Analysis on influencing factors of detecting chemical oxygen demand in water by three-dimensional spectroscopy

Kunpeng Zhou, Zhiyang Liu, Menglong Cong, Shanxin Man

Optoelectronics Letters ›› 2023, Vol. 20 ›› Issue (1) : 42-47. DOI: 10.1007/s11801-024-3082-9
Article

Analysis on influencing factors of detecting chemical oxygen demand in water by three-dimensional spectroscopy

Author information +
History +

Abstract

This paper focuses on the standard chemical oxygen demand (COD) liquid and studies the impact of pH, nitrite nitrogen, nitrate nitrogen, heavy metals, salinity, and other factors on fluorescence intensity and fluorescence peak positions during the detection of COD in water using fluorescence spectrometry. The influence mechanisms of different environmental factors on fluorescence spectra are also analyzed. Results indicate that pH value affects the fluorescence emission wavelength (Em), resulting in a red shift from 1.5 to 7.2, and a blue shift from 7.2 to 12.3. Nitrate nitrogen can react with organic matter in water to form nitro compounds, leading to a decrease in fluorescence intensity. Salinity has a negligible effect on T1 peak but a relatively large effect on T2 peak. Heavy metal ion concentration has a significant impact on T2 peak, while T1 peak position shifts with an increase in heavy metal ions. This study aims to explore the factors that can impact the detection of COD in water using three-dimensional fluorescence spectrometry, providing references to improve accuracy and practicability for COD detection based on three-dimensional fluorescence spectrometry.

Cite this article

Download citation ▾
Kunpeng Zhou, Zhiyang Liu, Menglong Cong, Shanxin Man. Analysis on influencing factors of detecting chemical oxygen demand in water by three-dimensional spectroscopy. Optoelectronics Letters, 2023, 20(1): 42‒47 https://doi.org/10.1007/s11801-024-3082-9

References

[[1]]
Dong X X, Yang F W, Yu H, et al.. Study on rapid nondestructive detection of pork lean freshness based on Raman spectroscopy. Spectroscopy and spectral analysis, 2023, 43(2): 484-488 [J]
[[2]]
Yan W H, Yang X Y, Geng X, et al.. Rapid identification of fish products using handheld laser induced breakdown spectroscopy combined with random forest. Spectroscopy and spectral analysis, 2022, 42(12): 3714-3718 [J]
[[3]]
Hu G T, Shang H W, Tan R H, et al.. Research on model transfer method of organic matter content estimation of different soils using VNIR spectroscopy. Spectroscopy and spectral analysis, 2022, 42(10): 3148-3154 [J]
[[4]]
Li F S, Zeng X L. Quantitative analysis method of soil elements combining sensitivity dimensionality reduction and support vector regression. Laser & optoelectronics progress, 2023, 60(5): 0530002 [J]
[[5]]
Cheng Z, Zhao N J, Yin G F, et al.. Identification of algae community discrete three-dimensional fluorescence spectrum based on SWTATLD. Acta optica sinica, 2021, 41(14): 1430001 [J]
[[6]]
Li F X, Tang B, Zhao M F, et al.. Research on correction method of water quality ultraviolet-visible spectrum data based on compressed sensing. Journal of spectroscopy, 2021, 2021: 6650630, J]
CrossRef Google scholar
[[7]]
Li Y, Luo H, Fan X, et al.. Open craniocerebral hematoma imaging based on near-infrared spectroscopy. Laser physics letters, 2022, 19(4): 045601, J]
CrossRef Google scholar
[[8]]
Gao X Y, Zhang Z S Y, Lu C C, et al.. Quantitative analysis of hemoglobin based on SiPLS-SPA wavelength optimization. Spectroscopy and spectral analysis, 2023, 43(1): 50-56 [J]
[[9]]
Nan D N, Dong L Q, Fu W X, et al.. Fast identification of hazardous liquids based on Raman spectroscopy. Spectroscopy and spectral analysis, 2021, 41(6): 1806-1810 [J]
[[10]]
Huo W, Wang J F, Liu Y R. Spectral pattern recognition and traceability analysis of human fingernail based on machine learning. Laser & optoelectronics progress, 2022, 59(18): 1830002 [J]
[[11]]
Dong Q, Wang W, Cao X, et al.. Plasmonic nanostructure characterized by deep-neural-network-assisted spectroscopy. Chinese optics letters, 2023, 21(1): 010004, J]
CrossRef Google scholar
[[12]]
Zheng X F, Li C, Fan X Y, et al.. Influence of temperature and turbidity on Rhodamine B tracer detection and correction. Infrared and laser engineering, 2022, 51(12): 20220243 [J]
[[13]]
Li F X, Tang B, Zhao M F, et al.. Research on correction method of water quality ultraviolet-visible spectrum data based on compressed sensing. Journal of spectroscopy, 2021, 2021: 6650630, J]
CrossRef Google scholar
[[14]]
Zhou K P, Liu Z Y, Cong M L, et al.. Detection of chemical oxygen demand in water based on UV absorption spectroscopy and PSO-LSSVM algorithm. Optoelectronics letters, 2022, 18(4): 251-256, J]
CrossRef Google scholar
[[15]]
Zhou K P, Liu S S, Cui J, et al.. Detection of chemical oxygen demand (COD) of water quality based on fluorescence emission spectra. Spectroscopy and spectral analysis, 2020, 40(4): 1143-1148 [J]

Accesses

Citations

Detail

Sections
Recommended

/