Abstract
Phenoxy acid herbicides are used worldwide and are potential contaminants of drinking water. Reversed phase high-performance liquid chromatography (RP-HPLC) is commonly used to monitor phenoxy acid herbicides in water samples. RP-HPLC retention of phenoxy acids is affected by both mobile phase composition and pH, but the synergic effect of these two factors, which is also dependent on the structure and pKa of solutes, cannot be easily predicted. In this paper, to support the setup of RP-HPLC analysis of phenoxy acids under application of linear mobile phase gradients we modelled the simultaneous effect of the molecular structure and the elution conditions (pH, initial acetonitrile content in the eluent and gradient slope) on the retention of the solutes. In particular, the chromatographic conditions and the molecular descriptors collected on the analyzed compounds were used to estimate the retention factor k by Partial Least Squares (PLS) regression. Eventually, a variable selection approach, Genetic Algorithms, was used to reduce the model complexity and allow an easier interpretation. The PLS model calibrated on the retention data of 15 solutes and successively tested on three external analytes provided satisfying and reliable results.
Highlights
Phenoxy acid herbicides are synthetic analogues of the plant regulator auxins and are extensively used to control broad-leaved weeds in many crops, such as rice, wheat and soybeans [1]
The set of solutes investigated in this work consists of eight phenoxy acid herbicides (2,4-D, 2,4,5-T, 2,4,5-TP, MCPA, dichlorprop, mecoprop, clopyralid and trichlopyr) and ten derivatives of benzoic acid, phenylacetic acid and phenoxyacetic acid, pKa of these compounds ranging between 2.29 and 4.31
The reversed-phase high-performance liquid chromatography (RP-HPLC) retention data of the 18 analytes were collected at three different values of eluent pH, namely 2, 3 and 4
Summary
Phenoxy acid herbicides are synthetic analogues of the plant regulator auxins and are extensively used to control broad-leaved weeds in many crops, such as rice, wheat and soybeans [1]. Phenoxy acids in water or food can be detected and quantified by reversed-phase high-performance liquid chromatography (RP-HPLC). Routine monitoring of trace pollutants in water requests chromatographic methods able to simultaneously detect and quantify many structural congeners or multi-class mixtures. In this context, the empirical trial-and-error strategies still used to develop chromatographic methods may be inefficient or excessively time-consuming. The empirical trial-and-error strategies still used to develop chromatographic methods may be inefficient or excessively time-consuming To overcome such limitation various multivariate statistical approaches have been proposed [15,16,17,18] with the aim of providing predictive tools for the chromatographic retention
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