Saavedra-Ruiz Andrés, Resto-Irizarry Pedro J
Bioengineering Graduate Program, University of Puerto Rico Mayagüez, Mayagüez, PR 00680, USA.
Mechanical Engineering Department, University of Puerto Rico Mayagüez, Mayagüez, PR 00680, USA.
Biosensors (Basel). 2025 Apr 30;15(5):284. doi: 10.3390/bios15050284.
Bacteriological water quality monitoring is of utmost importance for safeguarding public health against waterborne diseases. Traditional methods such as membrane filtration (MF), multiple tube fermentation (MTF), and enzyme-based assays are effective in detecting fecal contamination indicators, but their time-consuming nature and reliance on specialized equipment and personnel pose significant limitations. This paper introduces a novel, portable, and cost-effective UV-LED/RGB water quality sensor that overcomes these challenges. The system is composed of a multi-well self-loading microfluidic device for sample-preparation-free analysis, RGB sensors for data acquisition, UV-LEDs for excitation, and a portable incubation system. Commercially available defined substrate technology, most probable number (MPN) analysis, and machine learning (ML) are combined for the real-time monitoring of bacteria colony-forming units (CFU) in a water sample. Fluorescence signals from individual wells are captured by the RGB sensors and analyzed using Multilayer Perceptron Neural Network (MLPNN) and Support Vector Machine (SVM) algorithms, which can quickly determine if individual wells will be positive or negative by the end of a 24 h period. The novel combination of ML and MPN analysis was shown to predict in 30 min the bacterial concentration of a water sample with a minimum prediction accuracy of 84%.
细菌学水质监测对于保障公众健康免受水传播疾病的侵害至关重要。传统方法如膜过滤(MF)、多管发酵(MTF)和基于酶的检测方法在检测粪便污染指标方面是有效的,但它们耗时的特性以及对专业设备和人员的依赖构成了重大限制。本文介绍了一种新颖、便携且经济高效的紫外发光二极管/红绿蓝水质传感器,该传感器克服了这些挑战。该系统由一个用于无需样品制备分析的多孔自加载微流控装置、用于数据采集的红绿蓝传感器、用于激发的紫外发光二极管以及一个便携式培养系统组成。将市售的特定底物技术、最大可能数(MPN)分析和机器学习(ML)相结合,用于实时监测水样中的细菌菌落形成单位(CFU)。来自各个孔的荧光信号由红绿蓝传感器捕获,并使用多层感知器神经网络(MLPNN)和支持向量机(SVM)算法进行分析,这两种算法可以在24小时结束时快速确定各个孔是呈阳性还是阴性。机器学习和MPN分析的新组合在30分钟内就能预测出水样中的细菌浓度,最低预测准确率为84%。