EARLY DETECTION OF E. COLI AND TOTAL COLIFORM USING AN AUTOMATED, COLORIMETRIC AND FLUOROMETRIC FIBER OPTICS-BASED DEVICE

A system for detecting the presence of E. coli and total coliform in a water sample includes a sample holder that holds smaller, divided volumes of the sample and a testing reagent. A plurality of light sources are disposed above sample holder. The divided sample volumes are are illuminated with first and second light sources emitting light at different wavelengths. A bundle of optical fibers is provided with having an input end located adjacent to the divided sample volumes and is configured to receive light passing through the sample volumes. Light is output from the bundle of optical fibers and is captured with a camera. Image processing software is provided and is configured to calculate a light intensity in first and second wavelength channels at different times and outputs a positive/negative indication for E. coli and total coliform for the water sample.

TECHNICAL FIELD

The technical field generally relates to devices and methods used to detect and quantify the number of bacteria in a water sample. More specifically, the technical field relates to a small, portable device that can automatically detect the presence of bothEscherichia coli(E. coli) and total coliform in drinking water within ˜16 hours, down to a level of one colony-forming unit (CFU) per 100 mL.

This invention was made with government support under Grant Number W911NF-17-1-0161, awarded by the U.S. Army, Army Research Office. The government has certain rights in the invention.

BACKGROUND

The World Economic Forum's “The Global Risks Report 2019” states that water crises have been one of the top 5 global risks in terms of impact, and the top societal risk for the past 5 years. According to the World Health Organization (WHO), 785 million people lack safe drinking water, and at least 2 billion people use water sources contaminated with feces. Lack of access to safe, contaminant-free water, severely threaten public health due to waterborne illnesses. It is estimated that 1 million people die every year due to water, sanitation or hygiene related problems, and every 2 minutes a child dies due to poor quality water. Therefore, effective monitoring of water quality is urgently needed to prevent waterborne diseases, improve public health, and save lives.

Solving these problems is no simple task. Water can contain hundreds of different microorganisms, making the analysis of all possible pathogenic microorganisms very challenging. However, the presence ofE. coliand total coliform in a water sample is widely accepted as evidence for contamination of a water supply. Total coliform bacteria are commonly found in the environment. Therefore, the presence of total coliform in water samples is an indicator of contamination from the surrounding environment. While the coliform bacteria do not necessarily cause disease, their presence can indicate that other pathogens may potentially exist within the water sample. On the other hand,E. coliis a member of the fecal coliform group, which exists in the intestines and feces of human and other warm-blooded animals. Therefore, the presence of fecal coliform, more specificallyE. coli,indicates the presence of disease-causing pathogens. In practice, monitoring onlyE. coliand total coliform contamination is sufficient to analyze water quality for health-related risks. According to the United States Environmental Protection Agency (EPA) in order to determine whether or not a water source is safe for drinking, the sensitivity of the measurement technique must be at least 1 CFU/100 mL.

There are several EPA-approved methods used to monitor water quality which employ conventional microbiological techniques such as multiple tube fermentation and membrane filtration. However, these microbiological methods have some limitations, such as a long total analysis time, interference from non-coliform bacteria, limited detection of slow-growing or stressed coliform, viable but non-culturable (VBNC) bacteria, and requiring transportation to central lab facilities with trained professionals. There are other emerging methods such as immunological assays and polymerase chain reaction (PCR) based methods which in general provide faster detection. However, these methods require relatively complex procedures and trained specialists. Additionally, these methods do not allow on-site water quality monitoring. Instead, water samples need to be transported to central lab facilities, resulting in an additional delay. Water quality can also be monitored by optical, electrochemical, piezoelectric or plasmonic biosensors. However, such biosensor technologies typically lack sensitivity and/or are constrained to very small sample volumes, in addition to requiring complex and expensive benchtop equipment to operate.

