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Microbial volatile organic compounds (MVOCs) play an essential role in many environmental fields, such as indoor air quality. Long-term exposure to odorous and toxic MVOCs can negatively affect the health of occupants. Recently, the involvement of surface reservoirs in indoor chemistry has been realized, which signifies the importance of the phase partitioning of volatile organic pollutants. However, reliable partition coefficients of many MVOCs are currently lacking. Equilibrium partition coefficients, such as Henry's law constant

Discriminant-type analyses arise from the need to classify samples based on their measured characteristics (variables), usually with respect to some observable property. In the case of samples that are difficult to obtain, or using advanced instrumentation, it is very common to encounter situations with many more measured characteristics than samples. The method of Partial Least Squares Regression (PLS-R), and its variant for discriminant-type analyses (PLS-DA) are among the most ubiquitous of these tools. PLS utilises a rank-deficient method to solve the inverse least-squares problem in a way that maximises the co-variance between the known properties of the samples (commonly referred to as the Y-Block), and their measured characteristics (the X-block). A relatively small subset of highly co-variate variables are weighted more strongly than those that are poorly co-variate, in such a way that an ill-posed matrix inverse problem is circumvented. Feature selection is another common way of reducing the dimensionality of the data to a relatively small, robust subset of variables for use in subsequent modelling. The utility of these features can be inferred and tested any number of ways, this are the subject of this review.

Odorous clothing can be an annoying and unpleasant problem, particularly when odorants are not effectively removed via laundering. Cotton and polyester knit fabrics were soiled with three selected odorants, representing different polarities and lipophilicities (i.e. octanoic acid, 2-nonenal, dodecane). Fabrics were subjected to 1, 5 and 10 soil/wash cycles using a regular liquid detergent (Tide® Free and Gentle) or a sport liquid detergent (Tide® Plus Febreze Sport). Odorants released into the headspace were collected using solid phase micro-extraction, and odorants retained within the fabric were collected using solvent extraction. Analysis of odorant peaks was carried out using gas chromatography-flame ionization detection. Prior to laundering, higher amounts of all odorants were released into the headspace above polyester fabrics than above cotton fabrics. Cotton fabrics retained more octanoic acid within the fabric and lower amounts of 2-nonenal than polyester. Laundering was more effective at removing odorants from cotton than from polyester, and the polar octanoic acid was more readily removed than the two non-polar odorants from both fabrics. Accumulation of odorants occurred as soil/wash cycles increased from 1 to 5 cycles. However, between 5 and 10 soil/wash cycles the amounts of compounds did not significantly increase, with significantly lower amounts of octanoic acid extracted from cotton at 10 cycles compared to 5 cycles. The results from this study indicate that incomplete removal of odorants during washing, especially from oleophilic polyester fabrics, is a cause for odor build-up in clothing.

Comprehensive two-dimensional gas chromatography (GC × GC) provides enhanced separation power over its one-dimensional counterpart – gas chromatography (GC). This enhancement is achieved by the inclusion of a secondary column, the choice of which is a major determinant on the quality of the ultimate separation. When developing and optimizing a new GC × GC method, the choices of stationary phase chemistries, geometries and configurations (which phase serves in which dimension) are of fundamental importance, and must often be addressed even before the manipulation of instrumental conditions. These choices are often made using educated guesses, literature searches, or trial and error. Thermodynamic models of GC separations; however, provide a fast and easy means of acquiring information for guiding these choices. By using characteristic thermodynamic parameters (characteristic temperatures, Tchar, and characteristic thermal constants, θchar), we demonstrate the generation of maps that can inform the choices of column chemistries, phase ratios and configurations for GC × GC separations.

