This study seeks to solve the lack of low-cost, accurate water quality measuring tools by creating an inexpensive spectrophotometer utilizing a smartphone for the measurement of various water contaminants. The device consists of a mount, LEDs, and a smartphone, and uses advanced software tools, including machine learning, for data processing, analysis, and contaminant concentration prediction. The machine learning models were trained on 2,282 prepared test samples and 15 gathered field samples from eight sources, which were separately tested using standard techniques. The overall device and analysis software was then evaluated using eight separate testing samples from eight different locations, and was used to conduct a proof-of-concept mini-study where samples taken at various distances from a drainage pipe were analyzed. Overall, the predictive system reached an accuracy of 83.3%, cost $29.14, and was able to be completed in under three minutes by each of three volunteers. The main conclusion of this work is that the system developed is cheap, effective, and easy to use, providing a template for future mass production and better testing of water quality worldwide.
Keywords: Water Quality Analysis, Spectrophotometry, Machine Learning, SVM algorithm, Multivariate Multiple Regression
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