Machine Learning-Assisted Chromium Speciation Using a Single-Well Ratiometric Fluorescent Nanoprobe

This study introduces a single-well fluorometric sensor that utilizes orange emissive thioglycolic acid stabilized CdTe quantum dots (TGA-QDs) and blue emissive carbon dots (CDs) to detect and differentiate between various chromium species, such as Cr (III) and Cr (VI) (i.e., CrO42- and Cr2O72-). The variations of fluorescence spectra of the proposed probe upon chromium species addition were analyzed using machine learning techniques such as linear discriminant analysis and partial least squares regression as a classification and multivariate calibration technique, respectively. Linear discriminant analysis (LDA) demonstrated exceptional accuracy in differentiating single-component and bicomponent samples. Additionally, the findings from the partial least squares regression (PLSR) showed that the sensor created has strong linearity within the 1.0-100.0, 1.0-100.0, and 0.1-15 μM range for Cr2O72-, CrO42-, and Cr3+, respectively. Furthermore, appropriate detection limits were successfully achieved, which were 2.6, 2.9, and 0.7 μM for Cr2O72-, CrO42-, and Cr3+, respectively. Ultimately, the successful capability of the sensing platform in the identification and quantification of chromium species in environmental water samples provides innovative insights into general speciation analytics.PMID:38614401 | DOI:10.1016/j.chemosphere.2024.141966
Source: Chemosphere - Category: Chemistry Authors: Source Type: research