One of the EPA-approved methods forE. coliand total coliform detection is Colilert® (IDEXX Laboratories, Inc., Westbrook, Me.). This is one of the most widely used technique, and is an enzymatic method which uses Defined Substrate Technology (DST) to simultaneously detectE. coliand total coliform in drinking water. Within the Colilert® reagent there are two substrates: o-nitrophenyl-β-D-galactopyranoside (ONPG) and 4-methylumbelliferyl-β-D-glucuronide (MUG), which are metabolized by the coliform enzyme β-galactosidase andE. colienzyme β-glucuronidase respectively. When total coliform bacteria are present in the water sample, they use the β-galactosidase to metabolize ONPG, which releases o-nitrophenol and changes the sample from being colorless to yellow.E. coliuse β-glucuronidase to metabolize MUG and release 4-methylumbelliferone (4-MU), which is a fluorescent molecule and emits blue light when excited by ultraviolet (UV) light. This method is sensitive and can be used to detect concentrations as low as 1 CFU/100 mL, and quantification is available using a most probable number (MPN) table or software. However, when used to quantify coliform bacteria concentration in water samples, the Colilert® method has drawbacks as well. Notably, the total process takes 24 to 28 hours, and similar to those listed above, it is not an on-site method (i.e., the samples must be transported to a lab with trained personnel and special equipment). In case of fecal contaminated water sources, it is crucial to detect the presence of the bacteria as early as possible to prevent illness. To achieve this, a sensitive, portable and cost-effective water quality sensor or device which can be operated by non-specialists is urgently needed.

SUMMARY

In one embodiment, a cost-effective and highly sensitive water quality monitoring device (or sensor) is provided which can perform automatic early detection of bothE. coliand total coliform using the Colilert® reagent, mixed with the sample water under test, which is then placed inside a custom-designed 40-well plate to be automatically imaged all in parallel using fiber optic cables. The device, in one specific implantation, weighs 1.66 kg and can automatically detect 1 CFU/100 mL in less than 16 h, which allows the sample to be processed using limited laboratory equipment and without requiring specialized personnel. At higher concentrations ofE. coliand/or total coliform the automated detection time can be further decreased.

In one embodiment, a system for detecting the presence ofE. coliand total coliform in a water sample includes a portable device that contains a sample holder therein configured to hold a plurality of smaller volumes of the sample and a testing reagent therein. These may include vials, tubes, wells, or the like which are optically transparent. A plurality of light sources are disposed above the plurality of smaller volumes of the sample, wherein each of the plurality of smaller volumes of the sample within the sample holder is illuminated with first and second light sources. These may include an array of LEDs with two LEDs (blue and ultraviolet (UV) emitters) situated above each smaller volume of sample. A bundle of optical fibers is provided having an input end and an output end, the input end of the bundle comprising a plurality of optical fibers located adjacent to the plurality of smaller volumes of the sample and configured to receive light passing through the plurality of smaller volumes of the sample from the first and second light sources. A long-pass filter may be interposed between the sample holder and the input end of the bundle of optical fibers. A lens is disposed in the device and configured to receive light emitted from the output end of the bundle of optical fibers. A camera is included in the device/sensor and is configured to capture images of the light passing through the lens from the bundle of optical fibers for the plurality of smaller volumes of the sample. The system includes image processing software configured to calculate a light intensity in first and second channels corresponding to the first and second light sources at different times, wherein the image processing software outputs a positive/negative indication forE. coliand total coliform for the water sample. The change in intensity over time (over successive measurements) is used to make the positive/negative determination. The output may also include the concentration (or concentration range ofE. coliand total coliform). The image processing software may be run using a separate computing device (e.g., laptop, PC, server, tablet, mobile phone, etc.) that executes the image processing software. For example, image files may be transferred or offloaded to this other computing device for processing. Of course, the computing device may also be integrated into the portable device itself in other embodiments. In this embodiment, there would be no need for the transfer or offloading of the images as they would be processed directly by the device/sensor.

In one embodiment, a system for detecting the presence ofE. coliand total coliform in a water sample is disclosed. The system includes a sample holder configured to hold a plurality of smaller volumes of the sample and a testing reagent therein. A plurality of light sources are disposed above the plurality of smaller volumes of the sample, wherein each of the plurality of smaller volumes of the sample within the sample holder are illuminated with first and second light sources of the plurality of light sources emitting light at different wavelengths. The system includes a bundle of optical fibers having an input end and an output end, the input end of the bundle comprising a plurality of optical fibers located adjacent to the plurality of smaller volumes of the sample and configured to receive light passing through the plurality of smaller volumes of the sample from the first and second light sources. A lens is provided and is configured to receive light emitted from the output end of the bundle of optical fibers. The lens focuses the light from the output end of the bundle of optical fibers to a camera configured to capture images of the light passing through the lens from the bundle of optical fibers. The system includes computing device that runs image processing software that configured to calculate a light intensity in a first wavelength channel and a second wavelength channel at different times, wherein the image processing software outputs a positive/negative indication forE. coliand total coliform for the water sample. The image processing software may also output a concentration or concentration range ofE. coliand total coliform for the water sample. The computing system may be integrated as part of the testing device or a separate computing device and image files are transferred from the device to the separate computing device.

In another embodiment, a method for detecting the presence ofE. coliand total coliform in a water sample includes mixing the water sample and a testing reagent; dividing the mixture into a plurality of smaller volumes of the water sample and the testing reagent; loading the plurality of smaller volumes of the water sample and the testing reagent into the sample holder and inserted into the testing device. The testing device then periodically illuminates the plurality of smaller volumes of the water sample and the testing reagent with light from the first light source and capturing images of the light passing through the lens from the bundle of optical fibers associated with the plurality of smaller volumes of the water sample. The testing device also periodically illuminates the plurality of smaller volumes of the water sample and the testing reagent with light from the second light source and capturing images of the light passing through the lens from the bundle of optical fibers associated with the plurality of smaller volumes of the water sample. The images captured at the camera are then processed with image processing software configured to calculate a light intensity in first and second channels at the periodic times, wherein the image processing software outputs a positive/negative indication forE. coliand total coliform for the water sample based on a change in light intensity over time. The image processing software may also output a concentration or concentration range ofE. coliand total coliform for the water sample.

DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENTS

FIGS. 1A-1Cillustrates a device10for detecting the presence ofE. coliand total coliform in a water sample12. While water is the preferred fluid to be tested, other liquid fluids may also be tested in a similar manner.FIG. 1Cillustrates a system100that includes the device10. With reference toFIGS. 1A and 1B, the device10includes a housing14that contains the various components of the device10. The device10is small and portable, weight less than 2 kg in one embodiment. The small portable device10may be located in an incubator110(illustrated inFIG. 1C) or the like during the testing process to maintain the desired temperature and environmental conditions for bacterial growth. The housing14also prevents ambient light from interfering with the imaging operation although the incubator110may also be used to prevent ambient light from interfering with imaging operations as discussed herein.

The device10includes a sample holder16configured to hold a plurality of smaller volumes of the water sample12and a testing reagent therein. That is to say, in one embodiment, a single water sample12is divided into smaller volume samples which are loaded into their own vessels or containers18(e.g., vials, tubes, receptacle, well, or the like). In one embodiment, the individual vessels or containers18are detachable vials that are mounted in a sample holder16that receives the vessels or containers18. For example, the sample holder16may be manufactured using three-dimensional printing with receptacles that receive each vessel or container18. In the illustrated embodiment, the sample holder16can hold forty (40) vessels or containers18containing the water sample to be tested. It should be appreciated, however, that more or fewer vessels or containers18may be used in conjunction with the device10. The sample holder16may be made from an optically transparent material or have apertures or holes that allow for the transmission of light therethrough. The sample holder16with the vessel or containers18(loaded with fluid) contained therein may be inserted into the device10. The vessels or container18may be covered with a UV transmissible film, cover, or cap to seal the liquid contained therein.

A plurality of light sources20(e.g., LEDs, laser diode, or the like) are disposed in the housing14above the array of vessels or containers18contained sample holder16that is loaded into the device. An optional diffuser (not shown) may be used to diffuse the light emitted from the plurality of light sources20. The plurality of light sources20thus are used to illuminate the plurality of smaller volumes of the water sample12. In one embodiment, the plurality of light sources20includes at least one light source20that emits light at a first wavelength and at least one light source20that emits light at a second wavelength. For example, as seen inFIG. 1A, in this embodiment, there are a plurality of light sources20arrayed on a printed circuit board (PCB)22. Driver circuitry (not shown) may also be located in the PCB22. In this embodiment, the plurality of light sources20include a plurality of LEDs that emit ultraviolet (UV) light (i.e., LED20UVinFIG. 1A) and a plurality of LEDs that emit blue light (i.e., LED20BLinFIG. 1A). As seen inFIG. 1A, there are a total of eighty (80) LEDs with forty LEDs being blue LEDs20BLand forty LEDs being UV LEDs20UV. Of course, different numbers of LEDs or light sources may be used. At least one UV light source20UVand at least one blue light source20BLis needed to illuminate the smaller volumes of water sample12. Of course, additional light sources20will more evenly illuminate the array of vessels or containers18. Thus, in one embodiment, each vessel or container18is associated with a single UV light source20UVand a single blue light source20BL.

As explained herein, the UV light source(s)20UVand the blue light source(s)20BLilluminate the plurality of smaller volumes separately. This means that when the UV light source(s)20UVare “ON” the blue light source(s)20BLare “OFF.” Conversely when the blue light source(s)20BLare “ON” the UV light source(s)20UVare “OFF.” Driver circuitry and/or a microcontroller40or other processor may be used to power the plurality of light sources20. The microcontroller40or other processor may also be used to control the timing or sequencing of the UV light source(s)20UVand the blue light source(s)20BL. The microcontroller40or other processor may also control the camera50and acquisition and optional transfer of images90. In some embodiments, the image processing software106may also be executed by the microcontroller40or other processor. As explained herein, images90of transmitted and/or fluorescent light passing through or emitting from the vessels or containers18are captured with a camera50. The captured images90enable the system100to determine the intensity of the measured light at various time intervals which are then used to determine whether the particular vessel or container18is positive (+) or negative (−).

The device10includes a bundle of optical fibers24having an input end and an output end. The input end of the bundle of optical fibers24are located adjacent to the vessels or containers18containing the smaller volumes of the water sample12and are configured to receive light from the respective samples contained in the vessels or containers18in response to illumination from the plurality of light sources20. The light that is received by the input end of the bundle of optical fibers24is either light that is transmitted through the water sample(s)12within the vessels or containers18or fluorescent light that is generated within the water sample(s)12within the vessels or containers18.

In one embodiment, as best seen inFIG. 1B, a plurality of optical fibers from the bundle of optical fibers24are located in a header26that is used to collect light from a single vessel or container18. For example, a header26is illustrated that contains thirteen (13) optical fibers25from the bundle of optical fibers24. The header26may be disposed adjacent to the bottom of the vessel or container18so that light that is transmitted (or generated therein in the case of emitted fluorescence) is captured by these thirteen (13) optical fibers25from the bundle of optical fibers24. The light is then transmitted and captured by the camera50.

For the samples12that fluoresce, the fluorescent light is filtered using, for example, a long-pass filter28that is interposed between the vessels or containers18that contain the smaller volumes of sample12and the input end of the bundle of optical fibers24. The long-pass filter28does not affect the blue light emitted by the blue light source(s)20BL. A lens30is disposed adjacent to the output end of the bundle of optical fibers24and is configured to receive light emitted from the output end of the bundle of optical fibers24and focus/direct the light onto the camera50. The camera50is configured to capture images90of the light passing through the lens30from the bundle of optical fibers24for each of the vessels or containers18containing the plurality of smaller volumes of the sample. The images90may include spots or regions of light intensity corresponding to each fiber25of the bundle of optical fibers24. The individual optical fibers25that make up the bundle of optical fibers24may be mapped to specific pixels within the image90so that intensity values can be associated with a particular vessel or container18. The camera50illustrated in the device10used to generate the experimental results was a Raspberry Pi camera but it should be appreciated that other cameras50may be used. The camera50includes an image sensor52(FIG. 1C) that is used to capture the actual image90. The image sensor52may include a standard CMOS image sensor50known to those skilled in the art.

In a preferred embodiment, the light intensity at the first and second channels (i.e., fluorescence channel and absorption channel) is monitored at periodic intervals (e.g., 15 minutes) and the change in intensity is used to determine the positive/negative indication forE. coliand total coliform. In one embodiment, so long as one of the plurality of smaller volumes contained in the vessel or container18undergoes a change in intensity that meets an established threshold value, the sample may be classified as “positive.” The testing may continue for several hours or longer than a day while the plurality of smaller samples are incubated and periodically imaged.

With reference toFIG. 1C, the system100includes a computing device102that includes one or more processors104therein that are used to execute image processing software106that is configured to calculate a light intensity in the fluorescence and absorption channels. The image processing software106may include MATLAB® (The MathWorks, Inc.) or other commercially known software packages. First, the exact location of the center of each optical fiber of the bundle of optical fibers24is located by using the first image90illuminated with the blue light source(s)20BL. These optical fibers are then associated or mapped with the different vessels or containers18using a pre-set manually labelled groupings (done previously to associate or map particular fibers to a particular vessel or container18). To account for minor shifts, later images90are then registered to the initial image using cross correlation.

After the above pre-processing operations, the respective fiber intensities in each image90are then measured by summing the intensities of all the pixels within a radius of the fiber's center (e.g., 14-pixel radius used herein). Because there may be some crosstalk between different vessels or containers18in the fluorescence channel, the intensity values from the fibers associated with each vessel or container18are reduced according to the intensity of those around them. This is a normalization process di done by multiplying the average intensity of the thirteen (13) optical fibers under each vessel or container18by an empirically determined constant and dividing it by the square of the distance between the vessels or containers18.

The normalized intensities of the fibers25in each vessel or container18are averaged for the final classification. The absorption channel, which is used for the detection of total coliform, is in one embodiment, classified as positive when the intensity drops by 5% over ten (10) successive images90. The fluorescence channel is classified using a manually-chosen threshold, where the vessel or container18is marked as positive if the intensity increases more than 20% of the value after the first 75 min, indicating thatE. coliis present in the sample12. In both cases, the first five (5) time points (i.e., the first hour after loading the sample inside the incubator110) are ignored as there are significant fluctuations in the signal intensities due to the liquid in the vessels or containers18slowly warming to the temperature of the incubator110.

As seen inFIG. 1C, the image processing software106outputs a positive (+)/negative (−) indication forE. coliand total coliform for the water sample12. The output may also include the concentration (or concentration range ofE. coliand total coliform). This may be based on the number of vessels or containers18that are positive at a particular point in time. See Eq. 2 herein. In one embodiment, the sample12is presumed to be negative until a single vessel or container18is established to be positive (+) forE. coliand/or total coliform. For example, forE. colithe particular test may deem the entire sample12positive when only a single vessel or container18is deemed positive. Alternatively, the entire sample12may be deemed positive when a plurality of vessels or containers18is deemed positive. The cutoff for a positive or negative sample may depend on the number of single vessel or container18as well as regulatory or legal cut-offs for positive or negative samples which may vary with different jurisdictions. In some embodiments, the computing device102may be integrated into the device10. For example, the software may be run using the microcontroller40or other on-board processors in some embodiments. Alternatively, the images90may be transferred or offloaded to a local or remote computer that contains the image processing software106. The device is powered by a power source108(FIG. 1C) such as a battery that is used to power the plurality of light sources20, camera50, and microcontroller40or other electronics. The device10may be provided with its own incubator110or, alternatively, existing laboratory incubators110may be used with the device10being inserted therein.

Experimental

Materials

Culture Based Assays and Sample Preparation

Pure cultures ofE. coli(ATCC 25922) andE. aerogenes(ATCC 49701) were grown on Tryptic Soy Agar (TSA) for 24 h in a microbiological incubator at 37° C. and 35° C., respectively.C. freundii(ATCC 43864) was cultured on Nutrient Agar (NA) at 37° C. in an incubator. TSA and NA plates were prepared according to the manufacturer's specifications. Following this, 20 mL was poured into each 100 mm diameter plate and used or stored at 4° C. until use. The agar plates are used to culture the bacteria and to perform quantitative measurements of the bacteria concentration for comparison to the presented method. This plate count, in which bacteria were grown on agar plates and counted, was used as the “gold standard” for determining the concentration added to the device during testing. Bacteria from an overnight culture were resuspended in 1 mL sterile reagent grade water and serially diluted (10-fold) as required and 100 μl of bacteria contaminated water (BCW) sample was added to each agar plate (n=5). Samples were spread onto the agar surface with sterile glass beads using the Copacabana method and the plates were incubated for 24 h. After overnight incubation, individual colonies of bacteria on the agar plates were counted and averaged (seeFIGS. 6A-6C). In addition to the bacteria samples, negative control experiments were performed using the same procedures without any addition of bacteria (seeFIGS. 7A-7D).

To prepare the samples12used to validate the performance of the device10, 100 μL of the same BCW sample described above was added to 100 mL of sterile reagent grade water and mixed with the Colilert® reagent until dissolved. 2.5 mL of the contaminated water sample12was then added to each sterile glass vial18within the 40-well plate16. The filled vials18were then sealed with sterile, non-fluorescent, UV-transmitting sealing film, put into the device10and incubated for 24 h at 35° C. inside the incubator110. Following the incubation, the concentration of bacteria which was determined with the plate count (n=5) was compared with the automated counting results of the device10(seeFIGS. 6A-6C). Negative control experiments were performed using the same procedure without bacteria added to the initial sample.

To test the performance of the device10at higher concentrations of bacteria, a modified version of the above test was used. The sample preparation steps for these tests can be visualized inFIGS. 8A-8C. For these tests, eight differentE. coliconcentrations were prepared with 10-fold serial dilution between each. The Colilert® reagent was added to 100 mL sterile reagent grade water and 900 μl of this sample was in turn added to each vial18of the 40-well plate16. 100 μl of eachE. coliconcentration was added to five (5) vials18, and similar to the procedures outlined above, the 40-well plates16were sealed and incubated for 24 h at 35° C. To quantitatively determine the precise bacteria concentrations used for these tests, the plate count method (n=3) was applied following the procedure described herein. Since higher concentrations are too numerous to count (TNTC), the plate count method is applied to only the four lowest concentrations.

Device Design

To use the device10a water sample12of interest is split evenly into forty (40) disposable glass vials18which are held by a custom 3D-printed 40-well plate sample holder16. There are two LEDs20(one UV, one blue) above each one of these wells/vials18which illuminate the sample12, and thirteen (13) optical fibers25below each well/vial18collect the sample's signal. The blue LEDs20BLare used to detect the presence of total coliform, with the image sensor of the camera50indirectly measuring the absorption of the transmitted light. The UV LEDs20UVare used to detect the presence ofE. coliby exciting fluorophores in the sample12. Therefore, when fluorescence is detected in a vial18, it is classified as containingE. coli. The optical fibers25in the bundle of optical fibers24are used to map the light passing through the forty (40) wells/vials18onto the camera50, without the use of any mechanical scanning.

The device design is shown inFIGS. 1A, 1Band the system100inFIG. 1C. The device10uses a 3D-printed structure14to hold the components together, and the entire device is placed within an incubator110to ensure a constant temperature of 35° C. (other temperatures may be used). A Raspberry Pi microcontroller40controls the illumination and a Raspberry Pi camera50is used to periodically detect the light from all the forty (40) wells18. A total of 520 fibers25are used, with thirteen (13) collecting light from a given well/vial18. A plano-convex lens30is used below the output of the bundle of optical fibers24to help focus the light on the image sensor52of the camera50.

The blue LEDs20BLare used to detect the colorimetric/absorption signal indicating the presence of total coliform and they operate at a peak wavelength of 400 nm. This gives a strong overlap with o-nitrophenol's absorption spectrum, which is centered at 420 nm. The UV LEDs20UVused to detect the fluorometric channel operate at a peak wavelength of 365 nm, and are used to excite the 4-MU fluorophores. To eliminate the need for expensive and bulky UV excitation filters, a UV LED20UVwith minimal emission above 400 nm was chosen, which allows the light to be blocked solely by an emission filter28. Between the LEDs20BL,20UVand the glass vials18there is a UV-transmitting glass diffuser (not shown), which is used to make the illumination more uniform and reduce the effects of any small movement of the device10. The LEDs20are powered by constant current drivers, which output a current of 20 mA. All of the LEDs20are surface mounted to a custom printed circuit board (PCB)22. To allow for flexibility, the device can either be powered by a rechargeable battery or plugged into a standard outlet.

A 3 mm thick UF-5 Plexiglas sheet is used as a long-pass filter28, which blocks light below 400 nm, and filters out the light produced by the UV LEDs20UV. This Plexiglas sheet is an ideal UV filter28for this application as it completely blocks the wavelengths desired, does not produce auto-fluorescence, and unlike custom-designed filters, is very cost-effective. This cost-effectiveness is particularly useful as the filter needs to be large (165×110 mm) to cover all the fibers25.

Once the device10has been loaded with the 40 vials18, the Raspberry Pi microcontroller40begins to activate the LEDs20and takes an image90of the fibers25using one wavelength at a time. When the images90have been taken, the device10waits for 15 minutes with the LEDs20off before taking another image90, which are all saved as raw ‘.mat’ files for processing (other formats may be used). The images90using UV excitation have an exposure time of 30 ms while the blue excitation images90use an exposure time of 2 ms.

Image Processing

The raw images90were processed using MATLAB (The MathWorks, Inc., release R2018a). First, the exact location of the center of each fiber25is determined using the first image illuminated by the blue LEDs20BL. These fiber locations are then associated with the different wells18using pre-set manually labeled groupings. To account for minor shifts of the setup, subsequent images90are then registered to the initial image90using cross correlation.

Following these pre-processing steps, the fiber intensities in each image90are measured by summing up the intensities of all the pixels within a 14-pixel radius of the fiber's25center. As there is some crosstalk between the different wells18in the fluorescence channel, the intensity values for the fibers15in each well18are reduced according to the intensity of those around them. This normalization is done by multiplying the average intensity of the thirteen (13) fibers25under each well or vial18by an empirically determined constant and then dividing it by the square of the distance between the wells18.

The normalized intensities of the fibers25in each well18are averaged for the final classification. The absorption channel, which is used for the detection of total coliform, is classified as positive when the intensity drops by 5% over 10 successive images90. The fluorescence channel is classified using a manually-chosen threshold, where the well18is marked as positive if the intensity increases more than 20% of the value after the first 75 min, indicating thatE. coliis present in the sample12. In both cases, the first five (5) time points (i.e., the first hour after loading the sample12inside the incubator110) are ignored as there are significant fluctuations in the signal intensities due to the liquid in the vials18slowly warming to the temperature of the incubator110. This causes condensation to form on the sealing film covering the wells18over the course of the first hour. The classification threshold for the fluorescence channel was set to be higher than the colorimetric channel as the crosstalk between wells18cannot be completely eliminated in the fluorescence detection channel. A visualization of the intensity for the fibers25in each well can be seen inFIG. 6C.

The detection device10was validated usingE. coliand two different types of total coliform bacteria. Using the procedures described herein, 51 tests were performed usingE. colisamples, 27 usingE. aerogenessamples, and 19 usingC. freundiisamples as well as 3 negative samples, in which no bacteria was added, to determine the device10performance, sensitivity and limit of detection.FIGS. 2A-2Cprovides a comparison of the counting efficiency of the device10against the gold standard plate count method. In this plot, the number of wells18that turned out to be positive is compared with the average of five (5) plate counts. Plate count measurements are required to quantify the concentration of bacteria in the sample tested by the device10and constitute the ground truth measurements. The FIGS. also shows the statistically expected number of wells18that should be positive for a given plate count measurement. The horizontal error bars for these values are calculated by finding the standard deviation of the plate count (n=5), while the vertical error bars are calculated using the 95% confidence interval of a Monte Carlo simulation, based on the experimental measurement. This simulation was performed by taking the plate count, adding or subtracting a random number of bacteria corresponding to a normal distribution using the measured standard deviation, and finally randomly placing the bacteria into the wells18of the 40-well plate.

InFIGS. 2A-2C, the line of best fit is calculated as:

where N=40 is the number of wells per plate, P is the ground truth bacteria number in the sample (which is the plate count in this case) and α is the constant being fitted for, which represents the efficiency of the device at measuring bacteria concentration compared to the ground truth. When α is equal to one, Eq. (1) gives the theoretical number of positive wells18containing bacteria for a given P. ForE. colidetection experiments, α was found to be 1.154 (95% confidence between 1.064 and 1.244), which is close to the theoretical detection efficiency. The two total coliform tests had larger deviations from the plate count method, withE. aerogeneshaving an α of 0.602 (95% confidence between 0.46 and 0.745), andC. freundiihaving an α of 1.707 (95% confidence between 1.126 and 2.288). These measurements show that there is no consistent trend for either over counting or under counting the bacteria.

The lower α value ofE. aerogenesexperiments is likely due to a portion of the bacteria being stressed or injured, and not multiplying efficiently for the test to report positive within the 24-hour window. It is important to note that for the undercountedE. aerogenesmeasurements, the device10was able to obtain better results than a standard visual count of the positive wells since the automated detection is more sensitive than the human eye. One example of this can be seen inFIGS. 4C and 4D. For all other bacteria measurements, the visual positive well counts and the device counts were the same.

The overcounting of theC. freundiisamples compared to the plate counts, with an α of 1.707, can potentially be due to the bacteria being in the viable but non-culturable (VBNC) state. Bacteria can enter the VBNC state for reasons such as environmental stress. When this happens, they can preserve some metabolic activity (detected by the device10) but lose their ability to grow on an agar plate. Therefore, the concentration of bacteria detected by enzyme-based methods, such as the presented device10, can be higher than the concentration detected using culture-based methods.

If the number of bacteria detected by the device needs to be quantified, Eq. 1 can be rearranged to provide a probabilistic estimation:

where W is the number of positive wells counted by the device. At low concentrations the number of bacteria tested by the sample can be accurately estimated using Eq. (2). However, at higher concentrations as the probability of multiple bacteria to end up at the same well increases, Eq. (2) will undercount bacteria.

FIGS. 3A-3Cdemonstrate one of the important benefits of the presented device10: it can automatically detect bacteria several hours faster than the standard Colilert® method. While the exact detection time depends on several factors,FIGS. 3A-3Cshows that the device10is capable of bacteria detection in <16 hours, i.e., 8 hours faster than manual inspection. This is even the case at lower bacteria concentrations: for water samples12with a concentration of ≤5 CFU/100 mL, the device10was able to detect the presence of bacteria on average within 15.0 and 15.1 hours of the start of the incubation period forE. coliandC. freundii,respectively. The exception to this trend is a portion of theE. aerogenessamples, which, as discussed earlier, take significantly longer to be detected, e.g., 21.4 hours after the start of the incubation for the same concentration range. It should be noted that for theseE. aerogenessamples12it also took significantly longer than 24 hours to exhibit color change or fluorescence signal using the standard Colilert® method, which indicates potentially damaged or stressed bacteria. See for exampleFIGS. 4A-4D, which reports that the visual signal change, indicative of the presence of the bacteria, occurred only after 28 hours for some of the vials in thisE. aerogenessample, whereas for the same vials the device10detected the presence of the bacteria several hours earlier compared to the visual inspection.

This significant decrease in the detection time when compared to the traditional Colilert® method is a result of two factors. First, since the device10is completely automated, it performs measurements at regular intervals rather than waiting for the full 24 hours. Additionally, since it performs comparative quantitative analysis, the device10can make more sensitive measurements than is possible with a manual end-point qualitative measurement using the Colilert® method.FIGS. 4C and 4Dshow a demonstration of this increased sensitivity, showing that the device10can detect all of the five (5) positive wells18under 24 hours while a visual inspection at 24 hours is unable to detect any color changes that indicate the growth of theE. aerogenes.Only after the same samples12have been incubated for an additional four hours, visual inspection was then able to determine that all of these wells18are indeed positive.

FIGS. 5A-5Calso shows that as the bacteria concentration increases, the detection time further decreases as a larger number of bacteria can interact with the enzymes at an earlier time point. This point is illustrated inFIG. 5A, which reports the reduction in the detection time as the concentration of bacteria is raised to higher levels. Using the average of five (5) measured wells18at each concentration, the detection time is found to get ˜0.66 hours less for each order of magnitude increase in the concentration of bacteria over the range of 1 to 1.2×107CFU/mL. For example, at a concentration of 1 CFU/mL, the device10takes an average of 13.7 hours to automatically detect the presence of the bacteria, while at a concentration of 1.2×107CFU/mL it only takes 2.8 hours.FIGS. 5B and 5Cdemonstrate how the intensity measured by the device10changes over time for the fluorescence and absorption channels of the device, respectively.

As the device10is designed to test drinking water, all the testing has been performed using non-turbid water. EPA regulations require the turbidity in drinking water to be below 1 nephelometric turbidity unit (NTU) when direct or conventional filtration is used, and below 5 NTU otherwise. At a turbidity level of 5 NTU, less than 7% of the light passing through the sample will be absorbed. Since the device10makes a determination of bacterial growth according to the relative changes in the signal intensity (as a function of time), any absorption from these low levels of turbidity that drinking water might exhibit will therefore not impact the operation of the device10.

The device10including the housing14and may be 3D-printed that, in one embodiment, is controlled by a Raspberry Pi microcontroller40which can perform automated detection of bothE. coliand total coliform in a100mL water sample12using the Colilert® reagent. The device10is more sensitive than a manual count, and that, because of this increased sensitivity, it can automatically detect the presence of bacteria faster than it is possible through a manual measurement. The device10is able to perform these measurements according to the EPA standard, which requires testing of 100 mL of water sample, and it can identify a single organism in this 100 mL sample (i.e., 1 CFU/100 mL). Additionally, it is capable of performing this automated detection within less than 16 hours. This time can be further reduced as the bacteria concentration is increased.

The automatic classification of the wells18as positive (+) or negative (−) eliminates the need for a trained operator as well as the risk of a counting error. Additionally, since no specialized skills are required for its operation, it can be used with minimal training. This simplicity also allows the sample preparation to be performed in only a few minutes, while still being effective, giving a definitive result in less than 24 hours.

By using a Raspberry Pi microcontroller40and camera50with a CMOS image sensor52to perform the detection, optical fibers25to collect the light, Plexiglas as an inexpensive fluorescence emission filter28, and UV LEDs20UVfor illumination (without the need for an excitation filter), the blue LEDs20BL, the device10is rather cost-effective, with its parts costing ˜$600 under low volume manufacturing, which can be significantly reduced with economies of scale. Therefore, it is applicable in a variety of settings, particularly in areas where access to a central lab or transportation of the sample are not feasible. In the future, the device10can be modified to use a custom incubator110, which would allow the device to be field-portable and even more cost-effective to use. For example, the system100may be sold with the device10and incubator110together. Reagent(s) may also be provided as part of a kit.

While embodiments of the present invention have been shown and described, various modifications may be made without departing from the scope of the present invention. For example, while the testing reagent comprises a first substrate (o-nitrophenyl-β-D-galactopyranoside (ONPG)) and a second substrate (4-methylumbelliferyl-β-D-glucuronide (MUG)) it should be appreciated that other reagent(s) may be used to test for bacteria. The invention, therefore, should not be limited, except to the following claims, and their equivalents.