Kiani Hasan, Beheshti Babak, Borghei Ali Mohammad, Rahmati Mohammad Hashem
Department of Biosystem Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Biosystem Mechanical Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgān, Iran.
J Food Sci Technol. 2024 Jun;61(6):1126-1137. doi: 10.1007/s13197-024-05933-1. Epub 2024 Feb 12.
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.
在此,一种使用铅笔石墨电极、丝网印刷电极和玻碳电极的新型伏安法味觉传感器阵列(VTSA)被用于识别大豆油和菜籽油中的重金属(HM),包括镉(Cad)、铅(Pb)、锡(Sn)和镍(Ni)。将重金属以0.05、0.1和0.25 ppm三种浓度添加到食用油中,然后使用化学计量学分类方法对该装置的输出进行分类。根据主成分分析结果,PG电极分别解释了菜籽油和大豆油数据之间96%和81%的方差。此外,SP电极解释了菜籽油和大豆油数据之间91%的方差。而且,GC电极分别解释了菜籽油和大豆油数据之间100%和99%的方差。K近邻法在食用油中重金属分类方面表现出很高的能力。此外,VTSA组合中的偏最小二乘法在菜籽油中的预测准确率为99%。大豆食用油的最佳电极是GC电极。