Evaluation of settled floor dust for the presence of microbial metabolites and volatile anthropogenic chemicals in indoor environments by LC-MS/MS and GC-MS methods

This study reports on detection of a large number of biological and anthropogenic pollutants using LC-MS/MS and GC-MS technologies in settled floor dust (SFD). The latter technique was applied to obtain a general picture on the presence of microbial as well as non-microbial volatile organic compounds, whereas the targeted LC-MS/MS analysis focused on identification of species specific secondary metabolites. In the absence of moisture monitoring data the relevance of finding of stachybotrylactam and other metabolites of tertiary colonizers are confined only to accidental direct exposure to SFD. To the best of our knowledge 30 of the 71 identified volatile organic compounds (VOCs) are newly reported in SFD matrix. Coordinated application of “AMDIS and Spectconnect” was found beneficial for the evaluation and identification of prime volatile pollutants in complex environmental samples. Principal component analysis (PCA) of peak areas of 18 microbial volatile organic compounds (MVOCs) resulted in identification of nonanal as potential MVOC marker. Two more volatiles toluene and 1-tetradecanol though had discriminative influence, are not regarded as MVOC markers, considering their probable alternate origin from paints and cosmetics, respectively.     

Differentiation of the volatile profile of microbiologically contaminated canned tomatoes by dynamic headspace extraction followed by gas chromatography-mass spectrometry analysis

The aromatic profile of microbiologically contaminated canned tomatoes was analyzed by the dynamic headspace extraction technique coupled with gas chromatography-mass spectrometry. Canned tomatoes contaminated with Escherichia coli, Saccharomyces cerevisiae and Aspergillus carbonarius were analyzed after 2 and 7 days. About 100 volatiles were detected, among which alcohols, aldehydes and ketones were the most abundant compounds. Gas chromatographic peak areas were used for statistical purposes. First, principal component analysis was carried out in order to visualize data trends and clusters. Then, linear discriminant analysis was performed in order to detect the set of volatile compounds ables to differentiate groups of analyzed samples. Five volatile compounds, i.e. ethanol, β-myrcene, o-methyl styrene, 6-methyl-5-hepten-2-ol and 1-octanol, were found to be able to better discriminate between uncontaminated and contaminated samples. Prediction ability of the calculated model was estimated to be 100% by the “leave-one-out” cross-validation. An electronic nose device was then used to analyze the same contaminated and not contaminated canned tomato samples. Preliminary results were compared with those obtained by dynamic headspace gas chromatography-mass spectrometry, showing a good agreement.     

野生与栽培羌活药材挥发油含量及组分的比较分析

分析野生和栽培羌活药材挥发油含量及其组分的变化,为羌活药材的引种栽培及进一步的开发利用提供了依据。采用水蒸气蒸馏法提取羌活药材中的挥发油,通过GC-MS对挥发油成分进行分析鉴定;利用聚类分析对测定结果进行分类;通过相关分析研究海拔对羌活药材挥发油含量的影响。6份药材样品挥发油含量范围为1.60～7.98 mL/100g,6份羌活药材挥发油成分经GC-MS分析,共鉴定出57个化合物,共有成分12个;基于挥发油成分种类及相对含量的聚类分析显示了羌活药材挥发性成分种类及含量的种间差异;相关分析结果表明,海拔高度与挥发油含量存在显著正相关。野生和栽培羌活药材挥发油含量存在差异,总体上野生羌活药材的挥发油含量高于栽培种;羌活药材挥发油含量随海拔升高而增加。     

Application of headspace solid phase microextraction and gas chromatography to the screening of volatile compounds from some Brazilian aromatic plants

Summary An HS-SPME method was developed and applied for the isolation of volatile organic compounds from plants native or acclimatized to Brazil. Method optimization was performed using typical analytes from the target samples; fibers coated with 100 μm PDMS and 75 μm Carboxen/PDMS were tested. Using PDMS 100 μm fibers and GC-MS for separation and identification, up to 99.9% of the peak area in the chromatograms from plants were identified. The method was also applied to quantify the major volatile components of one of the samples (Aloysia gratissima) with results comparable to those from the conventional steam distillation method.     

