WO2025072013A1 - Technologies de surveillance de la qualité de l'eau avec analyse de composante de données d'image - Google Patents

Technologies de surveillance de la qualité de l'eau avec analyse de composante de données d'image Download PDF

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Publication number
WO2025072013A1
WO2025072013A1 PCT/US2024/047293 US2024047293W WO2025072013A1 WO 2025072013 A1 WO2025072013 A1 WO 2025072013A1 US 2024047293 W US2024047293 W US 2024047293W WO 2025072013 A1 WO2025072013 A1 WO 2025072013A1
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Prior art keywords
fluorescence signal
fluorescence
component
bacterial growth
blue
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English (en)
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Pedro J. RESTO IRIZARRY
Katerin RODRIGUEZ PADILLA
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University of Puerto Rico System
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University of Puerto Rico System
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/18—Water
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N21/6452—Individual samples arranged in a regular 2D-array, e.g. multiwell plates

Definitions

  • the present disclosure relates to devices and methods for water quality analysis. More particularly, the disclosure relates to devices and methods for water quality monitoring using visible light sensing and analysis.
  • Water is one of the most crucial natural resources for living beings. Water is utilized in daily activities such as cooking, bathing, drinking, and recreation.
  • the Environmental Protection Agency (EPA) provides a list of water pollutant testing guidelines in 40 C.F.R. Part 136. Testing procedures include, for example, biological indicators (bacteria, aquatic toxicity), inorganic indicators (metals), organic indicators, pesticides, radiological indicators, and microbiological indicators (bacteria, protozoa).
  • Biosensors are devices capable of transducing a biological or chemical signal into a mechanical or electrical signal capable of being measured, calibrated, and displayed. Developing a biosensor requires knowledge from different fields, such as engineering, physics, biology, and chemistry. Successful examples of biosensors can be found in the fields of medicine, in the military, and in environmental monitoring applications. One of the most important areas for the application of biosensors is in health care because of the increasing relevance for early, rapid, and in-si tu diagnostics.
  • a method comprises loading an aqueous medium into one or more reaction wells of a sample device, wherein each reaction well comprises a reagent to carry out a reaction, and wherein each reaction well is transparent; illuminating the one or more reaction wells of the device with a fluorescence excitation source; capturing, by a computing device, a fluorescence signal from each of the one or more reaction wells with a fluorescence emission sensor, wherein the fluorescence signal is indicative of visible light received from the one or more reaction wells; decomposing, by the computing device, the fluorescence signal into one or more color components; and determining, by the computing device, whether bacterial growth is present based on the one or more color components of the fluorescence signal.
  • the sample device comprises a plurality of reaction wells, an inlet coupled to the reaction wells, and an outlet coupled to the reaction wells.
  • decomposing the fluorescence signal into the one or more color components comprises decomposing the fluorescence signal into a blue component; and determining whether bacterial growth is present comprises determining whether bacterial growth is present based on the blue component.
  • determining whether bacterial growth is present based on the blue component comprises determining a change in intensity of the blue component and determining whether the change in intensity exceeds a predetermined threshold.
  • determining whether bacterial growth is present based on the blue component comprises determining a rate of change in intensity of the blue component and determining whether the rate of change in intensity exceeds a predetermined threshold.
  • decomposing the fluorescence signal into the one or more color components comprises decomposing the fluorescence signal into a red component and a blue component; and determining whether bacterial growth is present comprises determining whether a red/blue color inversion exists.
  • determining whether a red/blue color inversion exists comprises determining at an initial time that the red component exceeds the blue component and determining at a subsequent time that the blue component exceeds the red component.
  • determining whether a red/blue color inversion exists comprises determining a normalized red component based on the red component and a total intensity; determining a normalized blue component based on the blue component and a total intensity; and comparing the normalized blue component to the normalized red component.
  • decomposing the fluorescence signal into the one or more color components comprises decomposing the fluorescence signal into a red component, a blue component, and a green component.
  • the method further comprises heating the sample device at a predetermined temperature; wherein capturing the fluorescence signal comprises capturing the fluorescence signal while the sample device is at the predetermined temperature. In some embodiments, the method further comprises continuing to illuminate the one or more reaction wells, capture the fluorescence signal, decompose the fluorescence signal, and determine whether bacterial growth is present periodically while the sample device is at the predetermined temperature over a first time period up to 24 hours.
  • the method further comprises determining, by the computing device, a water quality metric based on determining whether bacterial growth is present.
  • determining the water quality metric comprises determining a number of the one or more reaction wells for which bacterial growth is present; and determining a most probable number (MPN) metric as a function of the number of the reaction wells for which bacterial growth is present.
  • the fluorescence excitation source comprises one or more ultraviolet (UV) light-emitting diodes (LEDs).
  • the fluorescence emission sensor comprises an image sensor.
