A research team at Hefei Institutes of Physical Science (HFIPS), Chinese Academy of Sciences (CAS), proposed a new strategy for fast and accurate detection of surface water quality by fusing the data of surface water ultraviolet-visible spectra (UV-Vis) and near infrared (NIR) spectra.
This research, published in the Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy recently, aims to enable real-time monitoring of water quality parameters, which is crucial for preventing and controlling surface water pollution.
Chemical oxygen demand (COD), ammonia nitrogen (AN), and total nitrogen (TN) are key indicators used to assess the extent of surface water pollution. Compared to traditional chemical detection methods, UV-Vis and NIR spectra offer rapid, simple, and multi-component analysis techniques that have significant advantages in water quality monitoring.
To enhance the accuracy of spectral methods for water quality detection, the research team developed a detection strategy by fusing UV-Vis and NIR spectral data (UV-Vis-NIR). They collected spectral data and conducted chemical determinations on 70 river samples with varying degrees of pollution. By combining UV-Vis and NIR spectra and employing different variable selection algorithms, they optimized the UV-Vis-NIR fusion models for surface water pollution indicators.
The results demonstrated that the UV-Vis-NIR data fusion strategy significantly improves the spectral prediction accuracy of COD, AN, and TN in surface water compared to using a single spectral technology.
Furthermore, this method exhibited better stability under different optimization conditions, ensuring more robust detection results than those achieved with single spectroscopic techniques.
Their findings provided exciting perspective for future application of spectral online monitoring technology for water quality assessment, according to the team.
By fusing the data of surface water ultraviolet-visible spectra (UV-Vis) and near infrared (NIR) spectra, scientists realized fast and accurate detection of surface water quality. (Image by XU Zhuopin)