Thermal desorption/gas chromatography/mass spectrometry approach for characterization of the volatile fraction from amber specimens: A possibility of tracking geological origins

Research on the chemical composition of fossil resins has evolved during the last decades as a multidisciplinary field and is strongly oriented toward the correlation with their geological and botanical origin. Various extraction procedures and chromatographic techniques have been used together for identifying the volatile compounds contained in the fossil resin matrix. Hyphenation between thermal desorption (TD), gas chromatography (GC) and mass spectrometry detection (MS) has been chosen to investigate the volatile compounds fraction from ambers with a focus on Romanite (Romanian amber) and Baltic amber species. A data analysis procedure was developed for the main purpose of fingerprinting ambers based on the MS identity of the peaks generated by the volatile fraction, together with their relative percentual area within the chromatogram. Chromatographic data analysis was based entirely on Automated Mass Spectral Deconvolution & Identification System (AMDIS) software to produce deconvoluted mass spectra which were used to build-up a mixed mass spectra and relative retention scale library. Multivariate data analysis was further applied on AMDIS results with successful discrimination between Romanite and Baltic ambers. A special trial was conducted to generate pyrolysis “like” macromolecular structure breakdown to volatile compounds by gamma irradiation with a high absorbed dose of 500 kGy. Contrary to our expectations the volatile fraction fingerprints were not modified after irradiation experiments. A complementary non-destructive new approach by ESR spectroscopy was also proposed for discriminating between Romanite and Baltic ambers.     

Development of a volatile amine sensor for the monitoring of fish spoilage

A sensor with potential for the development of a “chemical barcode” for real-time monitoring of fish freshness is described. This on-package sensor contains a pH sensitive dye, bromocresol green, that responds through visible colour change to basic volatile spoilage compounds, such as trimethylamine (TMA), ammonia (NH₃) and dimethylamine (DMA) collectively known as Total Volatile Basic Nitrogen (TVB-N). The sensor characteristics were studied as well as its response with standard ammonia gas. Trials on cod and under-utilised species have verified that the sensor response correlates with bacterial growth patterns in fish samples thus enabling the “real-time” monitoring of spoilage in various fish species. The sensor response can be interrogated with a simple, inexpensive reflectance colorimeter that we have developed based on two light emitting diodes (LEDs) and a photodetector.     

Chemical profiling and classification of illicit heroin by principal component analysis, calculation of inter sample correlation and artificial neural networks

Artificial neural networks (ANNs) were utilised to validate illicit drug classification in the profiling method used at “Institut de Police Scientifique” of the University of Lausanne (IPS). This method established links between samples using a combination of principal component analysis (PCA) and calculation of a correlation value between samples.Heroin seizures sent to the IPS laboratory were analysed using gas chromatography (GC) to separate the major alkaloids present in illicit heroin. Statistical analysis was then performed on 3371 samples. Initially, PCA was performed as a preliminary screen to identify samples of a similar chemical profile. A correlation value was then calculated for each sample previously identified with PCA. This correlation value was used to determine links between drug samples. These links were then recorded in an Ibase^® database. From this database the notion of “chemical class” arises, where samples with similar chemical profiles are grouped together. Currently, about 20 “chemical classes” have been identified.The normalised peak areas of six target compounds were then used to train an ANN to classify each sample into its appropriate class. Four hundred and sixty-eight samples were used as a training data set. Sixty samples were treated as blinds and 370 as non-linked samples. The results show that in 96% of cases the neural network attributed the seizure to the right “chemical class”.The application of a neural network was found to be a useful tool to validate the classification of new drug seizures in existing chemical classes. This tool should be increasingly used in such situations involving profile comparisons and classifications.     

GC—MS分析不同方法提取的大黄挥发性成分

分别采用顶空进样法和超临界CO2萃取法提取大黄挥发性成分,采用GC-MS结合保留指数进行检测分析。顶空进样法分析鉴定出8种挥发性成分,超临界CO2萃取法鉴定出15种挥发性成分。两种提取方法分别侧重于不同类型的挥发性成分,利用GC-MS结合保留指数进行定性分析,更快速准确。     

An electronic tongue for gliadins semi-quantitative detection in foodstuffs

An all-solid-state potentiometric electronic tongue with 36 polymeric membranes has been used for the first time to detect gliadins, which are primarily responsible for gluten intolerance in people suffering from celiac disease. A linear discriminant model, based on the signals of 11 polymeric membranes, selected from the 36 above using a stepwise procedure, was used to semi-quantitatively classify samples of a “Gluten-free” foodstuff (baby milked flour), previously contaminated with known amounts of gliadins (<10, 20-50 or >50 mg/kg), as “Gluten-free”, “Low-Gluten content” or “Gluten-containing”. For this food matrix, the device had sensitivity towards gliadins of 1-2 mg/kg and overall sensitivity and specificity of 77% and 78%, respectively. Moreover, the device never identified an ethanolic extract containing gliadins as “Gluten-free”. Finally, the system also allowed distinguishing “Gluten-free” and “Gluten-containing” foodstuffs (15 foods, including breads, flours, baby milked flours, cookies and breakfast cereals) with an overall sensitivity and specificity greater than 83%, using the signals of only 4 selected polymeric membranes (selected using a stepwise procedure). Since only one “Gluten-containing” foodstuff was misclassified as “Gluten-free”, the device could be used as a preliminary tool for quality control of foods for celiac patients.     

