Regularized Linear Discriminant Analysis of Wavelet Compressed Ion Mobility Spectra

Abstract

Wavelet compression (WC) coupled to a regularized version of linear discriminant analysis (LDA) was evaluated as an effective classifier for identification of benzene, toluene, ethyl benzene, and the xylene isomers (BTEX) from their positive ion mobility spectra. A novel LDA classifier was devised that uses pseudoinverses of the within-group covariance matrices. The pseudoinverse allows LDA to be applied directly to underdetermined data. Wavelet compression improved the classification by removing noise from the spectra. Spectra compressed with the daublet 22 filter yielded better prediction than data compressed by principal components. Ion mobility spectrometry (IMS) affords rapid and sensitive measurements that may be used for process monitoring. This study demonstrates that classification by LDA directly on the wavelet-compressed (WTLDA) positive ion spectra can be used for identification of BTEX compounds. The accompanying benefits include fast model building, removal of noise, and improved classification. Benzene, toluene, ethyl benzene, and o-, m-, and p-xylene at high and low concentrations were used to construct the classification models. Spectra from intermediate concentrations were used for evaluation. Optimization of prediction favored shorter wavelet filters than those obtained from filters optimized for reconstruction.