Dunaliella tertiolecta is a marine microalgae that has been studied extensively as a potential carbon-neutral biofuel source [1]. Microalgae oil contains high quantities of energy-rich fatty acids and lipids, but is not yet commercially viable as an alternative fuel. Carefully optimised growth conditions, and more recently, algal-bacterial co-cultures have been explored as a way of improving the yield of Dunaliella tertiolecta microalgae oils. The relationship between the host microalgae and bacterial co-cultures is currently poorly understood. Here, a complete workflow is proposed to analyse the global metabolomic profile of co-cultured Dunaliella tertiolectra and Phaeobacter italicus R11, which will enable researchers to explore the chemical nature of this relationship in more detail.Graphical AbstractHighlightsA method for direct microalgae sample introduction is proposed.Advanced chemometric tools can extract, useful and discriminating metabolomic features even from very noisy complex datasets.

Thermodynamics-based models have been demonstrated to be useful for predicting retention time and peak widths in gas chromatography and two-dimensional gas chromatography separations. However, the collection of data to train the models can be time consuming, which lessens the practical utility of the method. In this contribution, a method for obtaining thermodynamic-based data to predict peak widths in temperature-programmed gas chromatography is presented. Experimental work to collect data for peak width prediction is identical to that required to collect data for retention time prediction using approaches that we have presented previously. Using this combined approach, chromatograms including retention times and peak widths are predicted with very high accuracy. Typical errors in retention time are < 0.5%, while errors in peak width are typically < 5% as demonstrated using polycycic aromatic hydrocarbons and a mixture containing compounds with aldehyde, ketone, alkene, alkane, alcohol, and ester functionalities.

Comprehensive gas chromatography with time of flight mass spectrometry is a powerful tool in the analysis of complex samples. Chemometric analysis of raw chromatographic data is more useful in one- and two-dimensional separations relative to peak tables. The data volume from such experiments generally necessitates the use of data reduction tools. Such tools often sacrifice some of the multivariate information in the mass to charge ratio dimension. The unique ion filter reduces the over-redundancy in two-dimensional gas chromatography-mass spectrometry data by limiting the data to a few unique/pseudo-unique ions, sub-peaks/slices in the first dimension, and spectra in the second dimension. We explore the performance of this algorithm through careful inspection of two-dimensional gas chromatography-mass spectrometry data before and after application of the filter. A reduction (99%) in the number of variables in a two-dimensional gas chromatography-mass spectrometry chromatogram passed on to subsequent analysis was observed. Feature selection times for model optimization reduced from 229 (±13) to 6.8 (±0.5) min when the filter was applied. An estimate of two unique/pseudo-unique ions, one sub-peak in the first dimension and five spectra in the second dimension were considered to provide a true representation of each chromatogram and provided enough information to achieve 100% model prediction accuracy.

Dunaliella tertiolecta is a marine microalgae that has been studied extensively as a potential carbon-neutral biofuel source (Tang et al., 2011). Microalgae oil contains high quantities of energy-rich fatty acids and lipids, but is not yet commercially viable as an alternative fuel. Carefully optimised growth conditions, and more recently, algal-bacterial co-cultures have been explored as a way of improving the yield of D. tertiolecta microalgae oils. The relationship between the host microalgae and bacterial co-cultures is currently poorly understood. Here, a complete workflow is proposed to analyse the global metabolomic profile of co-cultured D. tertiolectra and Phaeobacter italicus R11, which will enable researchers to explore the chemical nature of this relationship in more detail. To the best of the authors' knowledge this study is one of the first of its kind, in which a pipeline for an entirely untargeted analysis of the algal metabolome is proposed using a practical sample preparation, introduction, and data analysis routine. Agency for Science, Technology and Research (A*STAR) The authors wish to acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the support given to The Metabolomics Innovation Centre (TMIC) through grants from Genome Alberta, Genome Canada, and The Canada Foundation for Innovation. This work was also supported by A*STAR SFS IAF-PP grant (A20H7a0152) awarded to Rebecca Case.

