Quantification of nitrite in beverages and pickled foods with a high-sensitivity amperometric biosensor enhanced by a multilayer perceptron neural network
Abstract
). 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.
Fields of Study & Subjects
Electrochemical sensors and biosensors
Advanced Chemical Sensor Technologies
Biosensors and Analytical Detection
Publication details
- Journal / source
- Analytical Methods
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 18
- Issue
- 10
- Pages
- 2109–2121
- Publication date
- 1 January 2026
- Language
- EN