WO2015120144A1

WO2015120144A1 - Methods and systems for diagnostics

Info

Publication number: WO2015120144A1
Application number: PCT/US2015/014609
Authority: WO
Inventors: Halil Berberoglu; Thomas E. Murphy
Original assignee: University of Texas System; University of Texas at Austin
Current assignee: University of Texas System; University of Texas at Austin
Priority date: 2014-02-05
Filing date: 2015-02-05
Publication date: 2015-08-13
Anticipated expiration: 2016-08-05

Abstract

Disclosed herein is method for probing algal cultures using multispectral image analysis. This spectral information can be used to measure algal biomass concentration, detect invasive species, and monitor culture health in real time. These methods can be extended to field applications to provide delay free process control feedback for efficient operation of large scale algae cultivation systems.

Description

METHODS AND SYSTEMS FOR DIAGNOSTICS

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Application No. 61/936,076, filed February 5, 2014, which is hereby incorporated herein by reference in its entirety.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with government support under Grant No. 201 100203-001 awarded by the National Science Foundation. The government has certain rights in this invention.

BACKGROUND

Microalgae cultivation is of commercial interest for production of biofuel feedstocks, nutritional supplements, and agricultural feed. Moreover, microalgae can grow using waste carbon dioxide and nutrients from wastewater streams, providing an added benefit of environmental pollutant mitigation. Algae can be cultivated as suspended cultures in a liquid medium or attached cultures on solid or porous substrate, with each regime having distinct advantages and disadvantages. Photobioreactors and open ponds, which employ suspended cultures, enable fast gas and nutrient transfer rates to and from the cells, which enhances productivity but requires large water volumes and energy inputs for operation. Photobioreactors employing attached cultures offer high microorganism concentrations, thus diminishing the water and energy input requirements However, photobioreactors employing attached cultures often suffer from lower biomass productivities due to mass transfer limitations. Many algal strains have been shown to grow as both attached and suspended cultures.

Efficient and cost effective algae cultivation requires real time algal biomass quantification, culture health monitoring, and biological contaminant detection. However, conventional methods for accomplishing these tasks require culture sampling, large analysis times, and use of sophisticated equipment, which make the feedback highly localized, prone to large lag times, and expensive. For example, the algal biomass concentration of open ponds, typically about 0.5 g/1, is usually quantified either by direct biomass weighing of a culture sample or by measuring a proxy for biomass, typically optical density. The biomass

concentration is then recovered from the optical density using previously generated correlations. Such methods require expensive equipment and incur large lag times, precluding the possibility of real time productivity monitoring. Improved methods for real time algal biomass quantification, culture health monitoring, and biological contaminant detection offer the opportunity to improve existing methods for microalgae cultivation SUMMARY

Provided herein are systems and methods for the detection, quantification, and/or monitoring of analytes, including one or more types of cells (e.g., algae and/or cyanobacteria), in liquid samples. The systems and methods can be used, for example, to rapidly and non- invasively monitor cell cultures (e.g., to quantify biomass, detect invasive species, monitor cell culture health, or combinations thereof). In some embodiments, the systems and methods can be used, for example, to rapidly and non-invasively monitor algal cultures (e.g., to quantify algal biomass, detect invasive species, monitor algal culture health, or combinations thereof).

Optionally, the systems and methods can be used for real-time monitoring of cell cultures, such as algal cultures. The systems and methods can be used to provide process control feedback for laboratory scale cell cultivation systems, large scale cell cultivation systems, laboratory scale algal cultivation systems, large scale algal cultivation systems, and combinations thereof (e.g., in conjunction with petri dishes and/or cell culture bottles for laboratory cell cultivation, conventional open ponds used as a platform for algal cultivation, etc.).

Provided herein are systems that comprise a liquid sample comprising an analyte (e.g., one or more types of cells, such as one or more species of algae, one or more species of cyanobacteria, or combinations thereof). The liquid sample can be, for example, a suspended cell culture, such as a cell culture grown in a conventional petri dish or cell culture flask. In some examples, the liquid sample can be a suspended algal culture, such as an algal culture grown or maintained in a conventional open pond. The system can further include a first imaging platform comprising a first optical reference surface and a second imaging platform comprising a second optical reference surface. The first imaging platform can be positioned such that the first optical reference surface is not immersed within the liquid sample. The second imaging platform can be positioned such that the second optical reference surface is immersed within the liquid sample. The system can further include an instrument configured to capture an optical signal from the first optical reference surface and the second optical reference surface. Optionally, the system can further include a light source configured to illuminate the first optical reference surface and the second optical reference surface.

Optionally, the first optical reference surface can be substantially parallel to the surface of the liquid sample. Optionally, the second optical reference surface can be substantially parallel to the surface of the liquid sample. Optionally, the first optical reference surface can be substantially parallel to the second optical reference surface.

Optionally, the first imaging platform and the second imaging platform can be provided as components of a monitoring stage, for example, to facilitate positioning of the first imaging platform and the second imaging platform. The monitoring stage can comprise one or more vertical positioning members, each of the one or more vertical positioning members having a top end and a bottom end; a first imaging platform comprising a first optical reference surface attached to the top end of one or more of the vertical positioning members; and a second imaging platform comprising a second optical reference surface attached to the bottom end of one or more of the vertical positioning members. The first optical reference surface and the second optical reference surface are positioned such that the instrument can capture an optical signal from the first optical reference surface and the second optical reference surface. Optionally, the monitoring stage can be configured such that the first optical reference surface and the second optical reference surface are facing in the same direction. Optionally, the monitoring stage can be configured such that the first imaging platform and the second imaging platform are substantially parallel.

The system can further comprise a computing device configured to receive and process optical signals from the instrument to obtain information about the liquid sample and/or one or more analytes present in the liquid sample (i.e., one or more sample characteristics). Analysis of the optical signal can involve multispectral image analysis. In certain embodiments, the computing device can comprise a processor and a memory operably coupled to the processor, the memory having further computer-executable instructions stored thereon that, when executed by the processor, cause the processor to receive an optical signal from the instrument; process the optical signal to identify a first analysis region and a second analysis region, wherein the first analysis region comprises at least a portion of the first optical reference surface and the second analysis region comprises at least a portion of the second optical reference surface; analyze the first analysis region to determine a reference parameter; analyze the second analysis region to determine a sample parameter; compare the sample parameter and the reference parameter to obtain a sample value; process the sample value to obtain a sample characteristic; and output the sample characteristic.

Methods for the detection, quantification, and/or monitoring of analytes, including algae and cyanobacteria, in liquid samples are also provided. The methods can involve the use of multispectral image analysis to detect, quantify, and/or monitor analytes, including algae and cyanobacteria, in a liquid sample. The methods can be performed using the systems described above.

Methods for the detection, quantification, and/or monitoring of analytes can comprise providing a first imaging platform comprising a first optical reference surface and a second imaging platform comprising a second optical reference surface, and positioning the first imaging platform and the second imaging platform relative to a liquid sample, such that the first optical reference surface is not immersed within the liquid sample, and the second optical reference surface is immersed within the liquid sample (e.g., such that a volume of the liquid sample is disposed between the second optical reference surface and an instrument configured to capture an optical signal from the second optical reference surface). Methods can further include capturing an optical signal from a first optical reference surface and a second optical reference surface (e.g., with the instrument), and processing the optical signal to obtain information about the liquid sample and/or one or more analytes present in the liquid sample (i.e., one or more sample characteristics).

The methods described herein can be used to obtain a wide variety of sample

characteristics that can provide quantitative or qualitative information about analytes (e.g., cells, such as algae and/or cyanobacteria) in the liquid sample. Examples of sample characteristics that can be determined and provided using the methods described herein include, for example, information regarding the concentration of the analyte in the liquid sample. In certain embodiments, the liquid sample comprises a first analyte and a second analyte. In some of these embodiments, the sample characteristic can comprise information regarding the relative amounts of the first analyte and the second analyte present in the liquid sample.

In certain embodiments, the analyte can include a microorganism (e.g., a species of algae, a species of cyanobacteria, or a combination thereof). In some embodiments, the sample characteristic can comprise information regarding the biomass concentration of the

microorganism. Such sample characteristics can be used to monitor algal and/or cyanobacterial cultures in real time during cultivation.

In some embodiments, the sample characteristic can comprise information regarding the health of the microorganism that can be used to monitor the health of algal and/or cyanobacterial cultures in real time during cultivation. Accordingly, the methods described herein can be used to prevent and/or identify algal and/or cyanobacterial culture crashes.

In some embodiments, the liquid sample comprises a first analyte and a second analyte, wherein the first analyte comprises a species of algae and the second analyte comprises a species of cyanobacteria. In these embodiments, the sample characteristic can comprise information regarding the relative biomass of the algae and cyanobacteria. Such sample characteristics can be used to monitor algal cultures in real time during cultivation for contamination. DESCRIPTION OF FIGURES

Figure 1 is an example system for the detection, quantification, and/or monitoring of analytes, including algae and cyanobacteria, in liquid samples.

Figure 2 displays example sample vessels: (A) cylindrical, (B) spherical, and (C) capped. Figure 3 displays an example flask.

Figure 4 displays an example dish and lid.

Figure 5 displays an example (A) lid sized relatively larger than the dish such that when the lid is placed over the dish (B) the lid encompasses the dish. Top view (C) and side view (D).

Figure 6 displays an example (A) lid sized relatively larger than the dish such that when the lid is placed over the dish (B) the lid encompasses the dish. Top view (C) and side view (D).

Figure 7 illustrates example marked targets.

Figure 8 displays example sample vessels with example marked targets disposed thereon. Figure 9 illustrates example fixed imaging platforms in open ponds.

Figure 10 illustrates example fixed imaging platforms in closed photobioreactors.

Figure 1 1 illustrates an example monitoring stage.

Figure 12 illustrates an example monitoring stage.

Figure 13 illustrates an example monitoring stage.

Figure 14 is an example computing device.

Figure 15 is an example system for the detection, quantification, and/or monitoring of analytes, including algae and cyanobacteria, in liquid samples.

Figure 16 illustrates an example monitoring stage.

Figure 17 illustrates example monitoring stages.

Figure 18 illustrates examples of non-flocculated (a) and flocculated (b) liquid samples. Figure 19 shows a cross-section schematic of the photobox.

Figure 20 shows a (a) schematic of the experimental setup used for measuring biofilm thickness and (b) biofilm thickness vs. areal biomass concentration.

Figure 21 shows the normalized color intensity as a function of areal biomass concentration for attached and suspended cultures of the cyanobacteria Anabaena variabilis at two culture ages for (a, b) red, (c, d) green, and (e, f) blue.

Figure 22 shows the normalized spectral intensities of the three light sources used for image acquisition, along with the wavelength bands of the color filters of the RGB camera.

Figure 23 shows digital images of an (a) attached culture at 7.8 g/m² and (c) suspended culture at 6.1 g/m² under the six lighting and background combinations, as well as the predicted areal biomass concentration plotted against measured areal biomass concentration for (b) attached and (d) suspended cultures under the six background and lighting combinations. Figure 24 shows (a) Reference platform used for imaging the cultures and (b) the reference platform in a culture of Chlorella sp. at a biomass concentration of 0.4 g/1.

Figure 25 shows the (a) red, (b) green, and (c) blue values as a function of Chlorella sp. biomass concentration.

Figure 26 shows the (a) red, (b) green, and (c) blue values as a function of A. variabilis biomass concentration.

Figure 27 shows the predicted versus measured biomass concentration of monocultures of (a) Chlorella sp. and (b) A. variabilis.

Figure 28 shows the red value as a function of blue value for monocultures of Chlorella sp. and A. variabilis.

Figure 29 shows the red values of a Chlorella sp. culture with different amounts of A. variabilis, as well as the red value that would be expected for a Chlorella sp. monoculture.

Figure 30 shows the (a) red and blue values and (b) fluorometric parameters as a function of culture temperature for a monoculture of A variabilis.