Recent increases in marijuana use and legalization without adequate knowledge of the risks necessitate the characterization of the billions of nanoparticles contained in each puff of smoke. Tobacco smoke offers a benchmark given that it has been extensively studied. Tobacco and marijuana smoke particles are quantitatively similar in volatility, shape, density and number concentration, albeit with differences in size, total mass and chemical composition. Particles from marijuana smoke are on average 29% larger in mobility diameter than particles from tobacco smoke and contain 3.4× more total mass. New measurements of semi-volatile fractions determine over 97% of the mass and volume of the particles from either smoke source are comprised of semi-volatile compounds. For tobacco and marijuana smoke, respectively, 4350 and 2575 different compounds are detected, of which, 670 and 536 (231 in common) are tentatively identified, and of these, 173 and 110 different compounds (69 in common) are known to cause negative health effects through carcinogenic, mutagenic, teratogenic, or other toxic mechanisms. This study demonstrates striking similarities between marijuana and tobacco smoke in terms of their physical and chemical properties.

Although polyester is well known for smelling strongly after wear, little is known about the propensity of nylon to retain and emit body odor. In this study, we investigated whether odor intensity differed between nylon and polyester fabrics. A secondary aim was to compare odor on fabrics frozen prior to sensory assessment with fabrics stored at room temperature. Eight participants wore T-shirts with fabrics in the underarm. Odor intensity was rated by 13 assessors. Odor reduction rate (ORR) was measured using ISO 17299-3. Overall, no differences were found in odor intensity between nylon and polyester. Any differences found between the two fabrics were likely dependent on the individual who wore the fabric. The ORR was higher for nylon than polyester, indicating that nylon absorbed more odorants. There was some evidence to suggest that odor intensity could increase on nylon fabrics stored at room temperature, but this was less apparent for polyester.

This is the third part of a three-part series of papers. In Part I, we presented a method for determining the actual effective geometry of a reference column as well as the thermodynamic-based parameters of a set of probe compounds in an in-house mixture. Part II introduced an approach for estimating the actual effective geometry of a target column by collecting retention data of the same mixture of probe compounds on the target column and using their thermodynamic parameters, acquired on the reference column, as a bridge between both systems. Part III, presented here, demonstrates the retention time transfer and prediction from the reference column to the target column using experimental data for a separate mixture of compounds. To predict the retention time of a new compound, we first estimate its thermodynamic-based parameters on the reference column (using geometric parameters determined previously). The compound's retention time on a second column (of previously determined geometry) is then predicted. The models and the associated optimization algorithms were tested using simulated and experimental data. The accuracy of predicted retention times shows that the proposed approach is simple, fast, and accurate for retention time transfer and prediction between gas chromatography columns.

A method to predict the crystal structure of equiatomic ternary compositions based only on the constituent elements was developed using cluster resolution feature selection (CR-FS) and support vector machine (SVM) classification. The supervised machine-learning model was first trained with 1037 individual compounds that adopt the most populated ternary 1:1:1 structure types (TiNiSi-, ZrNiAl-, PbFCl-, LiGaGe-, YPtAs-, UGeTe-, and LaPtSi-type) and then validated using an additional 519 compounds. The CR-FS algorithm improves class discrimination and indicates that 113 variables including size, electronegativity, number of valence electrons, and position on the periodic table (group number) influence the structure preference. The final model prediction sensitivity, specificity, and accuracy were 97.3%, 93.9%, and 96.9%, respectively, establishing that this method is capable of reliably predicting the crystal structure given only its composition. The power of CR-FS and SVM classification is further demonstrated by segregating the crystal structure of polymorphs, specifically to examine polymorphism in TiNiSi- and ZrNiAl-type structures. Analyzing 19 compositions that are experimentally reported in both structure types, this machine-learning model correctly identifies, with high confidence (0.7), the low-temperature polymorph from its high-temperature form. Interestingly, machine learning also reveals that certain compositions cannot be clearly differentiated and lie in a "confused" region (0.3-0.7 confidence), suggesting that both polymorphs may be observed in a single sample at certain experimental conditions. The ensuing synthesis and characterization of TiFeP adopting both TiNiSi- and ZrNiAl-type structures in a single sample, even after long annealing times (3 months), validate the occurrence of the region of structural uncertainty predicted by machine learning.

