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Chromatography and Chemometrics 2021

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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 14215

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Special Issue Editors

Prof. Dr. Michal Daszykowski

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Guest Editor

Institute of Chemistry, University of Silesia in Katowice, Katowice, Poland
Interests: chemometrics; modeling of instrumental signals; calibration; classification; fraud detection; authenticity studies; chemical fingerprints; hyperspectral imaging

Dr. Joaquim Jaumot

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Guest Editor

Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034 Barcelona, Spain
Interests: chemometrics; data analysis; multiomics; untargeted analysis; imaging; spectroscopy

Prof. Dr. Hadi Parastar

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Guest Editor

Department of Chemistry, Sharif University of Technology, Tehran, Iran
Interests: chemometrics; hyphenated chromatography; two-dimensional gas chromatography; metabolomics; environmental analytical chemistry; food authenticity

Special Issue Information

Dear Colleagues,

The combination of chromatography and chemometrics is becoming a precious tool to improve and increase the level of information that can be extracted from any complex system under study. In particular, hyphenated chromatographic techniques have achieved advanced technological developments. They can provide a vast amount of comprehensive, multivariate, and/or multimodal data. In addition to classic one-dimensional chromatography, comprehensive two-dimensional chromatographic techniques such as two-dimensional gas chromatography (GC×GC) and two-dimensional liquid chromatography (LC×LC), coupled with different detectors (mass spectrometry, diode-array, fluorescence, flame ionization detector, etc.), have demonstrated outstanding potential for the analysis of complex mixtures concerning enhanced sensitivity and the resolution power. The data collected from these systems are much more complex and need chemometric techniques to acquire meaningful information. Integrating chemometrics and chromatography can help exploit information that may not be provided by traditional data analysis approaches.

We intend to highlight the applications of chemometric methods. Among them, there are, for instance, multivariate resolution methods (assisting in resolving overlapping peaks and thus improving analyte identification and quantification), multivariate calibration methods (used to quantify compounds or to model different physicochemical properties of complex samples, e.g., quantitative structure–retention/property relationships based on chromatographic data), and multivariate discrimination/classification techniques (helping to assign samples to a priori defined groups and to explore data patterns or fingerprints).

This Special Issue aims to present the state-of-the-art and the benefits of this powerful combination exercised in different research fields, provide comprehensive reviews, discuss diverse applications, and encourage practitioners to use chemometric methods daily.

We are looking forward to receiving your proposals.

Prof. Dr. Michal Daszykowski
Dr. Joaquim Jaumot
Prof. Dr. Hadi Parastar
Guest Editors

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 submissions that pass pre-check are 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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

Chemometrics
Chromatography
Hyphenated techniques
Comprehensive two-dimensional chromatography
Multichannel detection
Experimental design for enhancement of information content
Chromatographic data processing
Multivariate calibration
Quantitative structure–retention relationships (QSRR)
Quantitative structure–property relationships (QSPR)
Multivariate classification
Multivariate resolution
Advanced modeling of chromatographic data, including the second-order advantage
Chromatographic data fusion strategies
Analysis of chromatographic fingerprints
Targeted and nontargeted chromatographic analysis
Chemometrics and chromatography in omics, environmental, and food analysis applications

Published Papers (6 papers)

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Research

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26 pages, 3591 KiB

Open AccessArticle

Flexible Implementation of the Trilinearity Constraint in Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) of Chromatographic and Other Type of Data

by Xin Zhang and Romà Tauler

Molecules 2022, 27(7), 2338; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27072338 - 05 Apr 2022

