A Multivariate Statistical Approach to Determine the Most Effective Factors for Biological Monitoring of Pesticides Using Voltammetric Sensors

Nowadays, there are increasing concerns about pesticides occupational and environmental adverse effects despite the wide use. Therefore, it is very important to develop reliable methods for pesticides detection, extraction, and quantification in different samples. This study was aimed to develop an electrochemical Nano-composite sensor based on molecularly imprinted polymer (MIP) for selective determination of pesticides. The MIPs and non-imprinted polymers (NIPs) for diazinon and dicloran pesticides synthesized and were used in the composition of the carbon paste electrode. The square wave voltammetry (SWV) differential technique based on a fractional 28 factorial design was used to evaluate the effects of variables on the sensor response. The results of MIP-CP electrodes showed very high recognition abilities compared to the NIP-CP. The linear ranges for diazinon and dicloran were obtained 5×10-10 to 1×10-6, 1×10-9 to 1×10-6 mol L-1, and detection limits were 2.7×10-10 and 4.1×10-10 mol L-1, respectively. Modified sensors were provided the prominent selectivity and sensitivity for quantification of pesticides in urine and water real samples considering optimized conditions. Therefore, the special sample pre-treatment wasn’t required prior to the analysis process. The experimental design was verified the existence of interaction between factors. Consequently, the variables including square wave frequency, square wave amplitude, and deposition potential were indicated more significant effects on the sensor response than the other factors. It can be concluded that the interaction evaluation between variables is very important to look for the optimum conditions for pesticides analysis using voltammetric sensors.