  • the fluorescence emission sensor comprises one or more RGB color sensors.
  • the aqueous medium comprises lake water, river water, or ocean water.
  • the predetermined temperature is between about 20 °C to about 60 °C.
  • the reaction is a biological reaction, a chemical reaction, a biochemical reaction, or any combination thereof.
  • the reagent comprises a nutrient and optionally an inhibitor.
  • the nutrient comprises o- nitrophenyl-
  • the inhibitor comprises a salt, a sugar, a phenol, or any combination thereof.
  • the present disclosure also provides a system comprising a sample device, a fluorescence excitation source, a fluorescence emission sensor, and a computing device coupled to the fluorescence excitation source and the fluorescence emission sensor.
  • the sample device comprises one or more reaction wells. Each reaction well comprises a reagent to carry out a reaction, and each reaction well is transparent.
  • the computing device is configured to cause the fluorescence excitation source to illuminate the one or more reaction wells of the sample device when an aqueous medium is loaded into the one or more reaction wells, to capture a fluorescence signal from each of the one or more reaction wells with the fluorescence emission sensor, wherein the fluorescence signal is indicative of visible light received from the one or more reaction wells, to decompose the fluorescence signal into one or more color components, and to determine whether bacterial growth is present based on the one or more color components of the fluorescence signal.
  • the sample device comprises a plurality of reaction wells, an inlet coupled to the reaction wells, and an outlet coupled to the reaction wells.
  • to decompose the fluorescence signal into the one or more color components comprises to decompose the fluorescence signal into a blue component; and to determine whether bacterial growth is present comprises to determine whether bacterial growth is present based on the blue component. In some embodiments, to determine whether bacterial growth is present based on the blue component comprises to determine a change in intensity of the blue component and determine whether the change in intensity exceeds a predetermined threshold.
  • to determine whether bacterial growth is present based on the blue component comprises to determine a rate of change in intensity of the blue component and determine whether the rate of change in intensity exceeds a predetermined threshold.
  • to decompose the fluorescence signal into the one or more color components comprises to decompose the fluorescence signal into a red component and a blue component; and to determine whether bacterial growth is present comprises to determine whether a red/blue color inversion exists.
  • to determine whether a red/blue color inversion exists comprises to determine at an initial time that the red component exceeds the blue component and determine at a subsequent time that the blue component exceeds the red component.
  • to determine whether a red/blue color inversion exists comprises to determine a normalized red component based on the red component and a total intensity; determine a normalized blue component based on the blue component and a total intensity; and compare the normalized blue component to the normalized red component.
  • to decompose the fluorescence signal into the one or more color components comprises to decompose the fluorescence signal into a red component, a blue component, and a green component.
  • the system further comprises a microcontroller coupled to a heater.
  • the microcontroller is configured to cause the heater to heat the sample device at a predetermined temperature.
  • To capture the fluorescence signal comprises to capture the fluorescence signal while the sample device is at the predetermined temperature.
  • the computing device is further to continue to cause the fluorescence excitation source to illuminate the one or more reaction wells, capture the fluorescence signal, decompose the fluorescence signal, and determine whether bacterial growth is present periodically while the sample device is at the predetermined temperature over a first time period up to 24 hours.
  • the computing device is further to determine a water quality metric based on a determination of whether bacterial growth is present.
  • to determine the water quality metric comprises to determine a number of the one or more reaction wells for which bacterial growth is present; and determine a most probable number (MPN) metric as a function of the number of the reaction wells for which bacterial growth is present.
  • MPN most probable number
  • the fluorescence excitation source comprises one or more ultraviolet (UV) light-emitting diodes (LEDs).
  • the fluorescence emission sensor comprises an image sensor.
  • the fluorescence emission sensor comprises one or more RGB color sensors.
  • the aqueous medium comprises lake water, river water, or ocean water.
  • the predetermined temperature is between about 20 °C to about 60 °C.
  • the reaction is a biological reaction, a chemical reaction, a biochemical reaction, or any combination thereof.
  • the reagent comprises a nutrient and optionally an inhibitor.
  • the nutrient comprises o- nitrophenyl-P-D-galactopyranoside (ONPG), 4-methylumbelliferyl-P-D-glucuronide (MUG), 4- methylumbelliferyl-P-D-glucoside, or any combination thereof.
  • the inhibitor comprises a salt, a sugar, a phenol, or any combination thereof.
  • FIG. 1 shows a system according to the present disclosure including a computing device and a sample device
  • FIG. 2 is a simplified block diagram of various environments that may be established by the system of FIG. 1 ;
  • FIG. 3 is a simplified flow diagram of at least one method for water quality analysis that may be performed by the system of FIGS. 1-2;
  • FIG. 4 is a simplified flow diagram of at least one embodiment of a method for blue component analysis that may be executed by a computing device of FIGS. 1-2;
  • FIG. 5 is a simplified flow diagram of at least one embodiment of a method for red/blue inversion analysis that may be executed by a computing device of FIGS. 1-2; and [0034] FIGS. 6 and 7 are charts depicting experimental results that may be achieved by a system in accordance with the present disclosure.
