Pattern Recognition for Sample Classification Using Elemental Composition—Application for Inductively Coupled Plasma Atomic Emission Spectrometry

Abstract

Three pattern recognition techniques were investigated as tools for automatic recognition of samples: k -Nearest Neighbors, Bayesian Classification and the C4.5 inductive learning algorithm. Their abilities to classify 20 geological reference materials were compared. Each training and test example used 13 elemental concentrations. The data set was composed of 2582 examples obtained from CANMET in the form of results of analyses performed on these reference materials by different laboratories. It was found that all three pattern recognition techniques performed extremely well with a large data set of real samples. Bayesian Classification and k -Nearest Neighbors worked very well with small data sets.