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15 pages, 743 KiB

Open AccessArticle

Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

by Fan Yang, Denice van Herwerden, Hugues Preud’homme and Saer Samanipour

Molecules 2022, 27(19), 6424; https://doi.org/10.3390/molecules27196424 - 29 Sep 2022

Cited by 7 | Viewed by 2174

Abstract

High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification [...] Read more.

High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence level. Thanks to the advances in analytical instrumentation, an increasing application of IMS hybrid with high-resolution mass spectrometry (HRMS) in NTS has been reported in the recent decades. Several CCS prediction tools have been developed. However, limited CCS prediction methods were based on a large scale of chemical classes and cross-platform CCS measurements. We successfully developed two prediction models using a random forest machine learning algorithm. One of the approaches was based on chemicals’ super classes; the other model was direct CCS prediction using molecular fingerprint. Over 13,324 CCS values from six different laboratories and PubChem using a variety of ion-mobility separation techniques were used for training and testing the models. The test accuracy for all the prediction models was over 0.85, and the median of relative residual was around 2.2%. The models can be applied to different IMS platforms to eliminate false positives in small molecule identification. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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14 pages, 2159 KiB

Open AccessArticle

Metabolomics Approach on Non-Targeted Screening of 50 PPCPs in Lettuce and Maize

by Weifeng Xue, Chunguang Yang, Mengyao Liu, Xiaomei Lin, Mei Wang and Xiaowen Wang

Molecules 2022, 27(15), 4711; https://doi.org/10.3390/molecules27154711 - 23 Jul 2022

Cited by 2 | Viewed by 1520

Abstract

The metabolomics approach has proved to be promising in achieving non-targeted screening for those unknown and unexpected (U&U) contaminants in foods, but data analysis is often the bottleneck of the approach. In this study, a novel metabolomics analytical method via seeking marker compounds [...] Read more.

The metabolomics approach has proved to be promising in achieving non-targeted screening for those unknown and unexpected (U&U) contaminants in foods, but data analysis is often the bottleneck of the approach. In this study, a novel metabolomics analytical method via seeking marker compounds in 50 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked into lettuce and maize matrices was developed, based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) output results. Three concentration groups (20, 50 and 100 ng mL⁻¹) to simulate the control and experimental groups applied in the traditional metabolomics analysis were designed to discover marker compounds, for which multivariate and univariate analysis were adopted. In multivariate analysis, each concentration group showed obvious separation from other two groups in principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) plots, providing the possibility to discern marker compounds among groups. Parameters including S-plot, permutation test and variable importance in projection (VIP) in OPLS-DA were used for screening and identification of marker compounds, which further underwent pairwise t-test and fold change judgement for univariate analysis. The results indicate that marker compounds on behalf of 50 PPCPs were all discovered in two plant matrices, proving the excellent practicability of the metabolomics approach on non-targeted screening of various U&U PPCPs in plant-derived foods. The limits of detection (LODs) for 50 PPCPs were calculated to be 0.4~2.0 µg kg⁻¹ and 0.3~2.1 µg kg⁻¹ in lettuce and maize matrices, respectively. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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21 pages, 4694 KiB

Open AccessArticle

An Integrated Mass Spectrometry-Based Glycomics-Driven Glycoproteomics Analytical Platform to Functionally Characterize Glycosylation Inhibitors

by Michael Russelle S. Alvarez, Qingwen Zhou, Sheryl Joyce B. Grijaldo, Carlito B. Lebrilla, Ruel C. Nacario, Francisco M. Heralde III, Jomar F. Rabajante and Gladys C. Completo

Molecules 2022, 27(12), 3834; https://doi.org/10.3390/molecules27123834 - 14 Jun 2022

Cited by 7 | Viewed by 2911

Abstract

Cancer progression is linked to aberrant protein glycosylation due to the overexpression of several glycosylation enzymes. These enzymes are underexploited as potential anticancer drug targets and the development of rapid-screening methods and identification of glycosylation inhibitors are highly sought. An integrated bioinformatics and [...] Read more.

