A fluorescence based dual sensor for Zn and PO and the application of soft computing methods to predict machine learning outcomes.

A phenolphthalein-based Schiff base, 3,3-bis-{4-hydroxy-3-[(pyridine-2-ylmethylimino)-methyl]-phenyl}-3-isobenzofuran-1-one (PAP), has been synthesized and used for selective fluorescence 'turn on' and 'turn off' sensing of Zn and PO respectively. The limit of detection using the 3 method for Zn is found to be 19.3 nM and that for PO is 8.3 μM. The sensing mechanism of PAP for Zn ions has been explained by H NMR, C 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 Zn and PO 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 Zn and PO. 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.