Membrane breakage can lead to filtration failure, which allows harmful substances to enter the effluent, posing potential hazards to human health and the environment. This study is an innovative combination of fluorescence and ultraviolet-visible (UV–Vis) spectroscopy to identify membrane breakage. It aims to unravel more comprehensive information, improve detection sensitivity and selectivity, and enable real-time monitoring capabilities. Fluorescence and UV–Vis data are extracted through variance partitioning analysis (VPA) and integrated through a decision tree algorithm to form a superior system with enhanced discrimination capabilities. VPA improves discrimination efficiency by extracting key information from spectral data and eliminating redundancy. The decision tree algorithm, on the other hand, can process large amounts of data simultaneously. In addition, the method has a wide range of applications and can be used in various scenarios accurately. The scenarios include domestic sewage, micropollutant water, aquaculture wastewater, and secondary treated sewage. The experimental results validate the application of machine learning classifiers in membrane breakage detection with an accuracy rate of 96.8 % to 97.4 %.

中文翻译：

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基于光谱融合的机器学习分类器，用于区分多种场景下的膜破损


膜破裂会导致过滤失败，使有害物质进入废水，对人体健康和环境造成潜在危害。这项研究是荧光和紫外可见 (UV-Vis) 光谱的创新组合，用于识别膜破损。其目的是揭示更全面的信息，提高检测灵敏度和选择性，并实现实时监控能力。通过方差分配分析 (VPA) 提取荧光和紫外-可见数据，并通过决策树算法进行集成，形成具有增强辨别能力的卓越系统。 VPA 通过从光谱数据中提取关键信息并消除冗余来提高辨别效率。另一方面，决策树算法可以同时处理大量数据。此外，该方法适用范围广泛，可以准确地应用于各种场景。场景包括生活污水、微污染物水、养殖废水、二级处理污水。实验结果验证了机器学习分类器在膜破损检测中的应用，准确率达到96.8%~97.4%。