WO2017019762A1 - Image based photometry - Google Patents

Abstract

An image based photometer includes a light source adapted to hold a well plate having multiple wells for testing samples, the light source providing a source of broadband white light for transmission through the multiple wells. A sensing surface is positioned opposite the light source to receive light transmitted through the multiple wells and shield ambient light from transmission through the wells.

Description

IMAGE BASED PHOTOMETRY

Cross-Reference to Related Application

[0001] This application claims benefit of the U. S. Provisional Patent

Application Serial No. 62/197,300, filed July 27, 2015, which is incorporated by reference herein in its entirety.

Government Funding

[0002] This invention was made with Government support under Grant

Number HDTRA1 -14-C-0037 awarded by Defense Threat Reduction Agency. The United States Government has certain rights in the invention. Background

[0003] Absorbance spectroscopy is the most common analytical technique used for chemical and biochemical analyses. Test kits (e.g., tube test kits and microtiter plate ELISA kits) are commercially available for hundreds of important analytes (e.g., metabolites, proteins, environmental pollutants), can provide accurate and precise results, and are inexpensive (the cost-per-sample of these test kits is typically < $5). Although these characteristics make absorbance spectroscopy attractive for use in laboratories and clinics across the world, the required instrumentation (e.g., spectrophotometers and microplate

spectrophotometers) can cost from $2,000 to $50,000, depending on their specifications, versatility, and throughput.

[0004] Laboratories and clinics in low- and middle- income countries typically cannot afford this expensive instrumentation, do not have access to the components, personnel, and expertise required to maintain the equipment; and/or lack the infrastructure (e.g., continuous and stable electrical power) necessary to keep it running.

[0005] Previous approaches to low-cost optical analysis of samples in wells of microtiter plates have used flatbed scanners in reflectance mode. Using reflected light to estimate absorbance, however, is problematic because the path that reflected light takes through each sample is complex. Depending on the geometry and divergence angle of the light source, the many different air/liquid/plastic interfaces present around the sample scatter the light at multiple locations and in different ways. This scattering cause the colors (RGB values) of the image to be neither uniform across each well, nor uniform from well to well (i.e., when comparing identical samples in different wells). In an attempt to compensate for these artifacts, approaches based on reflectance-mode imaging use custom software to spatially average the RGB values of all pixels of the image of each well, and then to calibrate the relationship between the average (mean) values of each well and concentration of the analyte, separately for each well.

[0006] Establishing a well understood relationship between these mean values and the concentration of the analyte, however, remains complicated for two reasons: i) the definition of "mean" value is ambiguous because there is typically a multimodal distribution of the RGB color values across each well and ii) the complex and highly variable optical path-length precludes the

establishment of a well-defined analytical relationship between the intensity of light that reaches the detector and the concentration of the sample or analyte.

Summary

[0007] An image based photometer includes a light source adapted to hold a well plate having multiple wells for testing samples, the light source providing a source of broadband white light for transmission through the multiple wells. A sensing surface is positioned opposite the light source to receive light transmitted through the multiple wells and shield ambient light from transmission through the wells.

[0008] A method includes directing broadband white light through multiple samples to obtain transmitted light, obtaining multiple red, green, and blue (RGB) values for each sample, averaging the obtained multiple RGB values for each sample, and correlating the averaged values to provide an RGB- resolved absorbance of each sample to identify each sample.

[0009] A processor readable storage device having instructions to cause a circuit based processor to perform operations including receiving an image of multiple samples based on broadband white light directed through the multiple samples, opening the image, selecting multiple red, green, and blue (RGB) pixels from the opened image for each sample, determining values for each of the RGB components of the pixels, averaging the obtained multiple RGB values for each sample, and correlating the averaged values to provide an RGB- resolved absorbance of each sample to identify each sample.

Brief Description of the Drawings

[0010] FIG. 1 is a block perspective diagram of an image based photometer using a camera according to an example embodiment.

[0011] FIG. 2 is a perspective representation of an image based photometer utilizing a flatbed scanner based photometer according to an example embodiment.

[0012] FIG. 3 is a top view of a microtiter plate having multiple wells to hold samples for an image based photometer according to an example embodiment.

[0013] FIG. 4A is a histogram illustrating green channel pixel counts of a sample from a flatbed scanner photometer operating in transmittance mode according to an example embodiment.

[0014] FIG. 4B is a histogram illustrating green channel pixel counts of a sample from a flatbed scanner operating in reflectance mode.

[0015] FIG. 4C is a histogram illustrating green channel pixel counts of a sample from a CSI based flatbed scanner.

[0016] FIG. 4D is a table containing statistics corresponding to the counts of FIGs. 4 A, 4B, and 4C according to an example embodiment.

[0017] FIG 5 A is a histogram illustrating green channel pixel counts of a sample from a camera based photometer according to an example embodiment.

[0018] FIG. 5B is a histogram illustrating green channel pixel counts of a sample from a camera.

[0019] FIG. 5C is a table containing statistics corresponding to the counts of FIGs. 5 A and 5B according to an example embodiment.

[0020] FIG. 6A is a graph illustrating a flatbed scanner photometer light source intensity versus wavelength according to an example embodiment. [0021] FIG. 6B is a graph illustrating sample transmittance versus wavelength using a flatbed scanner photometer according to an example embodiment.

[0022] FIG. 6C is a graph illustrating sensor sensitivity versus wavelength using a flatbed scanner photometer according to an example embodiment.

[0023] FIG. 6D is a graph illustrating captured light intensity versus wavelength using a flatbed scanner photometer according to an example embodiment.

[0024] FIG. 6E is a graph illustrating a camera photometer light source intensity versus wavelength according to an example embodiment.

[0025] FIG. 6F is a graph illustrating sample transmittance versus wavelength using a camera photometer according to an example embodiment.

[0026] FIG. 6G is a graph illustrating sensor sensitivity versus wavelength using a camera photometer according to an example embodiment.

[0027] FIG. 6H is a graph illustrating captured light intensity versus wavelength using a camera photometer according to an example embodiment.

[0028] FIG. 7 is a table illustrating analytical characteristics of calibration lines fitted to peak absorbance values for multiple different dye samples according to an example embodiment.

