and efficiently classify chromatographic data by reducing to a S(R) of ～15 before alignment, and then reducing down to S(R) of ～2 before performing PCA. Indeed, following Path C, results from an average of 15 different column length-with-temperature ramp rate combinations spanning a broad range of separation conditions resulted in only a ～15% loss in classification capability (via PCA) when the loss in chromatographic resolution was ～36%.     

柱前衍生化-气相色谱-质谱法定量测定食品中丙烯酰胺的含量

建立了气相色谱-质谱(GC-MS)定量测定食品中丙烯酰胺含量的分析方法。选择13C3-丙烯酰胺作为内标物。通过超纯水提取食品中的丙烯酰胺,经正己烷脱脂两次后,在酸性条件下选用溴化钾/溴酸钾为衍生剂进行衍生化反应,再采用乙酸乙酯进行液-液萃取两次,最后用三乙胺将丙烯酰胺转化为更稳定的产物2-溴丙烯酰胺,利用质谱检测器在选择离子扫描模式下测定2-溴丙烯酰胺。该方法在0.05～2.00 mg/kg范围具有良好的线性(r2=0.9995);检出限和定量限分别达到3 μg/kg和7 μg/kg;回收率范围为62.7%～65.5%。通过与前期建立的液相色谱-串联质谱(HPLC-MS/MS)方法进行对比,该法在薯片和面包样品中丙烯酰胺的检测结果略偏高,是一种可以用于常见食品中丙烯酰胺含量测定的分析方法。     

Classification of byzantine glass samples by principal component analysis

Summary Nine samples of byzantine glass classified previously by cluster analysis are classified by principal component analysis (PCA). A visual inspection of plots in coordinates of the first two principal components gives essentially the same results as cluster analysis. In addition, PCA indicates relationships among the classification variables.

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Klassifizierung byzantinischer Glasproben durch Analyse der Hauptbestandteile

     

Trace analysis of pesticide residues in water by high-speed narrow-bore capillary gas chromatography-mass spectrometry with programmable temperature vaporizer

A method for the rapid trace analysis of 17 residual pesticides in water by narrow-bore capillary (I.D. 100 microm) gas chromatography-mass spectrometry (GC-MS) using a programmable temperature vaporizer (PTV) was discussed. The method consisted of a large-volume injection (40 microl) by a PTV, high-speed analysis using a narrow-bore capillary column and MS detection. The PTV with solvent vent mode was very useful for large-volume injection into a narrow-bore capillary column because the injected solvent volume could be reduced to less than 2 microl. The analysis time was 8.5 min [less than 50% of the analysis time using conventional columns (I.D. 250 microm)]. A 10-ml volume of river water was extracted by dichloromethane (4 ml), and then the extract was condensed to 1 ml. This extract was analyzed. Mean recoveries for river water spiked at 100 pg/ml ranged from 83.4 to 96.7%. The limit of detections of the 17 pesticides ranged from 1 to 100 pg/ml.     

Classification of bacteria by pyrolysis-capillary column gas chromatography-mass spectrometry and pattern recognition

Pyrolysis followed by capillary column gas chromatography-mass spectrometry has been applied to the classification of five bacterial species of the genus Flacobacterium. Data were subjected to computerized pattern recognition using the ARTHUR software package. The results indicate that classification potential is greatly improved by the use of mass fragmentography. Other factors such as growth media, harvesting and storage conditions were found to be relatively unimportant.     