Cited by 4 | Viewed by 2828

Abstract

Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) can analyze three-way data under the assumption of a trilinear model using the trilinearity constraint. However, the rigid application of this constraint can produce unrealistic solutions in practice due to the inadequacy of the analyzed data to the characteristics and requirements of the trilinear model. Different methods for the relaxation of the trilinear model data requirements have been proposed, like in the PARAFAC2 and in the direct non-trilinear decomposition (DNTD) methods. In this work, the trilinearity constraint of MCR-ALS is adapted to different data scenarios where the profiles of all or some of the components of the system are shifted (not equally synchronized) or even change their shape among different slices in one of their data modes. This adaptation is especially useful in gas and liquid chromatography (GC and LC) and in Flow Injection Analysis (FIA) with multivariate spectroscopic detection. In a first data example, a synthetic LC-DAD dataset is built to investigate the possibilities of the proposed method to handle systematic changes (shifts) in the retention times of the elution profiles and the results are compared with those obtained using alternative methods like ATLD, PARAFAC, PARAFAC2 and DNTD. In a second data example, multiple wine samples were simultaneously analyzed by GC-MS where elution profiles presented large deviations (shifts) in their peak retention times, although they still preserve the same peak shape. Different modelling scenarios are tested and the results are also compared. Finally, in the third example, sample mixtures of acid compounds were analyzed by FIA under a pH gradient and monitored by UV spectroscopy and also examined by different chemometric methods using a different number of components. In this case, however, the departure of the trilinear model comes from the acid base speciation of the system depending on the pH more than from the shifting of the FIA diffusion profiles. Full article

(This article belongs to the Special Issue Chromatography and Chemometrics 2021)

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17 pages, 2141 KiB

Open AccessFeature PaperArticle

Gas Chromatographic Fingerprint Analysis for the Comparison of Seized Cannabis Samples

by Amorn Slosse, Filip Van Durme, Nele Samyn, Debby Mangelings and Yvan Vander Heyden

Molecules 2021, 26(21), 6643; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26216643 - 02 Nov 2021

Cited by 3 | Viewed by 1796

Abstract

Cannabis sativa L. is widely used as recreational illegal drugs. Illicit Cannabis profiling, comparing seized samples, is challenging due to natural Cannabis heterogeneity. The aim of this study was to use GC–FID and GC–MS herbal fingerprints for intra (within)- and inter (between)-location variability evaluation. This study focused on finding an acceptable threshold to link seized samples. Through Pearson correlation-coefficient calculations between intra-location samples, ‘linked’ thresholds were derived using 95% and 99% confidence limits. False negative (FN) and false positive (FP) error rate calculations, aiming at obtaining the lowest possible FP value, were performed for different data pre-treatments. Fingerprint-alignment parameters were optimized using Automated Correlation-Optimized Warping (ACOW) or Design of Experiments (DoE), which presented similar results. Hence, ACOW data, as reference, showed 54% and 65% FP values (95 and 99% confidence, respectively). An additional fourth root normalization pre-treatment provided the best results for both the GC–FID and GC–MS datasets. For GC–FID, which showed the best improved FP error rate, 54 and 65% FP for the reference data decreased to 24 and 32%, respectively, after fourth root transformation. Cross-validation showed FP values similar as the entire calibration set, indicating the representativeness of the thresholds. A noteworthy improvement in discrimination between seized Cannabis samples could be concluded. Full article

(This article belongs to the Special Issue Chromatography and Chemometrics 2021)

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16 pages, 1554 KiB

Open AccessArticle

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics

by Miao Tian, Zhonglong Lin, Xu Wang, Jing Yang, Wentao Zhao, Hongmei Lu, Zhimin Zhang and Yi Chen

Molecules 2021, 26(9), 2715; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26092715 - 05 May 2021

Cited by 2 | Viewed by 2444

Abstract

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics. Full article

(This article belongs to the Special Issue Chromatography and Chemometrics 2021)

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14 pages, 2529 KiB

Open AccessArticle

Essential Oils of New Lippia alba Genotypes Analyzed by Flow-Modulated Comprehensive Two-Dimensional Gas Chromatography (GC×GC) and Chemometric Analysis

by Leila Gimenes, Júlio César R. Lopes Silva, Roselaine Facanali, Leandro Wang Hantao, Walter José Siqueira and Marcia Ortiz Mayo Marques

