Adulteration detection of edible oil by one‐class classification and outlier detection

Citations Over Time

Abstract

Abstract Edible oil adulteration is a mostly practiced phenomenon. However, the traditional discriminant methods fail to detect oil adulteration involving more than one adulterant. Recently, one‐class classifiers were built for food or oil authentication. Unfortunately, as it is hard to determine the application domain of the one‐class classifier, high prediction error was obtained for real samples in market surveillance. In this study, a new method was developed based on one‐class classification and outlier detection for edible oil adulteration detection in market surveillance. The model population was constructed using Monte Carlo sampling of unidentified inspected samples to select the plateau region exhibiting the highest accumulated absolute centered residual (ACR) values. Subsequently, the number of models in the plateau region was validated by the theoretical ones calculated by the classical probability model. The models in the plateau region with the highest cumulative accumulated ACR values were used to identify adulterated oils. Furthermore, the cross‐validation was conducted by comparing identification results from two different Monte Carlo sampling ratios to ensure the accuracy of our method. Both single adulteration and multiple adulteration of peanut oils were prepared to validate our method. Moreover, this method was used to detect adulteration of sesame oils, which have already been identified by the markers in our previous study. The validation results of three datasets indicated that this method could effectively identify adulterated samples and therefore provide a novel solution for inspecting potential adulteration in practice.

Related Papers

• → A study of outliers in the exponential smoothing approach to forecasting(2011)38 cited
• → Outliers Identification Model in Point-of-Sales Data Using Enhanced Normal Distribution Method(2019)7 cited
• The robustness data processing method about eliminating outlier(2004)
• → One needs to be careful when dismissing outliers: a realistic example(2016)1 cited
• → Outlier Robust Prediction(2012)