DETAILED DESCRIPTION

Provided herein are systems and methods for the detection, quantification, and/or monitoring of analytes, including one or more types of cells, such as algae and/or cyanobacteria, in liquid samples. The cells can be any type of cells, including animal cells (e.g., mammalian cells), plant cells, fungal cells, algae, bacteria (e.g., cyanobacteria), and combinations thereof. The systems and methods can be used, for example, to rapidly and non-invasively monitor cell cultures (e.g., to quantify biomass, detect invasive species, monitor cell culture health, or combinations thereof). In some embodiments, the systems and methods can be used, for example, to rapidly and non-invasively monitor algal cultures (e.g., to quantify algal biomass, detect invasive species, monitor algal culture health, or combinations thereof). Optionally, the systems and methods can be used for real-time monitoring of cell cultures, such as algal cultures. The systems and methods can be used to provide process control feedback for laboratory scale cell cultivation systems, large scale cell cultivation systems, laboratory scale algal cultivation systems, large scale algal cultivation systems, and combinations thereof (e.g., in conjunction with petri dishes and/or cell culture bottles for laboratory cell cultivation, conventional open ponds used as a platform for algal cultivation, etc.).

Referring now to Figure 1, example systems (100) can comprise a liquid sample (101) comprising an analyte (e.g., one or more types cells, such as one or more species of algae, one or more species of cyanobacteria, or combinations thereof). The liquid sample can be, for example, a suspended cell culture, such as a cell culture grown in a conventional petri dish or cell culture flask. In some examples, the liquid sample can be a suspended algal culture, such as an algal culture grown or maintained in a conventional open pond. The system (100) can further include a first imaging platform (104) comprising a first optical reference surface (105) and a second imaging platform (102) comprising a second optical reference surface (103). The first imaging platform (104) can be positioned such that the first optical reference surface (105) is not immersed within the liquid sample (101). The second imaging platform (102) can be positioned such that the second optical reference surface (103) is immersed within the liquid sample (101). The system can further include an instrument (107) configured to capture an optical signal from the first optical reference surface (105) and the second optical reference surface (103). In some cases, the system (100) can further comprise a computing device (108) configured to receive and process optical signals from the instrument (107), as discussed in more detail below. In some cases, the system can further include a light source (106) configured to illuminate the first optical reference surface (105) and the second optical reference surface (103).

In some examples, the system (100) can comprise a liquid sample (101) comprising an analyte; a first imaging platform (104) comprising a first optical reference surface 105; a second imaging platform (102) comprising second optical reference surface (103); and an instrument (107) configured to capture an optical signal from the first optical reference surface (105) and the second optical reference surface (103). The first imaging platform (104) can be positioned relative to the instrument (107) and the liquid sample (101) such that an electromagnetic signal traveling from the first optical reference surface (105) to the instrument (107) does not traverse the liquid sample (101) (e.g., such that the electromagnetic signal does not pass through the liquid sample). The second imaging platform (102) can be positioned relative to the instrument (107) and the liquid sample (101) such that an electromagnetic signal traveling from the second optical reference surface (103) to the instrument (107) traverses the liquid sample (101) (e.g., such that the electromagnetic signal passes through the liquid sample).

The electromagnetic signal can comprise, for example, one or more wavelengths of interest. For example, the electromagnetic signal can comprise one or more wavelength of visible light, UV light, infrared light, or combinations thereof.

In some examples, the system can further comprise a sample vessel. The sample vessel can be any vessel or container such that the liquid sample (101) can be contained within an interior volume of the sample vessel. The sample vessel can be, for example, a Petri dish, bottle, flask, beaker, carboy, cuvette, graduated cylinder, test tube, carafe, decanter, pitcher, jug, urn, ewer, vial, jar, vat, tank, drum, tub, barrel, or a combination thereof.

Referring now to Figure 2, the sample container (400) can have an exterior surface (402) and an interior surface (404), wherein the liquid sample (101) can be contained in the volume defined by the interior surface (404) of the sample vessel (400). The sample vessel can have any shape consistent with the systems and methods described herein. For example, Figure 2A displays a sample vessel (400) with a cylindrical shape and Figure 2B displays a sample vessel (400) with a spherical shape. In some examples, the sample vessel (400) can have a removable cap (406), such as shown in Figure 2C.

In some embodiments, the sample vessel (400) can comprise a flask, such as a conventional cell culture flask. Referring now to Figure 3, the sample vessel (400) can, in some embodiments, comprise a flask (410) comprising a plurality of walls, said plurality of walls including a front wall (412), a back wall (414), a first side wall (416), a second side wall (418), a first end wall (420), and a second end wall (422); wherein the front wall (412) and the back wall (414) are joined by the first side wall (416), the second side wall (418), the first end wall (420) and the second end wall (422); and the first end wall (422) has an outwardly-extending open- ended neck (424) that forms a conduit through which the liquid sample (101) has access to the volume defined by the plurality of walls. In some embodiments, the front wall (412) and the back wall (414) are substantially parallel. In some embodiments, the first side wall (416) and the second side wall (418) are substantially parallel. In some examples, the second imaging platform (102) can comprise at least a portion of the back wall (414) of the flask (410) (e.g., the second imaging platform can be disposed on or in the back wall). In some examples, the first imaging platform (104) can comprise at least a portion of the front wall (412) of the flask (410) (e.g., the first imaging platform can be disposed on or in the front wall). In some embodiments, the front wall (412), back wall (414), first side wall (416), second side wall (418), first end wall (420), second end wall (422), and neck (424) can be integrally formed.

The flask can be fabricated from any suitable material or combination of materials compatible with the systems and methods described herein. Suitable materials include, but are not limited to, polymers, silicones, glasses, ceramics, inorganic materials, and combinations thereof. In some examples, the flask can be substantially optically transparent.

In some embodiments, the sample vessel (400) can comprise a dish, such as a conventional Petri dish or cell culturing dish. Referring now to Figure 4, the sample vessel (400) can, in some embodiments, comprise a dish (430) comprising a base plate (432) and an upstanding wall (434) extending continuously around the periphery of the dish (430), and the liquid sample (101) can be contained within the volume defined by the base plate (432) and the upstanding wall (434). In some embodiments, the upstanding wall (434) can be integrally formed with the base plate (432). In some embodiments, the second imaging platform (102) can comprise at least a portion of the base plate (432) (e.g., the second imaging platform can be disposed on or in the base plate). In some embodiments, the sample vessel can further comprise a lid (440) comprising a top plate (442) and a depending wall (444) extending continuously around the periphery of the lid (440). The lid (440) can be sized such that when the lid (440) is placed over the dish (430), the top plate (442) is located above and substantially parallel to the base plate (432). In some embodiments, the depending wall (444) is integrally formed with the top plate (406). In some embodiments, the first imaging platform (104) can comprise at least a portion of the top plate (442) (e.g., the first imaging platform can be disposed on or in the top plate).

The dish and/or lid can be fabricated from any suitable material or combination of materials compatible with the systems and methods described herein. Suitable materials include, but are not limited to, polymers, silicones, glasses, ceramics, inorganic materials, and combinations thereof. In some examples, the dish and/or lid can be substantially optically transparent.

The dish and lid can be relatively sized such that they have an interference fit when the lid is placed over the dish. In some embodiments, the lid can be sized relatively larger than the dish. In some embodiments, the lid can be sized relatively larger than the dish such that when the lid is placed over the dish the lid encompasses the dish. Example configurations where the lid is sized relatively larger than the dish are illustrated in Figure 5 and Figure 6. In the example configuration shown in Figure 5, the first optical platform could comprise a portion of the top plate, such as a portion not located above the liquid sample.

The imaging platforms can independently have any orientation compatible with the methods and systems described herein. In some cases, for example, one or more of the imaging platforms can be positioned at an angle with respect to the surface of the liquid sample (109). If the second platform is immersed in the liquid sample and oriented at an angle, the methods and systems described herein can be used to glean information about the liquid sample as a function of depth. In some cases, the first optical reference surface (105) is substantially parallel to the surface of the liquid sample (109). In some cases, the second optical reference surface (103) is substantially parallel to the surface of the liquid sample (109). In some cases, the first optical reference surface (105) is substantially parallel to the second optical reference surface (103).

The optical reference surfaces can have any optical properties consistent with the systems and methods described herein. In some cases, the second optical reference surface (103) can further comprise a marked target which has different optical properties (e.g., a different color) than the rest of the surface. Example marked targets are shown in Figure 7. The marked target can, for example, be used to assess the amount of scattering from the liquid sample. In some embodiments, the marked targets can be disposed on the sample vessel. Examples of marked targets disposed on sample vessels are shown in Figure 8. In some cases, the first optical reference surface (105) can be optically equivalent to the second optical reference surface (103), where optically equivalent means the reflective, transmissive and absorptive properties of the first optical reference surface at all wavelengths and orientations of interest are substantially the same as those of the second optical reference surface.

In some cases, the first optical reference surface (105) and the second optical reference surface (103) are both highly reflective. As used herein, "highly reflective" indicates that at least 80% of the incident radiation at the wavelength(s) of interest is reflected from the surface. In some cases, the first optical reference surface (105) and the second optical reference surface (103) are both white.

In some cases, the first imaging platform and the second imaging platform can be provided as fixed stages. For example, the second imaging platform can be provided as a component affixed to the container holding the liquid sample (e.g., a column affixed to the bottom of the container, a ledge affixed to the side of the container, etc.). For example, the first imaging platform can be affixed to the container holding the liquid sample, or at a location completely removed from the liquid sample. Example possibilities for integration of fixed imaging platforms into open algae pond systems are illustrated in Figure 9. In these cases, the first imaging platform can be affixed above the liquid sample (e.g., to the side of the pond), or entirely outside of the pond, meanwhile the second imaging platform can be affixed to the pond such that it is immersed in the liquid sample (e.g. to the side or bottom of the pond directly, or via some other connector). In some cases, the fixed imaging platforms can be applied to closed photobioreactors. Example possibilities for integration of fixed imaging platforms into closed photobioreactors are illustrated in Figure 10. In some cases, the closed photobioreactor can be a tubular photobioreactor and a cylindrical shell concentric with the tubular photobioreactor can be used as the imaging platform.

Optionally, the first imaging platform and the second imaging platform can be provided as components of a monitoring stage, for example, to facilitate positioning of the first imaging platform and the second imaging platform. An example monitoring stage (200) is illustrated in Figure 1 1. The monitoring stage (200) can comprise one or more vertical positioning members (210), each of the one or more vertical positioning members having a top end (211) and a bottom end (212); a first imaging platform (250) comprising a first optical reference surface (240) attached to the top end (211) of one or more of the vertical positioning members (210); and a second imaging platform (230) comprising a second optical reference surface (220) attached to the bottom end (212) of one or more of the vertical positioning members (210). In some cases, the monitoring stage (200) can be configured such that the first optical reference surface (240) and the second optical reference surface (220) are facing in the same direction. In some cases, the monitoring stage (200) can be configured such that first imaging platform (250) and the second imaging platform (230) are substantially parallel.

In some embodiments, the monitoring stage can include a plurality of vertical positioning members. Referring now to Figure 12, the monitoring stage (300) can comprise at least two vertical positioning members (310), each of the one or more vertical positioning members having a top end (311) and a bottom end (312); a first imaging platform (350) comprising a first optical reference surface (340) attached to the top end (311) of one or more of the vertical positioning members (310); and a second imaging platform (330) comprising a second optical reference surface (320) attached to the bottom end (312) of one or more of the vertical positioning members (310). In some cases, the monitoring stage (300) can be configured such that the first optical reference surface (340) and the second optical reference surface (320) are facing in the same direction. In some cases, the monitoring stage (300) can be configured such that first imaging platform (350) and the second imaging platform (330) are substantially parallel.

Optionally, the monitoring stage (300) can further comprise a stabilizing platform (360) configured to stabilize the monitoring stage (300) in a liquid sample (e.g., to minimize prevent movement of the monitoring stage during imaging). In some embodiments, the stabilizing platform can be attached to the bottom end (312) of one or more of the vertical positioning members (310), as shown in Figure 12. The stabilizing platform can also be otherwise attached to the monitoring stage. For example, the stabilizing platform can also be attached to the top end (311) of one or more of the vertical positioning members (310).

The monitoring stage, as well as the components thereof, can be fabricated from any suitable material or combination of materials compatible with the methods described below. Examples of suitable materials including polymers, silicones, glasses, metals, ceramics, inorganic materials, and combinations thereof. In some cases, the monitoring stage, as well as the components thereof, can be fabricated from a suitable material or combination of materials that prevent biofouling (e.g., the accumulation of microorganisms, cells, plants, algae, animals or other debris on surfaces). For example, to prevent biofouling, the monitoring stage and/or the components thereof may be fabricated from microtextured materials, hydrophobic materials, hydrophilic materials, or combinations thereof. In some cases, the monitoring stage and/or the components thereof can be coated with a coating to prevent biofouling. For example, to prevent biofouling, the monitoring stage and/or components thereof can be coated with a biocide, hydrophilic substance, hydrophobic substance, or combinations thereof. In some cases, the monitoring stage and/or components thereof can be equipped with a wiper that can function to periodically wipe the surface to manually prevent biofouling.

