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Present study is dedicated to the problem of electrochemical analysis ofmulticomponent mixtures such as milk. A combination of cyclic voltammetryfacilities and machine learning technique made it possible to create a patternrecognition system for antibiotic residues in skimmed milk. A multielectrodesensor including copper, nickel and carbon fiber was fabricated for thecollection of electrochemical data. Chemical aspects of processes occurring atthe electrode surface were discussed and simulated with the help of moleculardocking and density functional theory modelling. It was assumed that theantibiotic fingerprint reveals as potential drift of electrodes owing to redoxdegradation of antibiotic molecules followed by pH change or complexation withions present in milk. Gradient boosting algorithm showed the best efficiencytowards training the machine learning model. High accuracy was achieved forrecognition of antibiotics in milk. The elaborated method may be incorporatedinto existing milking systems at dairy farms for monitoring the residueconcentrations of antibiotics.

The development of an electrochemical sensor for antibiotics in milk based on machine learning algorithms