Monitoring toxic mancozeb residues in tropical fruits using AI-driven Tukey's biweight regression integrated CuV2O6/Zn-bentonite clay electrocatalyst.

Abstract

Improper use of the toxic fungicide mancozeb risks food contamination, necessitating its rapid detection to ensure food safety and public health. In this study, a novel triclinic copper vanadate (CuV2O6)/zinc-bentonite (Zn-BNT) nanocomposite was prepared to fabricate of an intelligent AI-assisted sensing system for detecting mancozeb in tropical fruits. The CuV2O6/Zn-BNT-modified screen-printed electrode (CuV2O6/Zn-BNT/SPE) displayed a broad sensing range (0.04-53.15 μM), low detection limit (1 nM), and high sensitivity (11.20 μAμM-1 cm-2). Furthermore, a robust regression model using Tukey's biweight function integrated into the proposed sensor achieved excellent predictive performance with a root mean squared error of 1.11, mean absolute error of 0.88, and R2 of 0.99. This smart sensor exhibited superior reproducibility, stability, and selectivity. To validate its application potential, the sensor was used to quantify mancozeb in real fruit samples, including mango, papaya, banana, and pineapple. The recovery values (96.6-99.4%) highlight the practicality of CuV2O6/Zn-BNT/SPE for monitoring mancozeb contamination in foods.

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