Research on Rapid Detection for TOC in Water Based on UV-VIS Spectroscopy and 1D-SE-Inception Networks
Yu Li,
Weihong Bi,
Yajie Jia,
Bing Wang,
Wa Jin,
Guangwei Fu,
Xinghu Fu
Affiliations
Yu Li
School of Information Science and Engineering, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao 066004, China
Weihong Bi
School of Information Science and Engineering, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao 066004, China
Yajie Jia
School of Information Science and Engineering, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao 066004, China
Bing Wang
Qinhuangdao Hongyan Photoelectric Technology Co., Ltd., Qinhuangdao 066100, China
Wa Jin
School of Information Science and Engineering, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao 066004, China
Guangwei Fu
School of Information Science and Engineering, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao 066004, China
Xinghu Fu
School of Information Science and Engineering, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao 066004, China
In recent years, the rapid monitoring of total organic carbon (TOC) in natural waters has attracted increasing attention. Optical methods are a valid tool for measurement. Nevertheless, how to more accurately establish the mapping relationship between spectroscopy and TOC concentrations is currently a challenge. A new method based on UV-VIS spectroscopy with a deep convolutional network is proposed for the quantification of TOC in water in this paper. The Inception network, originally used to process two-dimensional image data, was redesigned as a model capable of processing one-dimensional spectral data, while the convolution and pooling scale were modified to adapt to one-dimensional data. Simultaneously, squeeze and extraction (SE) blocks were applied to the designed network to enhance feature information and to suppress interference from useless information in the regression process. The method was tested on samples collected from the sea and river estuaries in several provinces in China. When compared to the classical least squares support vector machine (LSSVM), the experimental results showed that the proposed 1D-Inception network structure can provide more accurate regression results. The SE block can significantly improve the feature extraction and expression capabilities of the 1D-Inception network structure and suppress redundant information, thereby achieving better model performance.
