Classification of detectors for ion chromatography using principal components regression and linear discriminant analysis

Principal components regression (PCR) and linear discriminant analysis (LDA) have been applied to the classification of ion chromatographic detectors using information about the sample and other IC method conditions (19 attributes in total), a training set of 12 693 cases and a randomly-chosen test set of 1410 cases. Missing data was entered as a separate `unknownʹ code. When the value of each attribute was coded in a simple cardinal series (e.g., column=1, 2, 3, etc.), PCR correctly predicted the detector in 27% of the training set and 28% of the test set. By creating a variable (taking a value between 0 (absent) and 1 (present)) for each value of each attribute, the PCR prediction for both sets increased to 60%. LDA was more successful, predicting 69% of the detectors of each set, using a prior probability of the frequency of a given detector in the database, but this included zero hits for detectors that were poorly represented in the database. If equal prior probabilities were chosen the overall success rate dropped to 33% but now the classification of less frequently used detectors was improved. The ability of these numerically-oriented methods to classify discrete, non-numerical data, is surprisingly good and compares with induction methods, neural networks and expert systems reported previously.