Multi-label classification for Oil Authentication

Oil Authentication influences the life of the human being substantially. In tradition, NIR (near infrared ray) is followed by the single-label learning or the feature transformation to distinguish the pure oil and the mixed oil. In our work, we adopt the multi-label AdaBoost.RMH algorithm to proceed the chromatographic images of edible oil from high performance liquid chromatography. Furthermore, we rectify the predict results of the multi-label AdaBoost.RMH with the binary AdaBoost.RMH algorithm. Finally, the detect rate and the accuracy for the multi-label classification are proposed to measure the ability of the algorithm on recognizing the pureness property and the composite of the oil, respectively. The experiments from the dataset on 9 kinds of edible oil and their mixture shows our algorithm (AdaBoost.REC) can achieve the remarkable improvements than AdaBoost.RMH.