Development of an impurity‐profiling method for source identification of spilled benzene series compounds by gas chromatography with mass spectrometry: Toluene as a case study

In this study, an impurity profiling method was established for the source identification of spilled benzene series compounds. Toluene was used as a case study to demonstrate the feasibility of this approach. Gas chromatography with mass spectrometry was applied for identification and quantification of the impurities including ethyl benzene, p‐xylene, m‐xylene, and o‐xylene in toluene. Impurities in toluene were detected at very low levels by applying mass spectrometry in selected‐ion monitoring mode. Eight authentic toluene samples collected from different manufacturers were analyzed by the developed gas chromatography with mass spectrometry method to construct the characteristic impurity profiling of toluene. Then, combined with scatter distribution, similarity analysis and t‐test, a suite of diagnostic ratios based on the impurity distribution was used for the differentiation of toluene from different sources. Results indicated that scatter distribution method can discriminate the original toluene samples from different manufacturers. Similarity calculation and t‐test methods can identify effectively the weathered toluene samples. The proposed impurity profiling method was useful for discrimination between toluene samples from different sources. Statistical analysis of these impurity profiles demonstrated the potential to investigate whether two questioned spilled toluene samples encountered in forensic casework are from the same source.     

Quantitative analysis of trace‐level benzene,toluene, ethylbenzene,and xylene in cellulose acetate tow using headspace heart‐cutting multidimensional gas chromatography with mass spectrometry

This study describes a method for the quantification of trace‐level benzene, toluene, ethylbenzene, and xylene in cellulose acetate tow by heart‐cutting multidimensional gas chromatography with mass spectrometry in selected ion monitoring mode. As the major volatile component in cellulose acetate tow samples, acetone would be overloaded when attempting to perform a high‐resolution separation to analyze trace benzene, toluene, ethylbenzene, and xylene. With heart‐cutting technology, a larger volume injection was achieved and acetone was easily cut off by employing a capillary column with inner diameter of 0.32 mm in the primary gas chromatography. Only benzene, toluene, ethylbenzene, and xylene were directed to the secondary column to result in an effective separation. The matrix interference was minimized and the peak shapes were greatly improved. Finally, quantitative analysis of benzene, toluene, ethylbenzene, and xylene was performed using an isotopically labeled internal standard. The headspace multidimensional gas chromatography mass spectrometry system was proved to be a powerful tool for analyzing trace volatile organic compounds in complex samples.     

主成分-线性判别法对大气易挥发性有机化合物的预警

应用遥感傅里叶变换红外光谱,采用主成分提取-线性判别分析(PCA-LDA)技术,对丙酮、二氯甲烷、甲苯、苯、氯仿和甲醇等六组分的任意混合体系进行定性鉴别。被选用的这6种大气有毒有机化合物的红外光谱图相互间存在着严重的混叠,并和反向传播人工神经网络(BP-ANN)的预测结果进行了比较。PCA-LDA的鉴别判对率达92.2%,识别率94.4%,误判率7.8%;BP-ANN分别为91.1%、95.6%和8.9%。结果表明PCA处理克服了LDA对多变量数据预测的局限性,预测性能和BP-ANN相当。鉴于BP-ANN计算耗时和繁琐,PCA-LDA模型被确定为建立VOCs预警模型最适当的方法。     

Rapid hydrocarbon analysis using a miniature rectilinear ion trap mass spectrometer

A miniature ion trap mass analyzer was applied to the analysis of traces of hydrocarbons and simple heteroatomics in the vapor phase and in aqueous solution. Vapors of acetone, acetic acid, acetonitrile, benzene, butanethiol, carbon disulfide, hexane, dichloromethane, naphthalene, toluene and xylenes were detected and quantified using solid sorbent trapping and, in some cases, by passage through a membrane interface. Aqueous solutions of benzene, toluene, xylenes, hexane and a petroleum naphtha distillate were examined using the membrane interface. Sampling, detection and identification of all compounds was completed in times of less than one minute. The gas-phase samples of toluene and benzene were detected at 200 ppt (limit of detection, LOD) for toluene and 600 ppt for benzene. Identification of benzene and xylene in aqueous solutions was readily achieved with LODs of 200 and 400 ppb, respectively. Quantification over a linear dynamic range of two orders of magnitude for the aqueous samples and three orders of magnitude for the vapor-phase samples was demonstrated.     

