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Prediction of pesticide retention time in reversed-phase liquid chromatography using quantitative-structure retention relationship models: A comparative study of seven molecular descriptors datasets
- Source :
- Chemosphere, Chemosphere, Elsevier, 2021, pp.130036. ⟨10.1016/j.chemosphere.2021.130036⟩
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- International audience; Predicting chromatographic retention times of pesticides has become more and more important for suspect and non-target screening. Indeed, high-resolution mass spectrometry hyphenated (HRMS) to liquid chromatography (LC) are of growing interest for research and monitoring of pesticides, their metabolites and transformation products. The development of quantitative structure-retention relationship models require selecting the most adequate and best set of molecular descriptors and the best machine-learning algorithm. Here, we used seven molecular descriptor sets extracted from four well-known studies and applied them to roughly 800 pesticides and their chromatographic reversed-phase retention times. We used and optimized five different machine-learning algorithms with these descriptor sets to carry out predictions. Our results show that a support-vector machine regression algorithm with only eight molecular descriptors gave the best compromise between the number of molecular descriptors, processing time and model complexity to optimize prediction performance for this specific gradient LC method.
- Subjects :
- High-resolution mass spectrometry
MESH: Molecular descriptors
Environmental Engineering
Monitoring
Health, Toxicology and Mutagenesis
0208 environmental biotechnology
Liquid chromatography
Quantitative Structure-Activity Relationship
02 engineering and technology
010501 environmental sciences
Mass spectrometry
01 natural sciences
Mass Spectrometry
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
Molecular descriptor
Environmental Chemistry
MESH: Pesticides
Pesticides
Machine-learning
Chromatography, High Pressure Liquid
MESH: Reversed-phase liquid chromatography
0105 earth and related environmental sciences
Mathematics
Chromatography, Reverse-Phase
MESH: Machine Learning
Public Health, Environmental and Occupational Health
Quantitative structure
General Medicine
General Chemistry
Reversed-phase chromatography
Pesticide
Pollution
Model complexity
020801 environmental engineering
Regression algorithm
Biological system
MESH: Environmental Monitoring
Retention time
Chromatography, Liquid
Subjects
Details
- ISSN :
- 00456535
- Volume :
- 275
- Database :
- OpenAIRE
- Journal :
- Chemosphere
- Accession number :
- edsair.doi.dedup.....f24f8afff2cb7f0c6c040d6ca92a5310