The use of principal component analysis and discriminant analysis in differential sensing routines

Statistical analysis techniques such as principal component analysis (PCA) and discriminant analysis (DA) have become an integral part of data analysis for differential sensing. These multivariate statistical tools, while extremely versatile and useful, are sometimes used as "black boxes". Our aim in this paper is to improve the general understanding of how PCA and DA process and display differential sensing data, which should lead to the ability to better interpret the final results. With various sets of model data, we explore several topics, such as how to choose an appropriate number of hosts for an array, selectivity compared to cross-reactivity, when to add hosts, how to obtain the best visually representative plot of a data set, and when arrays are not necessary. We also include items at the end of the paper as general recommendations which readers can follow when using PCA or DA in a practical application. Through this paper we hope to present these statistical analysis methods in a manner such that chemists gain further insight into approaches that optimize the discriminatory power of their arrays.