[0029] FIG. 8 is a graph illustrating calibration curves of absorbance values for different photometer embodiments and a laboratory

spectrophotometer according to an example embodiment.

[0030] FIG. 9A is a graph illustrating correlation plots of peak absorbance values for different photometer embodiments and a laboratory spectrophotometer according to an example embodiment.

[0031] FIG. 9B is a graph illustrating correlation plots of chromogenic compounds peak absorbance values for different photometer embodiments and a laboratory spectrophotometer according to an example embodiment.

[0032] FIG. 10 is a table showing a comparison of analytical characteristics of correlations lines fitted to peak absorbance values for different photometer embodiments and a laboratory spectrophotometer according to an example embodiment. [0033] FIGs. 11 A, 1 IB, and 11C are graphs illustrating absorbance of various samples versus wavelength according to an example embodiment.

[0034] FIG. 12 is a block diagram of programmable circuitry to perform methods, execute apps and applications, and process images according to example embodiments.

[0035] FIG. 13 is flowchart illustrating a method performing image based photometry according to an example embodiment.

[0036] FIG. 14 is a flowchart illustrating a method of accounting for spatial gradients in an intensity of broadband light of an image based photometer according to an example embodiment.

Detailed Description

[0037] In the following description, reference is made to the

accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments which may be practiced. These

embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and that structural, logical and electrical changes may be made without departing from the scope of the present invention. The following description of example embodiments is, therefore, not to be taken in a limited sense, and the scope of the present invention is defined by the appended claims.

[0038] The functions or algorithms described herein may be

implemented in software or a combination of software and human implemented procedures in one embodiment. The software may consist of computer executable instructions stored on one or more non-transitory storage devices. Examples of such non-transitory storage devices include computer readable media or computer readable storage devices such as one or more memory or other type of hardware based storage devices, either local or networked and other non-transitory storage devices. The term "module" may be used to represent code stored on a storage device for execution by circuitry, such as one or more processors, which together form specifically programmed circuitry or computer. Modules may also include combinations of code, circuitry, firmware or any combination thereof capable of performing functions associated with the module. Multiple functions may be performed in one or more modules as desired, and the embodiments described are merely examples. The code or software may be executed on a digital signal processor, ASIC, microprocessor, or other type of processor operating on a computer system, such as a personal computer, server or other computer system.

[0039] Photometry of one or more samples is performed using a light source to illuminate the samples on one side of the samples and measuring light transmitted through the samples by RGB based imaging sensors. By using transmitted, rather than reflected or scattered light, this approach enables simple, reproducible measurements of broadband absorbance calculated using the RGB color values (RGB-resolved absorbance) of the image of each sample.

[0040] In one embodiment, the samples may be imaged using a flatbed scanner operating in transmittance mode. Multiple samples may be held in a microtiter plate, such as a 96 well plate. In a further embodiment, the plate with samples may be placed on a planar light source within a chamber and imaged by a camera, such as a smartphone or tablet based camera. One or more pixels corresponding to the transmittance of each sample may be used to calculate the RBG resolved absorbance which can be compared to RGB resolved absorbance values of known samples to identify each sample.

[0041] The linearity and precision of absorbance measurements of solutions of common dyes performed with an image based photometer demonstrates the applicability of these devices.

[0042] A variety of assays (e.g., enzymatic, ELISA) demonstrate that these low-cost photometers can be used in a broad range of clinical,

environmental, and chemical analyses.

[0043] FIG. 1 is a perspective block schematic diagram of a photometer

100 that utilizes a camera 110. The camera 110 may be a cellphone based camera in one embodiment, such as for example, an LG OPTFMUS F3 4G LTE. The camera 110 is supported by a box 115, which may be formed of cardboard covered with black fabric, metal, plastic, or other opaque material to reduce or eliminate external light from reaching a detection region within the box.

[0044] In one embodiment, a low-cost (US$6) planar light source 120 is positioned in the box 115 opposite the camera 110. The box 115 also has a hole about the size of a camera lens, such as a 6-mm hole, with the camera 110 body blocking external light from entering the hole. The planar light source 120 may be an edge-lit LED backlight module, such as that currently found in many smartphones and tablet computers.

[0045] The inside of the box forms a detection region, with the light source 120 serving to illuminate samples 125 from below. The samples, which may be wells of a microtiter plate 126, are thus positioned between the light source and camera such that the camera receives light transmitted through the samples. In one embodiment, the plate 126 maybe placed directly on top of the light source 120 such that it is supported by the light source 120. Guides may be used to repeatably and consistently position the plate 126 on the light source 120. Utilizing light transmitted from a uniform light source 120 provides a high- quality absorbance measurement. The alignment between the light source 120 and the detector (camera) establishes an optical path length that is simple, well- defined, and spatially uniform across the plate containing samples.

[0046] The box 115 may have four sides and a top having a size in one embodiment to define a fixed imaging distance of 15.5 cm and to provide a field of view sufficient to capture light transmitted through the samples. The top of the box 115 in one embodiment has the hole for the camera. The top of the box is thus a sensing surface positioned opposite the light source to receive light transmitted through the multiple wells. The box 115 may also operate to shield ambient light from transmission through the wells.

[0047] A base 127 may be used in one embodiment to mate with the box

115 to form the detection area. The base 127 in one embodiment may be formed by adhering a sheet of foam (1" thick) underneath a perfboard provided a surface on which to solder components and fix them in place. On to this perfboard, a series circuit consisting of two 9-V batteries 130 may be connected electrically in parallel and in separate battery holders, a toggle switch 135, a potentiometer 140 (to adjust brightness), and the planar light source 120. The box may 1 15 be placed on the base 127. The hole may be formed with the use of a biopsy punch, and may be centered on the planar light source 120, for the camera 110. Note that in further commercial embodiments, the toggle switch 135 and

potentiometer 140 may be located outside of the box for easy access. [0048] If the light source 120 is smaller than the microtiter plate 126

(with samples 125), the plate 126 may be moved to capture multiple images which may cover all the wells of the plate. In one embodiment, the planar light source may be large enough to illuminate 32 wells of a standard 96-microtiter plate. In this arrangement, three separate images may be obtained, moving the plate 126 between each image acquisition to capture the entire plate 126. In further embodiments, a larger backlight may be used to illuminate an entire sample set of the plate 126, and the box may be sized and camera optics selected to provide a field of view of an entire illuminate plate in one image.

