Application of Multiple Classifier Fusion in the Discriminant Analysis of near Infrared Spectroscopy for Agricultural Products

Abstract

Near infrared spectroscopy combined with chemometrics and pattern recognition has become a primary focus in the discriminant analysis of agricultural products. To date, most studies have focused on using a single classifier to discriminate the origins, varieties and grades of products. Others have focused on using multiple classifier fusion by weighted voting. Due to their attributes of continuity and internal similarity, discriminant models sometimes present poor performance. In this study, we achieved better performance by applying multiple classifier fusion models, including support vector machine (SVM), discriminant partial least squares (DPLS) and principal component and Fisher criterion (PPF). PPF showed continuity and similarity among different parts of tobacco leaves [i.e. upper (B), cutter (C) and lug (X)]. The similarities between each class and the others were quantified to values, and the sum of the similarity values of each class was defined as its similarity. SVM–DPLS–PPF fusion by voting and similarity constraint for decision resulted in better performance, with the correct discriminant rate improved on average by 14.1%, 8.2%, 17.3% and 4.6% compared with those achieved using SVM, DPLS, PPF and SVM–DPLS–PPF fusion by weighted voting for decision, respectively; in addition, the incorrect discriminant rate between B and X was reduced to zero. Therefore, we demonstrated the feasibility of using SVM–DPLS–PPF fusion by voting and similarity constraint for decision to discriminate between different parts of tobacco leaves. This technique could provide a new method for tobacco quality management, computer-aided grading and intelligent acquisition. It also provides a new discriminant method for analysing the attributes of continuity and similarity of agricultural products using near infrared spectroscopy.