In some cases, the monitoring stage, as well as the components thereof, can be fabricated from a suitable material or combination of materials that provide for proper positioning of the monitoring stage in the liquid sample (e.g., positioning of the monitoring stage such that the first optical reference surface is not immersed within the liquid sample and the second optical reference surface is immersed within the liquid sample). For example, the monitoring stage, as well as the components thereof, can be fabricated from a suitable material or combination of materials that allow the monitoring stage to float in the liquid sample such that the first optical reference surface is not immersed within the liquid sample and the second optical reference surface is immersed within the liquid sample.

If desired, the monitoring stage further comprises a buoyancy member attached to the monitoring stage. The buoyancy member can be configured to position the monitoring stage in the liquid sample such that the first optical reference surface is not immersed within the liquid sample and the second optical reference surface is immersed within the liquid sample. In certain embodiments, the one or more vertical positioning members are configured to position the monitoring stage in the liquid sample such that the first optical reference surface is not immersed within the liquid sample and the second optical reference surface is immersed within the liquid sample. Referring now to Figure 13, in some cases the buoyancy member (370) can be attached to the monitoring stage (300). The buoyancy member can be attached to any part of the monitoring stage, such as the vertical positioning members (310) or first imaging platform (350).

The system (100) can further comprise a computing device (108) configured to receive and process optical signals from the instrument (107). Figure 14 illustrates an example computing device upon which embodiments of the invention may be implemented. The computing device (108) may include a bus or other communication mechanism for

communicating information among various components of the computing device 108. In its most basic configuration, computing device 108 typically includes at least one processing unit 112 (a processor) and system memory 114. Depending on the exact configuration and type of computing device, system memory 114 may be volatile (such as random access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in Figure 14 by dashed line 110. The processing unit 112 may be a standard programmable processor that performs arithmetic and logic operations necessary for operation of the computing device 108.

Computing device 108 may have additional features/functionality. For example, computing device 108 may include additional storage such as removable storage 116 and non- removable storage 118 including, but not limited to, magnetic or optical disks or tapes.

Computing device 108 may also contain network connection(s) 124 that allow the device to communicate with other devices. Computing device 108 may also have input device(s) 122 such as a keyboard, mouse, touch screen, antenna or other systems configured to communicate with the instrument in the system described above, etc. Output device(s) 122 such as a display, speakers, printer, etc. may also be included. The additional devices may be connected to the bus in order to facilitate communication of data among the components of the computing device 108. All these devices are well known in the art and need not be discussed at length here.

The processing unit 112 may be configured to execute program code encoded in tangible, computer-readable media. Computer-readable media refers to any media that is capable of providing data that causes the computing device 108 (i.e., a machine) to operate in a particular fashion. Various computer-readable media may be utilized to provide instructions to the processing unit 112 for execution. Common forms of computer-readable media include, for example, magnetic media, optical media, physical media, memory chips or cartridges, a carrier wave, or any other medium from which a computer can read. Example computer-readable media may include, but is not limited to, volatile media, non- volatile media and transmission media. Volatile and non- volatile media may be implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data and common forms are discussed in detail below. Transmission media may include coaxial cables, copper wires and/or fiber optic cables, as well as acoustic or light waves, such as those generated during radio-wave and infra-red data communication. Example tangible, computer- readable recording media include, but are not limited to, an integrated circuit (e.g., field- programmable gate array or application-specific IC), a hard disk, an optical disk, a magneto- optical disk, a floppy disk, a magnetic tape, a holographic storage medium, a solid-state device, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices.

In an example implementation, the processing unit 112 may execute program code stored in the system memory 114. For example, the bus may carry data to the system memory 114, from which the processing unit 112 receives and executes instructions. The data received by the system memory 114 may optionally be stored on the removable storage 116 or the nonremovable storage 118 before or after execution by the processing unit 112.

Computing device 108 typically includes a variety of computer-readable media.

Computer-readable media can be any available media that can be accessed by device 108 and includes both volatile and non- volatile media, removable and non-removable media. Computer storage media include volatile and non-volatile, and removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. System memory 114, removable storage 116, and non-removable storage 118 are all examples of computer storage media.

Computer storage media include, but are not limited to, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing device 108. Any such computer storage media may be part of computing device 108.

It should be understood that the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination thereof. Thus, the methods, systems, and associated signal processing of the presently disclosed subject matter, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computing device, the machine becomes an apparatus for practicing the presently disclosed subject matter. In the case of program code execution on programmable computers, the computing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. One or more programs may implement or utilize the processes described in connection with the presently disclosed subject matter, e.g., through the use of an application programming interface (API), reusable controls, or the like. Such programs may be implemented in a high level procedural or object-oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language and it may be combined with hardware implementations.

In certain embodiments, system memory 114 comprises computer-executable instructions stored thereon that, when executed by the processor (112), provide for analysis of optical signals captured by the instrument (107) to obtain information about the liquid sample and/or one or more analytes present in the liquid sample (i.e., one or more sample characteristics, as discussed in more detail below). In certain embodiments, the system memory 114 can comprise computer- executable instructions stored thereon that, when executed by the processor (112), cause the processor to: receive an optical signal from the instrument; process the optical signal to identify a first analysis region and a second analysis region, wherein the first analysis region comprises at least a portion of the first optical reference surface and the second analysis region comprises at least a portion of the second optical reference surface; analyze the first analysis region to determine a reference parameter; analyze the second analysis region to determine a sample parameter; compare the sample parameter and the reference parameter to obtain a sample value; process the sample value to obtain a sample characteristic; and output the sample characteristic. In certain embodiments, the system memory 114 can further comprise computer-executable instructions stored thereon that, when executed by the processor (112), signals the instrument to obtain an optical signal that includes the first optical reference surface and the second optical reference surface.

In certain embodiments, the instrument can comprise a camera. In certain embodiments, the optical signal can comprise an image. Image analysis can involve multispectral image analysis. For example, each pixel of a digital image acquired by the camera can be represented by the color vector equal to [r_P, g_P, b_P], corresponding to the pixel's red (560-700 nm), green (490-590 nm), and blue (410-500 nm) intensities, respectively (Poynton C. Digital Video and HDTV: Algorithms and interfaces, San Francisco, CA: Morgan Kaufmann Publishers; 2003). Each element of the vector [r_P, g_P, b_P] has an integer value between 0 and 255, inclusive. First, a region of the image covering at least a portion of the first optical reference surface can be identified. This region can be used as a reference as it is not immersed in the liquid sample. The color vector of the reference region (the sample parameter, equal to [r_w, gw,

can then be calculated as the average of the red, green, and blue intensities of all the pixels in the region. Next, a region of the image containing at least a portion of the second optical reference surface can be identified. This region can be used to give information about the sample, as it is immersed in the liquid sample, whereas the other region is not. The raw color vector of this region (the sample parameter, ^~c^~ equal to \r₀, go, bo]) can then be calculated as the average of the red, green, and blue intensities of all the pixels in the region. The sample parameter and the reference parameter can then be compared to obtain a sample value (a normalized color vector c) that can be processed to obtain one or more sample characteristics. The normalized color vector c can be calculated by dividing each raw color intensity by the intensity of that color in the reference region, as shown below, to account for differences in the intensity and spectral content of the light source:

c( = ¾(0/Cw(0 ^{for 1} = 1, 2, 3 (1)

The sample value can be processed, for example by comparison to appropriate standard curves, to obtain one or more sample characteristics. In certain embodiments, the system memory 114 can further comprise computer- executable instructions stored thereon that, when executed by the processor (112), signals the camera to obtain an image that includes the first optical reference surface and the second optical reference surface. In certain embodiments, the camera simultaneously captures an image of the first optical reference surface and the second optical reference surface.

In some cases, the system can further comprise multiple additional imaging platforms. Referring now to Figure 15, the system (100) can further comprise a third imaging platform (130) comprising a third optical reference surface (140). The third imaging platform (130) can be positioned such that the third optical reference surface (140) is immersed in the liquid sample (101). The third imaging platform (130) may be positioned such that it is at the same or different depth in the liquid sample (101) than the second imaging platform (102). For example, in some cases the third imaging platform (130) may be positioned such that there is a larger volume of the liquid sample (101) between the third optical reference surface (140) and the instrument (107) than between the second optical reference surface (103) and the instrument (107).

Optionally, the multiple additional imaging platforms together with the first imaging platform and the second imaging platform can be provided as components of a monitoring stage, for example, to facilitate positioning of the first imaging platform, the second imaging platform, and the multiple additional imaging platforms. An example monitoring stage (200) is illustrated in Figure 16. The monitoring stage (200) can comprise one or more vertical positioning members (210), each of the one or more vertical positioning members having a top end (211) and a bottom end (212); a first imaging platform (250) comprising a first optical reference surface (240) attached to the top end (211) of one or more of the vertical positioning members

(210) ; a second imaging platform (230) comprising a second optical reference surface (220) attached to the bottom end (212) of one or more of the vertical positioning members (210); a third imaging platform (260) comprising a third optical reference surface (270) attached to the bottom end (212) of one or more of the vertical positioning members (210) with the top end

(211) attached to the second imaging platform (230). In some cases, the monitoring stage (200) can be configured such that the first optical reference surface (240), the second optical reference surface (220), and the third optical reference surface (270) are facing in the same direction. In some cases, the monitoring stage (200) can be configured such that first imaging platform (250), the second imaging platform (230), and the third imaging platform (260) are substantially parallel.

The optical reference surfaces can be of any shape consistent with the methods and systems described herein. For example, the optical reference surfaces can be polygonal (e.g., rectangular, cross-shaped), curved (e.g. circular, ellipsoidal) or combinations thereof. Example imaging platforms with various shaped optical references surfaces are illustrated in Figure 17. Figure 17a illustrates a cross pattern wherein four platforms could be immersed in the liquid sample. Figure 17b illustrates another cross pattern, with two layers of immersed platforms. Figure 17c illustrates a radial design with multiple layers of immersed platforms.

In some embodiments, the systems described above can further comprise a light source.

The light source can be any type of light source. In some embodiments, the system can include a single light source. In other embodiments, more than one light source can be included in the system. The one or more light sources present in the system can be configured to provide for substantially equivalent illumination of the first optical reference surface and the second optical reference surface. In some embodiments, the light source is part of the imaging platform(s). Examples of suitable light sources include natural light sources (e.g., sunlight), artificial light sources (e.g., incandescent light bulbs, light emitting diodes, gas discharge lamps, etc.), and combinations thereof. Furthermore, the light source can be a continuous light source (e.g., sunlight, incandescent light bulbs, light emitting diodes, gas discharge lamps, continuous wave lasers, etc.), a pulsed light source (e.g., pulsed lasers), or combinations thereof.

In some embodiments, the light source can be any light source that emits one or more wavelength between 300 and 2500 nm. In some embodiments, the light source emits a broad range of wavelengths and a filter can be used to select a wavelength of interest. In some embodiments, a range of wavelengths is selected. Any type of filter consistent with the systems and methods described herein can be used. For example, the filter can be an absorptive filter, a dichroic filter, a monochromatic filter, a longpass filter, a bandpass filter, a shortpass filter, or combinations thereof. In some embodiments, the filter is an external filter. In some embodiments, the filter is embedded in the light source. In some embodiments, the filter is embedded in the instrument.

The instrument can be any instrument consistent with the systems and methods herein.

For example, the instrument can be a hyperspectral imaging device, a spectrometer, a photometer, a camera, or combinations thereof. In some embodiments, the instrument is a camera. The camera can be any type of camera consistent with the systems and methods described herein. In some embodiments, the camera can have a high resolution. In some embodiments, the camera can have low resolution, as in, for example, an RGB camera. In some embodiments, the camera can be any suitable digital camera that can capture images from which RGB values can be determined. For example, the camera can be a charge-coupled device imager sensitive to light in the wavelength region of interest. Suitable camera platforms are known in the art and commercially available from companies such as Zeiss, Canon, Applied Spectral Imaging, and others, and such platforms are readily adaptable for use in the systems and methods described herein. The camera can feature simultaneous or sequential capture of multiple wavelengths using either embedded optical filters within the camera or external filters.

