Designing classification filters for integrated sensing and processing using optimal discriminant vectors

• Classification filters for new instrument design are proposed.
• The filters are derived from the optimal discriminant vectors.
• The filters are designed for classification of different drugs.
• The drugs can be identified by applying the filters onto their NIR spectra.

Integrated sensing and processing (ISP) is a new strategy for instrument design to simplify quantitative or qualitative analysis. One of the ISP approaches is processing the optical spectrum with filters to obtain analytical results directly. ISP filters based on optimal discriminant vectors are designed in this study for the problem of classification. The method starts with performing principal component analysis (PCA) on the spectra of multi-class samples, and then constructs the optimal orthogonal discriminant vectors using the PCA scores by maximizing Fisher's discriminant function. Therefore, the filters for discriminating the samples can be obtained by transforming the loadings with the discriminant vectors. Applying the filters onto the spectra of new samples, the difference between samples of different class can be obtained and the difference can be used for discrimination of these samples. NIR datasets of vitamins, cephalosporins and Chinese patent medicines are used to test the performance of the filters. The results show that, for each of the datasets, the samples of different class can be correctly identified.