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a powerful tool for the analysis of complex mixtures, and it is ideally suited to discovery studies where the entire sample is potentially of interest. Unfortunately, when unit mass resolution mass spectrometers are used, many detected compounds have spectra that do not match well with libraries. This could be due to the compound not being in the library, or the compound having a weak/nonexistent molecular ion cluster. While high-speed, high-resolution mass spectrometers, or ion sources with softer ionization than 70 eV electron impact (EI) may help with some of this, many GC×GC systems presently in use employ low-resolution mass spectrometers and 70 eV EI ionization. Scripting tools that apply filters to GC×GC-TOFMS data based on logical operations applied to spectral and/or retention data have been used previously for environmental and petroleum samples. This approach rapidly filters GC×GC-TOFMS peak tables (or raw data) and is available in software from multiple vendors. In this work, we present a series of scripts that have been developed to rapidly classify major groups of compounds that are of relevance to metabolomics studies including: fatty acid methyl esters, free fatty acids, aldehydes, alcohols, ketones, amino acids, and carbohydrates.

Alignment of fire debris data from GC-MS for chemometric analysis is challenged by highly variable, uncontrolled sample and matrix composition. The total ion spectrum (TIS) obviates the need for alignment but loses all separation information. We introduce the segmented total ion spectrum (STIS), which retains the advantages of TIS while retaining some retention information. We compare the performance of STIS with TIS for the classification of casework fire debris samples. TIS and STIS achieve good model prediction accuracies of 96% and 98%, respectively. Baseline removal improved model prediction accuracies for both TIS and STIS to 97% and 99%, respectively. The importance of maintaining some chromatographic information to aid in deciphering the underlying chemistry of the results and reasons for false positive/negative results was also examined.

Cluster resolution feature selection (CR-FS) is a hybrid feature selection algorithm which involves the evaluation of ranked variables via sequential backward elimination (SBE) and sequential forward selection (SFS). The implementation of CR-FS requires two main inputs, namely, start and stop number. The start number is the number of the highly ranked variables for the SBE while the stop number is the point at which the search for additional features during the SFS stage is halted. The setting of these critical parameters has always relied on trial and error which introduced subjectivity in the results obtained. The start and stop numbers are known to vary with each dataset. Drawing inspiration from overlapping coefficients, a method for comparing two probability density functions, empirical equations toward the estimation of start and stop number for a dataset were developed. All of the parameters in the empirical equations are obtained from the comparisons of the two probability density functions except the constant termed d. The equations were optimized using three real-world datasets. The optimum range of d was determined to be 0.48 to 0.57. An implementation of CR-FS using two new datasets demonstrated the validity of this approach. Partial least squares discriminant analysis (PLS-DA) model prediction accuracies increased from 90 and 96 to 100% for both datasets using start and stop numbers calculated with this approach. Additionally, there was a twofold increase in the explained variance captured in the first two principal components. Graphical abstract Here, we describe how to determine the start and stop numbers for an automated feature selection routine, ensuring that you get the best model you can for your data with minimal effort.

Alkyl phosphates in crude oils originate from their use as gellants used in some hydraulic fracturing processes or as additives used to improve physical properties of the oil for transportation. Alkyl phosphates are a potential issue for used oil re-refining and recycling operations because of their use as flame-retardants in, for example, aviation hydraulic fluids. The primary concern with alkyl phosphates in the refinery is their association with refinery fouling, including deposit formation and catalyst deactivation. A comprehensive two-dimensional gas chromatography method, with nitrogen phosphorus detection (GC × GC-NPD) and post-column Deans switching, was previously developed by our group for quantitative analysis of alkyl phosphates in petroleum samples. The original method employed an apolar/polar GC × GC column configuration and was shown to provide trace speciation of alkyl phosphates in distillate samples and some crude oils. However, when petroleum samples having a high concentration of heavy nitrogen-containing species were encountered, coelutions with alkyl phosphates ensued. Even in a phosphorus-optimized NPD configuration, the background signal from nitrogen-containing species masked the signals of some alkyl phosphates. Thus, a new column configuration using a polar/apolar GC × GC column set was developed. The results of this study are presented, showing the applicability of this method to the analysis of alkyl phosphates in petroleum samples with a high concentration of nitrogen-containing species. This method effectively separates the alkyl phosphates from nitrogen containing species with which they previously co-eluted. Additionally, it allows for trace speciation and quantification of alkyl phosphates in crude-oil with detection limits ranging from 0.001 to 0.02 μg phosphorus per mL.

