Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum

Food adulteration detection requires quick and simple methods. Spectral detection can significantly reduce the analysis time, but it needs to construct a detection model. In this study, a one-class classification method based on an autoencoder is proposed for the detection of food adulteration by spectroscopy. In the proposed method, the autoencoder is constructed to extract low-dimensional features from high-dimensional spectral data and reconstruct the original spectrum. Then the coding error and reconstruction error are used to determine the food sample is adulterated or not. The infrared spectral data of milk powder and its adulterated forms are used to verify the performance of the proposed model. Experimental results show that the proposed method has similar effects to soft independent modeling of class analogy and one-class partial least squares, and is significantly better than support vector data description. The proposed method can be flexibly applied to the spectral detection of food adulteration.

Automated summary

A one-class classification method based on an autoencoder is proposed in this study for detecting food adulteration by spectroscopy, using coding error and reconstruction error to determine adulteration status. The method shows similar performance to other models and outperforms support vector data description in spectral detection of food adulteration.