TY - JOUR

T1 - Development of a single retention time prediction model integrating multiple liquid chromatography systems

T2 - Application to new psychoactive substances

AU - Pasin, Daniel

AU - Mollerup, Christian Brinch

AU - Rasmussen, Brian Schou

AU - Linnet, Kristian

AU - Dalsgaard, Petur Weihe

N1 - Funding Information: The authors would like to sincerely thank all the contributors of HighResNPS, without whom this study would not be possible. Publisher Copyright: © 2021 The Author(s)

PY - 2021

Y1 - 2021

N2 - Database-driven suspect screening has proven to be a useful tool to detect new psychoactive substances (NPS) outside the scope of targeted screening; however, the lack of retention times specific to a liquid chromatography (LC) system can result in a large number of false positives. A singular stream-lined, quantitative structure-retention relationship (QSRR)-based retention time prediction model integrating multiple LC systems with different elution conditions is presented using retention time data (n = 1281) from the online crowd-sourced database, HighResNPS. Modelling was performed using an artificial neural network (ANN), specifically a multi-layer perceptron (MLP), using four molecular descriptors and one-hot encoding of categorical labels. Evaluation of test set predictions (n = 193) yielded coefficient of determination (R2) and mean absolute error (MAE) values of 0.942 and 0.583 min, respectively. The model successfully differentiated between LC systems, predicting 54%, 81% and 97% of the test set within ±0.5, ±1 and ±2 min, respectively. Additionally, retention times for an analyte not previously observed by the model were predicted within ±1 min for each LC system. The developed model can be used to predict retention times for all analytes on HighResNPS for each participating laboratory's LC system to further support suspect screening.

AB - Database-driven suspect screening has proven to be a useful tool to detect new psychoactive substances (NPS) outside the scope of targeted screening; however, the lack of retention times specific to a liquid chromatography (LC) system can result in a large number of false positives. A singular stream-lined, quantitative structure-retention relationship (QSRR)-based retention time prediction model integrating multiple LC systems with different elution conditions is presented using retention time data (n = 1281) from the online crowd-sourced database, HighResNPS. Modelling was performed using an artificial neural network (ANN), specifically a multi-layer perceptron (MLP), using four molecular descriptors and one-hot encoding of categorical labels. Evaluation of test set predictions (n = 193) yielded coefficient of determination (R2) and mean absolute error (MAE) values of 0.942 and 0.583 min, respectively. The model successfully differentiated between LC systems, predicting 54%, 81% and 97% of the test set within ±0.5, ±1 and ±2 min, respectively. Additionally, retention times for an analyte not previously observed by the model were predicted within ±1 min for each LC system. The developed model can be used to predict retention times for all analytes on HighResNPS for each participating laboratory's LC system to further support suspect screening.

KW - High-resolution mass spectrometry

KW - New psychoactive substances

KW - Retention time prediction

KW - Suspect screening

U2 - 10.1016/j.aca.2021.339035

DO - 10.1016/j.aca.2021.339035

M3 - Journal article

C2 - 34625246

AN - SCOPUS:85114785593

VL - 1184

JO - Analytica Chimica Acta

JF - Analytica Chimica Acta

SN - 0003-2670

M1 - 339035

ER -