Calibration Transfer for Food Recognition Models for E-Noses

Abstract

Gas sensors using the low-priced MOS technique face several problems that reduce their applicability for classification tasks. In literature, several methods can be found that try to alleviate these problems. In this extended abstract, we focus on the problem that the behavior of different sensors (of same type) results in different data even for the same gas concentrations. Hence, the models cannot be reused for sensors of same type. Additionally, it is hardly possible to record high amounts of this type of data which makes transfer learning techniques on such data sets necessary. We apply a calibration transfer procedure and present the results on a data set for food recognition, which we recorded simultaneously with two identical gas sensor boxes in a controlled environment. We compare the results on MOS-based gas sensors from the MQ series and a more modern sensor using MEMS fabrication. The applied technique shows increased recognition rates on this data set.