The optical signal can be any optical signal consistent with the systems and methods herein. In some embodiments, the optical signal is an image.

The liquid sample can comprise one or more analytes. Suitable analytes include absorbing (e.g., colored) and/or scattering analytes that have an optical signal that can be detected using the instrument described above. In certain embodiments, the analyte can be living organisms (e.g., a microorganism); dead organisms; single suspended cells; colonies of cells; flocks of cells; organic particles; inorganic particles; absorbing, fluorescing and/or luminescing cells, pigments, dyes or molecules; or combinations thereof. In certain

embodiments, the analyte can comprise mammalian cells, insect cells, photosynthetic cells, non- photosynthetic cells, or combinations thereof.

In some embodiments, the analyte comprises a species of algae. The species of algae can belong to any group. Examples of groups of algae include, but are not limited to, archaeplastida, chlorophyta (green algae), rhodophyta (red algae), glaucophyta, rhizaria, excavate,

chlorarachniphytes, euglenids, chromista, alveolata, heterokonts, bacillariophyceae (diatoms), azodine, bolidomonas, eustimatophyceae, phaeophyceae (brown algae), chrysophyceae (golden algae), raphidophyceae, synurophyceae (yellow-green algae), crytophyta, dinoflagellaes, haptophyta, or combinations thereof. In some embodiments, the species of algae comprises a species of the genus Chlorella. In some embodiments, the analyte comprises a mixture of multiple species of algae.

In some embodiments, the analyte comprises a species of cyanobacteria. Examples of cyanobacteria include, but are not limited to, any species of the following genera: Acaryochloris, Aamphithrix, Anabaena, Anabaenopsis, Aanacytis, Aanacytisgloeocapsa, Aphanizomenon, Arthrospira, Arthrosiphon, Aulosira, Brasilonema, Calothrix, Camptylonemopsis, Chrondocytis, Chroococcidiopsis, Coleodesmiopsis, Coleodesmium, Collenia, Crinalium, Cyanobiont, Cylindrospermopsis, Cylindrospermum, Chrondrocystis, Desmonema, Diplocolon, Dichothrix, Diplotrichia, Drilosiphon, Eoplectonema, Eucapsis, Fortiea, Gaillardotella, Gardnerula, Gloeobacter, Gleocapsa, Gloeotrichia, Gloiotrichia, Gunflintia, Hassallia, Heteractis,

Homoeothrix, Inomeria, Isactis, Kyrtuthrix, Loefgrenia, Lyngbya, Mastigonomea,

Merismopedia, Microchaete, Microcytis, Montanoa, Moorea, Morania, Nodularia, Nostoc, Ophiothris, Oscillaria, Oscillatoria, Paraortonella, Petalonema, Planktothrix,

Polycytismerismopedia, Polycystis anabaena, Primorivularia, Prochlorococcus, Prochloron, Radaisia, Rexia, Rivularia, Rivulariopsis, Sacconema, Scytonema, Scytonema, Scytonematopsis, Spirirestris, Spriulina, Streptostemon, Synechococcus, Synechocystis, Tolypothrix , Tildenia, Trichodesmium, Wollea, Zonotrichites, and combinations thereof. In some embodiments, the species of cyanobacteria comprises Anabaena variabilis. In some embodiments, the analyte comprises a mixture of multiple species of cyanobacteria.

In certain embodiments, the liquid sample comprises a first analyte and a second analyte. In some embodiments, the first analyte comprises a first species of algae and the second analyte comprises a second species of algae. In some embodiments, the first analyte comprises a first species of cyanobacteria and the second analyte comprises a second species of cyanobacteria. In some embodiments, the first analyte comprises a species of algae and the second analyte comprises a species cyanobacteria. In particular embodiments, the species of algae comprises a species of the genus Chlorella, and the species of cyanobacteria comprises Anabaena variabilis. In some embodiments, the first analyte comprises a mixture of multiple species of algae and the second analyte comprises a mixture of multiple species of cyanobacteria.

Methods for the detection, quantification, and/or monitoring of analytes can comprise providing a first imaging platform comprising a first optical reference surface and a second imaging platform comprising a second optical reference surface, and positioning the first imaging platform and the second imaging platform relative to a liquid sample, such that the first optical reference surface is not immersed within the liquid sample, and the second optical reference surface is immersed within the liquid sample (e.g., such that a volume of the liquid sample is disposed between the second optical reference surface and an instrument configured to capture an optical signal from the second optical reference surface). Methods can further include capturing an optical signal from a first optical reference surface and a second optical reference surface (e.g., with the instrument), and processing the optical signal to obtain information about the liquid sample and/or one or more analytes present in the liquid sample (i.e., one or more sample characteristics, as discussed in more detail below).

In some examples, methods for the detection, quantification, and/or monitoring of analytes can comprise providing a first imaging platform comprising a first optical reference surface and a second imaging platform comprising a second optical reference surface and positioning the first imaging platform and the second imaging platform relative to a liquid sample and an instrument, such that an electromagnetic signal traveling from the first optical reference surface to the instrument does not traverse the liquid sample, and an electromagnetic signal traveling from the second optical reference surface to the instrument traverses the liquid sample. Methods can further include capturing an optical signal from the first optical reference surface and the second optical reference surface (e.g., with the instrument), and processing the optical signal to obtain information about the liquid sample and/or one or more analytes present in the liquid sample (i.e., one or more sample characteristics, as discussed in more detail below). In some examples of the methods, the liquid sample is contained in a sample vessel, such as any of the sample vessels described herein above in more detail.

The imaging platforms can independently have any orientation compatible with the methods and systems described herein. In some cases, for example, one or more of the imaging platforms can be positioned at an angle with respect to the surface of the liquid sample. If the second platform (e.g., the platform immersed in the liquid sample) is oriented at an angle, the methods and systems described herein can be used to glean information about the liquid sample as a function of depth. In some cases, the first optical reference surface is substantially parallel to the surface of the liquid sample. In some cases, the second optical reference surface is substantially parallel to the surface of the liquid sample. In some cases, the first optical reference surface is substantially parallel to the second optical reference surface.

In some embodiments, methods for processing the optical signal can comprise processing the optical signal to identify a first analysis region and a second analysis region, wherein the first analysis region comprises at least a portion of the first optical reference surface and the second analysis region comprises at least a portion of the second optical reference surface; analyzing the first analysis region to determine a reference parameter; analyzing the second analysis region to determine a sample parameter; comparing the sample parameter and the reference parameter to obtain a sample value; and processing the sample value to obtain a sample characteristic.

In certain embodiments, the instrument can comprise a camera. In certain embodiments, the optical signal can comprise an image. Methods for processing the image can involve multispectral image analysis. For example, each pixel of the digital image acquired by the camera can be represented by the color vector equal to [r_P, g_P, b_P], corresponding to the pixel's red (560-700 nm), green (490-590 nm), and blue (410-500 nm) intensities, respectively (Poynton C. Digital Video and HDTV: Algorithms and interfaces. San Francisco, CA: Morgan Kaufmann Publishers; 2003). Each element of the vector [r_P, g_P, b_P] has an integer value between 0 and 255, inclusive. First, a region of the image covering at least a portion of the first optical reference surface can be identified. This region can be used as a reference as it is not immersed in the liquid sample. The color vector of the reference region (the sample parameter, equal to [r_w, gw, bw]) can then be calculated as the average of the red, green, and blue intensities of all the pixels in the region. Next, a region of the image containing at least a portion of the second optical reference surface can be identified. This region can be used to give information about the sample, as it is immersed in the liquid sample, whereas the other region is not. The raw color vector of this region (the sample parameter, equal to [r₀, go, bo]) can then be calculated as the average of the red, green, and blue intensities of all the pixels in the region. The sample parameter and the reference parameter can then be compared to obtain a sample value (a normalized color vector c) that can be processed to obtain one or more sample characteristics. The normalized color vector c can be calculated by dividing each raw color intensity by the intensity of that color in the reference region, as shown below, to account for differences in the intensity and spectral content of the light source:

c(0 = ¾ (i)/c^(0 for i = 1, 2, 3 (1)

The sample value can be processed, for example by comparison to appropriate standard curves, to obtain one or more sample characteristics.

The methods described herein can be used to obtain a wide variety of sample characteristics that can provide quantitative or qualitative information about analytes (e.g., algae and/or cyanobacteria) in the liquid sample. Examples of sample characteristics that can be determined and provided using the methods described herein include, for example, information regarding the concentration of the analyte in the liquid sample (e.g., the concentration of the analyte in the sample, graphs or other information regarding trends in the concentration of the analyte in the sample, values for changes in analyte concentration over time, alerts indicating that the concentration of the analyte in the sample has reached a predetermined set point, etc.). In certain embodiments, the liquid sample comprises a first analyte and a second analyte. In some of these embodiments, the sample characteristic can comprise information regarding the relative amounts of the first analyte and the second analyte present in the liquid sample (e.g., the ratio of the first analyte to the second analyte, graphs or other information regarding trends in the ratio of the first analyte to the second analyte in the sample, values for changes in ratio of the first analyte to the second analyte over time, alerts indicating that the ratio of the first analyte to the second analyte in the sample has reached a predetermined set point, etc.).

In some embodiments, the analyte can include a microorganism (e.g., a species of algae, a species of cyanobacteria, or a combination thereof). In some embodiments, the sample characteristic can comprises information regarding the biomass concentration of the

microorganism (e.g., the biomass concentration of the species of algae, the biomass

concentration of the species of cyanobacteria, graphs or other information regarding trends in the biomass concentration of the algae and/or cyanobacteria, values for changes in biomass concentration of the algae and/or cyanobacteria over time, alerts indicating that the biomass concentration of the algae and/or cyanobacteria has reached a predetermined set point, etc.). Such sample characteristics can be used to monitor algal and/or cyanobacterial cultures in real time during cultivation. Such sample characteristics can be determined from sample parameters comprising the value of one optical signal (e.g., the intensity of the red, blue or green band).

In some embodiments, the sample characteristic can comprise information regarding the health of the microorganism (e.g., values for photosynthetic yield of the microorganism in the sample, graphs or other information regarding trends in the photosynthetic yield of the microorganism in the sample, values for changes in the photosynthetic yield of the

microorganism in the sample over time, alerts indicating that the photosynthetic yield of the microorganism in the sample has reached a predetermined set point, etc.). Such sample characteristics can be used to monitor the health of algal and/or cyanobacterial cultures in real time during cultivation. In some embodiments, the sample characteristic can comprise an indicator of the health of the algae and/or cyanobacteria in the liquid sample (e.g., an alert indicating contamination of the algal and/or cyanobacterial culture). These sample

characteristics can be used to prevent and/or identify algal and/or cyanobacterial culture crashes.

In some embodiments, the sample parameter can comprise information regarding the spatial variance of the optical signal. In some embodiments, information regarding the spatial variance of the optical signal can be used to assess the amount of flocculation (sample characteristic). For example, in dilute cultures are often flocculated to hasten biomass harvesting. In this process, single cells form groups, which settle more quickly than single cells, and can also be harvested by filtration through membranes with larger pore size. In a non- flocculated sample, the optical signal from the analyte will be substantially homogeneous across the entire second analysis region; whereas the optical signal from the second analysis region in a flocculated sample will vary spatially. Thus, the standard deviation or variance in the optical signal spatially across the second analysis region can be used as the sample parameter to assess the amount of flocculation (sample characteristic) in a liquid sample. This concept is further illustrated in Figure 18. In some embodiments, the information regarding the spatial variance of the optical signal can be used to assess the presence of non-photosynthetic contaminants. Non- photosynthetic contaminants (e.g., non-photosynthetic microorganisms, dust, etc.) often scatter light without absorbing it. Therefore, the presence of non-photosynthetic contaminants can be identified as an increase in light scattering in the liquid sample. For example, if the second analysis region comprises a marked target, the effect of increased scattering can comprise blurring of the target, which can be identified as a smoother gradient in the optical signal with spatial variation. This concept is further illustrated in Figure 7.