The combined use of thermal desorption and selected ion flow tube mass spectrometry for the quantification of xylene and toluene in air

Thermal desorption (TD) is commonly employed for volatile chemical analysis, it being the method of choice for occupational health and safety monitoring. TD allows for offline capture of volatiles onto a solid sorbent followed by desorption and analysis at a later time. Although TD is routinely used in conjunction with gas chromatography (TD-GC), the assay throughput is low and requires the use of gas standards for quantification. Another technique increasingly employed for volatile chemical analysis, selected ion flow tube mass spectrometry (SIFT-MS), is capable of real-time absolute (i.e. without calibration standards) quantification of volatile chemicals present at single digit parts per billion or higher concentrations. SIFT-MS is, however, normally used for online direct analysis of gas samples rather than offline collection and analysis. The goal of this study was to determine whether a combination of TD and SIFT-MS could be used to quantify volatile compounds, specifically xylene and toluene, more rapidly than TD-GC and without the need for calibration standards. SIFT-MS was able to quantify xylene and toluene levels within 45 s of desorption. Due to the robustness of the SIFT-MS analysis in the presence of water vapour and other major components of air, the purging of tubes usually required to remove these constituents during the TD cycle was not required, therefore reducing the TD cycle time. Comparing the quantity of xylene and toluene applied to the TD tube with the absolute levels quantified by SIFT-MS subsequent to desorption suggested a recovery of over 95% of the applied compound. We conclude that the combination of TD and SIFT-MS allows more rapid and accurate quantification of xylene and toluene (compared with TD-GC) to be achieved without the need for calibration standards, features which may be advantageous in applications requiring rapid analysis and high throughput.     

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated.Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together).In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression.Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression.Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.     

苯酚、马尿酸和甲基马尿酸的反相高效液相色谱分析

本文提出了人体内苯、甲苯和二甲苯的代谢产物苯酚、马尿酸和甲基马尿酸的反相高效液相色谱分析法,讨论了其保留机制和样品预处理技术.以ODS为固定相,甲醇-水-醋酸为流动相时可实现良好分离.方法回收率为97.3%,相对标准偏差为1.02%.提出的方法可用于尿样分析,适用于临床与职业病防治的监测分析.     

R2 2(8) motifs in Aminopyrimidine sulfonate/carboxylate interactions: Crystal structures of pyrimethaminium benzenesulfonate monohydrate (2:2:1) and 2-amino-4,6-dimethylpyrimidinium sulfosalicylate dihydrate (4:2:2)

Background

The characterization of three types of Marche (Italy) honeys (Acacia, Multifloral, Honeydew) was carried out on the basis of the their quality parameters (pH, sugar content, humidity) and mineral content (Na, K, Ca, Mg, Cu, Fe, and Mn). Pattern recognition methods such as principal components analysis (PCA) and linear discriminant analysis (LDA) were performed in order to classify honey samples whose botanical origins were different, and identify the most discriminant parameters. Lastly, using ANOVA and correlations for all parameters, significant differences between diverse types of honey were examined.

Results

Most of the samples' water content showed good maturity (98%) whilst pH values were in the range 3.50 – 4.21 confirming the good quality of the honeys analysed. Potassium was quantitatively the most relevant mineral (mean = 643 ppm), accounting for 79% of the total mineral content. The Ca, Na and Mg contents account for 14, 3 and 3% of the total mineral content respectively, while other minerals (Cu, Mn, Fe) were present at very low levels. PCA explained 75% or more of the variance with the first two PC variables. The variables with higher discrimination power according to the multivariate statistical procedure were Mg and pH. On the other hand, all samples of acacia and honeydew, and more than 90% of samples of multifloral type have been correctly classified using the LDA. ANOVA shows significant differences between diverse floral origins for all variables except sugar, moisture and Fe.

Conclusion

In general, the analytical results obtained for the Marche honeys indicate the products' high quality. The determination of physicochemical parameters and mineral content in combination with modern statistical techniques can be a useful tool for honey classification.     

Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

Rapid diagnosis is important for efficient treatment in clinical medicine. This study aimed at development of a method for rapid and reliable diagnosis using near-infrared (NIR) spectra of human serum samples with the help of chemometric modelling. The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed. Discrete wavelet transform (DWT) and variable selection were adopted to extract the useful information from the spectra. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLSDA) were used for discrimination of the samples. Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection. DWT-LDA produced 93.8% and 83.3% of the recognition rates for the validation samples of the two classes, and 100% recognition rates were obtained using DWT-PLSDA. The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection, and the differences can be used for discrimination of the sera from healthy and possible patients. NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.     

Evaluation of indoor exposition to benzene, toluene, ethylbenzene, xylene, and styrene by passive sampling with a solid-phase microextraction device

A solid-phase microextraction (SPME) sampling method is developed to evaluate indoor exposure to benzene, toluene, ethylbenzene, xylene, and styrene with gas chromatography and flame ionization detection for quantitative analysis. An SPME holder with a 100-pm polydimethylsiloxane (PDMS) and 65-pm PDMS-divinylbenzene fiber coating is tested in different air relative humidity conditions. The method gives good resolution, shows a linear response, is repeatable, and presents high sensitivity. This method is compared with National Institute of Occupational Safety and Health (NIOSH) active sampling.     

