Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good performance (AUC $\sim 1$ for simple data sets and AUC $\sim 0.85$ for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromAlignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online

Analysis of gas chromatography/mass spectrometry data for catalytic lignin depolymerization using positive matrix factorization

Various catalytic technologies are being developed to efficiently convert lignin into renewable chemicals. However, due to its complexity, catalytic lignin depolymerization often generates a wide and complex distribution of product compounds. Gas chromatography/mass spectrometry (GC-MS) is a common analytical technique to profile the compounds that comprise lignin depolymerization products. GC-MS is applied not only to determine the product composition, but also to develop an understanding of the catalytic reaction pathways and of the relationships among catalyst structure, reaction conditions, and the resulting compounds generated. Although a very useful tool, the analysis of lignin depolymerization products with GC-MS is limited by the quality and scope of the available mass spectral libraries and the ability to correlate changes in GC-MS chromatograms to changes in lignin structure, catalyst structure, and other reaction conditions. In this study, the GC-MS data of the depolymerization products generated from organosolv hybrid poplar lignin using a copper-doped porous metal oxide catalyst and a methanol/dimethyl carbonate co-solvent was analyzed by applying a factor analysis technique, positive matrix factorization (PMF). Several different solutions for the PMF model were explored. A 13-factor solution sufficiently explains the chemical changes occurring to lignin depolymerization products as a function of lignin, reaction time, catalyst, and solvent. Overall, seven factors were found to represent aromatic compounds, while one factor was defined by aliphatic compounds

Enzymatic digestion and selective quantification of underivatised [delta]9-tetrahydrocannabinol and cocaine in human hair using gas chromatography-mass spectrometry.

Gas chromatography-mass spectrometric (GC-MS) methods for drug analysis routinely employ derivatising reagents. The aim of this paper was to develop a method for the analysis of two recreational drugs, delta-9-tetrahydrocannabinol ([delta](9)-THC) and cocaine in hair samples using GC-MS, without prior derivatisation, thus allowing the sample to be reanalysed in its original form. An enzymatic digestion technique was also developed. Ten hair samples, that were known positive for either [delta](9)-THC and/or cocaine, were enzymatically digested, extracted, and then analysed by GC-MS. All samples measured contained [delta](9)-THC and one sample contained cocaine. The limits of detection (LOD) and quantification (LOQ) were 0.02 ng/mg and 0.05 ng/mg, respectively, for cocaine and 0.015 ng/mg and 0.02 ng/mg, respectively, for [delta](9)-THC. The wide detection window, ease of direct analysis by GC-MS, lower detection limits of underivatised samples, and the stability of drugs using this technique may offer an improved method of analysis

A TGA/FTIR and Mass Spectral Study on the Thermal Degradation of Bisphenol A Polycarbonate

The thermal degradation of polycarbonate under nitrogen was studied using TGA/FTIR, GC/MS and LC/MS as a function of mass loss. The gases evolved during degradation were inspected by in situ FTIR and then the evolved products were collected and analysed using FTIR, GC–MS and LC–MS. The structures of the evolved products are assigned on the basis of FTIR and GC/MS results. The main thermal degradation pathways follow chain scission of the isopropylidene linkage, and hydrolysis/alcoholysis and rearrangement of carbonate linkages. In the case of chain scission, it was proposed that methyl scission of isopropylidene occurs first, according to the bond dissociation energies. The presence of carbonate structures, 1,1′-bis(4-hydroxyl phenyl) ethane and bisphenol A in significant amounts, supports the view that chain scission and hydrolysis/alcoholysis are the main degradation pathways for the formation of the evolved products

An application of data mining to fruit and vegetable sample identification using Gas Chromatography-Mass Spectrometry

