Improving chemometric results by optimizing the dimension reduction for Raman spectral data sets

In this contribution a new method for improving the accuracy of classification and identification experiments is presented. For this purpose the four most applied dimension reduction methods (principal component analysis, independent component analysis, partial least square dimension reduction and the linear discriminant analysis) are used as starting point for the optimization method. The optimization is done by a specially designed genetic algorithm, which is best suited for this kind of experiments. The presented multi-level chemometric approach has been tested for a Raman dataset containing over 2200 Raman spectra of eight classes of bacteria species (Bacillus anthracis, Bacillus cereus, Bacillus licheniformis, Bacillus mycoides, Bacillus subtilis, Bacillus thuringiensis, Bacillus weihenstephanensis and Paenibacillus polymyxa). The optimization of the dimension reduction improved the accuracy for classification by 6% compared with the accuracy, if the standard dimension reduction is applied. The identification rate is improved by 14% compared with the dimension reduction. The testing in a classification and identification experiment showed the robustness of the algorithm.