Abstract
Pesticide contamination of surface water and groundwater due to agricultural activities has been of concern for a long time. Water solubility indicates the tendency of a pesticide to be removed from soil by runoff or irrigation and to reach surface water and indicates the tendency to precipitate at the soil surface. The experimental procedures determining the solubility in water of pesticides are always time-consuming and expensive, and it is difficult to accurately distinguish species with similar physicochemical properties. A highly effective tool depending on a quantitative structure-property relationship (QSPR) can be utilised to predict solubility in water for those pesticide compounds with no literature values. QSPR models were developed using multiple linear regression, partial least squares and neural networks analyses. Following the removal of a small number of outliers, linear and non-linear QSPR models to predict the solubility of pesticide compounds in water were developed for the relevant descriptors. Consistent with experimental studies, the results obtained offer excellent regression models having good prediction ability.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 181-192 |
| Number of pages | 12 |
| Journal | Molecular Physics |
| Volume | 108 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2010 |
Keywords
- Partial least square (PLS)
- Pesticide compounds
- Principal components artificial neural networks (PC-ANN)
- Quantitative structure property relationships (QSPR)
- Solubility
ASJC Scopus subject areas
- Biophysics
- Molecular Biology
- Condensed Matter Physics
- Physical and Theoretical Chemistry