Pattern recognition for the study of structure—activity relationships: Uses of the adaptive least-squares method and linear discriminant analysis

The adaptive least-squares method (ALS) and linear discriminant analysis (LDA) were applied to structure—activity correlation studies including the antitumor activity of mitomycin derivatives, the relative binding affinities of 100 steroids for five receptors, assignment of the pharmacological category of 80 diarylmethane-derived drugs, and discrimination of the adverse reaction of 98 miscellaneous drugs that may induce liver and/or blood diseases. Generally, more satisfactory results were obtained by the use of ALS than by LDA, both in recognition and in leave-one-out predictions. However, LDA was not always inferior to ALS in the applications, especially those related to classification of independent categories.     

Fast Determination of Monocyclic Aromatic Hydrocarbons in Ambient Air Using a Portable Gas Chromatography–Photoionization Detector

Many air sampling methods are time consuming and require complex pre-treatment steps. Gas chromatography–photoionization detector (GC–PID) is a rapid method for sampling and analysis. However, although it has been used in a number of studies, its operating conditions and performance parameters have not been optimized systematically. In this study, a GC–PID method for analysis of monocyclic aromatic hydrocarbons in gas samples without pre-concentration or enrichment was developed and optimized. This GC–PID can perform both online and off-line analysis. In online analysis, the sample was pumped directly into a Teflon sample loop (pumped online injection), which resulted in minimal loss of sample. The optimum parameters were as follows: 30-s pumping time, 10 mL min^?1 of carrier gas flow rate, and 40 °C oven temperature. GC–PID was applied to analysis of benzene, toluene and xylene. The calibration curves showed good linearity for online analysis. The results obtained by GC–PID were accurate and reliable, with all the correlation coefficients ≥0.9972 and all the relative standard deviations <3%. A mixture of benzene, toluene, and o-, m-, and p-xylenes was separated satisfactorily in 10 min, except for m- and p-xylene. The performance of the portable GC–PID was compared with that of an ATD–GC–FID for quantification of benzene, toluene and xylene in calibration gas samples, and benzene, toluene, ethylbenzene, and the o-, m-, and p-xylenes in outdoor ambient air. The results indicated that GC–PID with pumped online injection was stable and accurate for analysis of these monocyclic aromatic hydrocarbons.     

Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils

An “electronic nose” has been used for the detection of adulterations of virgin olive oil. The system, comprising 12 metal oxide semiconductor sensors, was used to generate a pattern of the volatile compounds present in the samples. Prior to different supervised pattern recognition treatments, feature selection techniques were employed to choose a set of optimally discriminant variables. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and artificial neural networks (ANN) were applied. Excellent results were obtained in the differentiation of adulterated and non-adulterated olive oils and it was even possible to identify the type of oil used in the adulteration. Promising results were also obtained as regards quantification of the percentages of adulteration.     

Determination of single photon ionization cross sections for quantitative analysis of complex organic mixtures

Soft single photon ionization (SPI)-time-of-flight mass spectrometry (TOFMS) is well suited for fast and comprehensive analysis of complex organic gas mixtures, which has been demonstrated in various applications. This work describes a calibration scheme for SPI, which enables quantification of a large number of compounds by only calibrating one compound of choice, in this case benzene. Photoionization cross sections of 22 substances were determined and related to the yield of benzene. These substances included six alkanes (pentane, hexane, heptane, octane, nonane, decane), three alkenes (propene, butane, pentene), two alkynes (propyne, butyne), two dienes (butadiene, isoprene), five monoaromatic species (benzene, toluene, xylene, styrene, monochlorobenzene) and NO. The cross sections of organic compounds differ by about one order of magnitude but the photoionization properties of compounds belonging to one compound class are rather similar. Therefore, the scheme can also be used for an approximate quantification of compound classes. This is demonstrated by a fast characterization and pattern recognition of two gasoline samples with different origins (Germany and South Africa) and a diesel sample (Germany). The on-line capability of the technique and the scheme is demonstrated by quantitatively monitoring and comparing the cold engine start of four vehicles: a gasoline passenger car, a diesel van, a motorbike and a two-stroke scooter.     