In certain embodiments, the liquid sample comprises a first analyte and a second analyte, wherein the first analyte comprises a species of algae and the second analyte comprises a species of cyanobacteria. In these embodiments, the sample characteristic can comprise information regarding the relative biomass of the algae and cyanobacteria (e.g., the ratio of the biomass of the algae to the biomass of the cyanobacteria, graphs or other information regarding trends in the ratio of the biomass of the algae to the biomass of the cyanobacteria in the sample, values for changes in ratio of the biomass of the algae to the biomass of the cyanobacteria over time, alerts indicating that the ratio of the biomass of the algae to the biomass of the cyanobacteria in the sample has reached a predetermined set point, etc.). Such sample characteristics can be used to monitor algal cultures in real time during cultivation for contamination. In some embodiments, the sample characteristic can comprise an indicator of a contaminant in the liquid sample (e.g., an alert indicating contamination of the algal culture). Such sample characteristics can be determined from, for example, sample parameters comprising a ratio of optical signals (e.g., a ratio of absorption coefficients in the red and blue regions of the visible spectrum).

The examples below are intended to further illustrate certain aspects of the systems and methods described herein, and are not intended to limit the scope of the claims. EXAMPLES

The following examples are set forth below to illustrate the methods and results according to the disclosed subject matter. These examples are not intended to be inclusive of all aspects of the subject matter disclosed herein, but rather to illustrate representative methods and results. These examples are not intended to exclude equivalents and variations of the present invention which are apparent to one skilled in the art.

Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.) but some errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, temperature is in °C or is at ambient temperature, and pressure is at or near atmospheric. There are numerous variations and combinations of reaction conditions, e.g., component concentrations, temperatures, pressures and other reaction ranges and conditions that can be used to optimize the product purity and yield obtained from the described process.

Overview

Real time algal biomass quantification, culture health monitoring, and biological contaminant detection are important for efficient and cost effective algae cultivation. However, conventional methods for accomplishing these tasks require culture sampling, large analysis times, and use of sophisticated equipment, which make the feedback highly localized, prone to large lag times, and expensive. For example, the algal biomass concentration of open ponds, typically about 0.5 g/1, is usually quantified either by direct biomass weighing of a culture sample or by measuring a proxy for biomass, typically optical density. The biomass

concentration is then recovered from the optical density using previously generated correlations. Such methods require expensive equipment and incur large lag times, precluding the possibility of real time productivity monitoring.

The biomass concentration of suspended cultures is conventionally measured either by direct biomass weighing of a culture sample, or by measurement of a proxy for biomass, typically optical density or chlorophyll concentration. Dry biomass weighing entails weighing an empty, dry filter, filtering a liquid sample through the filter, drying the sample, and reweighing it. This method is very simple, but it requires instrumentation and is dependent on the sampling location. Moreover, nonalgal microorganisms and salts can be retained in the filtering process and counted as dry biomass, resulting in overestimation of the algal biomass concentration. Finally, the drying time is usually several hours, thus precluding the possibility of real-time biomass quantification.

Direct measurement of monochromatic optical density can be used as a proxy for biomass concentration. Optical density of a culture is measured at a specific wavelength in a spectrophotometer and correlated to biomass concentration using published calibration curves. However, the accuracy of said correlations is dependent on measurement specifications such as the spectral bandwidth and acceptance angle of the measuring instrument as well as the spectral distribution of the light source. Furthermore, the optical density of the sample can be dependent on sampling location.

Chlorophyll extraction entails centrifuging a sample, resuspending the concentrated sample in a solvent (usually ethanol or methanol), and measuring the optical density of the resulting chlorophyll suspension at specific wavelengths in a spectrophotometer. The chlorophyll concentration is calculated from the optical density using published correlations. This process can be performed in less than an hour, but it requires solvents, a centrifuge, and a

spectrophotometer. Moreover, the accuracy of the results depends on the similarity between the spectral content of the light source and the spectral bandwidth of the measuring instrument and the instrument used in obtaining the published correlations.

More recently, a method for using image analysis to measure the biomass concentration of a vertical tubular bioreactor was reported (Jung S-K, Lee SB. Biotechnol Progr. 2006, 22, 1443-1450). In this method, a red-green-blue image taken from the top of the reactor was converted to a grayscale image. The average gray value of the grayscale image was then correlated to the biomass concentration. However, in their study, the vertical tubular

photobioreactor was illuminated from the sides and imaged from the top. Therefore, the average gray value was dependent on tube diameter, as a culture in a larger diameter tube will appear darker than the same culture in a smaller diameter tube. Additionally, the correlation is also dependent on the spectral content of the light source, as an algae culture illuminated with green light will have a higher average gray value than one illuminated by red or blue light due to selective absorption by the photosynthetic pigments. Moreover, on a large scale, illumination from the top of a culture is logistically more feasible than uniform illumination from all sides.

The instantaneous photosynthetic rate of a culture can be measured electrochemically by measuring the dissolved oxygen concentration or fluorometrically with a pulse-amplitude- modulated (PAM) fluorometer. Dissolved oxygen electrodes can be inexpensively deployed into algal cultures, but they only measure the dissolved oxygen concentration. Additional information is needed to recover the oxygen production rate, which is proportional to the photosynthetic rate. On the other hand, PAM fluorometry can be used to measure the quantum yield of the photosynthetic light reactions in a culture, which is a relative indicator of photosynthetic health. However, due to the high cost of PAM fluorometers, a proxy parameter for quantum yield is sought.

Identification of invasive species in algal cultures is generally accomplished by microscopy or molecular analysis. Light microscopy requires a microscope and the ability to visually identify contaminants. Through molecular analysis, DNA fragments specific to known invasive species are amplified and identified using polymerase chain reaction (PCR) and gel electrophoresis. Both of these methods require expensive equipment and cannot be performed without removing a sample from the culture.

Multispectral image analysis is an optical diagnostic method that relies on the reflected and backscattered spectral radiation from the system being monitored. Any phenomenon that affects the reflected and backscattered radiation can in principle be detected and quantified by this technique. Thus, multispectral imaging provides a highly sensitive and versatile method for monitoring and diagnosing culture productivity, purity, and health.

Example 1

Materials and Methods

Stock culture cultivation

The cyanobacteria A. variabilis (ATCC 29413 -U) was used as an exemplary

microorganism due to its widespread use in experimental studies on photobiological CO2 mitigation and biohydrogen production. A. variabilis is a cyanobacteria composed of cells of approximately 5 μιη in diameter forming filaments more than 100 μιη long. Its cultivation as both suspended and attached cultures have been shown (Berberoglu H, Jay J, Pilon L. Ira , J Hydrogen Energ. 2008, 33, 1 172-1 184; Gaffhey AM, Markov SA, Gunasekaran M. Appl Biochem Biotechnol. 2001, 91-93, 185-193). Also, the pigmentation and the optical properties of A. variabilis have been reported (Berberoghi H, Pilon L. Int J Hydrogen Energ. 2007, 32, 4772-4785; Fogg GE, ei al. The Blue-Green Algae. London: Academic Press; 1973). Optically, the organisms have absorption peaks at 440 and 680 nm, corresponding to chlorophyll a, as well as an absorption peak at 620 nm, corresponding to phycocyanin, a light-harvesting

phycobiliprotein (Berberoghi H, Pilon L. Ini J Hydrogen Energ. 2007, 32, 4772-4785; Fogg GE, et al. The Blue-Green Algae. London: Academic Press; 1973). In lesser amounts, A. variabilis also contains carotenoids, with broad absorption between 400 and 500 nm, and the

phycobiliproteins, phycoerythrin and allophycocyanin, with absorption peaks at 565 and 650 nm, respectively (Fogg GE, et al The Blue-Green Algae. London: Academic Press; 1973).

The stock suspended culture for the experiments was cultivated in BG11 nutrient medium, sparged with air containing 2% by volume carbon dioxide, and continuously illuminated with 16 ± 2 W/m² irradiation (74 ± 8

in the photosynthetically active region (PAR) using cool white fluorescent bulbs (Philips, F32T8).

Biofilm preparation and imaging

Two suspended stock cultures, one 5-days old and the other 7-days old, were used to generate the biofilms to evaluate the effect of culture age on image analysis results. For each culture, biofilms of varying thickness were simulated by filtering a known volume of microorganism suspension from the stock culture onto glass fiber filter paper with an average pore diameter of 0.7 μιη (Whatman, GF/F) using a vacuum filtration apparatus (Kimax, 27070). The resulting biofilm had a diameter of 3.8 cm. Then, the biofilm was placed into a custom- made illuminated box (photobox) to ensure that each image acquired was subject to the same background and lighting conditions (Figure 19). Inside the photobox, the biofilm was illuminated with diffuse light provided by a fluorescent bulb (Underwriters Laboratories, Portable Luminaire) at an irradiance of 4.5 W/m² (21 μΕ/πι²/8). A digital camera with 8- megapixel resolution (Logitech, Pro 9000) was then placed into the camera port of the photobox. The automatic exposure and white balance features were disabled to avoid automatic increases in image brightness as the culture became darker on addition of microorganisms. The exposure and white balance were set to their minimum values and the gain was set to its maximum value. These settings were selected because they produced the greatest color contrast possible in the image.

After each image acquisition, an incremental volume of suspended stock culture was added to the biofilm by vacuum filtration. This process was repeated until there was no noticeable change in biofilm appearance. It was ensured that each volume of suspended stock culture filtered onto the paper had the same microorganism concentration by stirring the stock culture prior to each filtration. Given the short duration of the experiments (less than 5 min per sample) and the low light levels used (less than 21 μΕ/ιη²/8), no appreciable variations in either the biomass concentration or the optical properties of the cells were expected.

Biofilm thickness and areal biomass concentration

To measure biofilm thickness t, a modified version of the method presented by Norrman et al. was used (Norrman G, Characklis WG, Breyers JD. Dev Ind Microbiol . 1977, 18, 581- 590). In this method, a needle electrode was maintained at 2 V bias with respect to the biofilm- supporting filter paper using a Source-Meter (Keithley 2400; Figure 20a). With the needle electrode in air, the current through the circuit was at its noise level of approximately 20 pA. The needle electrode was lowered toward the biofilm surface at a rate of 1 μητ/s using a three axis differential translation stage (Thorlabs, PT3A) and a computer-controlled actuator (Thorlabs, ZST25B and TST001). The needle was lowered until the current through the circuit abruptly increased by four orders of magnitude to approximately 0.1 mA. This signaled the completion of the circuit, and the vertical needle position corresponded to the top of the biofilm. This process was repeated for the nonbiofilm-supporting region of filter paper. In this case, the vertical needle position at which the circuit was completed marked the bottom of the biofilm. For a given biofilm, this process was repeated at five biofilm locations to enable calculation of the spatial variance of the biofilm thickness.

Finally, the areal biomass concentration XA of the biofilms was calculated as the ratio of the dry algal biomass to the biofilm area. For this, the volumetric biomass concentration of the stock culture used in the experiments was determined according to the standard methods reported (Zhu CJ, Lee YK. JAppl Phycol. 1 97, 9, 189-194). This was done simultaneously while preparing the biofilms to eliminate the effects of growth. The dry biomass of each biofilm was then calculated by multiplying the volumetric biomass concentration of the suspended stock culture with the volume of culture used to make the biofilm.

Suspended culture preparation and imaging

The chamber for holding suspended cultures was a custom built acrylic box that measured 10.0 x 6.4 x 8.1 cm³ in length, width, and height, respectively. The top of the box was open to create a top-irradiated illumination scheme typical of scaled-up open raceway ponds, and the sides were covered with white paper to impose symmetry boundary conditions. Cultures of different biomass concentrations having a total volume of 400 mL were placed in the acrylic box and imaged. The concentration of the culture in the acrylic box was measured by filtering a known volume onto filter paper, drying, and weighing.

Image analysis

A custom computer code was developed to analyze the images of the algal cultures. Each pixel of a digital image acquired by the camera is represented by the color vector equal to [r_P, g_P, bp], corresponding to the pixel's red (560-700 nm), green (490-590 nm), and blue (410-500 nm) intensities, respectively (Poynton C. Digital Video and HDTV: Algorithms and interfaces. San Francisco, CA: Morgan Kaufmann Publishers; 2003). Each element of the vector [r_P, g_P, bp] has an integer value between 0 and 255, inclusive. First, a region of the image containing only the algal culture (green region) was identified. The raw color vector of the green region ^~c^~ equal to [r₀, go, bo] was then calculated as the average of the red, green, and blue intensities of all the pixels in the region. Then, a region of the image that contained a white reference background was identified. The biofilm- supporting filter paper and the white sides covering the acrylic box were used as the white reference regions for the images of the attached and suspended cultures, respectively. The color vector of the white reference region equal to [r_w, gw, bw] was calculated as the average of the red, green, and blue intensities of all the pixels in the region. The elements of the normalized color vector c used in the analysis were calculated by dividing each raw color intensity by the intensity of that color in the white region to account for differences in the intensity and spectral content of the light source:

c(0 = ¾ (i)/c^(0 for i = 1, 2, 3 (1)

Results and Discussion

Relationship between biofilm thickness and areal biomass concentration

Figure 20b shows the relationship between the areal biomass concentration and the biofilm thickness. A linear relationship was recovered using the electromechanical method. The thickness t is given in terms of areal concentration XA as, t = 3.696 XA, where t is in μιη and XA is in g/m². The coefficient of determination R² for this fit was 0.9989. Using these results, it was established that the volumetric microorganism concentration in the biofilm was 271 kg dry biomass per cubic meter (kg DW/m³).