Optimization of HS-SPME for GC-MS Analysis and Its Application in Characterization of Volatile Compounds in Sweet Potato

Volatile compounds are the main chemical species determining the characteristic aroma of food. A procedure based on headspace solid-phase microextraction (HP-SPME) coupled to gas chromatography-mass spectrometry (GC-MS) was developed to investigate the volatile compounds of sweet potato. The experimental conditions (fiber coating, incubation temperature and time, extraction time) were optimized for the extraction of volatile compounds from sweet potato. The samples incubated at 80 °C for 30 min and extracted at 80 °C by the fiber with a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) coating for 30 min gave the most effective extraction of the analytes. The optimized method was applied to study the volatile profile of four sweet potato cultivars (Anna, Jieshu95-16, Ayamursaki, and Shuangzai) with different aroma. In total, 68 compounds were identified and the dominants were aldehydes, followed by alcohols, ketones, and terpenes. Significant differences were observed among the volatile profile of four cultivars. Furthermore, each cultivar was characterized by different compounds with typical flavor. The results substantiated that the optimized HS-SPME GC-MS method could provide an efficient and convenient approach to study the flavor characteristics of sweet potato. This is the basis for studying the key aroma-active compounds and selecting odor-rich accessions, which will help in the targeted improvement of sweet potato flavor in breeding.     

Two Sides to One Story—Aroma Chemical and Sensory Signature of Lugana and Verdicchio Wines

Lugana and Verdicchio are two Italian white wines with a Protected Designation of Origin (PDO) label. These two wine types are produced in different regions using the same grape variety. The aim of this work is to investigate the existence of volatile chemical markers that could help to elucidate differences between Lugana and Verdicchio wines both at chemical and sensory levels. Thirteen commercial wine samples were analyzed by Gas Chromatography-Mass Spectrometry (GC-MS), and 76 volatile compounds were identified and quantified. Verdicchio and Lugana had been differentiated on the basis of 19 free and glycosidically bound compounds belonging to the chemical classes of terpenes, benzenoids, higher alcohols, C₆ alcohols and norisoprenoids. Samples were assessed by means of a sorting task sensory analysis, resulting in two clusters formed. These results suggested the existence of 2 product types with specific sensory spaces that can be related, to a good extend, to Verdicchio and Lugana wines. Cluster 1 was composed of six wines, 4 of which were Lugana, while Cluster 2 was formed of 7 wines, 5 of which were Verdicchio. The first cluster was described as “fruity”, and “fresh/minty”, while the second as “fermentative” and “spicy”. An attempt was made to relate analytical and sensory data, the results showed that damascenone and the sum of 3 of esters the ethyl hexanoate, ethyl octanoate and isoamyl acetate, was characterizing Cluster 1. These results highlighted the primary importance of geographical origin to the volatile composition and perceived aroma of Lugana and Verdicchio wines.     

“One-shot” analysis of polybrominated diphenyl ethers and their hydroxylated and methoxylated analogs in human breast milk and serum using gas chromatography-tandem mass spectrometry