Meat species identification by linear discriminant analysis of capillary electrophoresis protein profiles

The objective of this study was to utilize linear discriminant analysis (LDA) in the interpretation of capillary electrophoresis-sodium dodecyl sulfate polymer-filled capillary gel electrophoresis (CE-SDS) meat protein profiles for the identification of meat species. The specific objectives were 1) to collect quantitative data on water-soluble and saline-soluble proteins of different meat species obtained by CE-SDS and 2) to apply LDA on collected CE-SDS protein data for the development of a pattern recognition statistical model useful in the differentiation of meat species. Samples were raw beef top and eye round, boneless fresh pork ham and loin, turkey leg and breast meat, and mechanically deboned turkey meat collected on six different occasions, making a total of 42 samples. Additionally, 14 samples were used as test samples to determine the classification ability of the procedure. Quantitative protein data obtained by CE-SDS was used to generate separate LDA models for either water- or saline-soluble protein extracts. Although a saline solution was a more efficient meat protein-extracting agent, as shown by a higher total protein concentration and a larger number of peaks, water-soluble CE-SDS protein profiles gave more distinctive discrimination among meat species. The correct classification given by LDA on water-soluble protein data was 100% for all meat species, except pork (94%). Conversely, the correct classification on saline-soluble protein data was 88% for beef and mechanically deboned turkey meat, and 94% and 100% for turkey and pork meat, respectively. LDA proved to be a useful pattern recognition procedure in the interpretation of CE-SDS protein profiles for the identification of meat species.     

Trace analysis of benzene,toluene, ethylbenzene and xylene isomers in environmental samples by low-pressure gas chromatography-ion trap mass spectrometry

A rapid determination of benzene, toluene, ethylbenzene and the three xylene isomers (BTEX), including a nearly baseline separation of the xylene isomers in environmental samples within 1 min has been carried out using low-pressure gas chromatography-ion trap mass spectrometry (LP-GC-IT-MS). In order to evaluate the different parameters which may influence the performance of LP-GC-IT-MS, different column and mass spectral parameters were varied. Comparing LP-GC-IT-MS with the conventional equivalent, we obtained excellent detection limits as well as a good RSD of 8-13% in ition to a much shorter analysis time. In order to evaluate LP-GC-IT-MS for use in environmental samples, we determined BTEX in air.     

可见光响应碳掺杂的光催化剂涂覆于塞流反应器上分解气态芳香化合物(英文)

A plug‐flow reactor coated with carbon‐doped TiO2 (C‐TiO2 ) powder was investigated for the control of vaporous aromatics (benzene, toluene, ethylbenzene, and o‐xylene (BTEX)) under a range of experimental conditions. The characteristics of the as‐prepared C‐TiO2 and a reference Degussa P25 TiO2 powder were examined using X‐ray diffraction, scanning electron microscopy, diffuse‐reflectance ultraviolet‐visible‐near infrared spectroscopy, and Fourier transform infrared spectroscopy. The experimental conditions for the photocatalytic performance of the as‐prepared C‐TiO2 photocatalyst were controlled using three operational parameters, relative humidity, flow rate, and input concentration. Unlike other target compounds, very little benzene was removed by the C‐TiO2 photocatalyst under visible‐light irradiation. In contrast, the C‐TiO2 exhibited higher removal efficiencies for the other three target compounds (toluene, ethylbenzene, and xylene) compared with those achieved using unmodified TiO2 under visible‐light irradiation. The highest removal efficiency was obtained at a relative humidity value of 45%. Specifically, the toluene removal efficiency determined at a relative humidity of 45% was 78%, whereas it was close to 0%, 7.2%, and 5.5% for relative humidity values of 20%, 70%, and 95%, respectively. In addition, the removal efficiencies for the three target compounds decreased as the flow rate or input concentration increased. These findings indicate that the as‐prepared C‐TiO2 photocatalyst could be used for the removal of toxic vaporous aromatics under optimized operating conditions.     

FTIR spectroscopy coupled with multivariate classification methods to identify different powdered infant formulas adulterated with melamine and cyanuric acid

Fourier transform infrared spectroscopy (FTIR) is a nondestructive, simple, rapid, and cheap measurement technique for analysis of many multicomponent chemical systems, e.g., detection of adulterants in food samples. In this respect, this study proposes combining FTIR spectroscopy with multivariate classification methods for classification and discrimination of different samples of infant formulas adulterated by melamine or/and cyanuric acid. Different parametric and non-parametric multivariate classification methods including the linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and classification and regression tree (CART) approaches were used to classify the recorded FTIR data. Assessing the performance of the multivariate methods according to their sensitivity, specificity and percent of correct prediction results demonstrated that coupling FTIR spectroscopy with multivariate classification can be applied as a rapid and powerful technique to the simultaneous detection of melamine and cyanuric acid in powdered infant formulas. This combinatorial method is efficient for adulterant concentrations as low as 0.0001 w/w%.