Accurate nitrite detection in beverages and pickled foods is crucial for food safety but remains challenging due to matrix complexity, particularly interference from salinity. To address this, a highly sensitive electrochemical sensor was constructed by modifying a screen-printed electrode with an electrodeposited platinum-palladium nanoparticles/gold layer (Pt-Pd NPs/Au/SPE). The electrocatalytic effect of the bimetallic nanoparticles conferred a 1.60-fold sensitivity enhancement, enabling the sensor to achieve a wide linear range (1-7500 µM), a low detection limit of 0.11 µM, and high sensitivity (226.03 µA mM-1 cm-2). Crucially, quantification errors caused by salinity were corrected through a novel strategy that couples the developed Pt-Pd NPs/Au/SPE sensor with a commercial salinity meter. The NaCl concentration measured with the salinity meter served as the key input to a multilayer perceptron (MLP) neural network, which specifically compensated for the matrix effect. This intelligent compensation reduced the mean absolute error of nitrite quantification from 45.99% to 4.14%. The method was successfully applied to commercial beverages and pickled food, such as cola, milk, and pickled ginger, onion, garlic, and mustard, achieving recoveries of 92.77-106.56%. This work provides a reliable tool for food analysis and demonstrates a practical AI-assisted approach to overcome matrix interference in electrochemical sensing.
Liang et al. (Thu,) studied this question.