One of the uses of Gas Chromatography-Mass Spectrometry (GC-MS) is in the detection of pesticide residues in fruit and vegetables. In a high throughput laboratory there is the potential for sample swaps or mislabelling, as once a sample has been pre-processed to be injected into the GC-MS analyser, it is no longer distinguishable by eye. Possible consequences of such mistakes can be the destruction of large amounts of actually safe produce or pesticide-contaminated produce reaching the consumer. For the purposes of food safety and traceability, it can also be extremely valuable to know the source (country of origin) of a food product. This can help uncover fraudulent attempts of trying to sell food originating from countries deemed unsafe. In this study, we use the workflow environment ADAMS to examine whether we can determine the fruit/vegetable, and the country of origin of a sample from a GC-MS chromatogram. A workflow is used to generate data sets using different data pre-processing methods, and data representations from a database of over 8000 GC-MS chromatograms, consisting of more than 100 types of fruit and vegetables from more than 120 countries. A variety of classification algorithms are evaluated using the WEKA data mining workbench. We demonstrate excellent results, both for the determination of fruit/vegetable type and for the country of origin, using a histogram of ion counts, and Classification by Regression using Random Regression Forest with PLS-transformed data

Determination of gamma-hydroxybutyric acid in dried blood spots using a simple GC-MS method with direct 'on spot' derivatization

The objective of this study was the development of an accurate and sensitive method for the determination of gamma-hydroxybutyric acid (GHB) in dried whole blood samples using a GC-MS method. The complete procedure was optimized, with special attention for the sample pre-treatment, and validated. Therefore, dried blood spots (DBS) of only 50 µl were prepared and, after addition of internal standard GHB-d6, directly derivatized using 100 µl of a freshly prepared mixture of trifluoroacetic acid anhydride (TFAA) and heptafluorobutanol (HFB-OH) (2:1). The derivatized extract was injected into a gas chromatograph coupled to a mass spectrometer (GC-MS), operating in the electron impact mode (EI), with a total run time of 12.3 min. Method validation included the evaluation of linearity, precision, accuracy, sensitivity, selectivity and stability. A weighting factor of 1/x2 was chosen and acceptable intra-batch precision, inter-batch precision and accuracy were seen. The linear calibration curve ranged from 2 to 100 µg/ml, with a limit of detection of 1 µg/ml. Our procedure, utilizing the novel approach of direct “on spot” derivatization, followed by analysis with GC-MS, proved to be reliable, fast and applicable in routine toxicology

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

Pyrolysis-GC×GC-TOFMS to characterize carbonaceous chondrites

Using pyrolysis-GCxGC-TOFMS to analyze organic carbon in carbonaceous chondrites gives a massive increase in both sensitivity and structural information from samples when compared to traditional Py-GC-MS

Viking GC/MS mechanisms design and performance

The Viking Lander gas chromatograph/mass spectrometer will analyze pyrolyzed samples of the Martian surface for organic content. The surface-sample loader and pyrolyzer assembly (SSPLA) is described, along with the major problems encountered during design and testing. Three mechanisms were developed to implement the required SSLPA functions: (1) a soil loader that forces soil from a filled rotating funnel into each of three ovens located on a carriage, (2) a Geneva drive for rotating and precisely indexing the ovens to receive sample, and (3) a toggle-clamp mechanism for sealing the ovens by forcing circular double knife edges into gold sealing surfaces

Determination of propofol by GC/MS and fast GC/MS-TOF in two cases of poisoning

Two cases of suspected acute and lethal intoxication caused by propofol were delivered by the judicial authority to the Department of Sciences for Health Promotion and Mother-Child Care in Palermo, Sicily. In the first case a female nurse was found in a hotel room, where she lived with her mother; four 10 mg/mL vials and two 20 mg/mL vials of propofol were found near the decedent along with syringes and needles. In the second case a male nurse was found in the operating room of a hospital, along with a used syringe. In both cases a preliminary systematic and toxicological analysis indicated the presence of propofol in the blood and urine. As a result, a method for the quantitative determination of propofol in biological fluids was optimized and validated using a liquid-liquid extraction protocol followed by GC/MS and fast GC/MS-TOF. In the first case, the concentration of propofol in blood was determined to be 8.1 \u3bcg/mL while the concentration of propofol in the second case was calculated at 1.2 \u3bcg/mL. Additionally, the tissue distribution of propofol was determined for both cases. Brain and liver concentrations of propofol were, respectively, 31.1 and 52.2 \u3bcg/g in Case 1 and 4.7 and 49.1 \u3bcg/g in Case 2. Data emerging from the autopsy findings, histopathological exams as well as the toxicological results aided in establishing that the deaths were due to poisoning, however, the manner of death in each were different: homicide in Case 1 and suicide in Case 2