Cancer progression is linked to aberrant protein glycosylation due to the overexpression of several glycosylation enzymes. These enzymes are underexploited as potential anticancer drug targets and the development of rapid-screening methods and identification of glycosylation inhibitors are highly sought. An integrated bioinformatics and mass spectrometry-based glycomics-driven glycoproteomics analysis pipeline was performed to identify an N-glycan inhibitor against lung cancer cells. Combined network pharmacology and in silico screening approaches were used to identify a potential inhibitor, pictilisib, against several glycosylation-related proteins, such as Alpha1-6FucT, GlcNAcT-V, and Alpha2,6-ST-I. A glycomics assay of lung cancer cells treated with pictilisib showed a significant reduction in the fucosylation and sialylation of N-glycans, with an increase in high mannose-type glycans. Proteomics analysis and in vitro assays also showed significant upregulation of the proteins involved in apoptosis and cell adhesion, and the downregulation of proteins involved in cell cycle regulation, mRNA processing, and protein translation. Site-specific glycoproteomics analysis further showed that glycoproteins with reduced fucosylation and sialylation were involved in apoptosis, cell adhesion, DNA damage repair, and chemical response processes. To determine how the alterations in N-glycosylation impact glycoprotein dynamics, modeling of changes in glycan interactions of the ITGA5–ITGB1 (Integrin alpha 5-Integrin beta-1) complex revealed specific glycosites at the interface of these proteins that, when highly fucosylated and sialylated, such as in untreated A549 cells, form greater hydrogen bonding interactions compared to the high mannose-types in pictilisib-treated A549 cells. This study highlights the use of mass spectrometry to identify a potential glycosylation inhibitor and assessment of its impact on cell surface glycoprotein abundance and protein–protein interaction. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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18 pages, 2524 KiB

Open AccessArticle

Evaluation of Sample Preparation Methods for Non-Target Screening of Organic Micropollutants in Urban Waters Using High-Resolution Mass Spectrometry

by Nina Huynh, Emilie Caupos, Caroline Soares Peirera, Julien Le Roux, Adèle Bressy and Régis Moilleron

Molecules 2021, 26(23), 7064; https://doi.org/10.3390/molecules26237064 - 23 Nov 2021

Cited by 11 | Viewed by 2460

Abstract

Non-target screening (NTS) has gained interest in recent years for environmental monitoring purposes because it enables the analysis of a large number of pollutants without predefined lists of molecules. However, sample preparation methods are diverse, and few have been systematically compared in terms [...] Read more.

Non-target screening (NTS) has gained interest in recent years for environmental monitoring purposes because it enables the analysis of a large number of pollutants without predefined lists of molecules. However, sample preparation methods are diverse, and few have been systematically compared in terms of the amount and relevance of the information obtained by subsequent NTS analysis. The goal of this work was to compare a large number of sample extraction methods for the unknown screening of urban waters. Various phases were tested for the solid-phase extraction of micropollutants from these waters. The evaluation of the different phases was assessed by statistical analysis based on the number of detected molecules, their range, and physicochemical properties (molecular weight, standard recoveries, polarity, and optical properties). Though each cartridge provided its own advantages, a multilayer cartridge combining several phases gathered more information in one single extraction by benefiting from the specificity of each one of its layers. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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14 pages, 3416 KiB

Open AccessArticle

A New UPLC-qTOF Approach for Elucidating Furan and 2-Methylfuran Metabolites in Human Urine Samples after Coffee Consumption

by Simone Stegmüller, Nadine Beißmann, Jonathan Isaak Kremer, Denise Mehl, Christian Baumann and Elke Richling

Molecules 2020, 25(21), 5104; https://doi.org/10.3390/molecules25215104 - 03 Nov 2020

Cited by 8 | Viewed by 2202

Abstract

We have investigated urine samples after coffee consumption using targeted and untargeted approaches to identify furan and 2-methylfuran metabolites in urine samples by UPLC-qToF. The aim was to establish a fast, robust, and time-saving method involving ultra-performance liquid chromatography-quantitative time-of-flight tandem mass spectrometry [...] Read more.

