A perfect tandem: chemometric methods and microfluidic colorimetric twin sensors on paper. Beyond the traditional analytical approach

Abstract

Chemometrics has proven to be a powerful tool for processing multivariate analytical data aimed at locating and extracting useful information relating to a particular analyte or material system in a complex sample from non-specific analytical signals that have been previously acquired and recorded by one or more analytical instruments or devices. In this paper, the basis for the application of both classification and quantitation multivariate methods is described, using a colorimetric twin sensor produced on a microfluidic paper-based analytical device (μPAD) as instructive example. The selected twin sensor provides a dual analytical signal for four anions (acetate, cyanide, fluoride and phosphate) in aqueous solution (concentration interval 0–0.1 M) from two reagent dyes (alizarin and 4-nitrophenol), which are immobilised in parallel on the same device containing four microfluidic channels designed in the form of an X. In this way, a data vector is obtained from each test whose elements are the colour coordinates obtained from the four responses, which is then used to build the chemometric models to be applied. Two multivariate classification methods (partial least squares discriminant analysis and support vector machine classification) are explored and the latter makes it possible to detect the presence or absence of each anion in an aqueous solution mixture. Single (each dye dataset separately) and fused (merging the two dye datasets) models were built and a support vector machine was shown to be the best classification method, obtaining sensitivity and precision values of 100% in almost all cases.