Evaluation of normalization strategies for GC-based metabolomics.

INTRODUCTION: For many samples studied by GC-based metabolomics applications, extensive sample preparation involving extraction followed by a two-step derivatization procedure of methoximation and trimethylsilylation (TMS) is typically required to expand the metabolome coverage. Performing normalization is critical to correct for variations present in samples and any biases added during the sample preparation steps and analytical runs. Addressing the totality of variations with an adequate normalization method increases the reliability of the downstream data analysis and interpretation of the results. OBJECTIVES: Normalizing to sample mass is one of the most commonly employed strategies, while the total peak area (TPA) as a normalization factor is also frequently used as a post-acquisition technique. Here, we present a new normalization approach, total derivatized peak area (TDPA), where data are normalized to the intensity of all derivatized compounds. TDPA relies on the benefits of silylation as a universal derivatization method for GC-based metabolomics studies. METHODS: Two sample classes consisting of systematically incremented sample mass were simulated, with the only difference between the groups being the added amino acid concentrations. The samples were TMS derivatized and analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The performance of five normalization strategies (no normalization, normalized to sample mass, TPA, total useful peak area (TUPA), and TDPA) were evaluated on the acquired data. RESULTS: Of the five normalization techniques compared, TUPA and TDPA were the most effective. On PCA score space, they offered a clear separation between the two classes. CONCLUSION: TUPA and TDPA carry different strengths: TUPA requires peak alignment across all samples, which depends upon the completion of the study, while TDPA is free from the requirement of alignment. The findings of the study would enhance the convenient and effective use of data normalization strategies and contribute to overcoming the data normalization challenges that currently exist in the metabolomics community.

Discriminating extra virgin olive oils from common edible oils: Comparable performance of PLS-DA models trained on low-field and high-field 1H NMR data.

INTRODUCTION: Olive oil, derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or fraudulently mislabel oils as olive to increase profitability. Adulterated products can cause allergic reactions in sensitive individuals and can lack compounds which contribute to the perceived health benefits of olive oil, and its corresponding premium price. OBJECTIVE: There is a need for robust methods to rapidly authenticate olive oils. By utilising machine learning models trained on the nuclear magnetic resonance (NMR) spectra of known olive oil and edible oils, samples can be classified as olive and authenticated. While high-field NMRs are commonly used for their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate for routine screening purposes. Low-field benchtop NMR presents an affordable alternative. METHODS: We compared the predictive performance of partial least squares discrimination analysis (PLS-DA) models trained on low-field 60 MHz benchtop proton (1H) NMR and high-field 400 MHz 1H NMR spectra. The data were acquired from a sample set consisting of 49 extra virgin olive oils (EVOOs) and 45 other edible oils. RESULTS: We demonstrate that PLS-DA models trained on low-field NMR spectra are highly predictive when classifying EVOOs from other oils and perform comparably to those trained on high-field spectra. We demonstrated that variance was primarily driven by regions of the spectra arising from olefinic protons and ester protons from unsaturated fatty acids in models derived from data at both field strengths.

Antibacterial Activity and Untargeted Metabolomics Profiling of Acalypha arvensis Poepp.

The search for potent antimicrobial compounds is critical in the face of growing antibiotic resistance. This study explores Acalypha arvensis Poepp. (A. arvensis), a Caribbean plant traditionally used for disease treatment. The dried plant powder was subjected to successive extractions using different solvents: hexane (F1), dichloromethane (F2), methanol (F3), a 50:50 mixture of methanol and water (F4), and water (F5). Additionally, a parallel extraction was conducted using a 50:50 mixture of methanol and chloroform (F6). All the fractions were evaluated for their antimicrobial activity, and the F6 fraction was characterized using untargeted metabolomics using SPME-GC×GC-TOFMS. The extracts of A. arvensis F3, F4, and F5 showed antibacterial activity against Staphylococcus aureus ATCC 25923 (5 mg/mL), MRSA BA22038 (5 mg/mL), and Pseudomonas aeruginosa ATCC 27853 (10 mg/mL), and fraction F6 showed antibacterial activity against Staphylococcus aureus ATCC 29213 (2 mg/mL), Escherichia coli ATCC 25922 (20 mg/mL), Pseudomonas aeruginosa ATCC 27853 (10 mg/mL), Enterococcus faecalis ATCC 29212 (10 mg/mL), Staphylococcus aureus 024 (2 mg/mL), and Staphylococcus aureus 003 (2 mg/mL). Metabolomic analysis of F6 revealed 2861 peaks with 58 identified compounds through SPME and 3654 peaks with 29 identified compounds through derivatization. The compounds included methyl ester fatty acids, ethyl ester fatty acids, terpenes, ketones, sugars, amino acids, and fatty acids. This study represents the first exploration of A. arvensis metabolomics and its antimicrobial potential, providing valuable insights for plant classification, phytochemical research, and drug discovery.

