Determination of heavy metals in edible oils by a novel voltammetry taste sensor array.

Herein, a novel voltammetry taste sensor array (VTSA) using pencil graphite electrode, screen-printed electrode, and glassy carbon electrode was used to identify heavy metals (HM) including Cad, Pb, Sn and Ni in soybean and rapeseed oils. HMs were added to edible oils at three concentrations of 0.05, 0.1 and 0.25 ppm, and then, the output of the device was classified using a chemometric classification method. According to the principal component analysis results, PG electrode explains 96% and 81% of the variance between the data in rapeseed and soybean edible oils, respectively. Additionally, the SP electrode explains 91% of the variance between the data in rapeseed and soybean oils. Moreover, the GC electrode explains 100% and 99% of the variance between the data in rapeseed and soybean edible oils, respectively. K-nearest neighbor exhibited high capability in classifying HMs in edible oils. In addition, partial least squares in the combine of VTSA shows a predict 99% in rapeseed oil. The best electrode for soybean edible oil was GC.

A dedicated electronic nose combined with chemometric methods for detection of adulteration in sesame oil.

Sesame oil (SO), one of the most popular and expensive edible oils, is prone to adulteration. In this study, the fatty acid profiles of pure sesame seed oil and samples adulterated with two less expensive edible oils (canola and sunflower) were analyzed using Gas Chromatography. A dedicated e-nose system was developed and tested on 15 mixtures of sesame-canola and sesame-sunflower samples. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Multi-Layered Perceptron (MLP) methods were utilized to identify adulteration through the evaluation of Volatile Organic Compound. Result of chromatography showed that most samples of sesame oil containing impurities at levels less than 30% were recognized incorrectly in the standard range of SO fatty acids. This is while the developed e-nose system was able to detect adulteration at much lower levels. According to the results, PCA and LDA methods can describe the data set variance with precision of 95.6% and 97%, respectively. The MLP model had better results compared to PCA and LDA, with high determination coefficient (R2 = 0.981) and low RMSE (0.0178). Results indicate that the e-nose system provided an effective non-destructive method to detect SO adulteration at levels as low as 5%, which GC was unable to detect.

The process of producing tomato paste by ohmic heating method.

In this study, the effect of ohmic heating technique on electrical conductivity, water evaporation rate, heating rate, colour parameters, pH and energy consumption of tomato samples was investigated. Ohmic heating was accomplished till the moisture content of the tomato samples reduced from initial moisture content of as 9.33 (dry basis) to a safer level of 2.2. The results of the nonlinear mathematical model including the effects of voltage gradient level and the temperature on the electrical conductivity changes had good agreement (R ≥ 0.955) with the experimental data. Also, it was observed that the electrical conductivity increased along with concentration of tomato samples. The range of electrical conductivity during ohmic heating was 3.19-8.95 (S/m). It was found that processing time decreased from 28.32 to 4.3 min over the voltage gradient range studied (6 to 14 V/cm), which resulted in decreased specific energy consumption from 4.63 to 3.05 (MJ/kg water). Due to increasing of heating rate and water evaporation rate at high voltage gradient, the change of the pH was limited. Samples processed in high voltage gradient had higher L*, b* and hue angle (h), lower a* and Chroma (C) values as compared to low voltage gradient. The optimum value of processing time, pH, colour, specific energy consumption was obtained at 14 V/cm voltage gradient level.