[0049] In one embodiment, the settings on the camera of the cell phone

(LG OPTIMUS F3 4G LTE) during image capture were set to the following: i) Focus: Auto, ii) ISO: 400, iii) White balance: Auto, iv) Brightness: 0.0, v) Image size: 5M (2560x 1920), vi) Color effect: None. Images may be saved as Joint Photographic Experts Group (JPEG) files in one embodiment, which was a default of the camera. In some embodiments, the box 115 may be folded and the entire photometer 100 may be stored in a small bag.

[0050] In a further embodiment, a flatbed scanner 200 may be used to obtain images of samples as illustrated in a perspective view in FIG. 2. One example flatbed scanner suitable for operating as part of a photometer is an Epson Perfection V500 photo, Epson ($90 USD) employing a CCD image sensor in a strip, and operating in transmittance mode. The transmittance mode is designed for imaging photographic films, and uses a built-in transparency unit that employs a planar light source in a strip 210. Both the light source of the transparency unit and the image sensor scan across the imaging surface of the scanner simultaneously to capture an image of a 96-well microtiter plate 215 containing samples placed onto the imaging surface of the scanner. The imaging area of the flatbed scanner in transmittance mode (27 cm x 8.3 cm) is large enough to image up to two standard microtiter plates in parallel. Using software bundled with the scanner (Epson), all automatic correction functions may be unselected or disabled to ensure that the captured photometric data are not manipulated. Images may be saved as JPEG files.

[0051] FIG. 3 is a top view of an image of 96 well microtiter plate 400.

In one example which may be used for verification of the ability of the photometer 100 or 200 to provide suitable absorbance values of samples, different wells of the plate 300 may be filled with different color dyes in each row, with the first four columns containing a different concentration than the second four columns, with the last row, row 12, containing a blank solution. Note that in transmittance mode for both image based photometers (100 and 200), the image of plate 400 illustrates that the RGB color value obtained is substantially uniform across the well.

[0052] In one example, solutions of 1 1 compounds were prepared

(disperse orange 3, methyl orange, fluorescein, 2,2-diphenyl-l -picrylhydrazyl (DPPH), eosin Y, rhodamine B, trypan blue, prussian blue, malachite green, methylene blue, chlorophyll b) at different concentrations, and the absorption spectra, Α(λ), was measured.

[0053] The solutions of the dyes may be prepared in one embodiment with the following concentrations. Disperse orange 3 (2.81 , 8.43, 14.05, 28.10, 42.15, 56.20 μΜ), methyl orange (0.06, 0.24, 0.50, 1.00, 2.00, 4.00 μΜ), fluorescein (2.00, 4.00, 6.00, 8.00, 10.00, 16.00 uM), DPPH (1.40, 2.81 , 5.62, 8.43, 1 1.24, 16.86, 22.48, 28.10 uM), eosin Y (0.54, 1.00, 2.00, 2.69, 6.00, 8.00, 10.00, 13.45 μΜ), rhodamine B (0.16, 0.78, 1.17, 2.91 , 3.92, 5.83, 8.74, 1 1.66 μΜ), trypan blue (1.04, 2.08, 4.16, 6.24, 8.33, 10.41, 16.65, 20.82 μΜ), prussian blue (1.00, 2.00, 4.00, 6.00, 10.00, 14.04, 19.20 μΜ), malachite green (0.06, 0.24, 0.50, 1.00, 2.00, 4.00 μΜ), methylene blue (1.00, 2.00, 4.00, 6.00, 8.00, 10.00, 15.70, 21.98 μΜ), chlorophyll b (1.1 , 2.2, 4.4, 6.6, 8.8 μΜ).

[0054] 400 μΙ_, of different solutions may be added to each well of the

96-well microtiter plate (one plate per dye) and the absorbance may be read on a microplate spectrophotometer at the peak absorbance of these solutions (See Table 700 in FIG. 7). Then, images of the microtiter plates contained these solutions may be captured using the image based photometers (100 and 200). The RGB values of three pixels (chosen at random from within the image of each of the wells that contained these solutions) may be read with Image J or Microsoft Paint and the mean values Gt (where k = {R,G,B} color channel) of each group of RGB values recorded. RGB-resolved absorbance Ak of each sample can be calculated using the following equation ( A_k

the guidelines for the selection of appropriate color value. To use Microsoft Paint to obtain RGB values of pixels, an eyedropper tool may be used to select a pixel, then colors are selected, edit colors selected, and define custom colors. This results in a display of RGB values which may be read.

[0055] To eliminate the influence of slight spatial gradients in the intensity of light emitted by the planar light source, the following procedure may be used. For each assay, first capture a baseline image of 3 blank solutions and then calculate

for each well. Next, capture the sample image of, for example, 32 wells filled with 28 samples and 4 blank solutions. To account for any changes in the white balance from image to image (cell phone cameras typically adjust white balance automatically), estimate a white balance

are the RGB values of the 4 blank solutions in the sample image and t⁵-^baseliBe) _are the RGB values of the corresponding wells in the baseline image. Next, correct the color values of the sample image by C ^s =WC ^s*mw Finally, calculate the RGB-resolved absorbance Ak of each sample using these corrected values,

(A

color value.

[0056] Depending on the position of the peak absorbance of a compound, one color value of an RGB triplet typically provided more sensitive results than the other two. The blue color value is more suitable for absorbance peaks between 400-505 nm, the green color value is more suitable for peaks between 505-580 nm, and the red value for absorbance peaks between 580-700 nm. These guidelines may be used for the selection of the most appropriate color value (R, G, or B) to be used for the estimation of the RGB-resolved absorbance values of a compound. The RGB-resolved absorbance values obtained with photometers 100 and 200 may be compared to the peak absorbance values obtained using a microplate spectrophotometer. Calibration lines of absorbance versus concentration may also be prepared using the microplate spectrophotometer, the photometer 100, and the photometer 200 to verify the correlation between the RGB-resolved absorbance values and the peak absorbance values

[0057] Chromogenic compounds that exhibit absorbance peaks with very different spectral characteristics (e.g., shape, position, and intensity of the spectral peak) that span the entire visible spectrum can be identified. Images of the microtiter plates that contain these solutions may be captured using the photometers 100 and 200. RGB values of three pixels may then be read. The pixels may be chosen at random from within the image of each of the wells that contained solutions of the test compounds, using Image J or Microsoft Paint. Mean values Ck of each group of RGB values may be recorded. The recorded values may then be used to estimate the broadband, RGB-resolved absorption Ak, for each color channel.

[0058] The present embodiments utilize light transmitted through a sample. In this case, the alignment between the light source and the detector establishes an optical path length to an imaging device that is simple, well- defined, and spatially uniform across the plate. In one embodiment, the plate 300 may contain a bar code 310 that identifies the plate and may also indicate which wells contain specific reagents that may react with a sample. The bar code may be visible in one or more images of the plate 300 and may be decoded to obtain the information identifying the plate and wells containing reagents. In some embodiments, the information identifying the wells containing reagents and the reagents in such wells may be provided via a lookup table pointed to by information in the bar code.

[0059] FIG. 4A is a histogram illustrating green channel pixel values of an image of a well containing 8.00 μΜ Eosin Y solution captured using the flatbed scanner 200 in transmittance mode. FIG. 4B is a histogram illustrating green channel pixels values of a well obtained in reflectance mode. Note the wide variation in values compared to the green channel values of in FIG. 4A corresponding to the scanner operating in transmission mode. FIG. 4C is a histogram of green channel pixel values of the well obtained by a scanner using

CSI technology in reflectance mode. FIG. 4D is a table providing statistical characteristics of the different methods of obtaining green channel pixel values from FIGs. 4A-4C. In the case of Figure 4 A the mean and mode of the pixel values differed by only 0.4%, and the %RSD of the pixel values in each well was 0.4% (N = 10024).

[0060] FIG. 5 A is a histogram illustrating green channel pixel values of an image of a well containing 8.00 uM Eosin Y solution captured using the image based photometer 200. FIG. 5B is a histogram illustrating green channel pixel values of an image of a well containing 8.00 uM Eosin Y solution captured using a cell phone camera in FIG. 5B relying on ambient light. As seen in the table in FIG. 5C in the case of image based photometer the mean and mode of the pixel values differed by only 0.05%, and the %RSD of the pixel values in each well was 0.6% (N = 3532). These low values indicate that the distribution of pixel values is unimodal that the value of each pixel is nearly identical across the well.

[0061] The narrow distribution of the color values of the pixels for transmittance-based measurements enables a precise estimation of the mean value by sampling only a few pixels. Using Equation 1, which assumes that the distribution of color values follow a normal distribution in each well, the number of pixels N to read to achieve a sample mean (the average of the color values of N pixels) that does not deviate from the population mean (the average of the color values of all pixels) by more than ε (the percentage of difference between sample mean and population mean), was estimated within a confidence level determined by the value of z (which can be found in a standard tables for statistical analysis), and given the relative standard deviation RSD of the opulation.

[0062] For transmittance measurements using the photometers 100 and

200, the RSD value of color values of all pixels within each well is less than 0.006. With this RSD, achieving a sample mean that is within 1% of the population mean (ε = 0.01), with 95% confidence (z = 1.96), may be obtained sampling N= 1.38 (~ 2) pixels. This characteristic vastly reduces the image processing necessary to measure transmittance, simplifies the procedure, and makes it feasible without any specialized software. To eliminate erroneous results due to the sampling of an outlier, in one embodiment, the RGB values of three pixels chosen at random from the image of each well may be averaged.

[0063] By contrast, for reflectance measurements, the distribution of the color values of the pixels in each well is broad. For measurements taken using the scanner in reflectance mode, the mean and mode of the pixel values differed by 20.9%. This large difference indicates that the distribution of pixel values was not unimodal (in this case, bimodal). The %RSD of the pixel values in each well was 17.3% (N = 10024). For measurements taken using the scanner employing CIS technology in reflectance mode, the mean and mode of the pixel values differed by 10.3%. This large difference indicates that the distribution of pixel values was not unimodal. The %RSD of the pixel values in each well was 11.1% (N = 10024). The variations in the pixel values for reflectance

measurements originate from the angled configuration of the light source and the photodetector used by the scanners. For measurements taken using the cell phone camera without the box and the planar light source, the mean and mode of the pixel values differed by 8%. The %RSD of the pixel values in each well was 13.8%) (N = 6636). For a distribution that is not unimodal, a randomly chosen pixel in the well will not correlate well to the mean of the population. For a broad distribution, a randomly chosen pixel will be unlikely to be close to the mean of the population. To average out these variations and provide

reproducible results, in both cases, custom image processing and analysis are necessary to estimate the mean of the population.

[0064] There is a well-defined relationship between the absorbance spectrum of a sample, estimated using a spectrophotometer, and its

corresponding RGB-resolved absorbance estimated using an image based photometer.

[0065] Unlike traditional spectrophotometric instruments, which can detect light at a specific wavelength, photometers based on imaging devices (e.g., scanners, cell phone cameras) use RGB-based photodetectors that detect light over a broad bandwidth. Digital imaging devices use image sensors, which consist of an array of pixel sensors, to convert light intensity to electrical current.

Each individual pixel sensor employs CCD or CMOS photodetector with a spectral responsivity Κ(λ) (conversion of photons to electrons), and red, green and blue color filters, each with transmittance Fk(A), where k = {R, G, B} . The total spectra sensitivity of each pixel sensor is Sk(A) = R(A)Fk(A). Every pixel of an image, captured by a digital imaging device, has a set of raw, measured values

that are correlated with the spectral intensity / (X) by

k

ζ_κ = I (X)- S_k( )d where

λτ) define the range of wavelengths over which the sensor can detect light.

