title: Prediction of the processing factor for pesticides in apple juice by principal component analysis and multiple linear regression.
abstract: The main objective of this work was to establish a mathematical function that correlates pesticide residue levels in apple juice with the levels of the pesticides applied on the raw fruit, taking into account some of their physicochemical properties such as water solubility, the octanol/water partition coefficient, the organic carbon partition coefficient, vapour pressure and density. A mixture of 12 pesticides was applied to an apple tree; apples were collected after 10 days of application. After harvest, apples were treated with a mixture of three post-harvest pesticides and the fruits were then processed in order to obtain apple juice following a routine industrial process. The pesticide residue levels in the apple samples were analysed using two multi-residue methods based on LC-MS/MS and GC-MS/MS. The concentration of pesticides was determined in samples derived from the different steps of processing. The processing factors (the coefficient between residue level in the processed commodity and the residue level in the commodity to be processed) obtained for the full juicing process were found to vary among the different pesticides studied. In order to investigate the relationships between the levels of pesticide residue found in apple juice samples and their physicochemical properties, principal component analysis (PCA) was performed using two sets of samples (one of them using experimental data obtained in this work and the other including the data taken from the literature). In both cases the correlation was found between processing factors of pesticides in the apple juice and the negative logarithms (base 10) of the water solubility, octanol/water partition coefficient and organic carbon partition coefficient. The linear correlation between these physicochemical properties and the processing factor were established using a multiple linear regression technique.
authors.affiliation: Department of Hydrogeology and Analytical Chemistry, University of Almeria, Almeria, Spain.
authors.forename: L
authors.initials: L
authors.lastname: Martin
authors.forename: M
authors.initials: M
authors.lastname: Mezcua
authors.forename: C
authors.initials: C
authors.lastname: Ferrer
authors.forename: M D
authors.initials: MD
authors.lastname: Gil Garcia
authors.forename: O
authors.initials: O
authors.lastname: Malato
authors.forename: A R
authors.initials: AR
authors.lastname: Fernandez-Alba
mesh_terms: D001628:Beverages / Q000032:analysis*; D002849:Chromatography, Gas; D002853:Chromatography, Liquid; D005374:Filtration; D016014:Linear Models; D027845:Malus; D010575:Pesticides / Q000032:analysis*; D025341:Principal Component Analysis; D053719:Tandem Mass Spectrometry
chemical_list: D010575:Pesticides