Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis

Trinklein, Timothy J.; Cain, Caitlin N.; Ochoa, Grant S.; Schöneich, Sonia; Mikaliunaite, Lina; Synovec, Robert E.

doi:10.1021/acs.analchem.2c04235

Cited by 20 publications

(24 citation statements)

References 147 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The flavor profiles of the four e-liquids were characterized using nontargeted chemical analysis by two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC-TOFMS), which can be applied in nontargeted analyses to identify a wide variety of compounds of interest. − For each e-liquid, triplicate dilutions were prepared by first adding to a glass vial (for each dilution) 10 μL of the e-liquid and determining the precise mass (±0.0001 g) of the e-liquid. The next step was to dilute the e-liquid into 2 mL of isopropyl alcohol that contained 200 ng/mL of the internal standard (IS) acenaphthene- d 10 (and the other five isotopically labeled internal standards in the same mixture, as listed above).…”

Section: Methodsmentioning

confidence: 99%

Detection of the Synthetic Coolant Menthone 1,2-Glycerol Ketal in an e-Liquid and in Electronic Waterpipe Aerosols Therefrom

Klupinski

Strozier

Koebel

et al. 2023

Chem. Res. Toxicol.

View full text Add to dashboard Cite

The presence and toxicological risks of synthetic coolants in electronic nicotine delivery systems (ENDS) have not been thoroughly studied. We identified the synthetic coolant menthone 1,2-glycerol ketal (MGK) in a menthol-flavored e-liquid at a concentration of ∼170 μg/mL. We also detected MGK in aerosols resulting from heating the e-liquid with an electronic waterpipe. MGK was initially detected in the e-liquid by two-dimensional gas chromatography–time-of-flight mass spectrometry. To avoid potential analytical artifacts that could result from heating samples in the injection port of the gas chromatograph, quantitation of MGK in the e-liquid was accomplished using a liquid chromatography–tandem mass spectrometry method. Following recent reports identifying other synthetic coolants in e-liquids, these results add knowledge about inhalation exposures from ENDS use and suggest the importance of future research to study the potential inhalation toxicity related to the use of MGK-containing e-liquids in ENDS devices. Furthermore, the results demonstrate the ability to quantify ketals in e-liquids using liquid chromatography methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Detection of the Synthetic Coolant Menthone 1,2-Glycerol Ketal in an e-Liquid and in Electronic Waterpipe Aerosols Therefrom

Klupinski

Strozier

Koebel

et al. 2023

Chem. Res. Toxicol.

View full text Add to dashboard Cite

show abstract

“…A cluster window size of 4 modulations (10 s) × 300 ms was used for the redundant hit removal step. 1,8 For both data processing methods, i.e., tilebased RF and tile-based F-ratio, a signal-to-noise (S/N) threshold of 10 was set to exclude tiles with poor chromatographic signals. Additionally, only tiles with at least 3 m/z above the S/N threshold are accounted for in the analyses.…”

Section: ■ Principles and Theorymentioning

confidence: 99%

“…An importance score is then computed for every tile hit in every grid on a per-mass channel basis. Similar to the FRA, the redundant hits arising from the multigrid sampling are removed using the “pinning and clustering” algorithm. , …”

Section: Principles and Theorymentioning

confidence: 99%

“…Among the diverse range of available chemometric tools, one can enumerate data reduction techniques such as principal component analysis (PCA), regression-based methods such as partial least-squares (PLS), or even discriminant analysis approaches such as partial least-squares-discriminant analysis (PLS-DA), to name a few. These methods are harnessed to gain valuable insights into complex data sets, often serving different purposes, such as unveiling clustering patterns within a given data set, correlating chemical features to response factors, or performing classification tasks. , …”

mentioning

confidence: 99%

See 1 more Smart Citation

Tile-Based Random Forest Analysis for Analyte Discovery in Balanced and Unbalanced GC × GC-TOFMS Data Sets

Gaida,

Cain,

Synovec

et al. 2023

Anal. Chem.

View full text Add to dashboard Cite

“…This can be evaluated through the identification of compounds for which particular factors of interest have a significant effect. Various statistical techniques, such as compound variability assessments (e.g., Fisher ratio filtering, t -tests, partial least squares) or unsupervised screening techniques (e.g., principal component analysis (PCA)), are generally employed as dimensionality reduction tools that aid in the identification of significant compounds prior to further multivariate analysis. − Compound reduction can be complex for biological samples as the number of corresponding explanatory variables, which may impact compound presence or relative abundance, are increased with the expansion of the dataset . Furthermore, many explanatory biological variables with differing intralevel variabilities innately exist within biological datasets.…”

Section: Introductionmentioning

confidence: 99%

Insights into the Effects of Violating Statistical Assumptions for Dimensionality Reduction for Chemical “-omics” Data with Multiple Explanatory Variables

et al. 2023

View full text Add to dashboard Cite

Biological volatilome analysis is inherently complex due to the considerable number of compounds (i.e., dimensions) and differences in peak areas by orders of magnitude, between and within compounds found within datasets. Traditional volatilome analysis relies on dimensionality reduction techniques which aid in the selection of compounds that are considered relevant to respective research questions prior to further analysis. Currently, compounds of interest are identified using either supervised or unsupervised statistical methods which assume the data residuals are normally distributed and exhibit linearity. However, biological data often violate the statistical assumptions of these models related to normality and the presence of multiple explanatory variables which are innate to biological samples. In an attempt to address deviations from normality, volatilome data can be log transformed. However, whether the effects of each assessed variable are additive or multiplicative should be considered prior to transformation, as this will impact the effect of each variable on the data. If assumptions of normality and variable effects are not investigated prior to dimensionality reduction, ineffective or erroneous compound dimensionality reduction can impact downstream analyses. It is the aim of this manuscript to assess the impact of single and multivariable statistical models with and without the log transformation to volatilome dimensionality reduction prior to any supervised or unsupervised classification analysis. As a proof of concept, Shingleback lizard (Tiliqua rugosa) volatilomes were collected across their species distribution and from captivity and were assessed. Shingleback volatilomes are suspected to be influenced by multiple explanatory variables related to habitat (Bioregion), sex, parasite presence, total body volume, and captive status. This work determined that the exclusion of relevant multiple explanatory variables from analysis overestimates the effect of Bioregion and the identification of significant compounds. The log transformation increased the number of compounds that were identified as significant, as did analyses that assumed that residuals were normally distributed. Among the methods considered in this work, the most conservative form of dimensionality reduction was achieved through analyzing untransformed data using Monte Carlo tests with multiple explanatory variables.

show abstract

Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis

Cited by 20 publications

References 147 publications

Detection of the Synthetic Coolant Menthone 1,2-Glycerol Ketal in an e-Liquid and in Electronic Waterpipe Aerosols Therefrom

Detection of the Synthetic Coolant Menthone 1,2-Glycerol Ketal in an e-Liquid and in Electronic Waterpipe Aerosols Therefrom

Tile-Based Random Forest Analysis for Analyte Discovery in Balanced and Unbalanced GC × GC-TOFMS Data Sets

Insights into the Effects of Violating Statistical Assumptions for Dimensionality Reduction for Chemical “-omics” Data with Multiple Explanatory Variables

Contact Info

Product

Resources

About