[0066] To reproduce colors better as they appear in real life, digital imaging sensors typically impose a nonlinear "gamma" correction before each color value Gt is recorded, according to the relation Gt = ¾(¾!'*, where fik is a linear correction factor and ¾is an exponential correction factor, which can be different for each color channel. The value of each color channel encoded into the captured image is therefore described by Equation 2:

2)

[0068] For a sample with absorbance spectrum Α(λ) and for a light source with spectrum L( ), the intensity of the spectral intensity reaching the detector is Ι(λ) = L(X)- ΐθ^"Α(λ). The set of the recorded RGB values of each pixel of the image of the sample is therefore given by Equation 3 :

3)

[0070] The RGB-resolved absorbance Au of the sample (with measured color value c ^) to a blank solution (with measured color value C^), is defined by Equation 5. Substitution of Equation 3 into Equation 4 yields Equation 5, which relates Ak to Asfi) and Αβ(λ) :

4) [0072] A_k = -r_k

(Eq. 5)

[0073] For RGB-based sensors, Sufi) and yt are not provided by the manufacturers and vary between different imaging devices, and therefore, may be estimated experimentally. Spectral sensitivity and gamma correction factors for all the color channels of the photometers 100 and 200 may be estimated in one embodiment by using an external light source, a monochromator, and a fiber optic cable to deliver pseudo delta-function inputs to the pixel sensors and determining the relationship between measured color values and sensitivity of each color channel. Further details of the estimation process are provided below. Note that manufacturers of scanners and light sources may also provide such estimates for each device.

[0074] FIG. 6A and FIG. 6E respectively show examples of

characterization of the spectrum of light {Sk( )) emitted from the light source of the photometers 100 and 200. Estimates for ¾ for the photometer 100 (yn=

0.617, y_G= 0.472, y_B= 0.498) and for the photometer 200 (y_R= 0.724, y_G= 0.525, ye= 0.778) were also performed. Using Equation 5 and i) the L( ) of the light source of photometer 100 and the planar light source of the photometer 200, ii) the Α(λ) of 1 1 different dyes measured at a range of concentrations by the microplate spectrophotometer, and iii) the extracted values of Sufi) and ¾ the expected Ak was estimated for all the standard compounds. Using Equation 5 and the measured color values Gt , measured Ak was calculated for all the standard compounds.

[0075] As an example, FIGs. 6A, 6B, 6C, 6D, 6E, 6F, 6G, and 6H outline the steps to estimate Ak for a 10-μΜ solution of methylene blue for both the photometer 100 - FIGs. 6A-6D and the photometer 200 FIGs. 6E-6H. FIGs. 6B and 6F illustrate transmittance versus wavelength, FIGs. 6C and 6G illustrate imaging sensor spectral sensitivity versus wavelength, and FIGs. 6D and 6H illustrated intensity versus wavelength of captured light. For this sample, the peak absorbance lies primarily within the red channel; the expected RGB- resolved absorbance in the red channel is AR= 0.26 for the photometer 100 and AR= 0.25 for the photometer 200; the measured RGB-resolved absorbance is A_R= 0.27 for the photometer 100 and A_R= 0.25 for the photometer 200. For all other solutions, there is a strong correlation between the expected and measured values of Ak.

[0076] Inspection of FIGs. 6D and 6H, which are graphs illustrating intensity of captured light, enable establishment of simple guidelines for which of the R, G, or B values should be used to calculate the RGB-resolved absorbance of compounds that absorb in different wavelengths. For a given wavelength, the channel that provided the highest sensitivity should be chosen. FIGs. 6C and 6G show that the blue channel (B) of the CMOS/CCD detectors of the photometer 100 and the photometer 200 is most sensitive, compared to red and green channels, to light of wavelength between 400-505 nm, the green channel is the most sensitive to light of 505-580 nm, and the red channel to light of 580-700 nm. For absorbance peaks that have their maximum in the region of 400-505 nm, the blue color channel may be used; for peaks in the region of 505- 580 nm, the green channel; and for peaks in the region of 580-700 nm, the red color channel.

[0077] Although other available methods for representing color intensity may be used, R, G, or B values were selected for one embodiment because: i) most digital imaging devices use the RGB color system to digitize the image; ii) RGB values of the pixels of an image are easily read using commonly available software (e.g. Microsoft Paint, Adobe Illustrator, Image J); iii) RGB values can be considered as metrics of the total light intensity within certain bandwidths corresponding to the light that passes through the red, green and blue filters that are present on the surface of the CCD/CMOS photodetector. The intensity of the gray scale value (x) and the hue (H) component of hue-saturation- value (HSV) color system are other parameters that have been used in the past to correlate the color of the sample with the concentration of an analyte. These values are simply linear (gray scale) or nonlinear (hue) combinations of the recorded RGB values, and are defined by Equation 7 and 8.

[0078] x = 0.299i? + 0.587G + 0.1145, (Eq. 7)

(Eq. 8)

[0080] Spectral peaks, however, typically overlap most strongly with only one RGB color channel. Combining these RGB values into grayscale or HSV values, therefore, adds information about light intensity that is not related to the concentration of the sample, and consequently, makes these color values less sensitive to changes in concentration than the raw RGB values.

[0081] In one example, solutions of different concentrations of 1 1 chromophores that exhibit absorbance peaks with very different spectral characteristics (in terms of the shape, position, and intensity of each spectral peak) and that span the entire visible spectrum were prepared. The RGB- resolved absorbance Ak of each chromophore was measured with the

photometers 100 and 200 and then compared these measurements to the peak absorbance A_peak of the same solutions of chromophores with a laboratory standard microplate spectrophotometer. Calibration lines for Ak and A_peak vs. the concentration of all 1 1 chromophores was calculated, for each photometer. Example results are shown a table 700 in FIG. 7, where absorbance values were estimated by the mean value of N=7 measurements and the regression equation used was A = a + b x Ak. The spectral bandwidth is plus or minus 5 nm.

[0082] As an example, FIG. 8 shows the calibration curve of absorbance values for DPPH vs concentration. In all cases, the curves were linear, and the limits-of-detection of concentration of the dyes, measured by both the microplate spectrophotometer and the low-cost photometers, were comparable as observable in table 700. These results show that the photometers 100 and 200 can be used as photometric detectors in place of a microplate spectrophotometer in many assays, regardless of the shape, position, or intensity of the absorbance peak. Each data point corresponds to the mean value of seven measurements and the standard deviations are smaller than the symbols used for each point. [0083] Values of the peak absorbance A_peak (measured by the microplate spectrophotometer) and the RGB-resolved absorbance Ak (measured by the image based photometers) of solutions of different concentration of all 1 1 chromogenic compounds were directly compared. Graphs in FIGs. 9A and 9B show three examples of the correlation lines of A_peak vs. Ak for the photometers 100 and 200 respectively. Each data point corresponds to the mean value of seven measurements. The standard deviations are smaller than the symbols used for each point. A table 1000 in FIG. 10 lists the analytical characteristics of all the correlation lines. For all cases, a positive, near unity correlation may be observed between the Ak and A_peak (correlation coefficient r > 0.99). Because the value of A_peak is always, by definition, greater than the value of Ak, all slopes of the correlation lines were always greater than unity. Each data point corresponds to the mean value of seven measurements and the standard deviations are smaller than the symbols used for each point.

[0084] Equation 5 shows that this behavior occurs for at least two reasons: the polychromatic nature of RGB-resolved absorbance and the gamma correction. Relative to a standard narrowband measurement at X_peak, broadening the optical bandwidth of the measurement and misaligning X_peak with respect to the peak value of L(X)-Sk (λ) can only serve to decrease the sensitivity to changes in concentration. This effect occurs because §L(X)- 10^"Α(λ)·& )dX includes regions oiA(X) that are less absorptive than A_peak. In this case, the light that does not interact much (or at all) with the analyte will contribute more to the raw RGB color values ζκ than light near _peak; in this case, the ratio between the raw RGB values, captured from the light transmitted through the sample relative to the blank, will tend to unity ~ ^{¾ '} , and therefore, reduce the value of

Ak compared to a narrowband value of A_peak.

[0085] For example, solutions of rhodamine B and eosin Y that exhibit similar values oiA_peak as well as overall spectral shape Α(λ), shown in graphs of absorbance versus wavelength (nm) in FIGs. 1 1 A, 1 IB, and 1 1 C, may still exhibit very different values of green-resolved absorbance AG (~ 3-4x difference) because, compared to eosin Y, the position of _peak of rhodamine B is better- aligned with the peak of L(X) -SG(X). FIG. 1 1 A shows absorbance spectra of disperse orange 3 in ethanolic solution (7.0 μΜ), methyl orange in aqueous solution (29.3 μΜ), and fluorescein in aqueous solution (20.0 μΜ). FIG. 1 1B shows absorbance spectra of DPPH in methanolic solution (16.8 μΜ), eosin Y in aqueous solution (8.0 μΜ), and rhodamine B in aqueous solution (5.8 μΜ). FIG. l l C shows absorbance spectra of trypan blue in aqueous solution (10.0 μΜ), Prussian blue in aqueous solution (6.0 μΜ), malachine green in aqueous solution (19.2 μΜ), methylene blue in aqueous solution (10.0 μΜ), and chlorophyll b in aqueous solution (8.8 μΜ).

[0086] The nonlinear gamma correction that all imaging equipment imposes also serves to distort the magnitude of^. In particular, because ¾ < 1 for all the devices that were characterized, the gamma corrections always tended to further reduce Ak compared to a narrowband measurement centered at X_peak. Although the gamma correction reduces the sensitivity to changes in

concentration, it does not change the linearity of the Ak, and therefore, any image sensor can be calibrated easily for linear measurements of absorbance without express knowledge of the values of the gamma correction factors.

[0087] These results demonstrate that image based photometers can be useful for absorbance measurements, regardless of the shape, position, or intensity of the peak absorbance A_peak of the sample. The ultimate sensitivity of the measurements to changes in concentration of the sample, however, will depend strongly on the position and the shape of A_peak relative to the spectral peaks oiL(X) -Sk(X) for a chosen source of illumination and imaging sensor. The better the alignment of A_peak for a chosen sample, with the peaks of L(X) -S(X), for each color channel, the more sensitive the measurements will be. In the case that increased sensitivity is desired, however, there are two strategies to do so. i)

When developing an assay, choosing or engineering dyes with narrow peaks that are centered on the peaks of L(X) -S(X) will ensure maximal sensitivity to differences in concentration, ii) Addition of narrowband color filters centered on the absorption peaks of the samples would further improve sensitivity by transmitting the light that interacts most strongly with the sample and blocking the intense background light that depends only weakly on the concentration of the analyte. [0088] Image based photometers can, in principle, also enable quantification and discrimination between unknown concentrations of colored compounds in a mixture if they satisfy the following three criteria: i) the three values of Ak for each constituent compound are known (for example, at a standard, known concentration); ii) there are no more than three different compounds in the mixture (this condition arises because there are only three degrees of freedom in each measurement: AR, AG, and AB); iii) the values of Ak of the different compounds are linearly independent-that is, the column vectors

_ A^⁾;A^⁾;A_B ⁿ⁾} of RGB-resolved absorbances of each compound, indexed n ={ 1, 2, 3 } must satisfy det[{A⁽¹⁾,A⁽²⁾,A⁽³⁾}] = 0.

[0089] If all three criteria are satisfied, then a single photometric measurement may be used to estimate the concentrations of each of the three compounds. In cases where the Ak are not linearly independent-as may be the case if some or all of the absorption peaks occur within the same color channel- or if discrimination between more than three compounds is desired, it may be possible to satisfy these condition by adding extra degrees of freedom, for example, by comparing multiple measurements with and without narrow band color filters.

[0090] FIG. 12 is a block schematic diagram of a computer system 1200 to implement methods, execute apps, execute applications, and process images according to example embodiments. All components need not be used in various embodiments. One example computing device in the form of a computer 1200, may include a processing unit 1202, memory 1203, removable storage 1210, and non-removable storage 1212. Although the example computing device is illustrated and described as computer 1200, the computing device may be in different forms in different embodiments. For example, the computing device may instead be a smartphone, a tablet, smartwatch, or other computing device including the same or similar elements as illustrated and described with regard to FIG. 12. Devices such as smartphones, tablets, and smartwatches are generally collectively referred to as mobile devices. Further, although the various data storage elements are illustrated as part of the computer 1200, the storage may also or alternatively include cloud-based storage accessible via a network, such as the Internet.

