Ensemble Learning-Assisted Spectroelectrochemical Sensing Platform for Detection of Fluoride in Water

Fluoride is a crucial inorganic anion found in drinking water, which may pose serious health hazards to human health if consumed in excess amounts. The quantification of fluoride in drinking water with high sensitivity, selectivity, and cross-sensitivity is critical. Given these factors, the present work proposes a spectroelectrochemical sensing platform for fluoride sensing using 5,10,15,20-tetraphenyl-21H,23H-porphine iron (III) chloride (FeTPP), and tetrabutylammonium perchlorate (TBAP) as the electrolyte. The proposed spectroelectrochemistry (SEC) is a hybrid platform that concurrently provides spectroscopic and electrochemical information about a system susceptible to oxidation and reduction. An ensemble–based multivariate prediction model was developed to simultaneously analyze electrochemical and spectroscopic data to predict fluoride concentration with enhanced reliability and precision. The prediction model provided promising results with a coefficient of determination of 0.9923 ± 0.0063 and a MSE of 0.369 ± 0.0596. These encouraging results demonstrate the promising performance of the proposed spectroelectrochemical platform in complex real-world applications.