Henry's Law Constants and Indoor Partitioning of Microbial Volatile Organic Compounds.

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, H, are crucial for understanding the environmental behavior of chemicals. This study aims to experimentally determine the H values and their temperature dependence for key MVOCs under temperature relevant to the indoor environment. The H values were determined with the inert gas-stripping (IGS) method and variable phase ratio headspace (VPR-HS) technique. A two-dimensional partitioning model was applied to predict the indoor phase distribution of MVOCs and potential exposure pathways to the residences. The findings show that the MVOCs are likely distributed between the gas and weakly polar (e.g., organic-rich) reservoirs indoors. Temperature and the volume of reservoirs can sensitively affect indoor partitioning. Our results give a more comprehensive view of indoor chemical partitioning and exposure.

Unique ion filter-A data reduction tool for chemometric analysis of raw comprehensive two-dimensional gas chromatography-mass spectrometry data.

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.

Thermodynamics-based modelling of gas chromatography separations across column geometries and systems, including the prediction of peak widths.

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.

A simple, fast, and accurate thermodynamic-based approach for transfer and prediction of GC retention times between columns and instruments Part II: Estimation of target column geometry.

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.

A simple, fast, and accurate thermodynamic-based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part I: Estimation of reference column geometry and thermodynamic parameters.

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.

A simple, fast, and accurate thermodynamic-based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part III: Retention time prediction on target column.

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.

Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC.

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 chromatographic profiling and chemometric interpretation of the volatile profiles of sweat in knit fabrics.

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.

Estimation of start and stop numbers for cluster resolution feature selection algorithm: an empirical approach using null distribution analysis of Fisher ratios.

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.

Determination of Hydrocarbon Group-Type of Diesel Fuels by Gas Chromatography with Vacuum Ultraviolet Detection.

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.

Thermodynamic-based retention time predictions of endogenous steroids in comprehensive two-dimensional gas chromatography.

This work evaluates the application of a thermodynamic model to comprehensive two-dimensional gas chromatography (GC × GC) coupled with time-of-flight mass spectrometry for anabolic agent investigation. Doping control deals with hundreds of drugs that are prohibited in sports. Drug discovery in biological matrices is a challenging task that requires powerful tools when one is faced with the rapidly changing designer drug landscape. In this work, a thermodynamic model developed for the prediction of both primary and secondary retention times in GC × GC has been applied to trimethylsilylated hydroxyl (O-TMS)- and methoxime-trimethylsilylated carbonyl (MO-TMS)-derivatized endogenous steroids. This model was previously demonstrated on a pneumatically modulated GC × GC system, and is applied for the first time to a thermally modulated GC × GC system. Preliminary one-dimensional experiments allowed the calculation of thermodynamic parameters (ΔH, ΔS, and ΔC p ) which were successfully applied for the prediction of the analytes' interactions with the stationary phases of both the first-dimension column and the second-dimension column. The model was able to predict both first-dimension and second-dimension retention times with high accuracy compared with the GC × GC experimental measurements. Maximum differences of -8.22 s in the first dimension and 0.4 s in the second dimension were encountered for the O-TMS derivatives of 11ß-hydroxyandrosterone and 11-ketoetiocholanolone, respectively. For the MO-TMS derivatives, the largest discrepancies were from testosterone (9.65 ) for the first-dimension retention times and 11-keto-etiocholanolone (0.4 s) for the second-dimension retention times.

Application of thermodynamic-based retention time prediction to ionic liquid stationary phases.

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.

PARAFAC2×N: Coupled decomposition of multi-modal data with drift in N modes.

Analysis of GC×GC-TOFMS data for large numbers of poorly-resolved peaks, and for large numbers of samples remains an enduring problem that hinders the widespread application of the technique. For multiple samples, GC×GC-TOFMS data for specific chromatographic regions manifests as a 4th order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel is for all practical purposes nonexistent. A number of solutions to handling GC×GC-TOFMS data have been proposed: these involve reshaping the data to make it amenable to either 2nd order decomposition techniques based on Multivariate Curve Resolution (MCR), or 3rd order decomposition techniques such as Parallel Factor Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic drift along one mode, which has enabled its use for robust decomposition of multiple GC-MS experiments. Although extensible, it is not straightforward to implement a PARAFAC2 model that accounts for drift along multiple modes. In this submission, we demonstrate a new approach and a general theory for modelling data with drift along multiple modes, for applications in multidimensional chromatography with multivariate detection. The proposed model captures over 99.9% of variance for a synthetic data set, presenting an extreme example of peak drift and co-elution across two modes of separation.

Untargeted Metabolomic Profiling of Aqueous and Lyophilized Pooled Human Feces from Two Diet Cohorts Using Two-Dimensional Gas Chromatography Coupled with Time-of-Flight Mass Spectrometry.

