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This study investigated the potential effect of cold plasma on reducing residues of pesticides diazinon and chlorpyrifos in apples and cucumbers and its effects on property of products. Two separate concentrations of each pesticide with 500 and 1,000 ppm were prepared and the samples were inoculated by dipping them into the solutions. All samples treated with pesticides were exposed to cold plasma in a monopole cold plasma apparatus (DBD) run at 10 and 13 kV voltages. Liquid-liquid extraction (LLE) was used to remove pesticide residues from the samples. Eventually, high-performance liquid chromatography (HPLC) was used to measure the amount of pesticides in the samples. Also, to investigate generated metabolites, extracts were injected into a GC/MS apparatus. In addition, the effects of cold plasma on humidity, tissue hardness, color and the sugar percentage of products were analyzed. The results revealed that treatment of samples with cold plasma considerably reduced pesticide residues without leaving any traces of harmful or toxic substances. Furthermore, it did not have any undesirable effects on the color and texture of the samples. The efficiency of this method increased with higher voltage and longer exposure time. In general, the best results were obtained by the combination of 500 ppm concentration, 10 min exposure and 13 kV voltages. The residues of diazinon were reduced better than the residues of chlorpyrifos. Apples were detoxified much better than cucumbers. Also, cold plasma treatment transformed diazinon and chlorpyrifos pesticides into their less toxic metabolites. The results showed that with increased voltage and longer exposure time, cold plasma caused few changes in moisture and glucose content, texture hardness and color of products. Th ere were no significant difference between treated samples and control in all treatments.

The chemical composition of the volatile fraction obtained by head-space solid phase microextraction (HS-SPME), single drop microextraction (SDME) and the essential oil obtained by cold-press from the peels of C. sinensis cv. valencia were analyzed employing gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC-MS). The main components were limonene (61.34 %, 68.27 %, 90.50 %), myrcene (17.55 %, 12.35 %, 2.50 %), sabinene (6.50 %, 7.62 %, 0.5 %) and α-pinene (0 %, 6.65 %, 1.4 %) respectively obtained by HS-SPME, SDME and cold-press. Then a quantitative structure-retention relationship (QSRR) study for the prediction of retention indices (RI) of the compounds was developed by application of structural descriptors and the multiple linear regression (MLR) method. Principal components analysis was used to select the training set. A simple model with low standard errors and high correlation coefficients was obtained. The results illustrated that linear techniques such as MLR combined with a successful variable selection procedure are capable of generating an efficient QSRR model for prediction of the retention indices of different compounds. This model, with high statistical significance (R2 train = 0.983, R2 test = 0.970, Q2 LOO = 0.962, Q2 LGO = 0.936, REP(%) = 3.00), could be used adequately for the prediction and description of the retention indices of the volatile compounds.