Dual layer solid phase extraction based on molecular imprinting technology: Seeking a route to enhance selectivity for trace analysis of pesticide residues in olive oil.

Aiming to introduce a multiresidue analysis for the trace detection of pesticide residues belonging to organophosphorus and triazine classes from olive oil samples, a new sample preparation methodology comprising the use of a dual layer of “tailor-made” molecularly imprinted polymers (MIPs) SPE for the simultaneous extraction of both pesticides in a single procedure has been attempted. This work has focused on the implementation of a dual MIP-layer SPE procedure (DL-MISPE) encompassing the use of two MIP layers as specific sorbents. In order to achieve higher recovery rates, the amount of MIP layers has been optimized as well as the influence of MIP packaging order. The optimized DL-MISPE approach has been used in the preconcentration of spiked organic olive oil samples with concentrations of dimethoate and terbuthylazine similar to the maximum residue limits and further quantification by HPLC. High recovery rates for dimethoate (95%) and terbuthylazine (94%) have been achieved with good accuracy and precision. Overall, this work constitutes the first attempt on the development of a dual pesticide residue methodology for the trace analysis of pesticide residues based on molecular imprinting technology. Thus, DL-MISPE constitutes a reliable, robust, and sensitive sample preparation methodology that enables preconcentration of the target pesticides in complex olive oil samples, even at levels similar to the maximum residue limits enforced by the legislation.

An Assessment to Toxicological Risk of Pesticide Exposure

On the one hand, pesticides may be absorbed into the body orally, dermally, ocularly and by inhalation and the human exposure may be dietary, recreational and/or occupational where toxicity could be acute or chronic. On the other hand, the environmental fate and toxicity of the pesticide is contingent on the physico-chemical characteristics of pesticide, the soil composition and adsorption. Human toxicity is also dependent on the exposure time and individual’s susceptibility. Therefore, this work will focus on the development of an Artificial Intelligence based diagnosis support system to assess the pesticide toxicological risk to humanoid, built under a formal framework based on Logic Programming to knowledge representation and reasoning, complemented with an approach to computing grounded on Artificial Neural Networks. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting.