Aguilar, Ross; Graves, Spencer; Corcovilos, Theodore A.Department of Physics, Duquesne University; Pittsburgh Quantum Institute (pqi.org)

INTRODUCTION

We thank Duquesne University for financial and material support, particularlythe Bayer School of Natural and Environmental Science, the Office of the Provost, andthe Undergraduate Research Program. We also thank Dr. David Kahler for being apartner in the research conducted. Ross Aguilar was funded by the DuquesneDepartment of Physics.

ACKNOWLEDGEMENTS

CONTACT INFORMATION

Ted Corcovilos, Department of Physics, Duquesne University, 600 Forbes Ave. 317 Fisher Hall,Pittsburgh, PA 15282. Email: corcovilost@duq.edu Ph: (412) 396-5973

RESULTS

CONCLUSION

Our work sought to determine a method via software development to compute the

concentration of a given contaminant in water based on the optical intensity of the color

of a sample of water. This methodology can be applied to the Chemistry and

Environmental Science fields to simplify their work to analyze water in macro and

microecosystems. Red food dye was used to mimic a color-change test on

contaminated water and varying concentrations of dye were analyzed. The samples

were photographed with a mobile device through a RAW photo application to preserve

all color data. Software was developed in Python to determine the color of the water

based on an RGB scale and the data was fit along a variation of Beer’s Law. Blind tests

of unknown samples were then used to verify our method and the model we used. This

method will allow for the future development of a mobile application to simplify this

testing procedure and make it accessible to the general public.

BACKGROUND

Water is one of the most invaluable resources on the planet. Many of us in

developed countries take clean water for granted while many people and communities

around the world struggle with the purity of their water. Water contaminants and

impurities are commonplace but detection methods often are not. One goal that we

have in this work is to be able to provide a simple, inexpensive, and accessible way for

people and communities to conduct their own water testing. The most common use for

water testing is for the determination of the safety of drinking water. Clean drinking

water is pivotal to the sustainability of a community. However, the price point for a

thorough drinking water test kit renders this utility unattainable. (Fig 1a.) Our method

allows water to be tested for many different contaminants because it utilizes the

chromatic properties of a color-change water test. This project was conducted as one

part of a twofold project that searched for the best method of determining concentration

from a color-change test. The other project, conducted by Spencer Graves, is using a

homemade Arduino device that measures six color values.

Methods

For the red trials (Figure 2(a)) the cuvette was placed 10 cm away from the

camera and 10 cm away from the screen, and the green trials (Figure 2(b)), the cuvette

was placed 19 cm away from the camera and 15 cm away from the screen. As shown in

the Figures below, the background was a computer monitor displaying a plain white

screen and set to maximum brightness. The monitor served as the white background as

well as the light source for the test, as ambient light from the lab would add noise. The

green cuvettes were placed farther from the screen to eliminate any visible pixels that

would add noise to the data. As seen in Figures 2 and 3, each cuvette was placed on a

platform 2 cm tall (e.g. A lens case or petri dish) in order for the bottom of the cuvette to

be in alignment with the camera lens.

FUTURE WORK

REFERENCES

This research has the potential to be incredibly far reaching and beneficial to

numerous other fields and studies. Further development of this work would include the

development of a mobile app that would allow photos of water samples to be taken on

site and analyzed in real time. The application would be set up as a simple, easy to

use interface that would request the water test that is being analyzed, a photo of

untested water, and a photo of the water with the color-change test completed. The

software would then analyze the photos and return not only the concentration of the

contaminant with uncertainty but also with a recommendation as to how to remedy the

contamination. This would allow water testing to be done on-site with no special

equipment and make it accessible to the general public. This would also help to

educate the user about the importance of water purity and how to keep themselves

and their loved ones safe from contaminated water.

1. http://www.lamotte.com/en/drinking-water/combo-outfits/4783-03.html2. http://www.lamotte.com/en/pool-spa/kits-reagents/fas-dpd/7022-01.html3. http://life.nthu.edu.tw/~labcjw/BioPhyChem/Spectroscopy/beerslaw.htm

Photo Testing

Figure 2: (a) A cuvette of concentration 0.5 drop. Red dye was used for its opacity. (b) A cuvette of concentration 0.5 drop. Green was also used for its opacity.

