Rapid, Portable, and Low-Cost Water Quality Assessment Device Based on Machine Learning †
1
Bioengineering Graduate Program, University of Puerto Rico, Mayagüez Campus, Mayagüez, PR 00680, USA
2
Department of Mechanical Engineering, University of Puerto Rico, Mayagüez Campus, Mayagüez, PR 00680, USA
*
Author to whom correspondence should be addressed.
†
Presented at the 4th International Electronic Conference on Biosensors, 20–22 May 2024; Available online: https://sciforum.net/event/IECB2024.
Proceedings 2024, 104(1), 6; https://doi.org/10.3390/proceedings2024104006
Published: 28 May 2024
(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)
Abstract
:Water quality has a significant impact on public health. Inadequate water conditions are associated with diseases such as cholera, dysentery (shigella), hepatitis, and typhoid fever. Established techniques like Membrane Filtration (MF), Multiple Tube Fermentation (MTF), and enzyme-based defined substrate technology (DST) assays are used tomonitor bacteriological water quality, measuring indicators like Enterococcus faecalis (E. faecalis), Escherichia coli (E. coli), and total coliforms. Despite their high sensitivity and specificity, these methods take 24 to 48 h to produce results, as well as requiring access to laboratory facilities, specialized equipment, sample preparation steps, and trained personnel. This study presents a portable and low-cost UV-LED/RGB water quality sensor which includes a microfluidic device, a fluorogenic defined substrate assay for the detection of E. faecalis, RGB sensors for fluorescent data acquisition, ultraviolet-light-emitting diode (UV-LED) for sample excitation, a portable incubation system, and embedded systems for data storage and processing. The microfluidic device has a number of independent wells used to carry out Most Probable Number (MPN) analysis for bacteria quantification. The device is pre-loaded with the defined substrate assay and is self-loading when immersed in the target water sample for sample-preparation-free analysis. RGB sensors detect fluorescence from each well to automate the MPN results. Results from fluorescence-versus-time curves are used to generate a comprehensive database. Machine learning (ML) algorithms and real-time RGB data are used to predict whether each individual well will be positive or negative using only the first three hours of fluorescent data. Coupled with MPN, this method significantly reduces the timeframe of bacteria detection and quantification, making it a cost-effective and efficient solution for on-the-go water quality monitoring, addressing critical public health concerns, and underscoring the importance of swift and reliable water quality assessments.
Author Contributions
Conceptualization, P.J.R.-I. and A.S.-R.; methodology, P.J.R.-I. and A.S.-R.; software and analysis, A.S.-R.; resources, P.J.R.-I.; data curation, A.S.-R.; writing—original draft preparation, A.S.-R.; writing—review and editing, P.J.R.-I.; project administration, P.J.R.-I.; funding acquisition, P.J.R.-I. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by University of Puerto Rico Mayagüez Sea Grant College Program Grant number R/90-2-20, and Grant No. G21AP10624-01-State Water Resources Research Institutes, Water Resources Act, Section 104B.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors on request.
Conflicts of Interest
The authors declare no conflicts of interest.
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Saavedra-Ruiz, A.; Resto-Irizarry, P.J. Rapid, Portable, and Low-Cost Water Quality Assessment Device Based on Machine Learning. Proceedings 2024, 104, 6. https://doi.org/10.3390/proceedings2024104006
AMA Style
Saavedra-Ruiz A, Resto-Irizarry PJ. Rapid, Portable, and Low-Cost Water Quality Assessment Device Based on Machine Learning. Proceedings. 2024; 104(1):6. https://doi.org/10.3390/proceedings2024104006
Chicago/Turabian StyleSaavedra-Ruiz, Andrés, and Pedro J. Resto-Irizarry. 2024. "Rapid, Portable, and Low-Cost Water Quality Assessment Device Based on Machine Learning" Proceedings 104, no. 1: 6. https://doi.org/10.3390/proceedings2024104006
