A fluorescence based dual sensor for Zn2+ and PO43− and the application of soft computing methods to predict machine learning outcomes

Abstract

A phenolphthalein-based Schiff base, 3,3-bis-{4-hydroxy-3-[(pyridine-2-ylmethylimino)-methyl]-phenyl}-3H-isobenzofuran-1-one (PAP), has been synthesized and used for selective fluorescence ‘turn on’ and ‘turn off’ sensing of Zn²⁺ and PO₄³⁻ respectively. The limit of detection using the 3σ method for Zn²⁺ is found to be 19.3 nM and that for PO₄³⁻ is 8.3 μM. The sensing mechanism of PAP for Zn²⁺ ions has been explained by ¹H NMR, ¹³C NMR, TRPL, ESI-MS, FT-IR, and DFT based calculations. Taking advantage of this fluorescence ‘on–off’ behavior of PAP in the sequential presence of Zn²⁺ and PO₄³⁻ a two input fuzzy logic (FL) operation has been constructed. The chemosensor PAP can thus act as a metal ion and anion responsive molecular switch, and its corresponding emission intensity is used to mimic numerous FL functions. To replace various expensive, time-consuming experimental procedures, we implemented machine learning soft computing tools, such as fuzzy-logic, artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS), to correlate as well as predict the fluorescence intensity in the presence of any equivalent ratio of Zn²⁺ and PO₄³⁻. The statistical performance measures (MSE and RMSE, for example) show that the projected values of the cation and anion sensing data by the ANFIS network are the best and closer to the experimental values.