Forensic examination of fire debris evidence is a notoriously difficult analytical task due to the complexity and variability of sample composition. The use of comprehensive two-dimensional gas chromatography with mass spectrometry detection (GC × GC–MS) allows the coupling of orthogonal retention mechanisms and therefore a high peak capacity.We demonstrate recent innovations in combining chemometric techniques for data reduction and feature selection, with evaluation of the evidence for forensic questions pertaining to the detection and subsequent classification of ignitable liquid residue (ILR) in fire debris samples. Chromatograms are divided into non-overlapping spatially delimited regions; for each of these regions a Local Ion Signature (LIS) is computed by summing the intensities, per nominal mass/charge over all points contained within each region. This yields a reduced feature space representing the original data as a set of consolidated ion traces. Subsequent feature selection is performed by evaluating the individual efficacy of each feature using a univariate score-based likelihood ratio (LR) approach for discriminating between pairs of same or different type samples. The retained features are used to model each ILR class using linear discriminant analysis (LDA).Results are demonstrated for 155 arson samples containing a diversity of substrate compounds and several known ignitable liquids. ILR detection is performed at 84% accuracy with fewer than 1% false positives followed by subsequent classification. Likelihood ratio distributions are presented referring to both detection and classification tasks.

Human axillary sweat is a poorly explored biofluid within the context of metabolomics when compared to other fluids such as blood and urine. In this paper, we explore the volatile organic compounds emitted from two different types of fabric samples (cotton and polyester) which had been worn repeatedly during exercise by participants. Headspace solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) were employed to profile the (semi)volatile compounds on the fabric. Principal component analysis models were applied to the data to aid in visualizing differences between types of fabrics, wash treatment, and the gender of the subject who had worn the fabric. Statistical tools included with commercial chromatography software (ChromaTOF) and a simple Fisher ratio threshold-based feature selection for model optimization are compared with a custom-written algorithm that uses cluster resolution as an objective function to maximize in a hybrid backward-elimination forward-selection approach for optimizing the chemometric models in an effort to identify some compounds that correlate to differences between fabric types. The custom algorithm is shown to generate better models than the simple Fisher ratio approach. Graphical Abstract A route from samples and questions to data and then answers.

Infection of plants by pathogens can result in the upregulation of induced defenses; plants may be more or less susceptible to attack by insect herbivores following infection. We investigated the interaction between canola, Brassica napus L., plants infected with clubroot, Plasmodiophora brassicae Woronin, and a generalist herbivore the bertha armyworm (BAW) Mamestra configurata Walker using two canola cultivars that varied in susceptibility to clubroot disease. Volatile organic compounds released from experimental plants differed with infection and female adult BAW could discriminate between canola plants inoculated with P. brassicae and disease-free plants. Adult female moths preferentially laid eggs on disease-free plants of the susceptible cultivar to P. brassicae. Inoculation of resistant canola with P. brassicae, however, did not influence oviposition by female BAW. The fitness of BAW larvae was reduced when they were reared on susceptible canola inoculated with P. brassicae. Salicylic acid and its conjugates in susceptible canola plants were induced following P. brassicae inoculation as compared to disease-free susceptible plants. We conclude that suppression of BAW oviposition and offspring fitness may result in part from a change in the volatile profile of the plant as a result of inoculation and the induction of defenses in inoculated susceptible canola.

Partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) techniques were applied to develop a crystal structure predictor for binary AB compounds. Models were trained and validated on the basis of the classification of 706 AB compounds adopting the seven most common structure types (CsCl, NaCl, ZnS, CuAu, TlI, β-FeB, and NiAs), through data extracted from Pearson’s Crystal Data and ASM Alloy Phase Diagram Database. Out of 56 initial variables (descriptors based on elemental properties only), 31 were selected in as unbiased manner as possible through a procedure of forward selection and backward elimination, with the quality of the model evaluated by measuring the cluster resolution at each step. PLS-DA gave sensitivity of 96.5%, specificity of 66.0%, and accuracy of 77.1% for the validation set data, whereas SVM gave sensitivity of 94.2%, specificity of 92.7%, and accuracy of 93.2%, a significant improvement. Radii, electronegativity, and valence electrons, previously chosen i...