We have investigated urine samples after coffee consumption using targeted and untargeted approaches to identify furan and 2-methylfuran metabolites in urine samples by UPLC-qToF. The aim was to establish a fast, robust, and time-saving method involving ultra-performance liquid chromatography-quantitative time-of-flight tandem mass spectrometry (UPLC-qToF-MS/MS). The developed method detected previously reported metabolites, such as Lys-BDA, and others that had not been previously identified, or only detected in animal or in vitro studies. The developed UPLC-qToF method detected previously reported metabolites, such as lysine-cis-2-butene-1,4-dial (Lys-BDA) adducts, and others that had not been previously identified, or only detected in animal and in vitro studies. In sum, the UPLC-qToF approach provides additional information that may be valuable in future human or animal intervention studies. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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14 pages, 1453 KiB

Open AccessArticle

Ultraviolet Photodissociation for Non-Target Screening-Based Identification of Organic Micro-Pollutants in Water Samples

by Christian Panse, Seema Sharma, Romain Huguet, Dennis Vughs, Jonas Grossmann and Andrea Mizzi Brunner

Molecules 2020, 25(18), 4189; https://doi.org/10.3390/molecules25184189 - 12 Sep 2020

Cited by 5 | Viewed by 3367

Abstract

Non-target screening (NTS) based on the combination of liquid chromatography coupled to high-resolution mass spectrometry has become the key method to identify organic micro-pollutants (OMPs) in water samples. However, a large number of compounds remains unidentified with current NTS approaches due to poor [...] Read more.

Non-target screening (NTS) based on the combination of liquid chromatography coupled to high-resolution mass spectrometry has become the key method to identify organic micro-pollutants (OMPs) in water samples. However, a large number of compounds remains unidentified with current NTS approaches due to poor quality fragmentation spectra generated by suboptimal fragmentation methods. Here, the potential of the alternative fragmentation technique ultraviolet photodissociation (UVPD) to improve identification of OMPs in water samples was investigated. A diverse set of water-relevant OMPs was selected based on k-means clustering and unsupervised artificial neural networks. The selected OMPs were analyzed using an Orbitrap Fusion Lumos equipped with UVPD. Therewith, information-rich MS2 fragmentation spectra of compounds that fragment poorly with higher-energy collisional dissociation (HCD) could be attained. Development of an R-based data analysis workflow and user interface facilitated the characterization and comparison of HCD and UVPD fragmentation patterns. UVPD and HCD generated both unique and common fragments, demonstrating that some fragmentation pathways are specific to the respective fragmentation method, while others seem more generic. Application of UVPD fragmentation to the analysis of surface water enabled OMP identification using existing HCD spectral libraries. However, high-throughput applications still require optimization of informatics workflows and spectral libraries tailored to UVPD. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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21 pages, 4175 KiB

Open AccessArticle

Center-of-Mass iso-Energetic Collision-Induced Decomposition in Tandem Triple Quadrupole Mass Spectrometry

by Federico Maria Rubino

Molecules 2020, 25(9), 2250; https://doi.org/10.3390/molecules25092250 - 10 May 2020

Cited by 4 | Viewed by 4472

Abstract

Two scan modes of the triple quadrupole tandem mass spectrometer, namely Collision Induced Dissociation Precursor Ion scan and Neutral Loss scan, allow selectively pinpointing, in a complex mixture, compounds that feature specific chemical groups, which yield characteristic fragment ions or are lost as [...] Read more.