Calibration

Motivation

Objectives

The main objective of our work was to simplify the water testing process and

make it more affordable, more versatile, and more accessible to those who would need

to use it. Specifically, investigating the utility of a smartphone camera is one way that

this methodology could become more accessible. Many people in the world own a

smartphone and the development of an easy to use mobile application would put water

testing into the hands of each of those smartphone users. This project was inspired, in

part, by previous work done that used a similar device to measure the lead

concentration in water via a similar method. The key variation is that we wanted this

methodology to be applicable to a wider variety of color-change water tests besides

lead. Certain contaminants that can be found in naturally occurring swimming waters

are not present in drinking water. As seen in Figure 1(b), a swimming water testing kit

can be just as inaccessible and expensive as a drinking water testing kit.

The end goal of this Smartphone camera approach is to be able to use the

hardware of a common Smartphone and its processing capabilities to determine the

concentration of certain chemical or contaminant in a sample of water. To do this, we

must do a controlled study, testing multiple known concentrations of the contaminant. It

is critical that we use a RAW image format for the photos, as regular image formats (eg

JPEG, PNG) will compress images and, in doing so, omit valuable color data. From this

known data, an equation can be generated from written code that will model the

relationship between the color intensity and the concentration. Once this equation is

determined, we will be able to verify its legitimacy by conducting “Blind Tests” using

“unknown” concentrations and verifying that the fit holds true. If so, the fit will be able to

determine the concentration of any colored sample given only its color. Finally, the

Smartphone camera method will be compared to the Arduino device method to

determine which methodology is more accurate.

Figure 1: (a) An example of a drinking water test kit. (Ref. 1) (b) An example of a pool water test kit.

Both kits’ retail prices are greater than $100 USD. For each trial, a photo of the cuvette was taken through a RAW photo application

then was diluted by 50%. To do so, 2cc of the dyed water was removed and discarded

and 2cc of clear water was added. This was repeated until no visible color remained in

the sample—approximately 12 trials. Data was taken as RGB values through the use of

Python code that would take the RAW image and then extract the RGB values. This was

done by selecting a grid of pixels within the cuvette, as seen in Figure 4(a), and a grid of

pixels in the background screen, as seen in Figure 4(b). The program then averaged the

RGB values from the cuvette and the background and calculated the transmittance

values and an estimated error value, denoted as sigma (𝜎). The data that was collected

was fitted to a variation of the Beer-Lambert law in order to establish parameters of the

fit. The Beer-Lambert law illustrates that there exists a linear relationship between the

absorbance and concentration of a species (Ref. 3). These parameters would

standardize the function and allow us to compute a concentration from the RGB values in

any image.

Figure 3: (a) A side view of the cuvette photo setup. (b) An above view of the cuvette photo set up

Screen

Cuvette

Platform

Figure 4: An example of the grid of pixels inside the sample and on the background. These grids were used to determine the

Calibration (continued)

The fitting parameters for the equation were established. While the Beer-Lambert

Law of optics was the basis of our experiment, it was not general enough to

accommodate the flexibility we wished to incorporate into our methodology. As illustrated

in Figure 5(b), the data was fit to an exponential function: T = ⅇ𝑘0+𝑘1𝐶+𝑘2𝐶2. Using this

equation, along with the fitting parameters ko, k1, and k2, we can determine an unknown

concentration when the program computes transmission (T) values. In order to test the

validity of the equation, a series of “Blind Tests” were conducted in which five different

samples were made with varying concentrations of green dye. These samples were

photographed and analyzed using the Python program and transmission values were

given for red, green, and blue. The concentration of each trial was then calculated using

a minimized Chi-Square calculation based on the fit equation and the parameters

determined previously. The values calculated were then tested and compared to

measurements taken by the method developed by Spencer Graves. Figure 5 shows the

comparison between the actual concentration values and the values calculated by the

two methods.

Figure 5: (a) The comparison between the true concentrations and the two calculated

concentrations. (b) A plot of the calibration fit curves produced by the Python code.

The results of the experiments were as expected. The intensity of a color and the

concentration of a substance in a sample of water are inversely proportional on a

decaying model. Despite its technological simplicity compared to the Arduino photo

sensor, the Smartphone camera was able to collect the data required to support the

hypothesis. Improvements must be made insofar as to account for instances where a

dark room and a screen would not be used in the photo process. This is the limiting

factor that will need to be removed for the overall success of a future mobile device

application that is practical and accurate. As of now, the comparison graph shows that

both methods are effective and accurate in measuring the color of a sample of water.