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.

抗生素广泛用于医药和畜牧业。然而，由于抗生素对降解的抗性，大量抗生素进入环境，对生态系统和公众健康构成潜在风险。因此，对环境中的抗生素进行检测是很有必要的。然而，传统的检测方法通常涉及复杂的预处理技术和昂贵的仪器，这会带来大量的时间和经济成本。在本文中，我们提出了一种基于光谱机器学习简化预处理的混合抗生素快速检测方法。在改进的光谱仪的帮助下，生成了大量特征图像来映射抗生素信息。通过机器学习模型建立特征图像与抗生素浓度之间的关系。确定系数和均方根误差用于评估机器学习模型的预测性能。结果表明，训练有素的机器学习模型可以在几乎没有预处理的情况下同时准确预测多种抗生素浓度。本研究结果对推动环境检测技术和数字化环境管理策略的发展具有一定的参考价值。结果表明，训练有素的机器学习模型可以在几乎没有预处理的情况下同时准确预测多种抗生素浓度。本研究结果对推动环境检测技术和数字化环境管理策略的发展具有一定的参考价值。结果表明，训练有素的机器学习模型可以在几乎没有预处理的情况下同时准确预测多种抗生素浓度。本研究结果对推动环境检测技术和数字化环境管理策略的发展具有一定的参考价值。