  Present study is dedicated to the problem of electrochemical analysis of
multicomponent mixtures such as milk. A combination of cyclic voltammetry
facilities and machine learning technique made it possible to create a pattern
recognition system for antibiotic residues in skimmed milk. A multielectrode
sensor including copper, nickel and carbon fiber was fabricated for the
collection of electrochemical data. Chemical aspects of processes occurring at
the electrode surface were discussed and simulated with the help of molecular
docking and density functional theory modelling. It was assumed that the
antibiotic fingerprint reveals as potential drift of electrodes owing to redox
degradation of antibiotic molecules followed by pH change or complexation with
ions present in milk. Gradient boosting algorithm showed the best efficiency
towards training the machine learning model. High accuracy was achieved for
recognition of antibiotics in milk. The elaborated method may be incorporated
into existing milking systems at dairy farms for monitoring the residue
concentrations of antibiotics.
