Classical and modern algorithms for the evaluation of data from sensor-arrays

Abstract

Sensors have found wide application in process control, environmental analysis, and other analytical problems in recent years. Optical sensor arrays can be used to monitor organic solvent vapour mixtures by use of reflectometric interference spectroscopy. Lack in selectivity of the sensitive polymer films requires multivariate algorithms for evaluation. Two major aspects are of interest: the random error of calibration and the interpretation of the influence of a single sensor in an array with redundant information. Due to the partial selectivity of the different sensitive layers, non-linearities, cross-sensitivities, and differences in sensitivity, the selection of the most suitable sensitive polymer layers is not trivial. Model based algorithms allow the interpretation of variables whereas the model free algorithms provide better results concerning the random error of calibration. We choose the pruning algorithm to optimize a neural network topology in order to obtain the qualitative information on the sensor elements from the remaining links between the input layer and the hidden layer. We compare these results to the ones obtained for linear and non-linear PLS1 by partial least squares (PLS1) and calculate the errors for the calibration.