Total Organic Carbon Detection in Water Based on Attention Mechanism CNN

With the increasing demand for dynamic control of sewage quality, developing a fast and accurate detection method that can continuously monitor total organic carbon (TOC) in water has become a crucial issue. However, traditional methods for TOC detection require a high consumption of time and resources. To overcome these drawbacks, in this paper, a rapid and effective TOC detection method is developed based on near-infrared spectroscopy (NIRS) and an improved convolutional neural network (CNN). Firstly, the spectrum of the water samples is collected using a portable near-infrared spectrometer. Then a Convolutional Block Attention Module (CBAM) is integrated with one-dimensional CNN to effectively extract key characteristics of pollutants and achieve accurate quantitative analysis. The feature visualization results demonstrate the effectiveness of the feature extraction. And comparison experimental results shown that, the proposed method model has the greatest coefficient of determination and the smallest root mean square error compared with other models. Thus, the proposed method can accurately detect the total organic carbon in water and is suitable for practical applications.