[0091] Memory 1203 may include volatile memory 1214 and nonvolatile memory 1208. Computer 1200 may include - or have access to a computing environment that includes - a variety of computer-readable media, such as volatile memory 1214 and non-volatile memory 1208, removable storage 1210 and non-removable storage 1212. Computer storage includes random access memory (RAM), read only memory (ROM), erasable programmable readonly memory (EPROM) & electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD ROM), Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices capable of storing computer-readable instructions for execution to perform functions described herein.

[0092] Computer 1200 may include or have access to a computing environment that includes input 1206, output 1204, and a communication connection 1216. Output 1204 may include a display device, such as a touchscreen, that also may serve as an input device. The input 1206 may include one or more of a touchscreen, touchpad, mouse, keyboard, camera, one or more device-specific buttons, one or more sensors integrated within or coupled via wired or wireless data connections to the computer 1200, and other input devices. The computer may operate in a networked environment using a communication connection to connect to one or more remote computers, such as database servers, including cloud based servers and storage. The remote computer may include a personal computer (PC), server, router, network PC, a peer device or other common network node, or the like. The communication connection may include a Local Area Network (LAN), a Wide Area Network (WAN), cellular, WiFi, Bluetooth, or other networks.

[0093] Computer-readable instructions stored on a computer-readable storage device are executable by the processing unit 1202 of the computer 1200.

A hard drive, CD-ROM, and RAM are some examples of articles including a non-transitory computer-readable medium such as a storage device. The terms computer-readable medium and storage device do not include carrier waves. For example, a computer program 1218 capable of providing a generic technique to perform access control check for data access and/or for doing an operation on one of the servers in a component object model (COM) based system may be included on a CD-ROM and loaded from the CD-ROM to a hard drive. The computer-readable instructions allow computer 1200 to provide generic access controls in a COM based computer network system having multiple users and servers.

[0094] In one embodiment, a processor readable storage device has instructions to cause a circuit based processor to perform operations 1300 as illustrated in flowchart form in FIG. 13. The operations may include receiving an image at 1310 of multiple samples based on broadband white light directed through the multiple samples. The image may then be viewed or otherwise opened to allow access to pixel information at 1315. Several different applications are available as previously described to enable such pixel access. Once opened, multiple red, green, and blue (RGB) pixels from the opened image for each sample may be selected at 1320. Values for each of the RGB components of the pixels may then be determined and captured at 1325. The captured values may then be averaged for each sample at 1330. The averaged values may then be correlated to provide an RGB-resolved absorbance of each sample to identify each sample at 1335. The correlation may be performed via a lookup table in some embodiments with RGB-resolved absorbance correlated to a value of the sample to identify the sample, identify a concentration of the sample, or determine another parameter.

[0095] In a further embodiment, operations on the images may account for spatial gradients in an intensity of the broadband white light as indicated at operations 1400 in FIG. 14. Operations 1400 may begin by receiving at 1410 a baseline image of multiple blank solution samples held in multiple wells of a multiple well plate. Also, a sample image of multiple wells filed with samples is received at 1415, wherein some of the wells are filled with the blank solution. At 1420, a while balance correction factor is estimated based on the RGB values of the blank solution samples in the baseline image and RGB values of the blank solution samples in the sample image. At 1425, an RGB-resolved absorbance of each sample is determined as a function of the correction factor as previously described in detail.

[0096] Examples

[0097] 1. An image based photometer comprising:

a light source adapted to hold a well plate having multiple wells for testing samples, the light source providing a source of broadband white light for transmission through the multiple wells; and

a sensing surface positioned opposite the light source to receive light transmitted through the multiple wells and shield ambient light from

transmission through the wells.

[0098] 2. The image based photometer of example 1 wherein the sensing surface comprises flatbed scanner cover having photo sensitive elements for capturing red, green, and blue (RGB) information transmitted through the well plate, the flatbed scanner operating in a transmission mode.

[0099] 3. The image based photometer of any of examples 1-2 wherein the sensing surface comprises a container having a top and four sides with an opening in the top positioned above the light source to support a camera to obtain images of the well plate through the opening.

[00100] 4. The image based photometer of example 3 and further comprising:

a base to support the light source, wherein the light source is a planar light source;

a battery supported by the base and coupled to provide power to the light source; and

a switch coupled to the battery to control provision of the power to the light source.

[00101] 5. The image based photometer of any of examples 3-4 and further comprising a camera to provide joint picture experts group (JPEG) images of samples in the well plate.

[00102] 6. The image based photometer of example 5 wherein the camera comprises a camera of a smart phone and wherein the smart phone further comprises and app for execution by a processor of the smart phone to extract red green, and blue (RGB) values for multiple pixels corresponding to each sample, average the values, and correlate the averaged values to provide an RGB-resolved absorbance of each sample.

[00103] 7. The image based photometer of example 6 wherein three or more randomly selected RGB values for each sample are averaged.

[00104] 8. The image based photometer of any of examples 1-7 wherein the light source comprises a planar light source to emit light having wavelengths across the 400nm to 700nm range.

[00105] 9. A method comprising:

directing broadband white light through multiple samples to obtain transmitted light;

obtaining multiple red, green, and blue (RGB) values for each sample; averaging the obtained multiple RGB values for each sample; and correlating the averaged values to provide an RGB -resolved absorbance of each sample to identify each sample.

[00106] 10. The method of example 9 and further comprising accounting for spatial gradients in an intensity of the broadband white light by: capturing a baseline image of multiple blank solution samples held in multiple wells of a multiple well plate;

capturing a sample image of multiple wells filed with samples, wherein some of the wells are filled with the blank solution;

estimating a white balance correction factor based on the RGB values of the blank solution samples in the baseline image and RGB values of the blank solution samples in the sample image; and

determining an RGB-resolved absorbance of each sample as a function of the correction factor.

[00107] 11. The method of any of examples 9-10 and further comprising:

identifying a multiple well microtiter plate; and

correlating reagents to the wells based on the identified microtiter plate.

[00108] 12. The method of example 11 wherein identifying the multiple well microtiter plate comprises reading and decoding a bar code on the microtiter plate.

joint picture experts group (JPEG) images of samples in the well plate. [00109] 13. The method of any of examples 9-12 and further comprising block ambient light from impinging on the samples.

