Snapshots Analyses for Turbidity Measurements in Water
Turbidity has been used as an effective indicator of water quality and it is regulated by national control agencies as a water potability parameter. This work proposes a simple, low-cost, and easily reproducible method based on digitally processed snapshots of electromagnetic radiation beam through a scattering medium in order to measure turbidity. The formazin polymer was used as the standard reference in the experiments to prepare samples from 0.1 to 100 NTU for high turbidity range and from 0.02 to 10 FNU for low turbidity range. The device design is comprised of a webcam and an LED/laser as a light sensor and a radiation source, respectively. The captured scattered light snapshot can be decomposed digitally in color components values and correlated with the turbidity parameter. It is possible to obtain differently device performances changing the light sensor device configuration. The linear regression analyses have shown a distinct relationship among red, green, blue, and grayscale components and the turbidity. At high range, the green component present the best LOD and LOQ values 0.64 and 2.10, respectively, operating with an ordinary webcam and white LED. Nevertheless, the best device performances were obtained with dedicated Raspberry Pi camera modules and white LED for low range turbidity reaching LOD=0.027 and LOQ=0.087 FNU. The figures of merit show recovery between (97.50−101.95%), repeatability (2.11%), within-laboratory reproducibility (2.25%), and limits of quantification (0.087 FNU). The achieved performance device shows the effectiveness of digitally processed snapshots obtained with digital cameras for turbidity measurements.
KeywordsTurbidity analysis Image processing Formazin polymer CCD
We would like to thank Fundação Araucária for the undergraduate scientific research scholarship. We thank Juciane Wunsch and Matheus H. Lazzarin for their manuscript reading.
This study is financially supported by FUNASA under Grant 25100.011.260/2014-17.
- AOAC. (2017). Appendix f: guidelines for standard method performance requirements.Google Scholar
- APHA. (2005). Standard methods for the examination of water and wastewater, 21st edn. Washington: American Public Health Association.Google Scholar
- Dubal, P., Bhatt, S., Joglekar, C., Patii, S. (2017). Skin cancer detection and classification. In 2017 6th international conference on electrical engineering and informatics (ICEEI), pp. 1–6. https://doi.org/10.1109/ICEEI.2017.8312419.
- EPA, U. (1993). Method 180.1-determination of turbidity by nephelometry (revision 2.9).Google Scholar
- Gonzalez, R.C., & Woods, R.E. (2008). Digital image processing, 3rd edn. Upper Saddle River: Pearson Prentice Hall.Google Scholar
- Hargesheimer, E. (2002). Online monitoring for drinking water utilities. Denver: AWWA Research Foundation and American Water Works Association.Google Scholar
- Mangold, K., Shaw, J.A., Vollmer, M. (2013). The physics of near-infrared photography. European Journal of Physics, 34(6), S51. http://stacks.iop.org/0143-0807/34/i=6/a=S51.
- Peña, E.A., & Slate, E.H. (2015). Gvlma: global validation of linear models assumptions. https://CRAN.R-project.org/package=gvlma.
- Ribani, M., Bottoli, C.B.G., Collins, C.H., Jardim, I.C.S.F., Melo, L.F.C., et al. (2004). Validação em métodos cromatográficos e eletroforéticos. Química nova.Google Scholar
- William, W., Basaza-Ejiri, A.H., Obungoloch, J., Ware, A. (2018). A review of applications of image analysis and machine learning techniques in automated diagnosis and classification of cervical cancer from pap-smear images. In 2018 IST-Africa Week Conference (IST-Africa), pp. 1–11.Google Scholar