Attenuation of red, green, and blue light in the algal cultures

Figure 21 shows the normalized red, green, and blue intensities (r, g, and b) of attached and suspended cultures as a function of areal biomass concentration. The magnitude of each color intensity of a given image is a result of the combined effects of reflection from the culture surface and backscattering from within the culture. The intensity of the back-scattered light is governed by the RTE, which takes into account absorption and anisotropic scattering by the microorganisms and the medium (Berberoglu H, Pilon L, hit J Hydrogen Energ. 2010, 35, 500- 510; Siegel R, Howell J . Thermal Radiation Heat Transfer, 4th ed. New York: Taylor & Francis; 2002). Solving the RTE for the backscattered intensity from dense cultures poses a challenge as scattering phenomena gets into dependent regime where the radiative properties of the culture can no longer be described as linear functions of microorganism concentration (Berberoglu H, Piion L. hit J Hydrogen Energ, 2010, 35, 500-510;). Therefore, this analysis seeks an empirical correlation between areal biomass concentration and color intensity, taking into account reflected and back-scattered light from the culture. The equation form for the empirical model has been formulated by modeling light transfer through the algae culture with ray tracing analysis and is given by,

where the vector a represents the reflected intensity from the surface of the culture, and is assumed to be independent of biomass concentration based on the results presented by

Kazemipour et al (Kazemipour F, et al. J Quant Spectrosc Radiat Transf. 2011, 112, 131-142). Moreover, the vector β represents the reflectance of the bottom surface of the algae culture. The vector E_{p ex} contains the pseudo-mass extinction cross sections for red (i=l), green (i=2), and blue (i=3) light, which control the attenuation of irradiance of each color along the ray path.

The experimental data shown in Figure 21 were fitted with Eq. 2 using the linear least- squares method (Hastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning, 1st ed. Mew York: Springer; 2003). Table 1 summarizes the values οΐ ά, β , and E_{p ex}, as well as the R² value for each fit. All R² values obtained were greater than 0.97. The wideband pseudo- extinction cross sections of the red and blue bands were approximately equal to each other and greater than that for the green band by a factor of about 3.5. The more gradual attenuation of backscattered green light compared to red and blue light is a result of the relatively low absorptivity of A. variabilis at wavelengths between 490 and 590 nm, which is in turn a result of the organisms' lack of photosynthetic pigments that absorb light in that wavelength range (Berberoglu FT, Pilon L. Ira J Hydrogen Energ. 2007, 32, 4772-4785; Fogg GE, et al. The Blue- Green Algae. London: Academic Press; 1973). Moreover, the pseudo-extinction cross sections for the suspended cultures were greater than those for the attached ones by an average of 35%. This can be attributed to (i) light absorption by the liquid medium of the suspended culture and (ii) to dependent scattering in the biofilm, decreasing the effective extinction compared to that in an independently scattering medium (Siegel R, Howell J. Thermal Radiation Heat Transfer, 4th ed. New York: Taylor & Francis; 2002). Table 1. The curve fit parameters a and β. Wideband and pseudo-pass extinction cross sections

E ,ext, and coefficient of determination R² for fittin E . 2 to the ex erimental data.

Furthermore, in both cultures, the color intensities at areal biomass concentrations greater than 10 g/m² were greater than their respective intensities at areal biomass concentrations between 8 and 10 g/m² by approximately 0.02. This trend is especially clear for the red and blue intensities of the suspended cultures. This trend can be attributed to the increase in

backscattering that resulted from multiple scattering by organisms in a thin layer below the illuminated surface at higher concentrations. Finally, the components of the vector a were greater for the suspended cultures than for the attached ones by a factor of about 5, indicative of the greater surface reflectivity of water compared to a microorganism film layer (Kazemipour F, et al. J Quant Spectrosc Radiat Transf. 2011, 112, 131-142; Siege! R, Howell J. Thermal Radiation Heat Transfer, 4th ed. New York: Taylor & Francis; 2002).

The rate at which light energy is converted to chemical energy in photosynthetic systems is a function of the wavelength, known as the photosynthetic action spectrum, as well as the local irradiance. Thus, knowledge of spectral light attenuation within a culture can provide valuable information about the overall productivity of that system. Particularly, the local productivity in A variabilis cultures is highly dependent on the availability of red light as its photosynthetic action spectrum indicates one predominant peak at a center wavelength of 633 nm and a half width at half maximum of 37 nm (McLeod GC. J Gen Physiol. 1958, 42, 243- 250; Mimuro M, Fujita Y. Biochim Biophys Acta. 1977, 459, 376-389). Therefore, using the wideband red pseudo-extinction cross section obtained in this study, we can illustrate the areal biomass concentration, ΧΑ,Ϊ, for A. variabilis at which the local red irradiance drops to 10, 1, and 0.1% of its value incident on the culture as,

^XA,f = - ln(/) /E_Piext(red^~) (3) where / represents the attenuation fraction. In this analysis, it is assumed that the culture density is such that all red irradiance is absorbed before reaching the back surface. Table 2 presents the areal biomass concentrations at which the wideband red irradiance is attenuated to 10, 1, and 0.1% of its incident value within attached and suspended cultures of A variabilis. The table also shows the associated biofilm thicknesses and suspended photobioreactor depths that correspond to these concentrations, assuming attached and suspended volumetric microorganism concentrations of 271 kg/m³ and 500 g/m³, respectively (Borowitzka MA. JBiotechnol. 1999, 70, 313-321). Table 2. Areal biomass concentration (¾/), biofilm thickness (z_a), and suspended culture physical depth (z_s), at which the wideband red irradiance is attenuated to 10, 1, and 0.1% of its value at the light facing side of the culture, assuming attached and suspended columetric microorganism concentrations of 271 kg/m² and 500 g/m³, respectively.

Correlation between normalized green intensity and areal biomass concentration

The normalized green intensity displayed more gradual attenuation with increasing biomass concentration than the red and blue intensities. Therefore, the normalized green intensity was identified as the appropriate value to correlate to areal biomass concentration because such a correlation would be accurate within a larger range of concentrations. For attached cultures, rearranging Eq. 2 and using the coefficient values from Table 1 yields,

Equation 4 is recommended for areal biomass concentrations between 0.34 and 14 g/m², which was the range of areal biomass concentrations examined in this study. Similarly, for suspended cultures:

Equation 5 is recommended for areal biomass concentrations between 0.25 and 21 g/m². It is worth noting that the coefficients in Eqs. 4 and 5 are dependent on the cellular pigment concentrations. It is well known that cells can up- or down-regulate their pigment contents depending on cultivation conditions (Pontes AG, et al. J Plant Physiol, 1991, 137, 441 - 445). However, it is possible to reestablish the pigment-biomass correlation as necessary to account for these effects.

Validity of the correlation under different lighting conditions and backgrounds To take full advantage of this biomass quantification method, it must be insensitive to the magnitude, spectral content, and angle of incidence of the irradiance onto the culture, as well as to the spectral reflectance of the background surrounding the culture. Therefore, the accuracies of Eqs. 4 and 5 were measured on independently prepared sets of attached and suspended culture images acquired using combinations of three different light sources and two different backgrounds. The three light sources used were the fluorescent lamp of the photobox, fluorescent room lighting, and shaded diffuse solar illumination. The hemispherical PAR (from 400 to 700 nm) for the photobox, room, and sunlight were measured with a quantum sensor (Li- Cor, LH-100) to be 4.5 W/m² (21 μΕ/ιη²/8), 1.3 W/m² (6.0 μΕ/ιη²/8), and 1.6 W/m² (7.4 μΕ/ιη²/8), respectively. The normalized spectral intensities of the three light sources are shown in Figure 22. The spectra of the two fluorescent bulbs were measured using a monochromator (Newport, Cornerstone 260) with 3.7-nm spectral resolution, while the diffuse solar spectrum was reported by Gueymard et al (Gueymard CA, Myers D, Emery K. Sol Energy. 2002, 73, 443- 467). The two backgrounds were white paper (OfficeMax, Copy Paper) and black epoxy resin lab bench surface (VWR). Black and white materials were selected as backgrounds because the total reflectance in the visible range of any other color material will be between those of these two extremes.

Figure 23 a shows digital images acquired of the benthic cultures at an areal biomass concentration of 7.8 g/m². The color and brightness of each biofilm appears different to the naked eye due to the variations in the magnitude and spectral content of the incident irradiance. Figure 23b indicates that the benthic culture areal biomass concentration was predicted well by Eq. 4 under all six lighting conditions. Moreover, Table 3 shows the root mean square deviation (RMSD) between the areal biomass density predicted by Eq. 4 and the actual biomass density for each of the six conditions. For the case of photobox lighting with white background, which served as the validation data set, the RMSD was 0.67 g/m², which corresponds to an average error of 6.5%. The RMSD was highest for solar illumination with black background and room illumination with black background. The average percent error incurred in using Eq. 4 to predict areal biomass density across all six lighting and background scenarios was 25%. Similarly, Figure 23c shows the digital images acquired of the planktonic cultures at an areal biomass concentration of 6.1 g/m². Figure 23d shows the areal biomass concentration as a function of normalized green intensity for these cultures, as well as the areal biomass concentration predicted by Eq.5. Moreover, Table 4 shows the RMSD between the areal biomass density predicted by Eq. 5 and the actual biomass density for each of the six conditions. For the case of photobox lighting with white background, which serves as the validation data set, the RMSD was 1.64 g/m², which corresponds to an average error of 15%. The RMSD was highest for solar illumination with both white and black backgrounds. The average percent error incurred in using Eq. 5 to predict areal biomass density across all lighting and background combinations was 21%.

Table 3. Root mean square deviation (RSMD) between the areal biomass concentration predicted by Eq. 4 and the actual areal biomass concentration for the six lighting and background scenarios for the attached cultures.

Table 4. Root mean square deviation (RMSD) between the areal biomass concentration predicted by Eq. 5 and the actual areal biomass concentration for the six lighting and background scenarios for the attached cultures.

Although the traditional methods can provide more accurate results, they can take long analysis time and/or be highly sampling location-specific as explained previously. Thus, the temporal and spatial variations in biomass concentration in cultivation systems can blur the accuracy advantage of these more traditional methods with respect to the method presented here. Moreover, in this study, the lighting and background conditions have been identified that minimize the prediction errors. Thus, in practical implementation of the presented method, care can be taken to stay in these higher accuracy regimes.

Conclusions

Herein is reported a rapid, noninvasive method of biomass quantification in suspended and attached algae cultures using only a simple RGB camera and custom image analysis software. By parsing images of A. variabilis cultures into their red, green, and blue components and correlating the green intensity with biomass concentration, the biomass concentrations of independently prepared cultures imaged under a variety of lighting and background conditions were predicted with an average error of 23%.

Example 2

Materials and Methods

Culture selection and cultivation

The green alga Chlorella sp. (UTEX 2168) and the cyanobacterium Anabaena variabilis (ATCC 29413-U) were used in this study. Chlorella sp. is a spherical green alga approximately 5 μιη in diameter. It contains the pigments chlorophyll a, with absorption peaks at 440 nm and 680 nm, chlorophyll b, with peaks at 660 and 480 nm, and carotenoids, with an absorption band between 400 and 500 nm (Andersen RA. Algal Culturing Techniques. London: Elsevier Academic Press, 2005). Chlorella sp. is of interest in the biofuels market due to its high lipid productivity (Chisti Y. Biodiesel from algae. Biotechnol Adv. 2007, 25, 294-306), as well as in the health food market due to its richness in protein, vitamins, polysaccharides, polyunsaturated fatty acids, and microelements (Pulz O, Gross W. Appl Microbiol Biotechnol. 2004, 65, 635^18; G5rs M, et al. JAppl Phycol. 2009, 22,265-276).