Two scan modes of the triple quadrupole tandem mass spectrometer, namely Collision Induced Dissociation Precursor Ion scan and Neutral Loss scan, allow selectively pinpointing, in a complex mixture, compounds that feature specific chemical groups, which yield characteristic fragment ions or are lost as distinctive neutral fragments. This feature of the triple quadrupole tandem mass spectrometer allows the non-target screening of mixtures for classes of components. The effective (center-of-mass) energy to achieve specific fragmentation depends on the inter-quadrupole voltage (laboratory-frame collision energy) and on the masses of the precursor molecular ion and of the collision gas, through a non-linear relationship. Thus, in a class of homologous compounds, precursor ions activated at the same laboratory-frame collision energy face different center-of-mass collision energy, and therefore the same fragmentation channel operates with different degrees of efficiency. This article reports a linear equation to calculate the laboratory-frame collision energy necessary to operate Collision-Induced Dissociation at the same center-of-mass energy on closely related compounds with different molecular mass. A routine triple quadrupole tandem mass spectrometer can operate this novel feature (iso-energetic collision-induced dissociation scan; i-CID) to analyze mixtures of endogenous metabolites by Precursor Ion and Neutral Loss scans. The latter experiment also entails the hitherto unprecedented synchronized scanning of all three quadrupoles of the triple quadrupole tandem mass spectrometer. To exemplify the application of this technique, this article shows two proof-of-principle approaches to the determination of biological mixtures, one by Precursor Ion analysis on alpha amino acid derivatized with a popular chromophore, and the other on modified nucleosides with a Neutral Fragment Loss scan. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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14 pages, 1494 KiB

Open AccessArticle

Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome

by Joshua Morimoto, Marta Cialiè Rosso, Nicole Kfoury, Carlo Bicchi, Chiara Cordero and Albert Robbat, Jr.

Molecules 2019, 24(20), 3757; https://doi.org/10.3390/molecules24203757 - 18 Oct 2019

Cited by 16 | Viewed by 3678

Abstract

Identifying all analytes in a natural product is a daunting challenge, even if fractionated by volatility. In this study, comprehensive two-dimensional gas chromatography/mass spectrometry (GC×GC-MS) was used to investigate relative distribution of volatiles in green, pu-erh tea from leaves collected at two different [...] Read more.

Identifying all analytes in a natural product is a daunting challenge, even if fractionated by volatility. In this study, comprehensive two-dimensional gas chromatography/mass spectrometry (GC×GC-MS) was used to investigate relative distribution of volatiles in green, pu-erh tea from leaves collected at two different elevations (1162 m and 1651 m). A total of 317 high and 280 low elevation compounds were detected, many of them known to have sensory and health beneficial properties. The samples were evaluated by two different software. The first, GC Image, used feature-based detection algorithms to identify spectral patterns and peak-regions, leading to tentative identification of 107 compounds. The software produced a composite map illustrating differences in the samples. The second, Ion Analytics, employed spectral deconvolution algorithms to detect target compounds, then subtracted their spectra from the total ion current chromatogram to reveal untargeted compounds. Compound identities were more easily assigned, since chromatogram complexities were reduced. Of the 317 compounds, for example, 34% were positively identified and 42% were tentatively identified, leaving 24% as unknowns. This study demonstrated the targeted/untargeted approach taken simplifies the analysis time for large data sets, leading to a better understanding of the chemistry behind biological phenomena. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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14 pages, 2497 KiB

Open AccessArticle

Identification of Azoxystrobin Glutathione Conjugate Metabolites in Maize Roots by LC-MS

by Giuseppe Dionisio, Maheswor Gautam and Inge Sindbjerg Fomsgaard

Molecules 2019, 24(13), 2473; https://doi.org/10.3390/molecules24132473 - 05 Jul 2019

Cited by 6 | Viewed by 4684

Abstract

Xenobiotic detoxification in plant as well as in animals has mostly been studied in relationship to the deactivation of the toxic residues of the compound that, surely for azoxystrobin, is represented by its β-methoxyacrylate portion. In maize roots treated for 96 h with [...] Read more.

