Prediction of Collision Cross-Section Values for Small Molecules: Application to Pesticide Residue Analysis

Bijlsma, Lubertus; Bade, Richard; Celma, Alberto; Mullin, Lauren; Cleland, Gareth; Stead, Sara; Hernández, Félix; Sancho, Juan V.

doi:10.1021/acs.analchem.7b00741

Cited by 97 publications

(166 citation statements)

References 35 publications

(47 reference statements)

Supporting

Mentioning

161

Contrasting

Order By: Relevance

“…For this purpose, in-silico fragmentation matching [18,19] and prediction of retention time (RT) have been shown to reduce the list of potential compounds [1,20]. In-silico prediction of CCS and IMS drift times have utilized molecular modelling techniques [21][22][23]; however, models based on molecular descriptors have shown similar results while drastically reducing computing time [24][25][26][27], which corresponds to findings for prediction of the reduced ion mobility constants [28,29].…”

Section: Introductionmentioning

confidence: 99%

Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry

Mollerup

Mardal

Dalsgaard

et al. 2018

Journal of Chromatography A

View full text Add to dashboard Cite

Exact mass, retention time (RT), and collision cross section (CCS) are used as identification parameters in liquid chromatography coupled to ion mobility high resolution accurate mass spectrometry (LC-IM-HRMS). Targeted screening analyses are now more flexible and can be expanded for suspect and non-targeted screening. These allow for tentative identification of new compounds, and in-silico predicted reference values are used for improving confidence and filtering false-positive identifications. In this work, predictions of both RT and CCS values are performed with machine learning using artificial neural networks (ANNs). Prediction was based on molecular descriptors, 827 RTs, and 357 CCS values from pharmaceuticals, drugs of abuse, and their metabolites. ANN models for the prediction of RT or CCS separately were examined, and the potential to predict both from a single model was investigated for the first time. The optimized combined RT-CCS model was a four-layered multi-layer perceptron ANN, and the 95th prediction error percentiles were within 2 min RT error and 5% relative CCS error for the external validation set (n = 36) and the full RT-CCS dataset (n = 357). 88.6% (n = 733) of predicted RTs were within 2 min error for the full dataset. Overall, when using 2 min RT error and 5% relative CCS error, 91.9% (n = 328) of compounds were retained, while 99.4% (n = 355) were retained when using at least one of these thresholds. This combined prediction approach can therefore be useful for rapid suspect/non-targeted screening involving HRMS, and will support current workflows.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry

Mollerup

Mardal

Dalsgaard

et al. 2018

Journal of Chromatography A

View full text Add to dashboard Cite

show abstract

Section: Application Of Ion Mobility Spectrometry To the Analysis Of mentioning

confidence: 99%

“…The workflow demonstrated by Zhou et al. was particularly in‐depth as DTIMS experimental data from a total of ∼400 compounds were used to generate molecular descriptors to train a machine‐learning algorithm, which matched CCS values measured on their instrument with a median relative error of 3%. Alternative approaches to modeling of CCS are discussed in Section .…”

Section: Application Of Ion Mobility Spectrometry To the Analysis Of mentioning

confidence: 99%

“…The substantial challenge of exposomic studies, where 1000s of compounds including xenobiotics, secondary metabolites, and transformation products that remain currently unidentified, is a focus of high-throughput IM-MS studies whereby computational prediction of CCS values is suggested to be a way forward to allow rapid screening of samples [154]. Such a workflow requires substantial computational effort to predict CCS values, but some authors have presented promising results using different approaches in addressing this major challenge [155][156][157]. The workflow demonstrated by Zhou et al [157] was particularly in-depth as DTIMS experimental data from a total of ∼400 compounds were used to generate molecular descriptors to train a machine-learning algorithm, which matched CCS values measured on their instrument with a median relative error of 3%.…”

Section: Environmental Analysismentioning

confidence: 99%

“…Such a workflow requires substantial computational effort to predict CCS values, but some authors have presented promising results using different approaches in addressing this major challenge [155][156][157]. The workflow demonstrated by Zhou et al [157] was particularly in-depth as DTIMS experimental data from a total of ∼400 compounds were used to generate molecular descriptors to train a machine-learning algorithm, which matched CCS values measured on their instrument with a median relative error of 3%. Alternative approaches to modeling of CCS are discussed in Section 3.3.…”

Section: Environmental Analysismentioning

confidence: 99%

See 2 more Smart Citations

Adding a new separation dimension to MS and LC–MS: What is the utility of ion mobility spectrometry?

D’Atri

Causon

Hernandez‐Alba

et al. 2017

J of Separation Science

147

142

View full text Add to dashboard Cite

* These authors contributed equally.Ion mobility spectrometry is an analytical technique known for more than 100 years, which entails separating ions in the gas phase based on their size, shape, and charge. While ion mobility spectrometry alone can be useful for some applications (mostly security analysis for detecting certain classes of narcotics and explosives), it becomes even more powerful in combination with mass spectrometry and high-performance liquid chromatography. Indeed, the limited resolving power of ion mobility spectrometry alone can be tackled when combining this analytical strategy with mass spectrometry or liquid chromatography with mass spectrometry. Over the last few years, the hyphenation of ion mobility spectrometry to mass spectrometry or liquid chromatography with mass spectrometry has attracted more and more interest, with significant progresses in both technical advances and pioneering applications. This review describes the theoretical background, available technologies, and future capabilities of these techniques. It also highlights a wide range of applications, from small molecules (natural products,

show abstract

Trends in Multiresidue Analysis

Hernández‐Mesa

Lara

Moreno‐González

et al. 2020

Encyclopedia of Analytical Chemistry

View full text Add to dashboard Cite

The global concern about food safety and environment quality has produced the development of a legal framework to control residues of organic compounds (e.g. pesticides and veterinary and human drugs). In this context, the establishment of analytical methods achieving satisfactory performances in terms of sensitivity, selectivity, resolution, identification, sample throughput, and applicability, is mandatory. Multiresidue methods mainly based on chromatographic techniques [i.e., gas chromatography (GC) and liquid chromatography (LC)] coupled to mass spectrometry (MS) are currently applied in routine laboratories. Furthermore, capillary electrophoresis (CE) has emerged as an alternative to LC and GC, owing to the development of on‐line preconcentration strategies for sensitivity enhancement. The recent integration of ion mobility spectrometry (IMS) in LC‐MS workflows also involves new possibilities for the separation of isomeric and isobaric residues, as well as for the separation of analytes of interest from background noise. New trends in multiresidue analysis point towards ambient ionization mass spectrometry (AIMS) strategies, which avoid chromatographic separations and substantially reduce analysis time. On the other hand, sample preparation has evolved through the development of miniaturized and environmentally friendly procedures according to the Green Chemistry principles. In addition, generic extraction procedures for multiclass analysis have been optimized, increasing sample throughput. This article aims to show an overview of the current trends in the analysis of pesticide and drugs in food and environmental samples, discussing the relevant advances in sample treatments and the technological evolution of the instrumental techniques. We hope to offer with this article a critical review of the analytical possibilities for routine analysis laboratories focused on monitoring of residues.

show abstract

Prediction of Collision Cross-Section Values for Small Molecules: Application to Pesticide Residue Analysis

Cited by 97 publications

References 35 publications

Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry

Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry

Adding a new separation dimension to MS and LC–MS: What is the utility of ion mobility spectrometry?

Trends in Multiresidue Analysis

Contact Info

Product

Resources

About