The 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 techniques made
it possible to create a pattern recognition system for the detection
of antibiotic residues in skimmed milk. A multielectrode sensor including
copper, nickel, and carbon fiber was fabricated for the collection
of electrochemical data. Processes occurring at the electrode surface
were discussed and simulated with the help of molecular docking and
density functional theory modeling. It was assumed that the antibiotic
fingerprint reveals a potential drift of electrodes, owing to complexation
with metal ions present in milk. The gradient boosting algorithm showed
the best efficiency in training the machine learning model. High accuracy
was achieved for the 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.