Xenobiotic detoxification in plant as well as in animals has mostly been studied in relationship to the deactivation of the toxic residues of the compound that, surely for azoxystrobin, is represented by its β-methoxyacrylate portion. In maize roots treated for 96 h with azoxystrobin, the fungicide accumulated over time and detoxification compounds or conjugates appeared timewise. The main detoxified compound was the methyl ester hydrolysis product (azoxystrobin free acid, 390.14 m/z) thought to be inactive followed by the glutathione conjugated compounds identified as glutathione conjugate (711.21 m/z) and its derivative lacking the glycine residue from the GSH (654.19 m/z). The glycosylated form of azoxystrobin was also found (552.19 m/z) in a minor amount. The identification of these analytes was done by differential untargeted metabolomics analysis using Progenesis QI for label free spectral counting quantification and MS/MS confirmation of the compounds was carried out by either Data Independent Acquisition (DIA) and Data Dependent Acquisition (DDA) using high resolution LC-MS methods. Neutral loss scanning and comparison with MS/MS spectra of azoxystrobin by DDA and MSe confirmed the structures of these new azoxystrobin GSH conjugates. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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14 pages, 2138 KiB

Open AccessArticle

Mass Spectrometry-Based Untargeted Metabolomics and α-Glucosidase Inhibitory Activity of Lingzhi (Ganoderma lingzhi) During the Developmental Stages

by Dedi Satria, Sonam Tamrakar, Hiroto Suhara, Shuhei Kaneko and Kuniyoshi Shimizu

Molecules 2019, 24(11), 2044; https://doi.org/10.3390/molecules24112044 - 29 May 2019

Cited by 18 | Viewed by 4643

Abstract

Lingzhi is a Ganoderma mushroom species which has a wide range of bioactivities. Analysis of the changes in metabolites during the developmental stages of lingzhi is important to understand the underlying mechanism of its biosynthesis, as well as its bioactivity. It may also [...] Read more.

Lingzhi is a Ganoderma mushroom species which has a wide range of bioactivities. Analysis of the changes in metabolites during the developmental stages of lingzhi is important to understand the underlying mechanism of its biosynthesis, as well as its bioactivity. It may also provide valuable information for the cultivation efficiency of lingzhi. In this study, mass spectrometry based untargeted metabolomics was carried out to analyze the alteration of metabolites during developmental stages of lingzhi. Eight developmental stages were categorized on the basis of morphological changes; starting from mycelium stage to post-mature stage. GC/MS and LC/MS analyses along with multivariate analysis of lingzhi developmental stages were performed. Amino acids, organic acids, sugars, polyols, fatty acids, fatty alcohols, and some small polar metabolites were extracted as marker metabolites from GC/MS analysis, while, lanostane-type triterpenoids were observed in LC/MS analysis of lingzhi. The marker metabolites from untargeted analysis of lingzhi developmental stages were correlated with the α-glucosidase inhibitory activity. Two metabolites, compounds 34 and 35, were identified as potential contributors of the α-glucosidase inhibitory activity. The current result shows that some metabolites are involved in the developmental process and α-glucosidase inhibitory activity of lingzhi. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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Review

Jump to: Research

13 pages, 653 KiB

Open AccessReview

Towards Non-Targeted Screening of Lipid Biomarkers for Improved Equine Anti-Doping

by Kathy Tou, Adam Cawley, Christopher Bowen, David P. Bishop and Shanlin Fu

Molecules 2023, 28(1), 312; https://doi.org/10.3390/molecules28010312 - 30 Dec 2022

Viewed by 1771

Abstract

The current approach to equine anti-doping is focused on the targeted detection of prohibited substances. However, as new substances are rapidly being developed, the need for complimentary methods for monitoring is crucial to ensure the integrity of the racing industry is upheld. Lipidomics [...] Read more.

The current approach to equine anti-doping is focused on the targeted detection of prohibited substances. However, as new substances are rapidly being developed, the need for complimentary methods for monitoring is crucial to ensure the integrity of the racing industry is upheld. Lipidomics is a growing field involved in the characterisation of lipids, their function and metabolism in a biological system. Different lipids have various biological effects throughout the equine system including platelet aggregation and inflammation. A certain class of lipids that are being reviewed are the eicosanoids (inflammatory markers). The use of eicosanoids as a complementary method for monitoring has become increasingly popular with various studies completed to highlight their potential. Studies including various corticosteroids, non-steroidal anti-inflammatories and cannabidiol have been reviewed to highlight the progress lipidomics has had in contributing to the equine anti-doping industry. This review has explored the techniques used to prepare and analyse samples for lipidomic investigations in addition to the statistical analysis and potential for lipidomics to be used for a longitudinal assessment in the equine anti-doping industry. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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22 pages, 2607 KiB

Open AccessFeature PaperReview

Nontargeted Screening Using Gas Chromatography–Atmospheric Pressure Ionization Mass Spectrometry: Recent Trends and Emerging Potential

by Xiaolei Li, Frank L. Dorman, Paul A. Helm, Sonya Kleywegt, André Simpson, Myrna J. Simpson and Karl J. Jobst

Molecules 2021, 26(22), 6911; https://doi.org/10.3390/molecules26226911 - 16 Nov 2021

Cited by 13 | Viewed by 3720

Abstract

Gas chromatography–high-resolution mass spectrometry (GC–HRMS) is a powerful nontargeted screening technique that promises to accelerate the identification of environmental pollutants. Currently, most GC–HRMS instruments are equipped with electron ionization (EI), but atmospheric pressure ionization (API) ion sources have attracted renewed interest because: (i) [...] Read more.

Gas chromatography–high-resolution mass spectrometry (GC–HRMS) is a powerful nontargeted screening technique that promises to accelerate the identification of environmental pollutants. Currently, most GC–HRMS instruments are equipped with electron ionization (EI), but atmospheric pressure ionization (API) ion sources have attracted renewed interest because: (i) collisional cooling at atmospheric pressure minimizes fragmentation, resulting in an increased yield of molecular ions for elemental composition determination and improved detection limits; (ii) a wide range of sophisticated tandem (ion mobility) mass spectrometers can be easily adapted for operation with GC–API; and (iii) the conditions of an atmospheric pressure ion source can promote structure diagnostic ion–molecule reactions that are otherwise difficult to perform using conventional GC–MS instrumentation. This literature review addresses the merits of GC–API for nontargeted screening while summarizing recent applications using various GC–API techniques. One perceived drawback of GC–API is the paucity of spectral libraries that can be used to guide structure elucidation. Herein, novel data acquisition, deconvolution and spectral prediction tools will be reviewed. With continued development, it is anticipated that API may eventually supplant EI as the de facto GC–MS ion source used to identify unknowns. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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23 pages, 1178 KiB

Open AccessReview

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

by Charlotte Capitain and Philipp Weller

Molecules 2021, 26(18), 5457; https://doi.org/10.3390/molecules26185457 - 08 Sep 2021

Cited by 27 | Viewed by 5116

Abstract

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) [...] Read more.

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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21 pages, 4506 KiB

Open AccessFeature PaperReview

Guide to Semi-Quantitative Non-Targeted Screening Using LC/ESI/HRMS

by Louise Malm, Emma Palm, Amina Souihi, Merle Plassmann, Jaanus Liigand and Anneli Kruve

Molecules 2021, 26(12), 3524; https://doi.org/10.3390/molecules26123524 - 09 Jun 2021

Cited by 48 | Viewed by 5978

Abstract

Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, [...] Read more.

Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, semi-quantification strategies are, or at least should be, used to estimate the concentrations of the unknown compounds before final decision making. All steps in the analytical chain, from sample preparation to ionization conditions and data processing can influence the signals obtained, and thus the estimated concentrations. Therefore, each step needs to be considered carefully. Generally, less is more when it comes to choosing sample preparation as well as chromatographic and ionization conditions in NTS. By combining the positive and negative ionization mode, the performance of NTS can be improved, since different compounds ionize better in one or the other mode. Furthermore, NTS gives opportunities for retrospective analysis. In this tutorial, strategies for semi-quantification are described, sources potentially decreasing the signals are identified and possibilities to improve NTS are discussed. Additionally, examples of retrospective analysis are presented. Finally, we present a checklist for carrying out semi-quantitative NTS. Full article

(This article belongs to the Special Issue (Mass Spectrometric) Non Target Screening–Techniques and Strategies)

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