Antibiotics are widely used in medicine and animal husbandry.However,due to the resistance of antibiotics to degradation,large amounts of antibiotics enter the environment,posing a potential risk to the ecosystem and public health.Therefore,the detection of antibiotics in the environment is necessary.Nevertheless,conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation,which impose considerable time and economic costs.In this paper,we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning.With the help of a modified spectrometer,a large number of characteristic images were generated to map antibiotic information.The relationship between characteristic images and antibiotic concentrations was established by machine learning model.The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model.The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment.The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.

Yicai Huang;Jiayuan Chen;Qiannan Duan;Yunjin Feng;Run Luo;Wenjing Wang;Fenli Liu;Sifan Bi;Jianchao Lee

Laboratory of Environmental Aquatic Chemistry,Department of Environmental Science,School of Geography and Tourism,Shaanxi Normal University,Xi'an 710062,China;Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity,College of Urban and Environmental Sciences,Northwest University,Xi'an 710127,China

环境科学与工程前沿

[1]Yicai Huang;Jiayuan Chen;Qiannan Duan;Yunjin Feng;Run Luo;Wenjing Wang;Fenli Liu;Sifan Bi;Jianchao Lee-.A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning)[J].环境科学与工程前沿,2022(03):129-140

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