Molecules 2021, 26(8), 2332; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26082332 - 16 Apr 2021

Cited by 6 | Viewed by 2411

Abstract

Lippia alba (Mill.) N. E. Br. (Verbenaceae) is an aromatic shrub whose essential oils have stood out as a promising source for application in several industrial fields. In this study, the essential oils chemical characterization of eight new L. alba genotypes was performed. The selected materials were collected from the Active Germplasm Bank of the Agronomic Institute and the essential oils were extracted by hydrodistillation. Flow-modulated comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) was employed for chemical characterization and evaluation of possible co-eluted compounds. In addition, the chemical analyses were submitted to multivariate statistical analyses. From this investigation, 73 metabolites were identified in the essential oils of the genotypes, from which α-pinene, β-myrcene, 1,8-cineole, linalool, neral, geranial, and caryophyllene oxide were the most abundant compounds among the accessions. This is the first report disclosing α-pinene in higher amounts in L. alba (19.69%). In addition, sabinene, trans-verbenol, myrtenol, (E)-caryophyllene, α-guaiene, germacrene D, and α-bulnesene were also found in relevant quantities in some of the genotypes, and myrtenal and myrtenol could be well separated through the second dimension. Such results contributed to the understanding of the chemical composition of those new genotypes, being important to drive a future industrial applicability and studies in genetic breeding. Full article

(This article belongs to the Special Issue Chromatography and Chemometrics 2021)

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Review

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13 pages, 1136 KiB

Open AccessReview

Being Uncertain in Chromatographic Calibration—Some Unobvious Details in Experimental Design

by Łukasz Komsta, Katarzyna Wicha-Komsta and Tomasz Kocki

Molecules 2021, 26(22), 7035; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26227035 - 21 Nov 2021

Viewed by 1365

Abstract

This is an introductory tutorial and review about the uncertainty problem in chromatographic calibration. It emphasizes some unobvious, but important details influencing errors in the calibration curve estimation, uncertainty in prediction, as well as the connections and dependences between them, all from various perspectives of uncertainty measurement. Nonuniform D-optimal designs coming from Fedorov theorem are computed and presented. As an example, all possible designs of 24 calibration samples (3–8, 4–6, 6–4, 8–3 and 12–2, both uniform and D-optimal) are compared in context of many optimality criteria. It can be concluded that there are only two independent (orthogonal, but slightly complex) trends in optimality of these designs. The conclusions are important, as the uniform designs with many concentrations are not the best choices, contrary to some intuitive perception. Nonuniform designs are visibly better alternative in most calibration cases. Full article

(This article belongs to the Special Issue Chromatography and Chemometrics 2021)

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29 pages, 1764 KiB

Open AccessFeature PaperReview

Chromatographic Applications in the Multi-Way Calibration Field

by Fabricio A. Chiappini, Mirta R. Alcaraz, Graciela M. Escandar, Héctor C. Goicoechea and Alejandro C. Olivieri

Molecules 2021, 26(21), 6357; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26216357 - 21 Oct 2021

Cited by 7 | Viewed by 1905

Abstract

In this review, recent advances and applications using multi-way calibration protocols based on the processing of multi-dimensional chromatographic data are discussed. We first describe the various modes in which multi-way chromatographic data sets can be generated, including some important characteristics that should be taken into account for the selection of an adequate data processing model. We then discuss the different manners in which the collected instrumental data can be arranged, and the most usually applied models and algorithms for the decomposition of the data arrays. The latter activity leads to the estimation of surrogate variables (scores), useful for analyte quantitation in the presence of uncalibrated interferences, achieving the second-order advantage. Recent experimental reports based on multi-way liquid and gas chromatographic data are then reviewed. Finally, analytical figures of merit that should always accompany quantitative calibration reports are described. Full article

(This article belongs to the Special Issue Chromatography and Chemometrics 2021)

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