Classification of pyrolysis mass spectra by fuzzy multivariate rule induction-comparison with regression, K-nearest neighbour, neural and decision-tree methods

The fuzzy multivariate rule building expert system (FuRES) is applied to solve classification problems using two pyrolysis mass spectral data sets. The first data set contains three types of milk (from cow, goat and ewe) and the second data set contains two types of olive oils (adulterated and extra virgin). The performance of FuRES is compared with a selection of well-known classification algorithms: backpropagation artificial neural networks (ANNs), canonical variates analysis (CVA), classification and regression trees (CART), the K-nearest neighbour method (KNN) and discriminant partial least squares (DPLS). In terms of percent correct classification the DPLS and ANNs were best since all test set objects in both data sets were correctly classified. FuRES was second best with 100% correct classification for the milk data set and 91% correct classification for the olive oil data set, while the KNN method showed 100% and 61% for the two data sets. CVA had a 100% correct classification for the milk data set, but failed to form a model for the olive oil data set. The percent correct classifications for the CART method were 92% and 74%, respectively. When both model interpretation and predictive ability are taken into consideration, we consider that the ranking of these methods on the basis of these two data sets is in order of decreasing utility: DPLS, FuRES, ANNs, CART, CVA and KNN.