Determination of acetamiprid, imidacloprid, and spirotetramat and their relevant metabolites in pistachio using modified QuEChERS combined with liquid chromatography-tandem mass spectrometry.

A QuEChERS based methodology was developed for the simultaneous identification and quantification of acetamiprid, imidacloprid, and spirotetramat and their relevant metabolites in pistachio by liquid chromatography-tandem mass spectrometry for the first time. First, sample extraction was done with MeCN:citrate buffer:NaHCO3 followed by phase separation with the addition of MgSO4:NaCl. The supernatant was then cleaned by a primary-secondary amine (PSA), GCB, and MgSO4. The proposed method provides a linearity in the range of 5-200µgL-1, and the linear regression coefficients were higher than 0.99. LOD and LOQ were obtained to be 2 and 5µgkg-1 for the studied insecticides, respectively, with the exception of imidacloprid-olefin (5 and 10µgkg-1). Acceptable recoveries (91-110%) were obtained for all the analytes with good intra- and inter-precisions (0.4≥RSD ≤11.0). The method was then used for the pistachio samples collected from a field trial to estimate the maximum residue limits (MRLs) in next step.

A QSPR study on the GC retention times of a series of fatty, dicarboxylic and amino acids by MLR and ANN.

Quantitative structure-property relationship (QSPR) analysis has been carried out to a series of fatty, amino and dicarboxylic acids to model their GC retention times. A genetic partial least square method (GAPLS) was applied as a variable selection tool. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and artificial neural network (ANN). The neural network employed here is a connected back-propagation system with a 3-4-1 architecture. Three topological indices for these compounds, namely, mean information index on atomic composition (AAC), average connectivity index chi-0 (X0A) and total information index of atomic composition (IAC) taken as inputs for the regression models. The results indicate that the GA is a very effective variable selection approach for QSPR analysis. The comparison of the two regression methods used showed that ANN has better prediction ability than MLR. The statistical figure of merits of the two models showed the successful modeling of the retention times with molecular descriptors.