Anabaena variabilis is a cyanobacterium composed of cells of approximately 5 μιη in diameter forming filaments more than 100 μιη long (Berberoglu H, Pilon L. Int J Hydrogen Energ. 2007, 32, 4772^1785). It has been used ubiquitously in experimental studies on photobiological CO2 mitigation and biohydrogen production (Tsygankov AA, et al. FEMS Microbiol Lett. 1998, 167, 13-17; Gaffney AM, Markov SA, Gunasekaran M. Appl Biochem Biotechnol. 2001, 91-93, 185-193; Berberoglu H, Jay J, Pilon L. Int J Hydrogen Energ. 2008, 33, 1 172-1184). Similarly to Chlorella sp., A. variabilis contains chlorophyll a and carotenoids, but it does not contain chlorophyll b (Fogg GE, Stewart WDP, Fay P, Walsby AE. The blue- green algae. London: Academic Press, 1973). Additionally, A. variabilis contains the phycobiliproteins phycoerythrin, phycocyanin, and allophycocyanin, which have absorption peaks at 565 nm, 620 nm, and 650 nm, respectively (Fogg GE, Stewart WDP, Fay P, Walsby AE. The blue-green algae. London: Academic Press, 1973). As a result of the difference in pigment content between Chlorella sp. and A variabilis, monocultures of these strains appear different to the naked eye, with the former having a green-yellow color and the latter having more of a blue-green appearance.

Each strain was cultivated as a batch monoculture in the BG1 1 nutrient medium

(Andersen RA. Algal Culturing Techniques. London: Elsevier Academic Press, 2005), sparged with air containing 2% by volume carbon dioxide, and continuously illuminated with 16 ± 2 W/m² irradiation (74 ± 8 μΕ/ιη²-8) in the photosynthetically active region (PAR) using cool white fluorescent bulbs (Philips, F32T8).

Imaging platform

An acrylic box measuring 10.0 cm long by 6.4 cm wide by 8.1 cm high was constructed to mimic an open pond cultivation system. The top of the box was open and was illuminated with cool white fluorescent lamps (Philips, 4100 K), which provided diffuse irradiation at an irradiance of 3.8 ± 0.1 μΕ/ιη²-8. Cultures of known biomass concentrations of Chlorella sp. and A. variabilis were prepared and imaged in the box using an RGB webcam (Logitech, Pro 9000). Three sets of images were prepared for each strain using cultures with ages ranging between 7 and 14 days. It was ensured that the culture color was independent of culture age in this age range.

Due to absorption of visible light by the organisms, the red, green, and blue intensities of a culture image decrease with increasing biomass concentration (Murphy TE, Macon K, Berberoglu H. Biotechnol Progr. 2013, 29, 808-16). However, the optical thickness of most algae cultivation systems in the visible region of the spectrum is on the order of 100 (Borowitzka MA. J Biotechnol. 1999, 70, 313-321). As a result, a saturation effect occurs in the water- leaving radiance and cultures with biomass concentrations greater than about 0.1 g/1 are indistinguishable from each other (Murphy TE, Macon K, Berberoglu H. Biotechnol Progr. 2013, 29, 808-16). In response to this challenge, a custom reference platform, shown in Figure 24a, was constructed. When placed in an aqueous medium, the top plate rests above the surface of the water, whereas the bottom plate resides below the surface. This strategy increases the sensitivity of the water-leaving radiance to the microorganism concentration in the range of concentrations typically employed in algae cultivation systems (0 to 0.5 g/1). Moreover, normalizing the color intensities from the submerged region with the color intensities of the top region makes the method insensitive to the intensity and spectral content of the light source used to illuminate the culture.

In this study, the top plate of the reference platform consisted of a white square polystyrene sheet 25 mm on a side and 0.5 mm thick (Midwest Products Co., 701-02). The top plate was joined to two identical bottom plates using nylon nuts and bolts (McMaster-Carr, 94812A1 12 and 95868A260). The vertical distance between the top plate and the two bottom plates was 10 mm. A piece of styrofoam 12 mm long, 6 mm wide, and 2 mm thick was glued onto the bottom side of the top plate to make the platform buoyant. When placed into an aqueous medium, the top plate resided above the liquid surface whereas the two bottom plates resided 10 ± 0.5 mm below the liquid surface, as shown in Figure 24b.

Image analysis

A custom computer code was developed to analyze the images of the algal cultures. Each pixel of a digital image acquired by the camera is represented by the color vector c^; equal to [r_P, g_P, bp], corresponding to the pixel's red (560 to 700 nm), green (490 to 590 nm), and blue 153 (410 to 500 nm) intensities, respectively (Poynton C. Digital Video and HDTV: Algorithms and interfaces. San Francisco, CA: Morgan Kaufmann Publishers, 2003). Each element of the vector \r_P, g_P, bp] has an integer value between 0 and 255, inclusive. First, a region of the image containing the top plate above the liquid surface was identified, labeled "w" in Figure 24b. The color vector of this white region equal to [r_w, gw, bw] was calculated as the average of the red, green, and blue intensities of all the pixels in the region. Then, a region of the image containing a submerged plate was identified, labeled "g" in Figure 24b. The raw color vector of this green region equal to [r_g, g_g, b_g] was calculated as the average of the red, green, and blue intensities of all the pixels in the region. To minimize the effects of transmitted light from the side of the chamber, the submerged plate with the greater distance to the nearest wall was used for analysis. The elements of color vector c equal to [r, g, b] were calculated by dividing the color intensities of the immersed plate by the color intensities of the top plate to account for differences in the intensity and spectral content of the light source:

c(Q = ¾(0/Cw(0 ^{for 1} = 1, 2, 3 (6)

Photosynthetic yield

The quantum yield of Photosystem II (YII) is the fraction of the light incident onto the photosynthetic membrane that is converted to short term chemical energy carriers (Genty B, Briantais J-M, Baker NR. BBA-Gen Subjects. 1989, 990, 87-92; Campbell D, et al. Microbiol Mol Biol R. 1998, 62, 667-83). In this study, YII was measured using a pulse amplitude- modulated (PAM) fluorometer (Walz, JUNIOR-PAM). The photosynthetic yield was calculated as (Genty B, Briantais J-M, Baker NR. BBA-Gen Subjects. 1989, 990, 87-92; Campbell D, et al. Microbiol Mol Biol R. 1998, 62, 667-83),

where Fo is the baseline fluorescence from the culture and F_m is its maximum

fluorescence after being exposed to a saturating pulse. The difference F_m-Fo is known as the variable fluorescence F_v. The Y(II) value was used as a semi -quantitative indicator of the health of the cells (Campbell D, et al. Microbiol Mol Biol R. 1998, 62, 667-83; Masojidek J, et al. Journal of Plankton Research. 2001, 23, 57-66). Culture invasion simulation

Invasion of cyanobacterial species is often a problem facing large scale green algae cultivation (G5rs M, et al. JAppl Phycol. 2009, 22,265-276). In this study, culture invasion was simulated by adding a known amount of A. variabilis to an initially mono culture of Chlorella sp. The invasion ratio was defined as the ratio of the mass of A. variabilis to the mass of Chlorella sp.

Culture crash simulation

Images of an initially healthy culture of A variabilis were continuously acquired as the culture was slowly heated from 23°C to 53°C. A hot plate (Fisher, Isotemp) was used to heat and mix the culture, and a Type T thermocouple (Omega, TT-T-36-100) was used to measure the culture temperature. The maximum photosystem II fluorescence F_m and baseline fluorescence Fo were simultaneously monitored.

Results and Discussion

Image analysis of monocultures for biomass concentration quantification

Chlorella

Figure 25 shows the red, green, and blue values as a function of Chlorella biomass concentration, X, for three different monocultures. In the biomass concentration range of 0 to 0.37 g/1, the blue value was more sensitive to biomass concentration than were the red or green values due to selective absorption of blue photons. Therefore, a least squares regression line was fitted to the experimental data within this concentration range, resulting in the correlation, X = 0.477 - 0.522 b, with a coefficient of determination R² value of 0.920. However, at biomass concentrations greater than 0.37 g/1, the blue value was insensitive to biomass concentration due to saturation. Thus, for biomass concentrations between 0.37 and 1.0 g/1, a least squares line between the biomass concentration and the red value was calculated, resulting in the equation X = 1.70 - 2.20 r, with an R² value of 0.898.

Anabaena variabilis

Figure 26 shows the red, green, and blue values as a function of A. variabilis biomass concentration. The color values featured less variation between experimental runs than the Chlorella sp. monocultures. This is attributed to the lesser prevalence of flocculation of A.

variabilis, which led to more homogeneous cultures. Moreover, the red and green values declined more steeply with increasing A. variabilis concentration than with increasing Chlorella sp. concentration due to the presence of phycocyanin and phycoerythrin in variabilis, which have absorption peaks at 620 nm and 565 nm, respectively.

For biomass concentrations less than 0.33 g/1, the red value was more sensitive to biomass concentration than the green or blue values, and was therefore identified as the appropriate parameter for biomass estimation. In this concentration range, the least squares line was defined by the equation X = 0.434 - 0.512 r, with an R² value of 0.957. At biomass concentrations greater than 0.33 g/1, both the red and the blue values were saturated, whereas the green value was not. Therefore, in the concentration range of 0.4 to 1.0 g/1, the correlation X = 1.50 - 1.90 g was generated, with an R² value of 0.925. The equations and R² values of the least squares regression lines are summarized in Table 5. Table 5. Correlation between biomass concentration and red ®, green (g), and blue (b) values for Chlorella sp. and A. variabilis.

Accuracy of biomass correlations

To evaluate the accuracy of the multispectral imaging method for biomass quantification, independent sets of Chlorella sp. and A. variabilis cultures with known biomass concentrations were imaged. The biomass concentration was calculated using the correlations shown in Table 5. Figure 27 shows the predicted versus actual biomass concentration for the two strains. The average error between the actual and predicted biomass concentrations for Chlorella sp. and A. variabilis was 22% and 14%, respectively. This accuracy is similar to that of the previous imaging method presented for biomass quantification (Murphy TE, Macon K, Berberoglu H. Biotechnol Progr. 2013, 29, 808-16). However, the present method expands the measurable biomass range up to 0 to 1 g/1, which is the range of interest in open pond cultivation systems. Identification of invasion of a green algae culture by cyanobacteria

Healthy mono cultures of Chlorella sp. and A. variabilis have different red-to-blue ratios, as shown in Figure 28. Therefore, the red-to-blue ratio of a culture can be used to identify invasion of one group by another. To demonstrate this technique, images were acquired of an initially mono culture of Chlorella sp. culture as different amounts of A. variabilis were added. The red and blue values were calculated for each image. The blue value was also used to calculate the expected red value for a Chlorella sp. monoculture. Figure 29 shows the red value of the mixed cultures as a function of the invasion ratio, as well as the red value that would be expected for a monoculture. The figure indicates that contamination by A. variabilis can be detected for invasion ratios greater than 0.08. This invasion detection technique can be applied to detect invasion of a mono culture of one organism by another organism so long as the two organisms have different red to green to blue ratios. In large scale applications, invasion detection can be used to inform decisions regarding manipulation of the pH or nutrient concentrations, enabling the species of interest to out-compete the invaders. Identification of a temperature induced culture crash

RGB images and fluorometric photosynthetic yield parameters were continuously acquired of an A variabilis mono culture as it was slowly heated from 23°C to 53°C. Figure 30a and b show the color values and fluorometric parameters, respectively, as a function of culture temperature. At temperatures between 23°C and 33°C, all measured parameters were constant. However, between temperatures of 33°C and 48°C, the red and blue values increased at an average rate of 0.017°C^_1 and 0.021°C^_1 , respectively, meaning that the culture became paler, although this change was imperceptible to the naked eye. In this temperature range, the photosynthetic yield also decreased at an average rate of 0.022°C^_1, marking a decline in the health of the culture. Then, at a temperature of 49°C, the maximum fluorescence of the culture abruptly decreased from 53 a.u. to 26 a.u., signaling irreversible degradation of Photosystem II. At this same temperature, the red and blue values each abruptly decreased by about 20%. This analysis shows that steadily increasing red and blue values can be used as a "warning sign" of an impending crash caused by thermal stress. Real time image acquisition and analysis can therefore provide information for mitigating such crashes.