Determination of isophorone in food samples by solid-phase microextraction coupled with gas chromatography-mass spectrometry

A simple and sensitive method for the determination of isophorone in food samples was developed by headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Isophorone was separated within 10 min by GC-MS using a DB-1 capillary column and detected with selective ion monitoring mode. The HS-SPME using a polydimethylsiloxane/divinylbenzene (PDMS/DVB) fiber provided effective sample enrichment, and was carried out by fiber exposition at 60 degrees C for 45 min. The extracted isophorone was easily desorbed by fiber exposition in the injection port of a capillary GC-MS system, and carryover was not observed. Using this method, the calibration curve of isophorone was linear in the range 20-1000 pg/mL, with a correlation coefficient 0.9996 (n = 18), and the detection limit (S/N = 3) was 0.5 pg/mL. The HS-SPME/GC-MS method showed 25,000-fold higher sensitivity than the direct injection method (1 microL injection). The within-day and between-day precisions (relative standard deviations) at the concentration of 1 ng/mL isophorone were 3.9% and 6.1% (n=5), respectively. This method was successfully applied to the analysis of food samples without interference peaks. The recoveries of isophorone spiked into food sample were above 84% for a 50 or 500 pg/mL spiking concentration. The analytical results of the contents of isophorone in various food samples were presented.     

Structural analysis of commercial ceramides by gas chromatography-mass spectrometry

A simple method using gas chromatography-mass spectrometry was applied to analyse structures of ceramides. Identification of trimethylsilylated ceramides were obtained in short analysis times (derivatization of ceramides in 30 min at room temperature and 20 min gas chromatography mass spectrometry run) even for complex mixtures. For example in ceramide Type III, 18 peaks were observed which represent 27 various structures. The coeluted compounds were ceramides containing the same functional groups and the same carbon number but with a different distribution on the two alkyl chains of the molecule. They were accurately differentiated by mass spectrometry. Therefore, 83 structures of trimethylsilylated ceramides were identified in 11 different commercial mixtures. For 52 structures of these, mass spectral data were not described in the literature, neither full mass spectra nor characteristic fragments.     

固相萃取采样和气相色谱-质谱检测液化石油气中的芳烃杂质（英文）

建立石墨化碳(GCB)为吸附剂的动态采样系统,可实现液化石油气(LPG)中芳烃杂质的采样和同步萃取富集。LPG中的芳烃杂质(苯、甲苯、二甲苯、苯乙烯和萘)被快速捕集后,进行气相色谱-质谱(GC-MS)定性定量分析。与C18和苯乙烯二乙烯苯吸附剂(PS-DVB)相比,GCB填充柱对芳烃杂质的萃取效率最高。评价了基于GCB填充柱采样的吸附效率、重现性和贮存稳定性。采样和分析方法对氮气模拟气流中8种芳烃的定量分析线性范围为15~1 000μg/m~3。所开发的方法具有回收率高(92.9%~109.0%)、检出限低(1.0~6.2μg/m~3)、准确性好(相对标准偏差为0.6%~5.8%)和准确度高(标准偏差为0.8%~8.2%)等优点。     

The extraction and analysis of 1,4-dioxane from water using solid-phase microextraction coupled with gas chromatography and gas chromatography-mass spectrometry

In this study, two methods are developed for the extraction of 1,4-dioxane (dioxane) from water using 80-microm carboxen-polydimethylsiloxane solid-phase microextraction fibers followed by either gas chromatography (GC)-flame ionization detection (FID) or GC-mass spectrometry (MS). With GC-FID, the lower limit of detection (LOD) for 1,4-dioxane is 2.5 microg/L (ppb) with a linear range of 5 to 10,000 microg/L, obtained by immersing the fiber in the sample for 20 min with agitation. Using GC-MS, the lower limit of quantitation is 0.5 microg/L, and the LOD is 0.25 microg/L. The upper linear range limit is 100 microg/L. Samples are extracted in 20 min using either heated headspace with agitation or direct immersion with agitation.     

