Zhang Xu, Zhang Yihao, Yang Xuanyi, Wang Zhiyun, Liu Xianhua
School of Environmental Science and Engineering, Tianjin University, Tianjin 300354, China.
Sensors (Basel). 2025 Jan 24;25(3):711. doi: 10.3390/s25030711.
Biochemical oxygen demand (BOD) is an important indicator of the degree of organic pollution in water bodies. Traditional methods for BOD determination, although widely used, are complicated and dependent on accurate chemical measurements of dissolved oxygen. The aim of this study was to propose a facile method for predicting biochemical oxygen demand by fluorescence signals using three-dimensional fluorescence spectroscopy and parallel factor analysis in combination with a machine learning algorithm. The water samples were incubated for five days using the national standard method, during which the dissolved oxygen contents and three-dimensional fluorescence spectroscopy data were measured at eight-hour intervals. The maximum fluorescence intensity of three fluorescence components was decomposed and extracted by parallel factor analysis. The relationship between the maximum fluorescence of the three fluorescence components and the BOD values was established using a random forest model. The results showed that there was a good correlation between the fluorescence components and BOD values. The BOD values were effectively predicted by the random forest model with a high goodness of fit (R = 0.878) and low mean square error (MSE = 0.28). Although this method did not shorten the incubation time, successful BOD prediction was realized by the non-contact measurement of fluorescence signals. This avoids the complicated operation of DO determination, improves detection efficiency, and provides a convenient solution for analyzing large quantities of water samples and monitoring facile water quality.
生化需氧量（BOD）是水体有机污染程度的重要指标。传统的BOD测定方法虽然应用广泛，但操作复杂，且依赖于对溶解氧的精确化学测量。本研究的目的是提出一种简便的方法，利用三维荧光光谱和平行因子分析结合机器学习算法，通过荧光信号预测生化需氧量。采用国家标准方法对水样进行5天的培养，在此期间每隔8小时测量一次溶解氧含量和三维荧光光谱数据。通过平行因子分析分解并提取三种荧光成分的最大荧光强度。利用随机森林模型建立三种荧光成分的最大荧光与BOD值之间的关系。结果表明，荧光成分与BOD值之间具有良好的相关性。随机森林模型有效地预测了BOD值，拟合优度高（R = 0.878），均方误差低（MSE = 0.28）。虽然该方法没有缩短培养时间，但通过荧光信号的非接触测量实现了BOD的成功预测。这避免了溶解氧测定的复杂操作，提高了检测效率，为分析大量水样和简便水质监测提供了便利的解决方案。
