Detection Potential of Multi-Features Representation of E-Nose Data in Classification of Moldy Maize Samples

Abstract

In order to assess rapidly and timely the moldy degree of maize samples using electronic nose (E-nose) and improve the correct classification rate of E-nose, the different feature representation modes (DFRM) for E-nose data were explored in depth. A determining method for multi-features vector of E-nose based on Wilks Λ statistic was introduced so as to obtain the best multi-features vector for characterizing E-nose data. And then a selection method of representation features of each sensor signals based on elimination transform with pivoting of the Λ statistic was also introduced for the different excitation characteristic of each gas sensor. The research results show that the classification effect of multi-features representation mode (MFRM) is better than that of single feature representation mode (SFRM), and the MFRM is not a regular pattern, but the best multi-features vector of E-nose in MFRM can be obtained by the determining method. Moreover, it is necessary to select the representation features of each sensor signals in the MFRM using the selection method. The visual inspection results based on SFRM and MFRM were examined by Fisher discriminant analysis (FDA) and proved that the introduced methods were very effective, the highest correct discrimination rate based on SFRM is 80%, while the correct discrimination rate of the five features combination is 97%. As an outlook, we believe that the research findings may be universally applied for the classification of other food and agriculture products using E-nose.