PLS regression on wavelet compressed NIR spectra

Today, good compression methods are more and more needed, due to the ever increasing amount of data that is being collected. The mere thought of the computational power demanded to calculate a regression model on a large data set with many thousands of variables can often be depressing. This paper should be treated as an introduction to how the discrete wavelet transform can be used in multivariate calibration. It will be shown that by using the fast wavelet transform on individual signals as a preprocessing method in regression modelling on near-infrared (NIR) spectra, good compression is achieved with almost no loss of information. No loss of information means that the predictive ability and the diagnostics, together with the graphical displays of the data compressed regression model, are basically the same as for the original uncompressed regression model. The regression method used here is Partial Least Squares, PLS. In a NIR-VIS example, compression of the data set to 3% of its original size was achieved.