Principal component-wavelet neural networks as a new multivariate calibration method

A principal component-wavelet neural network (PC-WNN) was proposed as a multivariate calibration method for simultaneous determination of test samples of copper, iron, and aluminum. Principal component analysis has been applied to the data set for dimensionality reduction of the data matrix, and neural network with wavelet function has been employed as the function-learning method. The data sets consisted of 27 standard solutions, which were randomly divided into training and prediction sets. The WNN architecture and its parameters are optimized based on the minimum value for the root mean square errors of the prediction set to prevent over-fitting the model. The performance of the constructed model is evaluated by prediction of the metal concentrations in a validation set and also in the alloy samples.