The metabolic profiles of human feces are influenced by various genetic and environmental factors, which makes feces an attractive biosample for numerous applications, including the early detection of gut diseases. However, feces is complex, heterogeneous, and dynamic with a significant live bacterial biomass. With such challenges, stool metabolomics has been understudied compared to other biospecimens, and there is a current lack of consensus on methods to collect, prepare, and analyze feces. One of the critical steps required to accelerate the field is having a metabolomics stool reference material available. Fecal samples are generally presented in two major forms: fecal water and lyophilized feces. In this study, two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) was used as an analytical platform to characterize pooled human feces, provided by the National Institute of Standards and Technology (NIST) as Research-Grade Test Materials. The collected fecal samples were derived from eight healthy individuals with two different diets: vegans and omnivores, matched by age, sex, and body mass index (BMI), and stored as fecal water and lyophilized feces. Various data analysis strategies were presented to determine the differences in the fecal metabolomic profiles. The results indicate that the sample storage condition has a major influence on the metabolic profiles of feces such that the impact from storage surpasses the metabolic differences from the diet types. The findings of the current study would contribute towards the development of a stool reference material.

Use of reconstituted kefir consortia to determine the impact of microbial composition on kefir metabolite profiles.

Kefir is fermented traditionally with kefir grains, but commercial kefir production often relies on fermentation with planktonic cultures. Kefir has been associated with many health benefits, however, the utilization of kefir grains to facilitate large industrial production of kefir is challenging and makes to difficult to ensure consistent product quality and consistency. Notably, the microbial composition of kefir fermentations has been shown to impact kefir associated health benefits. This study aimed to compare volatile compounds, organic acids, and sugar composition of kefir produced through a traditional grain fermentation and through a reconstituted kefir consortium fermentation. Additionally, the impact of two key microbial communities on metabolite production in kefir was assessed using two modified versions of the consortium, with either yeasts or lactobacilli removed. We hypothesized that the complete kefir consortium would closely resemble traditional kefir, while the consortia without yeasts or lactobacilli would differ significantly from both traditional kefir and the complete consortium fermentation. Kefir fermentations were examined after 12 and 18 h using two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) to identify volatile compounds and high performance liquid chromatography (HPLC) to identify organic acid and sugar composition. The traditional kefir differed significantly from the kefir consortium fermentation with the traditional kefir having 15-20 log2(fold change) higher levels of esters and the consortium fermented kefir having between 1 and 3 log2(fold change) higher organic acids including lactate and acetate. The use of a version of kefir consortium that lacked lactobacilli resulted in between 2 and 20 log2(fold change) lower levels of organic acids, ethanol, and butanoic acid ethyl ester, while the absence of yeast from the consortium resulted in minimal change. In summary, the kefir consortium fermentation is significantly different from traditional grain fermented kefir with respect to the profile of metabolites present, and seems to be driven by lactobacilli, as evidenced by the significant decrease in multiple metabolites when the lactobacilli were removed from the fermentation and minimal differences observed upon the removal of yeast.

Limits of detection and quantification in comprehensive multidimensional separations. 1. A theoretical look.

Comprehensive multidimensional separations (e.g., GC×GC, LC×LC, etc.) are increasingly popular tools for the analysis of complex samples, due to their many advantages, such as vastly increased peak capacity, and improvements in sensitivity. The most well-established of these techniques, GC×GC, has revolutionized analytical separations in fields as diverse as petroleum, environmental research, food and flavors, and metabolic profiling. Using multidimensional approaches, analytes can be quantified at levels substantially lower than those possible by one-dimensional techniques. However, it has also been shown that the modulation process introduces a new source of error to the measurement. In this work, we present the results of a study into the limits of quantification and detection (LOQ and LOD) in comprehensive multidimensional separations using GC×GC and the more popular "two-step" integration algorithm as an example. Simulation of chromatographic data permits precise control of relevant parameters of peak geometry and modulation phase. Results are expressed in terms of the dimensionless parameter of signal-to-noise ratio of the base peak (S/N(BP)) making them transportable to any result where quantification is performed using a two-step algorithm. Based on these results, the LOD is found to depend upon the modulation ratio used for the experiment and vary between a S/N(BP) of 10-17, while the LOQ depends on both the modulation ratio and the phase of the modulation for the peak and ranges from a S/N(BP) of 10 to 50, depending on the circumstances.

Considerations for the automated collection of thermodynamic data in gas chromatography.

Thermodynamic modeling of retention times in gas chromatography depends on the accurate estimation of thermodynamic parameters. Previous research has used manual injections of samples with coinjection of a dead time marker to obtain accurate measurements of the retention factor of analytes. Ideally this process would be automated. Herein an approach is presented by which thermodynamic parameters can be estimated both autonomously and accurately. This method also allows for a consistent estimation of thermodynamic parameters regardless of factors such as data system delays and the nature of the void time marker employed. Ignoring these factors can lead to significant errors in the prediction of retention times when using thermodynamic models.