Oil oxidation is an undesirable series of chemical reactions involving oxygen that degrades the quality of an oil. Oxidation eventually produces rancidity in oil, with accompanying off-flavors and smells. An electronic nose was used in this study to detect the adulterations in edible oils. The acidity, peroxide, anisidine and Totox values of the edible oil samples were measured according to the official American Oil Chemist Society (AOCS) standard. The results were analyzed using cluster analysis, principal component analysis, support vector machine, quadratic discriminant analysis, and Partial least squares regression technique. In the sensor array, the TGS2602, and MQ136 sensors had the highest values of the Loudness coefficient and the MQ9, TGS822, TGS813, and TGS2620 had the lowest values. Based on the results obtained, the accuracy of the three methods; Support vector machine (SVM), Quadratic discriminant analysis and Partial least squares were 97%, 98.33%, and 100%, respectively. The results for the linear vector kernel support machine, training accuracy and validation for C-SVM and Nu-SVM were 98, 97, 97 and 95%, respectively. The results also indicated that the proposed method can be used as an alternative to the official AOCS methods to innovatively detect the edible oil oxidation with high accuracy.

中文翻译：

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使用嗅觉机对食用油氧化进行定性分析

油的氧化是一系列不希望的化学反应，其中涉及氧气，会降低油的质量。氧化最终会在油中产生酸败，并伴有异味和气味。在这项研究中，电子鼻被用于检测食用油中的掺假。食用油样品的酸度，过氧化物，茴香胺和Totox值均根据美国石油化学家协会（AOCS）官方标准进行测量。使用聚类分析，主成分分析，支持向量机，二次判别分析和偏最小二乘回归技术对结果进行了分析。在传感器阵列中，TGS2602和MQ136传感器的响度系数最高，而MQ9，TGS822，TGS813和TGS2620最低。根据获得的结果，三种方法的准确性；支持向量机（SVM），二次判别分析和偏最小二乘分别为97％，98.33％和100％。线性向量核支持机，C-SVM和Nu-SVM的训练准确性和验证的结果分别为98％，97％，97％和95％。结果还表明，该方法可作为官方AOCS方法的替代方法，以创新方式高精度检测食用油氧化。