  • the illustrative system 100 includes a computing device 102 coupled to a one or more ultraviolet LEDs 104, one or more visible light sensors 106, and a controller 112.
  • a sample device 108 may be loaded with a sample of an aqueous medium such as fresh water (lake water, river water) or marine water (salt water, sea water, ocean water) for water quality analysis.
  • the sample device 108 is brought to a predetermined temperature using a heater 110, and the UV LEDs 104 and the visible light sensors 106 are used to measure fluorescence generated by the sample in the sample device 108.
  • the computing device 102 decomposes sensor data from the visible light sensors 106 into multiple color components (e.g., red, green, and blue components), and then analyzes those components to identify bacterial growth or other pathogenic growth. Based on detected bacterial growth, the computing device 102 may determine a water quality metric. Accordingly, the system 100 may provide a cost-effective solution for in situ water quality testing. Additionally, as described further below, the system 100 may determine the water quality metric in a shorter amount of time (e.g., 12 hours, 10 hours, 5 hours, or another time) as compared to conventional water quality analysis, which may require at least 24 hours of incubation.
  • a shorter amount of time e.g., 12 hours, 10 hours, 5 hours, or another time
  • the computing device 102 may be embodied as any type of device capable of performing the functions described herein.
  • the computing device 102 may be embodied as, without limitation, a microcomputer, a workstation, a desktop computer, a laptop computer, a tablet computer, a smartphone, a server, a rack-mounted server, a blade server, a network appliance, a web appliance, a consumer electronic device, a distributed computing system, a multiprocessor system, and/or any other computing device capable of performing the functions described herein.
  • the computing device 102 may be embodied as a “virtual server” formed from multiple computing devices distributed across a network and operating in a public or private cloud.
  • the computing device 102 is illustrated in FIG. 1 as embodied as a single computing device, it should be appreciated that the computing device 102 may be embodied as multiple devices cooperating together to facilitate the functionality described below.
  • the illustrative computing device 102 includes a processor 120, an I/O subsystem 122, memory 124, a data storage device 126, and a communication subsystem 128.
  • the computing device 102 may include other or additional components, such as those commonly found in a desktop computer (e.g., various input/output devices), in other embodiments.
  • one or more of the illustrative components may be incorporated in, or otherwise form a portion of, another component.
  • the memory 124, or portions thereof may be incorporated in the processor 120 in some embodiments.
  • the processor 120 may be embodied as any type of processor or compute engine capable of performing the functions described herein.
  • the processor may be embodied as a single or multi-core processor(s), digital signal processor, microcontroller, or other processor or processing/controlling circuit.
  • the memory 124 may be embodied as any type of volatile or non-volatile memory or data storage capable of performing the functions described herein. In operation, the memory 124 may store various data and software used during operation of the computing device 102 such as operating systems, applications, programs, libraries, and drivers.
  • the memory 124 is communicatively coupled to the processor 120 via the I/O subsystem 122, which may be embodied as circuitry and/or components to facilitate input/output operations with the processor 120, the memory 124, and other components of the computing device 102.
  • the VO subsystem 122 may be embodied as, or otherwise include, memory controller hubs, input/output control hubs, firmware devices, communication links (i.e., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.) and/or other components and subsystems to facilitate the input/output operations.
  • the I/O subsystem 122 may form a portion of a system-on-a-chip (SoC) and be incorporated, along with the processor 120, the memory 124, and other components of the computing device 102, on a single integrated circuit chip.
  • SoC system-on-a-chip
  • the data storage device 126 may be embodied as any type of device or devices configured for short-term or long-term storage of data such as, for example, memory devices and circuits, memory cards, hard disk drives, solid-state drives, or other data storage devices.
  • the communication subsystem 128 of the computing device 102 may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications between the computing device 102 and other remote devices.
  • the communication subsystem 128 may be configured to use any one or more communication technology (e.g., wireless or wired communications) and associated protocols (e.g., Ethernet, Bluetooth®, Bluetooth Low Energy (BLE), Wi-Fi®, WiMAX, 3G LTE, 5G, etc.) to effect such communication.
  • communication technology e.g., wireless or wired communications
  • associated protocols e.g., Ethernet, Bluetooth®, Bluetooth Low Energy (BLE), Wi-Fi®, WiMAX, 3G LTE, 5G, etc.
  • the controller 112 may be illustratively embodied as a microcontroller, such as an iOS microcontroller, a microcomputer, such as a Raspberry Pi microcomputer, or other control device, is coupled to the heater 110. As described above, the controller 112 ensures that the heater 110 reaches and maintains a predetermined temperature.
