Kinetic Determination of Acetylsalicylic Acid Using a CdTe/AgInS2 Photoluminescence Probe and Different Chemometric Models

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Castro, RC (Author) Other; Pascoa, RNMJ (Author) Other; Lucia L M F S Saraiva (Author) FFUP; Joao L M Santos (Author) FFUP; Ribeiro, DSM (Author) Other

Journal

Title: Biosensors-Basel

Vol. 13

Final page: 437

ISSN: 2079-6374

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-00Y-5KT

DOI: 10.3390/bios13040437

Abstract (EN): The combination of multiple quantum dots (QDs) in a multi-emitter nanoprobe can be envisaged as a promising sensing scheme, as it enables obtaining a collective response of individual emitters towards a given analyte and allows for achieving specific analyte-response profiles. The processing of these profiles using adequate chemometric methods empowers a more sensitive, reliable and selective determination of the target analyte. In this work, we developed a kinetic fluorometric method consisting of a dual CdTe/AgInS2 quantum dots photoluminescence probe for the determination of acetylsalicylic acid (ASA). The fluorometric response was acquired as second-order time-based excitation/emission matrices that were subsequently processed using chemometric methods seeking to assure the second-order advantage. The data obtained in this work are considered second-order data as they have a three-dimensional size, I x J x K (where I represents the samples' number, J the fluorescence emission wavelength while K represents the time). In order to select the most adequate chemometric method regarding the obtained data structure, different chemometric models were tested, namely unfolded partial least squares (U-PLS), N-way partial least squares (N-PLS), multilayer feed-forward neural networks (MLF-NNs) and radial basis function neural networks (RBF-NNs).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20