Chemometric models for data processing in quantum dots-based photoluminescence methodologies

Abstract

The appealing properties of quantum dots (QDs) have drawn the scientific community's attention, leading to extensive research on using these nanomaterials as sensing platforms for the detection and quantification of a variety of analytes in environmental, biological, pharmaceutical and food samples. Despite the multiple inventive strategies that can be used to develop efficient QDs-sensing schemes, the defiant reactivity of these nano -materials, and their propensity to establish non-specific interactions, has significantly restrained their utilisation in situations demanding high selectivity, as is the case of the quantification of analytes in samples with interfering species or complex matrices, and in multiplexed detection. Several approaches have been proposed to overcome these selectivity issues, among which the chemometric analysis of photoluminescent (PL) data acquired from QDs-based analytical methodologies can be highlighted.This review details the application of chemometric models in the characterization and optimization of QDs-based analytical procedures, as well as for the analysis of data obtained from QDs-based PL methodologies, discussing how they can be used to circumvent selectivity issues and pointing out the corresponding advantages and limitations. In this work, we provide insights not only about probe arrangement strategies that could be designed to obtain efficient QDs-based sensing platforms but also regarding the requirements that must be observed to select both the most suitable type of data and the most effective chemometric model to assure the objectives of the methodology. Related advantages, namely second and higher-order advantages, constraints, and application prospects are also discussed.