  • the heater 110 may be embodied as an incubation heater, a temperature chamber, a hot plate, or a thermoelectric heater, for example.
  • the system 100 may also include one or more thermocouples, thermometers, thermistors, resistance temperature detectors, or other temperature sensors in order to control temperature of the heater 110 and thus the sample device 108 and/or other components of the system 100.
  • the sample device 108 may be embodied as a millifluidic, microfluidic, or other device that includes multiple reaction wells.
  • Each reaction well may be filled with an aqueous medium such as sampled water.
  • each well is separate, independent, and sealed off from the other wells such that there is no cross-contamination between wells.
  • the reaction well may comprise any shape and in some embodiments, if there are multiple reaction wells, the shape of each well may be the same or independently selected. For example, the shape of a reaction well may be independently selected from circular, oval, square, rectangular, and triangular.
  • the reaction well may be of any desired dimension and any desired depth I height.
  • the length and width of each side may be independently selected from about 1 mm to about 100 mm, such as from about 1 mm to about 80 mm, from about 1 mm to about 60 mm, from about 1 mm to about 40 mm, from about 1 mm to about 20 mm, from about 1 mm to about 10 mm, from about 10 mm to about 100 mm, from about 30 mm to about 100 mm, from about 50 mm to about 100 mm, or from about 70 mm to about 100 mm.
  • the depth / height of the reaction well 160 may be independently selected from about 1 mm to about 10 mm.
  • the height may be about 1 mm to about 9 mm, about 1 mm to about 8 mm, about 1 mm to about 7 mm, about 1 mm to about 6 mm, about 1 mm to about 5 mm, about 1 mm to about 4 mm, about 1 mm to about 3 mm, or about 1 mm to about 2 mm.
  • the sample device 108 may further include, for each reaction well, an inlet and an outlet that are each coupled to the corresponding reaction well by an associated channel. Accordingly, each reaction well may be in communication with an external environment via the corresponding inlet and outlet.
  • the dimensions of each inlet and/or outlet and associated channel are not particularly limited and may be selected by one of skill in the art. In some embodiments, the dimensions are chosen so that the sample device 108 is self-loading; that is, upon submersion in a liquid medium, the channel comprises a hydraulic resistance that is able to be overcome by a hydrostatic pressure of the liquid medium, thereby allowing the liquid of the external environment to enter the inlet and travel to the reaction well.
  • the reaction wells After being filled with the aqueous medium, the reaction wells may be sealed, for example by application of an impermeable top layer.
  • the sample device 108 and/or the walls of the reaction wells may be formed from a polymer material that is transparent to ultraviolet (UV) and visible light.
  • the polymer may be impermeable to solids, liquids, and/or gasses.
  • the polymer may be a thermoset polymer or a thermoplastic polymer, for example. Additionally, in some embodiments the polymer is transparent, semi-transparent, machinable, manufacturable, translucent, biocompatible, chemically inert, resistant to ultraviolet light, and/or any combination thereof.
  • the polymer comprises little or no autofluorescence.
  • Each reaction well further includes a reagent for water quality analysis.
  • the regent may be the same or different in each well.
  • the reagent comprises a nutrient and optionally an inhibitor.
  • Each well may have the same nutrient or a different nutrient. Further, each well may have the same inhibitor or a different inhibitor (or no inhibitor at all).
  • Nutrients include methylumbelliferyl derivatives, such as MUG and 4- methylumbelliferyl-P-D-glucoside, in addition to, for example, ONPG.
  • Other nutrients may be selected by one of ordinary skill in the art based on the target bacteria. For example, one of ordinary skill in the art can determine nutrients capable of producing a fluorescent signal when metabolized by an enzyme of the target bacteria.
  • Nutrients may be selected such that they are specific to the target bacteria.
  • the nutrient fluoresces when metabolized by an enzyme of the target bacteria.
  • the nutrient is hydrolyzed by the enzyme and the reaction product emits a fluorescent signal.
  • the enzyme used in the presently disclosed methods may be any enzyme of the target bacteria.
  • E. coli bacteria use P-glucuronidase to metabolize MUG and ONPG.
  • Coliform use P-galactosidase to metabolize MUG and ONPG.
  • P-glucuronidase and P ⁇ galactosidase are two illustrative examples of enzymes that can be used in connection with the presently disclosed devices and methods.
  • tests conducted using the presently disclosed microfluidic device can be conducted in accordance with one or more of the steps set forth in “Evaluation of Enterolert for Enumeration of Enterococci in Regulational Waters,” G.E. Budnick et al., Applied and Environmental Microbiology, Oct. 1996, Vol. 62, No. 10, p. 3881-3884, the contents of which are incorporated into the present disclosure in their entirety.
  • Inhibitors may be present in the reagent if it would be desirable to inhibit growth of non-target bacteria so that only the target bacteria would be able to grow.
  • Inhibitors include, but are not limited to, a salt, a sugar, a phenol, or any combination thereof.
  • Reagents, nutrients and inhibitors can be selected from those found in, for example, the ColilertTM and EnterolertTM products sold by IDEXX.
  • the computing device 102 is coupled to one or more UV lightemitting diodes (LEDs) 104.
  • the UV LEDs 104 may be specific to the excitation frequency of the reaction product produced by the enzyme / nutrient interaction. Additionally or alternatively, although illustrated as including UV LEDs 104, it should be understood that the fluorescence excitation source is not limited to UV LEDs, and in some embodiments the system 100 may include any appropriate fluorescence excitation source for the particular reaction product in use.
  • the computing device 102 is also coupled to one or more visible light sensor(s) 106.
  • the visible light sensors 106 may be embodied as, for example, a digital camera such as a CMOS camera, a CCD camera, or other imaging device, an RGB light sensor, one or more photodiodes, or other visible light sensor capable of sensing fluorescence emissions from the sample device 108.
  • sensor data generated by the visible light sensors 106 may be decomposed into color components or channels for multiple colors or wavelengths, such as red, green, and blue components.
  • the system 100 may include any other appropriate fluorescence emission sensor capable of sensing fluorescence emissions from the sample device 108, including infrared sensors, ultraviolet sensors, and/or other fluorescence emission sensors.
  • the computing device 102 establishes an environment 200 during operation.
  • the illustrative environment 200 includes an illumination controller 202, a sensor controller 204, an image data analyzer 206, and a water quality analyzer 208.
  • the various components of the environment 200 may be embodied as hardware, firmware, software, or a combination thereof.
  • one or more of the components of the environment 200 may be embodied as circuitry or a collection of electrical devices (e.g., illumination controller circuitry 202, sensor controller circuitry 204, image data analyzer circuitry 206, and/or water quality analyzer circuitry 208).
  • the illumination controller 202 is configured to cause a fluorescence excitation source to illuminate the one or more reaction wells of the sample device 108 when an aqueous medium is loaded into the one or more reaction wells.
  • the fluorescence excitation source may be embodied as, for example, one or more UV LEDs 104.
  • the sensor controller 204 is configured to capture a fluorescence signal from each of the one or more reaction wells with a fluorescence emission sensor.
  • the fluorescence emission sensor may be embodied as, for example, one or more visible light sensors 106.
  • the fluorescence signal is indicative of visible light received from the one or more reaction wells.
  • the image data analyzer 206 is configured to decompose the fluorescence signal into one or more color components and to determine whether bacterial growth is present based on the one or more color components of the fluorescence signal. Decomposing the fluorescence signal into the one or more color components may include decomposing the fluorescence signal into a red component, a blue component, and a green component. The image data analyzer 206 may be configured to determine whether bacterial growth is present periodically while the sample device is at a predetermined temperature over a first time period up to 24 hours.
  • decomposing the fluorescence signal into the one or more color components includes decomposing the fluorescence signal into a blue component, and determining whether bacterial growth is present includes determining whether bacterial growth is present based on the blue component. Determining whether bacterial growth is present based on the blue component may include determining a change in intensity or a rate of change in intensity of the blue component, and determining whether the change in intensity or rate of change in intensity exceeds a predetermined threshold.
  • decomposing the fluorescence signal into the one or more color components comprises decomposing the fluorescence signal into a red component and a blue component, and determining whether bacterial growth is present comprises determining whether a red/blue color inversion exists. Determining whether a red/blue color inversion exists comprises determining at an initial time that the red component exceeds the blue component, and determining at a subsequent time that the blue component exceeds the red component. In some embodiments, determining whether the red/blue color inversion exists comprises determining a normalized red component based on the red component and a total intensity and a normalized blue component based on the blue component and a total intensity, and comparing the normalized blue component to the normalized red component.
  • the water quality analyzer 208 is configured to determine a water quality metric based on the determination of whether bacterial growth is present. Determining the water quality metric may include determining a number of the one or more reaction wells for which bacterial growth is present, and determining a most probable number (MPN) metric as a function of the number of the reaction wells for which bacterial growth is present.
  • MPN most probable number
  • the controller 112 may establish an environment 220 during operation.
  • the illustrative environment 220 includes a heater controller 222, which may be embodied as hardware, firmware, software, or a combination thereof.
  • a heater controller 222 may be embodied as hardware, firmware, software, or a combination thereof.
  • one or more of the components of the environment 220 may be embodied as circuitry or a collection of electrical devices (e.g., heater controller circuitry 222).
  • the heater controller 222 is configured to cause the heater to heat the sample device at a predetermined temperature.
  • the fluorescence signal is captured by the computing device 102 while the sample device 108 is at the predetermined temperature.
  • the predetermined temperature may be between about 20 °C to about 60 °C.