固相萃取-气相色谱-质谱法测定食品中23种邻苯二甲酸酯

建立了同时检测食品中23种邻苯二甲酸酯类化合物的固相萃取-气相色谱-质谱(GC-MS)分析方法。样品经正己烷或乙腈提取、玻璃ProElut PSA固相萃取柱净化,GC-MS选择离子监测模式(SIM)测定。考察了不同种类食品的提取、净化方法。23种邻苯二甲酸酯的线性范围除邻苯二甲酸二异壬酯(DINP)和邻苯二甲酸二异癸酯(DIDP)为0.5～5 mg/L外,其余均为0.05～5 mg/L,相关系数(r)除DIDP外均大于0.99。方法的检出限(信噪比为3)为0.005～0.05 mg/kg,定量限(信噪比为10)为0.02～0.2 mg/kg。在10种食品基质中3个加标水平的平均回收率为77%～112%,相对标准偏差(RSD,n=6)为4.1%～12.5%。该方法稳定、可靠,操作简单,适用于食品中邻苯二甲酸酯类化合物的检测与确证。     

Analysis of chloroacetanilide herbicides in water samples by solid-phase microextraction coupled with gas chromatography-mass spectrometry

A solid-phase microextraction (SPME) method has been developed for the determination of 3 chloroacetanilide herbicides in both fresh and seawater samples. The extracted sample was analyzed by gas chromatography with mass spectrometry detection (GC-MS), and parameters affecting SPME operation including fibre type, sample pH, sample temperature, mixing speed and extraction time have been evaluated and optimized. The amount of dissolved organic matter (DOM) and the salt content both affected SPME extraction efficiency, but the presence of other competitive extractants such as organochlorine pesticides (OCPs) in the matrix showed no insignificance interference. The limit of detection (LOD) for acetochlor, metolachlor and butachlor were 1.2, 1.6 and 2.7 ng L⁻¹, respectively. The recoveries for the herbicides ranged from 79 to 102%, and the linear dynamic range was from 10 to 1000 ng L⁻¹. The developed method has been used to monitor herbicides contaminations in coastal water samples collected around Laizhou bay and Jiaozhou bay in Shandong peninsula, China. The concentrations of acetochlor and metolachlor ranged from undetectable to 78.5 ng L⁻¹ and undetectable to 35.6 ng L⁻¹, respectively. Butachlor was not observed but in only one sample and the concentration is lower than the limit of quantification (LOQ). The concentrations of the three herbicides in this study are low compared to most of the other places reported.     

固相微萃取-气相色谱法和气相色谱-质谱法测定茶叶中氟虫腈及其代谢物残留

基于固相微萃取-气相色谱(SPME-GC)建立了检测茶叶中氟虫腈及其代谢物(脱亚硫酰基氟虫腈、硫化氟虫腈、氟虫腈砜、酰胺氟虫腈)残留的分析方法。实验中采用85 μm聚丙烯酸酯(PA)萃取头,萃取温度为60 ℃,萃取缓冲体系的pH值为9。当添加水平为2～10 μg/kg时,回收率在71.2%～109.3%之间,相对标准偏差(RSD)在1.2%～7.1%之间。方法的检出限(LOD)和定量限(LOQ)分别在0.3～1.2 μg/kg和1.0～4.0 μg/kg之间。对所建立的方法采用气相色谱-质谱(GC-MS)进行确证,结果令人满意。本方法灵敏度、精密度和LOD均符合残留分析要求,具有快速、灵敏度高的特点,适合于茶叶中氟虫腈及其代谢物残留的痕量分析。     

Peak alignment and robust principal component analysis of gas chromatograms of fatty acid methyl esters and volatiles

Gas chromatograms of fatty acid methyl esters and of volatile lipid oxidation products from fish lipid extracts are analyzed by multivariate data analysis [principal component analysis (PCA)]. Peak alignment is necessary in order to include all sampled points of the chromatograms in the data set. The ability of robust algorithms to deal with outlier problems, including both sample-wise and element-wise outliers, and the advantages and drawbacks of two robust PCA methods, robust PCA (ROBPCA) and robust singular value decomposition when analysing these GC data were investigated. The results show that the usage of ROPCA is advantageous, compared with traditional PCA, when analysing the entire profile of chromatographic data in cases of sub-optimally aligned data. It also demonstrates how choosing the most robust PCA (sample or element-wise) depends on the type of outliers present in the data set.     

