Chen Yi, Dai Licheng, Zhang Fei, Zhao Tianqi, Jin Shangzhong
College of Optics and Electronic Science and Technology, China Jiliang University, Hangzhou, China.
J Fluoresc. 2024 Aug 24. doi: 10.1007/s10895-024-03872-w.
The monitoring of pathogenic microorganisms in water is important for public health and disease outbreaks prediction. Recently, optical detection techniques have drawn much attention due to the advantages of rapid response, security and high sensitivity. In this paper, a fluorescence spectrometer based on 375 nm exciting laser and the microchannel liquid sample flow technology is proposed. The 4 × 4 narrowband filter array was coupled to a Silicon Photomultiplier (SiPM) array with single-photon sensitivity. B500 fluorescent microspheres and Escherichia coli were used for performance evaluation of the spectrometer. As a result, it is feasible to use random particle counting method to detect the bacteria concentration level in water even low to several CFU/mL. In addition, based on Python tools and neural network algorithm models, the fluorescence spectra of different kinds of substances (biotic and abiotic) can be classified with an accuracy of more than 97%. The method was successfully applied to tap water samples. The results suggest that the proposed method is applicable for on-site bacteria detection.
水中致病微生物的监测对于公众健康和疾病爆发预测至关重要。近年来，光学检测技术因其响应迅速、安全且灵敏度高的优点而备受关注。本文提出了一种基于375 nm激发激光和微通道液体样品流动技术的荧光光谱仪。4×4窄带滤光片阵列与具有单光子灵敏度的硅光电倍增管（SiPM）阵列耦合。使用B500荧光微球和大肠杆菌对光谱仪进行性能评估。结果表明，即使细菌浓度低至几CFU/mL，使用随机粒子计数法检测水中细菌浓度水平也是可行的。此外，基于Python工具和神经网络算法模型，不同种类物质（生物和非生物）的荧光光谱分类准确率超过97%。该方法已成功应用于自来水样品检测。结果表明，所提出的方法适用于现场细菌检测。