  • the system 100 may be used to perform a method 300 for water quality analysis. It should be appreciated that, in some embodiments, one or more operations of the method 300 may be performed by one or more components of the environment 200 of the computing device 102 as shown in FIG. 2.
  • the method 300 begins in block 302, in which the sample device 108 is filled with water or other aqueous medium to be tested, such as fresh water (lake water, river water) or marine water (salt water, sea water, ocean water).
  • water or other aqueous medium to be tested such as fresh water (lake water, river water) or marine water (salt water, sea water, ocean water).
  • the sample device 108 may be immersed in the aqueous medium and then sealed.
  • the aqueous medium may include, for example, a target bacteria, such as Enterococcus faecalis, total coliforms, fecal coliforms, fecal streptococci, Enterococci, and/or Escherichia coli.
  • the reaction wells of the sample device 108 are also filled (or may be pre- filled) with a reagent, which may include a nutrient and/or an inhibitor.
  • a reagent which may include a nutrient and/or an inhibitor.
  • any other appropriate technique may be used to fill the reaction wells of the sample device 108 with the aqueous medium and/or reagent.
  • the sample device 108 is placed on or near the heater 1 10.
  • the controller 112 maintains the sample device 108 at a predetermined temperature using the heater 110.
  • the controller 112 may use a thermistor and a Peltier cell to maintain the sample device 108 at a predetermined temperature, such as 41 °C, or otherwise about 40 °C.
  • a predetermined temperature such as 41 °C, or otherwise about 40 °C.
  • other heaters and/or predetermined temperatures may be used in other embodiments.
  • the device may be heated to a temperature of about 25 °C to about 55 °C, about 30 °C to about 50 °C, about 35 °C to about 45 °C, or about 39 °C, about 40 °C, about 41 °C, about 42 °C, or about 43 °C.
  • one or more functions of the controller 112 may be performed by a different device, such as the computing device 102 and/or the heater 110.
  • the computing device 102 captures fluorescence data for each of the reaction wells included in the sample device 108.
  • the computing device 102 causes the UV LEDs 104 to illuminate each reaction well of the sample device 108 with ultraviolet light.
  • the system 100 may include a single UV LED 104 or other ultraviolet light source capable of illuminating all reaction wells in the sample device 108.
  • the system 100 may include multiple UV LEDs 104 that are each positioned adjacent to a reaction well or otherwise capable of illuminating one of the reaction wells. Other numbers and/or arrangements of UV LEDs 104 and/or other fluorescence excitation sources may be used in other embodiments.
  • the computing device 102 captures red, green, blue sensor data associated with each of the reaction wells using the visible light sensors 106.
  • the sensor data may be digital or analog data indicative of absolute intensity and/or relative intensity values for each color component of light generated by fluorescent excitation within the reaction wells of the sample device 108 and received by the visible light sensor 106.
  • the sensor data may be generated by a single visible light sensor 106 such as a CMOS camera or other imaging device.
  • sensor data may be generated for each reaction well by segmenting the sensor data spatially. For example, sensor data for each reaction well may be determined as a sub-image, a cropped image, or other selection of pixels from a two-dimensional image captured by the visible light sensor 106.
  • sensor data for each reaction well may be generated by multiple visible light sensors 106 that are each configured to sense fluorescence emissions from a corresponding reaction well.
  • an RGB sensor may be positioned adjacent to each reaction well or otherwise capable of sensing fluorescence emissions generated within the reaction well.
  • the computing device 102 determines whether a predetermined measurement time has elapsed.
  • the computing device 102 continues illuminating the reaction wells and capturing fluorescence data for the duration of the measurement time.
  • the computing device 102 may continually, periodically, responsively, or otherwise sample the fluorescence data over the measurement time.
  • the computing device 102 may capture fluorescence data every minute, every few minutes, or otherwise on a predetermined schedule during the measurement time.
  • the measurement time may be a predetermined duration such as 24 hours, more than 24 hours, between 12 and 24 hours, between 12 and 48 hours, more than 12 hours, more than 6 hours, or more than 3 hours.
  • the method 300 loops hack to block 306 to continue capturing fluorescence data. If the computing device 102 determines that the measurement time has elapsed, the method 300 advances to block 314.
  • the computing device 102 detects bacterial growth in the sample device 108 based on one or more of the red, green, and blue components of the fluorescence data. In particular, the computing device 102 may determine whether bacterial growth is detected or not in each reaction well of the sample device 108. Bacterial growth is detected by detecting fluorescence in each reaction well, which indicates that the target bacteria or other pathogen has interacted with the reagent within the corresponding reaction well. In block 316, the computing device 102 decomposes the fluorescence data into red, green, and blue components. The computing device 102 may, for example, extract each color channel from the fluorescence data into a separate data structure or otherwise decompose the sensor data into color components capable of independent processing.
