Special Issue "Chromatography and Chemometrics"

A special issue of Separations (ISSN 2297-8739).

Deadline for manuscript submissions: closed (31 May 2019).

Special Issue Editor

Prof. Giorgia Purcaro
Website
Guest Editor
Gembloux Agri-Bio Tech, University of Liege (Belgium)
Interests: analytical chemistry; chromatography; multidimensional and comprehensive chromatography; mass spectrometry; sample preparation; metabolomics; lipid analysis; food quality and authenticity; food contaminants
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The combination of chromatography and chemometric knowledge is becoming a precious tool to exponentially increase the level of information that can be extrapolated from any complex system under study. It is particularly true nowadays that chromatographic techniques, especially coupled to mass spectrometry, have achieved advanced technological developments that can provide a vast amount of data from which useful information can be extrapolated. The fields of application of the chromatography–chemometrics pair are wide, from more technical applications in chromatography (e.g., denoising of signals, signal enhancement, chromatographic alignment, etc.) to retention modeling, and from experimental design to unsupervised and supervised data analyses (e.g., classification, discrimination, etc.).

The aim of this Special Issue is to present the state-of-the-art of this powerful combination in different fields of chromatography, presenting comprehensive reviews and innovative applications in different fields.

Prof. Giorgia Purcaro
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Separations is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Chromatographic techniques
  • Comprehensive chromatography
  • Chemometrics
  • Sample preparation for -omics studies
  • Machine learning
  • Metabolomics
  • Lipidomics
  • Proteomics

Published Papers (6 papers)

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Research

Open AccessCommunication
Comparison of Pre-Processing and Variable Selection Strategies in Group-Based GC×GC-TOFMS Analysis
Separations 2019, 6(3), 41; https://doi.org/10.3390/separations6030041 - 21 Aug 2019
Cited by 1
Abstract
Chemometric analysis of comprehensive two-dimensional chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS) data has been reported with various workflows, yet little effort has been devoted to evaluating the impacts of workflow variation on study conclusions. The report presented herein aims to investigate the [...] Read more.
Chemometric analysis of comprehensive two-dimensional chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS) data has been reported with various workflows, yet little effort has been devoted to evaluating the impacts of workflow variation on study conclusions. The report presented herein aims to investigate the effects of different pre-processing and variable selection strategies on the scores’ plot outputs from GC×GC-TOFMS data acquired from lavender and tea tree essential oils. Our results suggest that pre-processing, such as applying log transformation to the data set, can result in significant differentiation of sample clustering when compared to only mean centering. Additionally, exploring differences between analysis of variance, Fisher-ratio, and partial least squares-discriminant analysis feature selection resulted in little variation in scores plots. This work highlights the effects different chemometric workflows can have on results to help facilitate harmonization efforts. Full article
(This article belongs to the Special Issue Chromatography and Chemometrics)
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Open AccessFeature PaperArticle
A Data-Challenge Case Study of Analyte Detection and Identification with Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry (GC×GC-MS)
Separations 2019, 6(3), 38; https://doi.org/10.3390/separations6030038 - 02 Aug 2019
Cited by 1
Abstract
This case study describes data analysis of a chromatogram distributed for the 2019 GC×GC Data Challenge for the Tenth Multidimensional Chromatography Workshop (Liege, Belgium). The chromatogram resulted from chemical analysis of a terpene-standards sample by comprehensive two-dimensional chromatography with mass spectrometry (GC×GC-MS). First, [...] Read more.
This case study describes data analysis of a chromatogram distributed for the 2019 GC×GC Data Challenge for the Tenth Multidimensional Chromatography Workshop (Liege, Belgium). The chromatogram resulted from chemical analysis of a terpene-standards sample by comprehensive two-dimensional chromatography with mass spectrometry (GC×GC-MS). First, several aspects of the data quality are assessed, including detector saturation and oscillation, and operations to prepare the data for analyte detection and identification are described, including phase roll for modulation-cycle alignment and baseline correction to account for the non-zero detector baseline. Then, the case study presents operations for analyte detection with filtering, a new method to flag false detections, interactive review to confirm detected peaks, and ion-peaks detection to reveal peaks that are obscured by noise or coelution. Finally, the case study describes analyte identification including mass-spectral library search with a new method for optimizing spectra extraction, retention-index calibration from preliminary identifications, and expression-based identification checks. Processing of the first 40 min of data detected 144 analytes, 21 of which have at least one percent response, plus an additional 20 trace and/or coeluted analytes. Full article
(This article belongs to the Special Issue Chromatography and Chemometrics)
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Open AccessArticle
Highly Informative Fingerprinting of Extra-Virgin Olive Oil Volatiles: The Role of High Concentration-Capacity Sampling in Combination with Comprehensive Two-Dimensional Gas Chromatography
Separations 2019, 6(3), 34; https://doi.org/10.3390/separations6030034 - 15 Jul 2019
Cited by 4
Abstract
The study explores the complex volatile fraction of extra-virgin olive oil by combining high concentration-capacity headspace approaches with comprehensive two-dimensional gas chromatography, which is coupled with time of flight mass spectrometry. The static headspace techniques in this study are: (a) Solid-phase microextraction, with [...] Read more.
The study explores the complex volatile fraction of extra-virgin olive oil by combining high concentration-capacity headspace approaches with comprehensive two-dimensional gas chromatography, which is coupled with time of flight mass spectrometry. The static headspace techniques in this study are: (a) Solid-phase microextraction, with multi-polymer coating (SPME- Divinylbenzene/Carboxen/Polydimethylsiloxane), which is taken as the reference technique; (b) headspace sorptive extraction (HSSE) with either a single-material coating (polydimethylsiloxane—PDMS) or a dual-phase coating that combines PDMS/Carbopack and PDMS/EG (ethyleneglycol); (c) monolithic material sorptive extraction (MMSE), using octa-decyl silica combined with graphite carbon (ODS/CB); and dynamic headspace (d) with either PDMS foam, operating in partition mode, or Tenax TA™, operating in adsorption mode. The coverage of both targeted and untargeted 2D-peak-region features, which corresponds to detectable analytes, was examined, while concentration factors (CF) for a selection of informative analytes, including key-odorants and off-odors, and homolog-series relative ratios were calculated and the information capacity was discussed. The results highlighted the differences in concentration capacities, which were mainly caused by polymer-accumulation characteristics (sorptive/adsorptive materials) and its amount. The relative concentration capacity for homologues and potent odorants was also discussed, while headspace linearity and the relative distribution of analytes, as a function of different sampling amounts, was examined. This last point is of particular interest in quantitative studies where accurate data is needed to derive consistent conclusions. Full article
(This article belongs to the Special Issue Chromatography and Chemometrics)
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Open AccessArticle
