基于机器学习算法的牛奶抗生素电化学传感器的研制
The development of an electrochemical sensor for antibiotics in milk based on machine learning algorithms
摘要：
本研究致力于牛奶等多组分混合物的电化学分析问题。循环伏安法设备和机器学习技术的结合使得有可能创建脱脂牛奶中抗生素残留的模式识别系统。制作了一种由铜、镍和碳纤维组成的多电极传感器，用于电化学数据的采集。在电极表面发生的过程的化学方面进行了讨论和模拟的帮助下，分子对接和密度泛函理论建模。据推测，抗生素指纹显示为电极的电位漂移，这是由于抗生素分子的氧化还原降解，随后是pH变化或与牛奶中存在的离子络合。梯度提升算法在训练机器学习模型方面表现出最好的效率。实现了牛奶中抗生素的高准确度识别。该方法可用于奶牛场现有的挤奶系统，以监测抗生素的残留浓度。
英文摘要：
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.
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