The presence of polybrominated diphenyl ethers (PBDEs) and their hydroxylated (OH-BDE) and methoxylated (MeO-BDE) analogs in humans is an area of high interest to scientists and the public due to their neurotoxic and endocrine disrupting effects. Consequently, there is a rise in the investigation of the occurrence of these three classes of compounds together in environmental matrices and in humans in order to understand their bioaccumulation patterns. Analysis of PBDEs, OH-BDEs, and MeO-BDEs using liquid chromatography-mass spectrometry (LC-MS) can be accomplished simultaneously, but detection limits for PBDEs and MeO-BDEs in LC-MS is insufficient for trace level quantification. Therefore, fractionation steps of the phenolic (OH-BDEs) and neutral (PBDEs and MeO-BDEs) compounds during sample preparation are typically performed so that different analytical techniques can be used to achieve the needed sensitivities. However, this approach involves multiple injections, ultimately increasing analysis time. In this study, an analytical method was developed for a “one-shot” analysis of 12 PBDEs, 12 OH-BDEs, and 13 MeO-BDEs using gas chromatography with tandem mass spectrometry (GC-MS/MS). This overall method includes simultaneous extraction of all analytes via pressurized liquid extraction followed by lipid removal steps to reduce matrix interferences. The OH-BDEs were derivatized using N-(t-butyldimethylsilyl)-N-methyltrifluoroacetamide (TBDMS-MTFA), producing OH-TBDMS derivatives that can be analyzed together with PBDEs and MeO-BDEs by GC-MS/MS in “one shot” within a 25-min run time. The overall recoveries were generally higher than 65%, and the limits of detection ranged from 2 to 14 pg in both breast milk and serum matrices. The applicability of the method was successfully validated on four paired human breast milk and serum samples. The mean concentrations of total PBDEs, OH-BDEs, and MeO-BDEs in breast milk were 59, 2.2, and 0.57 ng g⁻¹ lipid, respectively. In serum, the mean total concentrations were 79, 38, and 0.96 ng g⁻¹ lipid, respectively, exhibiting different distribution profiles from the levels detected in breast milk. This “one-shot” GC-MS/MS method will prove useful and cost-effective in large-scale studies needed to further understand the partitioning behavior, and ultimately the adverse health effects, of these important classes of brominated flame retardants in humans.     

Second-order standard addition for deconvolution and quantification of fatty acids of fish oil using GC-MS

In the present work two second-order calibration methods, generalized rank annihilation method (GRAM) and multivariate curve resolution-alternating least square (MCR-ALS) have been applied on standard addition data matrices obtained by gas chromatography-mass spectrometry (GC-MS) to characterize and quantify four unsaturated fatty acids cis-9-hexadecenoic acid (C16:1ω7c), cis-9-octadecenoic acid (C18:1ω9c), cis-11-eicosenoic acid (C20:1ω9) and cis-13-docosenoic acid (C22:1ω9) in fish oil considering matrix interferences. With these methods, the area does not need to be directly measured and predictions are more accurate. Because of non-trilinear conditions of GC-MS data matrices, at first MCR-ALS and GRAM have been used on uncorrected data matrices. In comparison to MCR-ALS, biased and imprecise concentrations (%R.S.D. = 27.3) were obtained using GRAM without correcting the retention time-shift. As trilinearity is the essential requirement for implementing GRAM, the data need to be corrected. Multivariate rank alignment objectively corrects the run-to-run retention time variations between sample GC-MS data matrix and a standard addition GC-MS data matrix. Then, two second-order algorithms have been compared with each other. The above algorithms provided similar mean predictions, pure concentrations and spectral profiles. The results validated using standard mass spectra of target compounds. In addition, some of the quantification results were compared with the concentration values obtained using the selected mass chromatograms. As in the case of strong peak-overlap and the matrix effect, the classical univariate method of determination of the area of the peaks of the analytes will fail, the “second-order advantage” has solved this problem successfully.     

The use of a combined portable X ray fluorescence and multivariate statistical methods to assess a validated macroscopic rock samples classification in an ore exploration survey

The combination of “ex situ” portable X ray fluorescence with unsupervised and supervised pattern recognition techniques such as hierarchical cluster analysis, principal components analysis, factor analysis and linear discriminant analysis have been applied to rock samples, in order to validate a “in situ” macroscopic rock samples classification of samples collected in the Boris Angelo mining area (Central Chile), during a drill-hole survey carried out to evaluate the economic potential of this Cu deposit. The analysed elements were Ca, Cu, Fe, K, Mn, Pb, Rb, Sr, Ti and Zn. The statistical treatment of the geological data has been arisen from the application of the Box-Cox transformation used to transform the data set in normal form to minimize the non-normal distribution of the data. From the statistical results obtained it can be concluded that the macroscopic classification applied to the transformed data permits at least, to distinguish quite well in relation to two of the rock classes defined (70.5% correctly classified (p < 0.05)) as well as for four of the five alteration types defined “in situ” (75% of the total samples).     