We have previously demonstrated the successful application of multivariate statistical (i.e. chemometric) techniques to the classification of casework fire debris based on gasoline content, with the objective to expedite the data interpretation process for the forensic analysis of fire debris. We have also shown that it is possible to classify simulated fire debris based on gasoline content using simulated fire debris; however, models trained on simulated debris are not applicable to casework samples without a significant loss in model accuracy. A previously developed simulation protocol that works well for generating debris to train human analysts was inadequate for training either partial least-squares discriminant analysis (PLS-DA) or soft independent modelling by class analogy (SIMCA) models to identify casework debris due mainly to its inability to generate a sufficient amount of benzene, toluene, ethylbenzene, and xylenes (BTEX) and non-aromatic hydrocarbon compounds. This method relied on pyrolyzing materials at 400 °C. Here we examine the effects of pyrolysis conditions on household materials, including spruce plywood, vinyl sheet flooring, polyethylene terephthalate (PET) carpet, Nylon 6 carpet, polyurethane (PU) foam carpet underlay, asphalt shingle, medium-density fireboard (MDF) shelving, and spruce timber at temperatures above 400 °C, in an attempt to generate additional BTEX and non-aromatic hydrocarbon compounds for the realistic simulation of fire debris. The work presented here showed that C 3 - to C 5 -alkylbenzenes, which are abundant in gasoline, were generally absent from the pyrolysates of all materials studied and at all temperatures studied (400, 700 and 900 °C), only appearing in trace amounts on rare occasions. Based on our results, we propose that accurate simulation of fire debris could be achieved with a mixture of carpets, carpet underlay, and vinyl flooring pyrolyzed at 700 °C; spruce plywood pyrolyzed at 900 °C, and asphalt shingles pyrolyzed at 400 and 700 °C. These conditions generated a substantial amount of BTEX and non-aromatic hydrocarbons in the debris matrix background. The testing of this ⿿recipe⿿ for generating simulated debris for training chemometric models that can classify casework debris is left for future experimentation.

Ignitable liquid detection canines have an extraordinarily sensitive sense of smell. Previous studies estimating canine sensitivity to ignitable liquids have been unable to demonstrate a level below which canines will not indicate ignitable presence. Forensic laboratories provide corroborating evidence and explain reasons for canine indications. Laboratories provide protection against false-positives and greater context for the exhibit contents to both investigators and the courts. However, the methods routinely used in forensic laboratories lack the sensitivity required to match canine sensitivity estimates, leading to frustration when canines provide a strong indication, but the laboratory findings are negative. In order to develop a laboratory method that can match the sensitivity of canines to trace ignitable liquids, an estimation of canine sensitivity is required. We present a protocol for producing extremely clean, porous tile substrates, and a scheme for quantitatively producing target samples with any desired abundance by using a solvent that does not elicit a response from the canines. Search experiments with two canine-handler teams validate the methods and provide the first estimate of the ignitable liquid detection canine sensitivity limit for gasoline: both canines repeatedly located gasoline at 5 pL but provided no indication at any level ≤3 pL. The protocol allows standardization of substrates and targets for canine olfactory testing, while the estimated canine sensitivity to gasoline provides a preliminary target for forensic laboratories performing confirmatory analysis on exhibits selected by ignitable liquid detection canines.