  • the computing device 102 may decompose the sensor data into a different number and/or arrangement of components. For example, in an embodiment, the computing device 102 may extract only the blue component or only the red and blue components. In some embodiments, the computing device 102 may extract other components from the sensor data (e.g., components for other colors, color spaces, chrominance, luminance, luma, saturation, or other image components).
  • the computing device 102 may detect bacterial growth based on growth of the value of the blue component. For example, if the computing device 102 determines that the value of the blue component exceeds an initial, baseline value by more than a predetermined threshold value, the computing device 102 may determine that bacterial growth is present.
  • a method for detection based on blue component growth is described below in connection with FIG. 4.
  • the computing device 102 may detect bacterial growth based on detection of a red/blue inversion. For example, the computing device 102 may determine whether the relative proportion of the red component to the blue component changes from being more red to being more blue.
  • the computing device 102 may determine that bacterial growth is present.
  • a method for detection based on a red/blue inversion is described below in connection with FIG. 5.
  • the computing device 102 may determine a time of detection. For example, the computing device 102 may determine an elapsed time (e.g., a number of hours) from the beginning of monitoring up until it is determined that bacterial growth is detected. This elapsed time may be used, for example, to quantify bacteria concentration, which may be used to predict contamination in certain embodiments.
  • the computing device 102 generates a water quality metric based on the detected bacterial growth.
  • the computing device 102 may compute a most probable number (MPN) as a function of the number of reaction wells with detected bacterial growth.
  • the number of fluorescence-positive wells is related to the MPN of contamination.
  • the unit of contamination is Colony Forming Units (CFU) and is an estimate of the number of viable bacteria in the water sample.
  • CFU Colony Forming Units
  • the computing device 102 may compute a confidence interval.
  • the computing device 102 reports the water quality metric.
  • the computing device 102 may display, store, transmit, or otherwise provide the water quality metric to a user and/or to another system for further analysis or processing. After reporting the water quality metric, the method 300 may loop back to block 302, in which additional water samples may be tested.
  • the computing device 102 may execute a method 400 for blue component analysis. As described above, in some embodiments the method 400 may be executed in connection with block 318 of FIG. 3. It should be appreciated that, in some embodiments, the operations of the method 400 may be performed by one or more components of the environment 200 of the computing device 102 as shown in FIG. 2.
  • the method 400 begins with block 402, in which the computing device 102 decomposes fluorescence data received from one or more visible light sensors 106 into red, green, and blue components.
  • the computing device 102 may, for example, extract each color channel from the fluorescence data into a separate data structure or otherwise decompose the sensor data into color components capable of independent processing.
  • the computing device 102 determines a baseline blue component value.
  • the baseline value may be an initial value, averaged initial value, predetermined value, or other value indicative of the blue component at the start of the analysis (i.e., before bacteria have had substantial time to react with any reagent in the sample device 108).
  • the computing device 102 may, for example, record an initial value or an averaged initial value for the blue component at the start of an analysis run.
  • the computing device 102 identifies any increase in blue component value from the baseline value.
  • the computing device 102 may, for example, determine whether the value of the blue component exceeds the baseline value by more than a predetermined threshold amount. If the blue component value exceeds the baseline by more than the predetermined threshold amount, this may indicate that bacterial growth is detected.
  • the method 400 is completed. In some embodiments, the method 400 may be invoked repeatedly, for example as fluorescence data is captured and analyzed by the computing device 102.
  • the computing device 102 may execute a method 500 for red/blue inversion analysis. As described above, in some embodiments the method 500 may be executed in connection with block 320 of FIG. 3. It should be appreciated that, in some embodiments, the operations of the method 500 may be performed by one or more components of the environment 200 of the computing device 102 as shown in FIG. 2.
  • the method 500 begins with block 502, in which the computing device 102 decomposes fluorescence data received from one or more visible light sensors 106 into red, green, and blue components.
  • the computing device 102 may, for example, extract each color channel from the fluorescence data into a separate data structure or otherwise decompose the sensor data into color components capable of independent processing.
  • the computing device 102 normalizes each red, green, and blue component value based on total intensity. For example, the computing device 100 may divide each of the red, green, and blue components by a value representing total intensity, such as value, luminance, luma, or other intensity value. These normalized values represent the relative values of each color component. For example, sensor data that is more red than blue will have a larger normalized red component than normalized blue component. As another example, sensor data that is more blue than red will have a larger normalized blue component than normalized red component. Additionally, although described as generating normalized red, green, and blue components, it should be understood that in some embodiments the computing device 102 may generate only a normalized red component and a normalized blue component or may generate another number and/or arrangement of normalized color components.
  • the computing device 102 compares relative values of the normalized red component and the normalized blue component.
  • the computing device 102 identifies an inversion in relative values of the red component and the blue component.