Identification and quantification of the main organic components of vinegars by high resolution 1H NMR spectroscopy

A detailed analysis of the proton high-field NMR spectra of vinegars (in particular of Italian balsamic vinegars) is reported. A large number of organic substances belonging to different classes, such as carbohydrates, alcohols, organic acids, volatile compounds and amino acids, were assigned. The possibility of quantification of the substances identified in the whole vinegar sample, without extraction or pre-concentration steps, was also tested. The data validity was demonstrated in terms of precision, accuracy, repeatability and inter-day reproducibility. The effects of the most critical experimental parameters (sample concentration, water suppression and relaxation time) on the analysis response were also discussed. ¹H NMR results were compared with those obtained by traditional techniques (GC-MS, titrations), and good correlations were obtained. The results showed that ¹H NMR with water suppression allows a rapid, simultaneous determination of carbohydrates (glucose and fructose), organic acids (acetic, formic, lactic, malic, citric, succinic and tartaric acids), alcohols and polyols (ethanol, acetoin, 2,3-butanediol, hydroxymethylfurfural), and volatile substances (ethyl acetate) in vinegar samples. On the contrary, the amino acid determination without sample pre-concentration was critical. The ¹H NMR method proposed was applied to different samples of vinegars, allowing, in particular, the discrimination of vinegars and balsamic vinegars.     

Study of the influence of NaOH treatment on the pyrolysis of different leather tanned using thermogravimetric analysis and Py/GC-MS system

This paper presents a study of the influence of a NaOH treatment on leather samples tanned using different tanning agents. Thermogravimetric analysis (TG/DTG) and flash pyrolysis (Py/GC-MS) was carried out on treated samples, and the results were compared with those obtained with the untreated samples. Treatment with sodium hydroxide seemed to decrease the temperature of maximum leather decomposition rate between 4 and 27 °C, depending on the tanning agent, and increased the range of temperatures where leather degradation occurs. From flash pyrolysis experiments, it was found that the volatile products obtained from NaOH-treated samples contain higher amounts of nitrogen compounds than those obtained from untreated samples. The results of a multivariate analysis applied to pyrolytic products obtained in the flash pyrolysis of samples showed greater similarity between the results obtained in all treated samples than between treated and untreated samples produced with the same tanning process.     

Profiling analysis of volatile compounds from fruits using comprehensive two-dimensional gas chromatography and image processing techniques

An image processing approach originating from the proteomics field has been transferred successfully to the processing of data obtained with comprehensive two-dimensional gas chromatographic separations data. The approach described here has proven to be a useful analytical tool for unbiased pattern comparison or profiling analyses, as demonstrated with the differentiation of volatile patterns (“aroma”) from fruits such as apples, pears, and quince fruit. These volatile patterns were generated by headspace solid phase microextraction coupled to comprehensive two-dimensional gas chromatography (HS-SPME-GC × GC). The data obtained from GC × GC chromatograms were used as contour plots which were then converted to gray-scale images and analyzed utilizing a workflow derived from 2D gel-based proteomics. Run-to-run variations between GC × GC chromatograms, respectively their contour plots, have been compensated by image warping. The GC × GC images were then merged into a fusion image yielding a defined and project-wide spot (peak) consensus pattern. Within detected spot boundaries of this consensus pattern, relative quantities of the volatiles from each GC × GC image have been calculated, resulting in more than 700 gap free volatile profiles over all samples. These profiles have been used for multivariate statistical analysis and allowed clustering of comparable sample origins and prediction of unknown samples. At present state of development, the advantage of using mass spectrometric detection can only be realized by data processing off-line from the identified software packages. However, such information provides a substantial basis for identification of statistically relevant compounds or for a targeted analysis.     

Organic Compounds in Surface and Deep Antarctic Snow

Abstract

Eight surface snow samples taken during the 1987/88, 1988/89 and 1990/91 Italian Antarctic Expeditions and six samples collected at different depths from two dissimilar sites during the 1990/91 Expedition, were analyzed for the non-chlorinated organic content using the GC capillary columns technique and GC-MS. Several biogenic and anthropogenic classes of organic compounds were identified and quantitatively determined. The data obtained give a more complete picture of the pollution level in Antarctica.     

RF-pulsed glow discharge time-of-flight mass spectrometry for glass analysis: Investigation of the ion source design

Radiofrequency (RF) millisecond pulsed glow discharge (PGD) coupled to time-of-flight mass spectrometry (TOFMS) was investigated for direct elemental analysis of glass samples. Aiming at achieving highest elemental sensitivity, appropriate discrimination from polyatomics, and good crater shapes on glasses, a new Grimm-type GD chamber (termed from now “UNIOVI GD”, designed and constructed in our laboratory) was coupled to TOFMS, and the results compared with those obtained with the former GD design (here denominated as “GD.1”) of the initial RF-PGD-TOFMS prototype. The critical differences distinguishing the two GDs under scrutiny are the GD chamber thickness (15.5 mm for the GD.1 and 7 mm for the UNIOVI GD) and the “flow tube” which is inserted in the GD.1 and inexistent in UNIOVI GD.