The transfer of retention times based on thermodynamic models between columns can aid in separation optimization and compound identification in gas chromatography. Although earlier investigations have been reported, this problem remains unsuccessfully addressed. One barrier is poor predictive accuracy when moving from a reference column or system to a new target column or system. This is attributed to challenges associated with the accurate determination of the effective geometric parameters of the columns. To overcome this, we designed least squares-based models that account for geometric parameters of the columns and thermodynamic parameters of compounds as they partition between mobile and stationary phases. Quasi-Newton-based algorithms were then used to perform the numerical optimization. In this first of three parts, the model used to determine the geometric parameters of the reference column and the thermodynamic parameters of compounds subjected to separation is introduced. As will be shown, the overall approach significantly improves the predictive accuracy and transferability of thermodynamic data (and retention times) between columns of the same stationary phase chemistry. The data required for the determination of the thermodynamic parameters and retention time prediction are obtained from fast and simple experiments. The proposed model and optimization algorithms were tested and validated using simulated and experimental data.

The transfer of thermodynamic parameters governing retention of a molecule in gas chromatography from a reference column to a target column is a difficult problem. Successful transfer demands a mechanism whereby the column geometries of both columns can be determined with high accuracy. This is the second part in a series of three papers. In Part I of this work we introduced a new approach to determine the actual effective geometry of a reference column and thermodynamic-based parameters of a suite of compounds on the column. Part II, presented here, illustrates the rapid estimation of the effective inner diameter (or length) and the effective phase ratio of a target column. The estimation model based on the principle of least squares; a fast Quasi-Newton optimization algorithm was developed to provide adequate computational speed. The model and optimization algorithm were tested and validated using simulated and experimental data. This study, together with the work in Parts I and III, demonstrates a method that improves the transferability of thermodynamic models of gas chromatography retention between gas chromatography columns.

Thermodynamic parameters of ΔH(T0), ΔS(T0), and ΔCP for 156 compounds comprising alkanes, alkyl halides and alcohols were determined for a 5% phenyl 95% methyl stationary phase. The determination of thermodynamic parameters relies on a Nelder-Mead simplex optimization to rapidly obtain the parameters. Two methodologies of external and leave one out cross validations were applied to assess the robustness of the estimations of thermodynamic parameters. The largest absolute errors in predicted retention time across all temperature ramps and all compounds were 1.5 and 0.3s for external and internal sets, respectively. The possibility of an in silico extension of the thermodynamic library was tested using a quantitative structure-retention relationship (QSRR) methodology. The estimated thermodynamic parameters were utilized to develop QSRR models. Individual partial least squares (PLS) models were developed for each of the three classes of the molecules. R(2) values for the test sets of all models across all temperature ramps were larger than 0.99 and the average of relative errors in retention time predictions of the test sets for alkanes, alcohols, and alkyl halides were 1.8%, 2.4%, and 2.5%, respectively.

Using raw GC/MS data as the X-block for chemometric modeling has the potential to provide better classification models for complex samples when compared to using the total ion current (TIC), extracted ion chromatograms/profiles (EIC/EIP), or integrated peak tables. However, the abundance of raw GC/MS data necessitates some form of data reduction/feature selection to remove the variables containing primarily noise from the data set. Several algorithms for feature selection exist; however, due to the extreme number of variables (10(6)-10(8) variables per chromatogram), the feature selection time can be prolonged and computationally expensive. Herein, we present a new prefilter for automated data reduction of GC/MS data prior to feature selection. This tool, termed unique ion filter (UIF), is a module that can be added after chromatographic alignment and prior to any subsequent feature selection algorithm. The UIF objectively reduces the number of irrelevant or redundant variables in raw GC/MS data, while preserving potentially relevant analytical information. In the m/z dimension, data are reduced from a full spectrum to a handful of unique ions for each chromatographic peak. In the time dimension, data are reduced to only a handful of scans around each peak apex. UIF was applied to a data set of GC/MS data for a variety of gasoline samples to be classified using partial least-squares discriminant analysis (PLS-DA) according to octane rating. It was also applied to a series of chromatograms from casework fire debris analysis to be classified on the basis of whether or not signatures of gasoline were detected. By reducing the overall population of candidate variables subjected to subsequent variable selection, the UIF reduced the total feature selection time for which a perfect classification of all validation data was achieved from 373 to 9 min (98% reduction in computing time). Additionally, the significant reduction in included variables resulted in a concomitant reduction in noise, improving overall model quality. A minimum of two um/z and scan window of three about the peak apex could provide enough information about each peak for the successful PLS-DA modeling of the data as 100% model prediction accuracy was achieved. It is also shown that the application of UIF does not alter the underlying chemical information in the data.