  • the captured fluorescence data will be initially more red than blue — that is, initially the normalized red component value is larger than the normalized blue component value. If bacterial growth is present, fluorescence emissions increase over time, which in the illustrative embodiment causes the captured fluorescence data to become gradually more blue than red over time. If bacterial growth is present, then at some time a red/blue inversion occurs — that is, the fluorescence data becomes more blue than red, meaning the normalized blue component value is larger than the normalized red component value.
  • the computing device 102 may indicate that bacterial growth is detected. After identifying any increase, the method 500 is completed. In some embodiments, the method 500 may be invoked repeatedly, for example as fluorescence data is captured and analyzed by the computing device 102.
  • diagram 600 shows illustrative fluorescence data captured during the experiment.
  • the diagram 600 plots measured intensity values over time for the red (R), green (G), and blue (B) components, as well as for total intensity (I).
  • R red
  • G green
  • B blue
  • I total intensity
  • the components increase toward a maximum value during a growth period 604.
  • the B component corresponds to a sigmoidal growth curve for the bacteria in the sample.
  • the computing device 102 may identify bacterial growth based on the value of the B component. For example, the computing device 102 may determine whether the value of the B component in the growth period 604 exceeds a particular predetermined threshold value. As another example, the computing device 102 may determine whether the value of the B component during the growth period 604 exceeds the baseline value of the B component during the initial period 602 by greater than a predetermined threshold.
  • diagram 700 shows illustrative normalized fluorescence data captured during the experiment.
  • the diagram 700 plots measured normalized intensity values over time for the red (R/I), green (G/I), and blue (B/I) components. For example, a measurement that is completely red would be shown as 100% R/I, a measurement that is completely blue would be shown as 100% B/I, and so on.
  • This change in relative values of the R/I and B/I components is due to increased fluorescence emissions (which have a bluish color) due to bacterial growth.
  • the computing device 102 may determine that bacterial growth exists. Samples without bacterial growth do not exhibit an R/B inversion, and instead remain at roughly the same relative values of the R/I and B/I components as shown in the initial period 702.
  • composition disclosed herein may comprise, consist of, or consist essentially of any element, component and/or ingredient disclosed herein or any combination of two or more of the elements, components or ingredients disclosed herein.
  • Any method disclosed herein may comprise, consist of, or consist essentially of any method step disclosed herein or any combination of two or more of the method steps disclosed herein.
  • the term "about” refers to the cited value being within the errors arising from the standard deviation found in their respective testing measurements, and if those errors cannot be determined, then “about” may refer to, for example, within 5% of the cited value.
  • the invention encompasses any and all possible combinations of some or all of the various embodiments described herein. It should also be understood that various changes and modifications to the presently preferred embodiments described herein will be apparent to those skilled in the art. Such changes and modifications can be made without departing from the spirit and scope of the invention and without diminishing its intended advantages. It is therefore intended that such changes and modifications be covered by the appended claims.

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Abstract

La présente divulgation concerne des dispositifs autonomes, des systèmes comprenant les dispositifs, et des procédés d'utilisation des dispositifs pour réaliser des dosages, tels que des dosages chimiques et/ou biologiques. Les systèmes comprennent un dispositif informatique couplé à une ou plusieurs sources d'excitation de fluorescence et à un ou plusieurs capteurs d'émissions de fluorescence. Un dispositif d'échantillon comprend un ou plusieurs puits de réaction comprenant un milieu aqueux, qui peut contenir, par exemple, une bactérie cible. Le dispositif informatique entraîne les sources d'excitation de fluorescence à éclairer les puits de réaction et à capturer un signal de fluorescence provenant de chacun des puits de réaction avec les capteurs d'émission de fluorescence. Le dispositif informatique décompose le signal de fluorescence en une ou plusieurs composantes de couleur, telles que le rouge, le vert et le bleu, et détermine si la croissance bactérienne est présente sur la base des composantes de couleur. La divulgation décrit et revendique d'autres modes de réalisation.
PCT/US2024/047293 2023-09-26 2024-09-18 Technologies de surveillance de la qualité de l'eau avec analyse de composante de données d'image Ceased WO2025072013A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080049974A1 (en) * 2006-08-23 2008-02-28 Thyagarajan Kadayam S Methods for processing images to detect objects below the surface of the water
US20080219499A1 (en) * 2007-03-10 2008-09-11 Walter Jay Gregory Method for reading test strips
WO2023154274A1 (fr) * 2022-02-08 2023-08-17 University Of Puerto Rico Dispositif microfluidique autonome pour analyser la qualité de l'eau

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080049974A1 (en) * 2006-08-23 2008-02-28 Thyagarajan Kadayam S Methods for processing images to detect objects below the surface of the water
US20080219499A1 (en) * 2007-03-10 2008-09-11 Walter Jay Gregory Method for reading test strips
WO2023154274A1 (fr) * 2022-02-08 2023-08-17 University Of Puerto Rico Dispositif microfluidique autonome pour analyser la qualité de l'eau

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