固相微萃取-气相色谱-质谱联用分析环境水样中痕量有机磷农药

研究了固相微萃取(SPME) 气相色谱 质谱联用(GC MS)同时测定环境水样中二嗪农、甲基对硫磷、对硫磷和水胺硫磷4种有机磷农药(OPPs)的分析方法。选择聚丙烯酸酯(PA)萃取纤维,对SPME的条件如萃取时间、萃取溶液的pH值和离子强度、解吸温度、解吸时间和GC MS的条件进行了优化。对二嗪农和水胺硫磷方法线性范围为0.001～10μg L,对甲基对硫磷和对硫磷方法线性范围为0.001～100μg L。二嗪农、甲基对硫磷、对硫磷、水胺硫磷的检出限分别为0.015,0.020,0.013和0.039μg L。分析加标自来水、矿泉水和湖水样品,回收率在89.0%～102%之间,RSD在2.1%～14.1%之间。适合于环境水样中痕量OPPs的快速分析。     

固相微萃取-气相色谱-质谱联用分析水中的痕量扑草净

研究了固相微萃取－气相色谱－质谱联用测定水中扑草净的方法，采用聚丙烯酸酯（PA）萃取头，对影响固相微萃取萃取效率的萃取时间、搅拌速度、pH值以及盐度等进行了优化。在优化实验条件下，方法线性范围在0．1mg/mL-1000ng/mL之间，检出限为1．5ng/L。用该法分析了合成井水、自来水和湖水样品，回收率在85．4％－89．1％之间，相对标准偏差在1．8％－5．2％之间。本方法适合于水中痕量扑草净的分析。     

Determination of benzylsuccinic acid in gasoline-contaminated groundwater by solid-phase extraction coupled with gas chromatography-mass spectrometry

Benzylsuccinic acid (BSA) and methylbenzylsuccinic acid (methyl-BSA) are unambiguous biotransformation products resulting from anaerobic toluene and xylene biodegradation, respectively. A solid-phase extraction method based on polystyrene-divinylbenzene sorbent was developed for the quantitative BSA determination in groundwater samples as an alternative to liquid-liquid extraction. Gas chromatography coupled with mass spectrometry was used for separation and detection. The recovery from spiked 11 groundwater samples was 88 to 100%. The precision of the method, indicated by the relative standard deviation, was +/- 4% and the method detection limit was 0.2 microg/l. The concentration of BSA and methyl-BSA in groundwater samples from anaerobic BTEX (benzene, toluene, ethylbenzene and xylenes)-contaminated sites ranged from below the detection limit (3 microg/l) to 155 microg/l.     

Fast quantitative analysis of boric acid by gas chromatography-mass spectrometry coupled with a simple and selective derivatization reaction using triethanolamine

A fast, selective, and sensitive GC-MS method has been developed and validated for the determination of boric acid in the drinking water by derivatization with triethanolamine. This analytic strategy successfully converts the inorganic, nonvolatile boric acid B(OH)₃ present in the drinking water to a volatile triethanolamine borate B(OCH₂CH₂)₃N in a quantitive manner, which facilitates the GC measurement. The SIM mode was applied in the analysis and showed high accuracy, specificity, and reproducibility, as well as reducing the matrix effect effectively. The calibration curve was obtained from 0.01 μg/mL to 10.0 μg/mL with a satisfactory correlation coefficient of 0.9988. The limit of detection for boric acid was 0.04 μg/L. Then the method was applied for detection of the amount of boric acid in bottled drinking water and the results are in accordance with the reported concentration value of boric acid. This study offers a perspective into the utility of GC-MS as an alternate quantitative tool for detection of B(OH)₃, even for detection of boron in various other samples by digesting the boron compounds to boric acid.