Fatty Acid Methyl Ester (FAME) Profiling Identifies Carbapenemase-Producing Klebsiella pneumoniae Belonging to Clonal Complex 258
Separations 2019, 6(2), 32; https://doi.org/10.3390/separations6020032 - 17 Jun 2019
Cited by 1
Abstract
Carbapenem-resistant Klebsiella pneumoniae (CRKP) is one of the most extensively antibiotic-resistant pathogens encountered in the clinical setting today. A few studies to-date suggest that CRKP and carbapenem-susceptible K. pneumoniae (CSKP) differ from one another not only with respect to their underlying genetics, but [...] Read more.
Carbapenem-resistant Klebsiella pneumoniae (CRKP) is one of the most extensively antibiotic-resistant pathogens encountered in the clinical setting today. A few studies to-date suggest that CRKP and carbapenem-susceptible K. pneumoniae (CSKP) differ from one another not only with respect to their underlying genetics, but also their transcriptomic and metabolomic fingerprints. Within this context, we characterize the fatty acid methyl ester (FAME) profiles of these pathogens in vitro. Specifically, we evaluated the FAME profiles of six Klebsiella pneumoniae carbapenemase (KPC)-producing isolates belonging to the CC258 lineage (KPC+/258+), six KPC-producing isolates belonging to non-CC258 lineages (KPC+/258), and six non-KPC-producing isolates belonging to non-CC258 lineages (KPC/258). We utilized a single-step sample preparation method to simultaneously lyse bacterial cells and transesterify the lipid fraction, and identified 14 unique FAMEs using gas chromatography-mass spectrometry. The machine learning algorithm Random Forest identified four FAMEs that were highly discriminatory between CC258 and non-CC258 isolates (9(Z)-octadecenoate, 2-phenylacetate, pentadecanoate, and hexadecanoate), of which three were also significantly different in relative abundance between these two groups. These findings suggest that distinct differences exist between CC258 and non-CC258 K. pneumoniae isolates with respect to the metabolism of both fatty acids and amino acids, a hypothesis that is supported by previously-acquired transcriptomic data. Full article
(This article belongs to the Special Issue Chromatography and Chemometrics)
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Open AccessArticle
Balancing Resolution with Analysis Time for Biodiesel–Diesel Fuel Separations Using GC, PCA, and the Mahalanobis Distance
Separations 2019, 6(2), 28; https://doi.org/10.3390/separations6020028 - 27 May 2019
Cited by 2
Abstract
In this work, a statistical metric called the Mahalanobis distance (MD) is used to compare gas chromatography separation conditions. In the two-sample case, the MD computes the distance between the means of the multivariate probability distributions of two groups. Two gas chromatography columns [...] Read more.
In this work, a statistical metric called the Mahalanobis distance (MD) is used to compare gas chromatography separation conditions. In the two-sample case, the MD computes the distance between the means of the multivariate probability distributions of two groups. Two gas chromatography columns of the same polarity but differing length and film thickness were utilized for the analysis of fatty acid methyl esters in biodiesel fuels. Biodiesel feedstock samples representing classes of canola, coconut, flaxseed, palm kernal, safflower, soy, soyabean, sunflower, tallow, and waste grease were used in our experiments. Data sets measured from each column were aligned with the correlated optimized warping (COW) algorithm prior to principal components analysis (PCA). The PC scores were then used to compute the MD. Differences between the data produced by each column were determined by converting the MD to its corresponding p-value using the F-distribution. The combination of COW parameters that maximized the p-value were determined for each feedstock separately. The results demonstrate that chromatograms from each column could be optimally aligned to minimize the MD derived from the PC-transformed data. The corresponding p-values for each feedstock type indicated that the two column conditions could produce data that were not statistically different. As a result, the slight loss of resolution using a faster column may be acceptable based on the application for which the data are used. Full article
(This article belongs to the Special Issue Chromatography and Chemometrics)
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Open AccessArticle
Estimating Detection Limits in Chromatography from Calibration Data: Ordinary Least Squares Regression vs. Weighted Least Squares
Separations 2018, 5(4), 49; https://doi.org/10.3390/separations5040049 - 08 Oct 2018
Cited by 5
Abstract
It is necessary to determine the limit of detection when validating any analytical method. For methods with a linear response, a simple and low labor-consuming procedure is to use the linear regression parameters obtained in the calibration to estimate the blank standard deviation [...] Read more.
It is necessary to determine the limit of detection when validating any analytical method. For methods with a linear response, a simple and low labor-consuming procedure is to use the linear regression parameters obtained in the calibration to estimate the blank standard deviation from the residual standard deviation (sres), or the intercept standard deviation (sb0). In this study, multiple experimental calibrations are evaluated, applying both ordinary and weighted least squares. Moreover, the analyses of replicated blank matrices, spiked at 2–5 times the lowest calculated limit values with the two regression methods, are performed to obtain the standard deviation of the blank. The limits of detection obtained with ordinary least squares, weighted least squares, the signal-to-noise ratio, and replicate blank measurements are then compared. Ordinary least squares, which is the simplest and most commonly applied calibration regression methodology, always overestimate the values of the standard deviations at the lower levels of calibration ranges. As a result, the detection limits are up to one order of magnitude greater than those obtained with the other approaches studied, which all gave similar limits. Full article
(This article belongs to the Special Issue Chromatography and Chemometrics)
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