First- and second-dimension retention times for a series of alkyl phosphates were predicted for multiple column combinations in GC×GC. This was accomplished through the use of a three-parameter thermodynamic model where the analytes' interactions with the stationary phases in both dimensions are known. Ionic liquid columns were employed to impart unique selectivity for alkyl phosphates, and it was determined that for alkyl phosphate compounds, ionic liquid columns are best used in the primary dimension. Retention coordinates for unknown phosphates are predicted from the thermodynamic parameters of a set standard alkyl phosphates. Additionally, we present changing retention properties of alkyl phosphates on some ionic liquid columns, due to suspected reaction between the analyte and column. This makes it difficult to accurately predict their retention properties, and in general poses a problem for ionic liquid columns with these types of analytes.

Detection and identification of ignitable liquids (ILs) in arson debris is a critical part of arson investigations. The challenge of this task is due to the complex and unpredictable chemical nature of arson debris, which also contains pyrolysis products from the fire. ILs, most commonly gasoline, are complex chemical mixtures containing hundreds of compounds that will be consumed or otherwise weathered by the fire to varying extents depending on factors such as temperature, air flow, the surface on which IL was placed, etc. While methods such as ASTM E-1618 are effective, data interpretation can be a costly bottleneck in the analytical process for some laboratories. In this study, we address this issue through the application of chemometric tools. Prior to the application of chemometric tools such as PLS-DA and SIMCA, issues of chromatographic alignment and variable selection need to be addressed. Here we use an alignment strategy based on a ladder consisting of perdeuterated n-alkanes. Variable selection and model optimization was automated using a hybrid backward elimination (BE) and forward selection (FS) approach guided by the cluster resolution (CR) metric. In this work, we demonstrate the automated construction, optimization, and application of chemometric tools to casework arson data. The resulting PLS-DA and SIMCA classification models, trained with 165 training set samples, have provided classification of 55 validation set samples based on gasoline content with 100% specificity and sensitivity.

A new method for calibrating thermodynamic data to be used in the prediction of analyte retention times is presented. The method allows thermodynamic data collected on one column to be used in making predictions across columns of the same stationary phase but with varying geometries. This calibration is essential as slight variances in the column inner diameter and stationary phase film thickness between columns or as a column ages will adversely affect the accuracy of predictions. The calibration technique uses a Grob standard mixture along with a Nelder–Mead simplex algorithm and a previously developed model of GC retention times based on a three-parameter thermodynamic model to estimate both inner diameter and stationary phase film thickness. The calibration method is highly successful with the predicted retention times for a set of alkanes, ketones and alcohols having an average error of 1.6 s across three columns.

Several refinery fouling incidents in North America have been due to the presence of alkyl phosphates in the crude oil feed. These phosphates originate in some cases from their use as gellants (vis...

A GC-vacuum ultraviolet (UV) method to perform group-type separations of diesel range fuels was developed. The method relies on an ionic liquid column to separate diesel samples into saturates, mono-, di-, and polyaromatics by gas chromatography, with selective detection via vacuum UV absorption spectroscopy. Vacuum UV detection was necessary to solve a coelution between saturates and monoaromatics. The method was used to measure group-type composition of 10 oilsands-derived Synfuel light diesel samples, 3 Syncrude light gas oils, and 1 quality control sample. The gas chromatography (GC)-vacuum UV results for the Synfuel samples were similar (absolute % error of 0.8) to historical results from the supercritical fluid chromatography (SFC) analysis. For the light gas oils, discrepancies were noted between SFC results and GC-vacuum UV results; however, these samples are known to be challenging to quantify by SFC-flame ionization detector (FID) due to incomplete resolution between the saturate/monoaromatic and/or monoaromatic/diaromatic group types when applied to samples heavier than diesel (i.e., having a larger fraction of higher molecular weight species). The quality control sample also performed well when comparing both methods (absolute % error of 0.2) and the results agreed within error for saturates, mono- and polyaromatics.