Standard substances free quantification makes LC/ESI/MS non-targeted screening of pesticides in cereals comparable between labs

Wang, Tingting; Liigand, Jaanus; Frandsen, Henrik Lauritz; Smedsgaard, Jørn; Kruve, Anneli

doi:10.1016/j.foodchem.2020.126460

Cited by 25 publications

(16 citation statements)

References 32 publications

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“…This approach aims to account for the effect of the structure of the compound, eluent composition, and instrument parameters (such as source geometry) on the response factor. The ionization efficiency-based quantification has been recently tested for quantification of pesticides in food samples [27].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of the quantification approaches for the non-targeted screening of micropollutants and their transformation products in groundwater

2021

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A wide range of micropollutants can be monitored with non-targeted screening; however, the quantification of the newly discovered compounds is challenging. Transformation products (TPs) are especially problematic because analytical standards are rarely available. Here, we compared three quantification approaches for non-target compounds that do not require the availability of analytical standards. The comparison is based on a unique set of concentration data for 341 compounds, mainly pesticides, pharmaceuticals, and their TPs in 31 groundwater samples from Switzerland. The best accuracy was observed with the predicted ionization efficiency-based quantification, the mean error of concentration prediction for the groundwater samples was a factor of 1.8, and all of the 74 micropollutants detected in the groundwater were quantified with an error less than a factor of 10. The quantification of TPs with the parent compounds had significantly lower accuracy (mean error of a factor of 3.8) and could only be applied to a fraction of the detected compounds, while the mean performance (mean error of a factor of 3.2) of the closest eluting standard approach was similar to the parent compound approach.

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Section: Introductionmentioning

confidence: 99%

Benchmarking of the quantification approaches for the non-targeted screening of micropollutants and their transformation products in groundwater

2021

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“…The performance of the LC/MS descriptors model was also compared to a model previously published by Liigand et al [ 14 ] This random forest model is based on PaDEL descriptors [ 31 ] calculated from SMILES representation of the chemical and include different molecular descriptors and fingerprints, for example, the number of nitrogen and hydrogen atoms, and presence of specific functional groups, but also topological and electronic descriptors. Additionally, mobile phase descriptors, such as pH of the water phase, viscosity, surface tension, and polarity index, are used to account for the effect of the mobile phase.…”

Section: Validationmentioning

confidence: 99%

“…To overcome these limitations, machine learning approaches for predicting the ionization efficiency of the detected compounds have recently been developed [ 10 , 11 , 12 , 13 ]. The predicted ionization efficiencies can be further used to quantify the tentatively identified compounds if analytical standards are lacking [ 11 , 14 , 15 ]. These approaches rely on descriptors deduced from the structure of the compound and, therefore, at minimum a tentatively known structure of the detected compound is required.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS

Palm

Kruve

2022

Molecules

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LC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the response of compounds from their structure, which can then be used for quantification in non-targeted analysis. Still, these models rely on tentatively known structures while for most detected compounds, a list of structural candidates, or sometimes only exact mass and retention time are identified. In this study, a quantification approach was developed, where LC/ESI/HRMS descriptors are used for quantification of compounds even if the structure is unknown. The approach was developed based on 92 compounds analyzed in parallel in both positive and negative ESI mode with mobile phases at pH 2.7, 8.0, and 10.0. The developed approach was compared with two baseline approaches— one assuming equal response factors for all compounds and one using the response factor of the closest eluting standard. The former gave a mean prediction error of a factor of 29, while the latter gave a mean prediction error of a factor of 1300. In the machine learning-based quantification approach developed here, the corresponding prediction error was a factor of 10. Furthermore, the approach was validated by analyzing two blind samples containing 48 compounds spiked into tap water and ultrapure water. The obtained mean prediction error was lower than a factor of 6.0 for both samples. The errors were found to be comparable to approaches using structural information.

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“…To illustrate this, we used data from Wang et al [60] and calculated the prediction errors of the concentration for pyridaben in a solvent matrix which had concentrations falling outside the linear range. The calibration curve and prediction errors are shown in Figure 4.…”

Section: Dilution Of the Samplementioning

confidence: 99%

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

et al. 2021

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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.

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Standard substances free quantification makes LC/ESI/MS non-targeted screening of pesticides in cereals comparable between labs

Cited by 25 publications

References 32 publications

Benchmarking of the quantification approaches for the non-targeted screening of micropollutants and their transformation products in groundwater

Benchmarking of the quantification approaches for the non-targeted screening of micropollutants and their transformation products in groundwater

Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS

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

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