[00110] 14. The method of any of examples 9-13 wherein the RGB values are obtained from an image of the samples via a camera.

[00111] 15. The method of any of examples 9-14 wherein the RGB values are obtained from an imager of a flatbed scanner operating in

transmittance mode.

[00112] 16 The method of any of examples 9-15 wherein the RGB values are obtained from an image of the samples and a program executing on a processor to extract red green, and blue (RGB) values for multiple pixels corresponding to each sample.

[00113] 17. The method of example 16 wherein three randomly selected RGB values for each sample are averaged.

[00114] 18. The method of any of examples 9-17 wherein the broadband light comprises wavelengths across the 400nm to 700nm range.

[00115] 19. A processor readable storage device having instructions to cause a circuit based processor to perform operations comprising:

receiving an image of multiple samples based on broadband white light directed through the multiple samples;

opening the image;

selecting multiple multiple red, green, and blue (RGB) pixels from the opened image for each sample;

determining values for each of the RGB components of the pixels;

averaging the obtained multiple RGB values for each sample; and correlating the averaged values to provide an RGB -resolved absorbance of each sample to identify each sample.

[00116] 20. The processor readable storage device of example 19, wherein the operations further comprise accounting for spatial gradients in an intensity of the broadband white light by:

receiving a baseline image of multiple blank solution samples held in multiple wells of a multiple well plate;

receiving a sample image of multiple wells filed with samples, wherein some of the wells are filled with the blank solution; estimating a white balance correction factor based on the RGB values of the blank solution samples in the baseline image and RGB values of the blank solution samples in the sample image; and

determining an RGB-resolved absorbance of each sample as a function of the correction factor.

[00117] Although a few embodiments have been described in detail above, other modifications are possible. For example, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. Other steps may be provided, or steps may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. Other embodiments may be within the scope of the following claims.

Claims

1. An image based photometer comprising:

a light source adapted to hold a well plate having multiple wells for testing samples, the light source providing a source of broadband white light for transmission through the multiple wells; and

a sensing surface positioned opposite the light source to receive light transmitted through the multiple wells and shield ambient light from

transmission through the wells.

2. The image based photometer of claim 1 wherein the sensing surface comprises flatbed scanner cover having photo sensitive elements for capturing red, green, and blue (RGB) information transmitted through the well plate, the flatbed scanner operating in a transmission mode.

3. The image based photometer of claim 1 wherein the sensing surface comprises a container having a top and four sides with an opening in the top positioned above the light source to support a camera to obtain images of the well plate through the opening.

4. The image based photometer of claim 3 and further comprising:

a base to support the light source, wherein the light source is a planar light source;

a battery supported by the base and coupled to provide power to the light source; and

a switch coupled to the battery to control provision of the power to the light source.

5. The image based photometer of claim 3 and further comprising a camera to provide joint picture experts group (JPEG) images of samples in the well plate.

6. The image based photometer of claim 5 wherein the camera comprises a camera of a smart phone and wherein the smart phone further comprises and app for execution by a processor of the smart phone to extract red green, and blue (RGB) values for multiple pixels corresponding to each sample, average the values, and correlate the averaged values to provide an RGB-resolved absorbance of each sample.

7. The image based photometer of claim 6 wherein three or more randomly selected RGB values for each sample are averaged.

8. The image based photometer of claim 1 wherein the light source comprises a planar light source to emit light having wavelengths across the 400nm to 700nm range.

9. A method comprising:

directing broadband white light through multiple samples to obtain transmitted light;

obtaining multiple red, green, and blue (RGB) values for each sample; averaging the obtained multiple RGB values for each sample; and correlating the averaged values to provide an RGB-resolved absorbance of each sample to identify each sample.

10. The method of claim 9 and further comprising accounting for spatial gradients in an intensity of the broadband white light by:

capturing a baseline image of multiple blank solution samples held in multiple wells of a multiple well plate;

capturing a sample image of multiple wells filed with samples, wherein some of the wells are filled with the blank solution;

estimating a white balance correction factor based on the RGB values of the blank solution samples in the baseline image and RGB values of the blank solution samples in the sample image; and

determining an RGB-resolved absorbance of each sample as a function of the correction factor.

11. The method of claim 9 and further comprising:

identifying a multiple well microtiter plate; and

correlating reagents to the wells based on the identified microtiter plate.

12. The method of claim 11 wherein identifying the multiple well microtiter plate comprises reading and decoding a bar code on the microtiter plate.

joint picture experts group (JPEG) images of samples in the well plate.

13. The method of claim 9 and further comprising block ambient light from impinging on the samples.

14. The method of claim 9 wherein the RGB values are obtained from an image of the samples via a camera.

15. The method of claim 9 wherein the RGB values are obtained from an imager of a flatbed scanner operating in transmittance mode.

16 The method of claim 9 wherein the RGB values are obtained from an image of the samples and a program executing on a processor to extract red green, and blue (RGB) values for multiple pixels corresponding to each sample.

17. The method of claim 16 wherein three randomly selected RGB values for each sample are averaged.

18. The method of claim 9 wherein the broadband light comprises wavelengths across the 400nm to 700nm range.

19. A processor readable storage device having instructions to cause a circuit based processor to perform operations comprising:

receiving an image of multiple samples based on broadband white light directed through the multiple samples;

opening the image;

selecting multiple multiple red, green, and blue (RGB) pixels from the opened image for each sample;

determining values for each of the RGB components of the pixels;

averaging the obtained multiple RGB values for each sample; and correlating the averaged values to provide an RGB -resolved absorbance of each sample to identify each sample.

20. The processor readable storage device of claim 19, wherein the operations further comprise accounting for spatial gradients in an intensity of the broadband white light by:

receiving a baseline image of multiple blank solution samples held in multiple wells of a multiple well plate;

receiving a sample image of multiple wells filed with samples, wherein some of the wells are filled with the blank solution;

estimating a white balance correction factor based on the RGB values of the blank solution samples in the baseline image and RGB values of the blank solution samples in the sample image; and

determining an RGB-resolved absorbance of each sample as a function of the correction factor.