In several parts of India, groundwater is the only reliable, year round source for drinking water. Prevention of fluorosis, a chronic disease resulting from excess intake of fluoride, requires the screening of all groundwater sources for fluoride in endemic areas. In this paper, the authors present a field deployable colorimetric analyzer based on an inexpensive smartphone embedded with digital camera for taking photograph of the colored solution as well as an easy-fit, and compact sample chamber (Akvo Caddisfly). Phones marketed by different smartphone makers were used. Commercially available zirconium xylenol orange reagent was used for determining fluoride concentration. A software program was developed to use with the phone for recording and analyzing the RGB color of the picture. Linear range for fluoride estimation was 0-2mgl(-1). Around 200 samples, which consisted of laboratory prepared as well as field samples collected from different locations in Karnataka, India, were tested with Akvo Caddisfly. The results showed a significant positive correlation between Ion Selective Electrode (ISE) method and Akvo Caddisfly (Phones A, B and C), with correlation coefficient ranging between 0.9952 and 1.000. In addition, there was no significant difference in the mean fluoride content values between ISE and Phone B and C except for Phone A. Thus the smartphone method is economical and suited for groundwater fluoride analysis in the field.
We present a proof of concept for quick screening and alerting of coliform/E. coli contamination in water samples using a device attached to a smartphone. Current methods of coliform detection rely upon relatively expensive laboratory-based time consuming techniques which require trained manpower and take at least 24-48 hours. This waiting time prevents quick action and the consequences can be severe since the contaminated water may already have been consumed by then. Instead an unattended smartphone can continuously monitor the sample and send an alert as soon as contamination is detected. Smartphones, especially older or unused ones, fitted with a customized compact incubator and a sample holder, can be set to take photos of the sample (mixed with a selective growth medium) at regular intervals. An image analysis algorithm would analyze the photos and predict contamination as soon as it notices any increase in turbidity and/or change in color of the sample under observation due to bacterial growth. On detection of contamination, alerts can be immediately sent out to the concerned parties and intervention can be made without any potentially harmful delay. To test this concept we built a prototype for the detection of coliform/E. coli contamination in water samples. With the initial bacterial counts varying from 1-10 to >108 colony forming units (CFU) per 100 ml of water samples, all the results were produced within a turnaround time of 4 to 12 hours and found to be comparable with conventional microbiological methods which require 24-48 hours of incubation.
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