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基于纳米材料的电化学传感器对食用油的分类和抗氧化活性评价。

Classification and Antioxidant Activity Evaluation of Edible Oils by Using Nanomaterial-Based Electrochemical Sensors.

机构信息

Department of Chemistry, Physics and Environment, Faculty of Sciences and Environment, "Dunărea de Jos" University of Galaţi, 47 Domneasca Street, 800008 Galaţi, Romania.

出版信息

Int J Mol Sci. 2023 Feb 3;24(3):3010. doi: 10.3390/ijms24033010.

Abstract

The classification of olive oils and the authentication of their biological or geographic origin are important issues for public health and for the olive oil market and related industries. The development of techniques for olive oil classification that are fast, easy to use, and suitable for online, in situ and remote operation is of high interest. In this study, the possibility of discriminating and classifying vegetable oils according to different criteria related to biological or geographical origin was assessed using cyclic voltammograms (CVs) as input data, obtained with electrochemical sensors based on carbonaceous nanomaterials and gold nanoparticles. In this context, 44 vegetable oil samples of different categories were analyzed and the capacity of the sensor array coupled with multivariate analysis was evaluated. The characteristics highlighted in voltammograms are related to the redox properties of the electroactive compounds, mainly phenolics, existing in the oils. Moreover, the antioxidant activity of the oils' hydrophilic fraction was also estimated by conventional spectrophotometric methods (1,1-diphenyl-2-picrylhydrazyl (DPPH) and galvinoxyl) and correlated with the voltammetric responses of the sensors. The percentage of DPPH and galvinoxyl inhibition was accurately predicted from the voltammetric data, with a correlation coefficients greater than 0.97 both in calibration and in validation. The results indicate that this method allows for a clear discrimination of oils from different biological or geographic origins.

摘要

橄榄油的分类和其生物或地理来源的鉴定对于公共卫生以及橄榄油市场和相关产业而言都是非常重要的议题。开发快速、易于使用且适用于在线、原位和远程操作的橄榄油分类技术具有重要意义。在本研究中,使用基于碳纳米材料和金纳米粒子的电化学传感器获得的循环伏安图(CV)作为输入数据,评估了根据与生物或地理来源相关的不同标准对植物油进行区分和分类的可能性。在这种情况下,分析了 44 种不同类别的植物油样本,并评估了传感器阵列与多元分析相结合的能力。CV 中突出的特征与存在于油中的电活性化合物(主要是酚类化合物)的氧化还原性质有关。此外,还通过传统分光光度法(1,1-二苯基-2-苦基肼(DPPH)和戊二醛)评估了油亲水部分的抗氧化活性,并将其与传感器的伏安响应相关联。DPPH 和戊二醛抑制率的百分比可从伏安数据中准确预测,校准和验证中的相关系数均大于 0.97。结果表明,该方法可明确区分来自不同生物或地理来源的油。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b04/9917972/1a4375740715/ijms-24-03010-g001.jpg

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