Conclusions

A multispectral imaging approach was presented for monitoring the backscattered light from algal cultures. The spectral signature of the backscattered light can in turn be used for culture diagnostics such as biomass concentration quantification, invasive species detection, and culture health monitoring. A floating reference platform and an RGB camera were used to measure the backscattered light from the cultures. Correlations were generated between the red, green, and blue backscattered intensities and the biomass concentrations of the green alga Chlorella sp. and the cyanobacterium Anabaena variabilis. These correlations predicted the biomass concentrations of independently prepared cultures with an accuracy of 22% and 14%, respectively. Moreover, a technique was described for using the spectral signature of a green algae culture to detect invasion by cyanobacteria. This technique was successful in detecting cyanobacteria-to-green algae mass ratios as small as 0.08. Finally, a culture crash was simulated by heating an ^. variabilis culture from 23°C to 53°C while simultaneously monitoring the photosynthetic yield and culture color. It was shown that a decrease in the photosynthetic yield coincided with an increase in the red and blue values of the culture image, enabling the red and blue values to be used as a proxy for culture health. These techniques enable real time culture diagnostics, which in turn enable efficient operation of large scale cultivation systems with respect to harvesting, nutrient delivery, and pH and temperature control. Other advantages which are obvious and which are inherent to the invention will be evident to one skilled in the art. It will be understood that certain features and sub-combinations are of utility and may be employed without reference to other features and sub-combinations. This is contemplated by and is within the scope of the claims. Since many possible

embodiments may be made of the invention without departing from the scope thereof, it is to be understood that all matter herein set forth or shown in the accompanying drawings is to be interpreted as illustrative and not in a limiting sense.

Claims

CLAIMS What is claimed is:

1. A system comprising:

a liquid sample comprising an analyte;

a first imaging platform comprising a first optical reference surface, wherein the first imaging platform is positioned such that the first optical reference surface is not immersed within the liquid sample;

a second imaging platform comprising a second optical reference surface, wherein the second imaging platform is positioned such that the second optical reference surface is immersed within the liquid sample; and

an instrument configured to capture an optical signal from the first optical reference surface and the second optical reference surface.

2. The system of claim 1, wherein the first optical reference surface is optically

equivalent to the second optical reference surface.

3. The system of any of claims 1-2, further comprising a computing device comprising a processor and a memory operably coupled to the processor, the memory having further computer-executable instructions stored thereon that, when executed by the processor, cause the processor to:

receive an optical signal from the instrument

process the optical signal to identify a first analysis region and a second analysis region, wherein the first analysis region comprises at least a portion of the first optical reference surface and the second analysis region comprises at least a portion of the second optical reference surface;

analyze the first analysis region to determine a reference parameter; analyze the second analysis region to determine a sample parameter; compare the sample parameter and the reference parameter to obtain a sample value;

process the sample value to obtain a sample characteristic; and

output the sample characteristic.

4. The system of any of claims 1-3, wherein the first imaging platform and the second imaging platform are provided as components of a monitoring stage.

5. The system of claim 4, wherein the monitoring stage comprises one or more vertical positioning members, each of the one or more vertical positioning members having a top end and a bottom end;

the first imaging platform attached to the top end of one or more of the vertical positioning members; and

the second imaging platform attached to the bottom end of one more of the vertical positioning members; and

wherein the first optical surface and the second optical surface are positioned such that the instrument can capture an optical signal from the first optical surface and the second optical surface.

6. The system of any of claims 1-5, wherein the first optical reference surface is

substantially parallel to the surface of the liquid sample

7. The system of any of claims 1-6, wherein the second optical reference surface is substantially parallel to the surface of the liquid sample.

8. The system of any of claims 1-7, wherein the first imaging platform and the second imaging platform are facing the same direction.

9. The system of any of claims 1-8, wherein the first optical reference surface is

substantially parallel to the second optical reference surface.

10. The system of any of claims 4-9, wherein the monitoring stage further comprises a stabilizing platform.

11. The system of any of claims 4-10, wherein the monitoring stage further comprises a buoyancy member attached to the monitoring stage, wherein the buoyancy member is configured to position the monitoring stage in the liquid sample such that the first optical reference surface is not immersed within the liquid sample and the second optical reference surface is immersed within the liquid sample.

12. The system of any of the claims 5-11, wherein the one or more vertical positioning members are configured to position the monitoring stage in the liquid sample such that the first optical reference surface is not immersed within the liquid sample and the second optical reference surface is immersed within the liquid sample.

13. The system of any of the claims 1-12, wherein the first optical reference surface and second optical reference surface exhibit high reflectivity.

14. The system of any of the claims 1-13, wherein the instrument is a camera.

15. The system of claim 14, wherein the optical signal is an image.

16. The system of claim 15, wherein the camera simultaneously captures an image of the first optical reference surface and the second optical reference surface.

17. The system of any of claims 1-16, wherein the system further comprises a light source configured to illuminate the first optical reference surface and the second optical reference surface.

18. The system of claim 17, wherein the light source comprises sunlight.

19. The system of claim 17, wherein the light source comprises an artificial light source.

20. The system of any of the claims 1-19, further comprising one or more additional imaging platforms comprising one or more additional optical reference surface wherein the one or more additional imaging platform is positioned such that the one or more optical reference surface is immersed within the liquid sample.

21. The system of claim 20, wherein the one or more additional imaging platforms are at different depths within the liquid sample.

22. The system of any of the claims 1-21, further comprising a sample vessel that

contains the liquid sample.

23. The system of claim 22, wherein the imaging platforms are attached to the sample vessel.

24. The system of any of the claims 1-23, wherein the analyte comprises a species of algae.

25. The system of claim 24, wherein the species of algae comprises a species of the genus Chlorella.

26. The system of any of the claims 1-23, wherein the analyte comprises a mixture of species of algae.

27. The system of any of the claims 1-23, wherein the analyte comprises a species of cyanobacteria.

28. The system of claim 27, wherein the species of cyanobacteria comprises Anabaena variabilis.

29. The system of any of the claims 1-23, wherein the analyte comprises a mixture of species of algae.

30. The system of any of the claims 1-29, wherein the liquid sample comprises a first analyte and a second analyte.

31. The system of claim 30, wherein the first analyte comprises a first species of algae and the second analyte comprises a second species of algae.

32. The system of claim 30, wherein the first analyte comprises a first species of

cyanobacteria and the second analyte comprises a second species of cyanobacteria.

33. The system of claim 30, wherein the first analyte comprises a species of algae and the second analyte comprises a species of cyanobacteria.

34. The system of claim 33, wherein the species of algae comprises a species of the genus Chlorella. and the species of cyanobacteria comprises Anabaena variabilis.

35. The system of claim 30, wherein the first analyte comprises a mixture of species of algae and the second analyte comprises a mixture of species of cyanobacteria.

36. The system of claim 26, wherein the first analyte comprises a first mixture of

species of algae and the second analyte comprises a second mixture of species of algae.

37. The system of claim 26, wherein the first analyte comprises a first mixture of

species of cyanobacteria and the second analyte comprises a second mixture of species of cyanobacteria.

38. The system of any of the claims 1-23, wherein the analyte comprises cells.

39. The system of claim 38, wherein the cells comprise single suspended cells; a colony of cells; a flock of cells; absorbing, fluorescing and/or luminescing cells; or combinations thereof.

40. The system of any one of claims 38-39, wherein the cells comprise mammalian cells, insect cells, plant cells, photosynthetic cells, non-photosynthetic cells, or combinations thereof.

41. A system 100 comprising:

a liquid sample comprising an analyte;

a first imaging platform comprising a first optical reference surface; a second imaging platform comprising second optical reference surface;

and an instrument configured to capture an optical signal from the first optical reference surface and the second optical reference surface;

wherein the first imaging platform is positioned relative to the instrument and the liquid sample such that an electromagnetic signal traveling from the first optical reference surface to the instrument does not traverse the liquid sample; and

wherein the second imaging platform is positioned relative to the instrument and the liquid sample such that an electromagnetic signal traveling from the second optical reference surface to the instrument traverses the liquid sample.

42. The system of claim 41, further comprising a sample vessel that contains the liquid sample.

43. The system of claim 42, wherein the sample vessel has an exterior surface and an interior surface, wherein the liquid sample is contained in the volume defined by the interior surface of the sample vessel.

44. The system of any one of claims 42-43, wherein the sample vessel comprises a flask comprising:

a plurality of walls, said plurality of walls including a front wall, a back wall, a first side wall, a second side wall, a first end wall, and a second end wall; wherein the front wall and the back wall are joined by the first side wall the second side wall, the first end wall and the second end wall; and the first end wall has an outwardly-extending open-ended neck that forms a conduit through which the liquid sample has access to the volume defined by the plurality of walls.

45. The system of claim 44, wherein the front wall and the back wall are substantially parallel.

46. The system of any one of claims 44-45, wherein the first side wall and the second side wall are substantially parallel

47. The system of any one of claims 44-46, wherein the second imaging platform is disposed on or in the back wall of the flask.

48. The system of any one of claims 44-47, wherein the first imaging platform is

disposed on or in the front wall of the flask.

49. The system of any one of claims 42-43, wherein the sample vessel comprises a dish comprising a base plate and an upstanding wall extending continuously around the periphery of the dish, and the liquid sample is contained within the volume defined by the base plate and the upstanding wall.

50. The system of claim 49, wherein the upstanding wall is formed integrally with the base plate.

51. The system of any one of claims 49-50, wherein the second imaging platform is disposed on or in the base plate.

52. The system of any one of claims 49-51 , wherein the sample vessel further comprises a lid comprising a top plate and a depending wall extending continuously around the periphery of the lid, wherein the lid is sized such that when the lid is placed over the dish, the top plate is located above and substantially parallel to the base plate.

53. The system of claim 52, wherein the depending wall is formed integrally with the top plate.

54. The system of any one of claims 52-53, wherein the first imaging platform is

disposed on or in the top plate.

55. The system of any one of claims 49-51, wherein the analyte comprises cells.

56. The system of claim 55, wherein the cells comprise single suspended cells; a colony of cells; a flock of cells; absorbing, fluorescing and/or luminescing cells; or combinations thereof.

57. The system of any one of claims 55-56, wherein the cells comprise mammalian cells, insect cells, plant cells, photosynthetic cells, non-photosynthetic cells, or combinations thereof.

58. A method, comprising:

providing a first imaging platform comprising a first optical reference surface and a second imaging platform comprising a second optical reference surface;

positioning the first imaging platform and the second imaging platform relative to a liquid sample, such that the first optical reference surface is not immersed within the liquid sample, and the second optical reference surface is immersed within the liquid sample;

capturing an optical signal from the first optical reference surface and the second optical reference surface;

processing the optical signal to identify a first analysis region and a second analysis region, wherein the first analysis region comprises at least a portion of the first optical reference surface and the second analysis region comprises at least a portion of the second optical reference surface;

analyzing the first analysis region to determine a reference parameter;

analyzing the second analysis region to determine a sample parameter;

comparing the sample parameter and the reference parameter to obtain a sample value;

processing the sample value to obtain a sample characteristic.

59. The method of claim 58, wherein the first imaging platform and the second imaging platform are provided as components of a monitoring stage.

60. The method of claim 59, wherein the monitoring stage comprises

one or more vertical positioning members, each of the one or more vertical positioning members having a top end and a bottom end;

the second imaging platform attached to the bottom end of one more of the vertical positioning members.

61. The method of any of claims 58-60, wherein the monitoring stage further

comprising a stabilizing platform.

62. The method of any of claims 59-61, wherein the monitoring stage further comprises a buoyancy member attached to the monitoring stage, wherein the buoyancy member is configured to position the monitoring stage in the liquid sample such that the first optical reference surface is immersed within the liquid sample and the second optical reference surface is not immersed within the liquid sample.

63. The method of any of claims 60-62, wherein the one or more vertical positioning members are configured to position the monitoring stage in the liquid sample such that the first optical reference surface is immersed within the liquid sample and the second optical reference surface is not immersed within the liquid sample.

64. The method of any of claims 58-63, wherein the first optical reference surface is substantially parallel to the surface of the liquid sample.

65. The method of any of claims 58-64, wherein the second optical reference surface is substantially parallel to the surface of the liquid sample

66. The method of any of claims 58-65, wherein the first optical surface and the second optical surface are facing the same direction.

67. The method of any of claims 58-66, wherein the first optical reference surface is substantially parallel to the second optical reference surface

68. The method of any of claims 58-67, wherein the first optical reference surface and second optical reference surface exhibit high reflectivity.

69. The method of any of the claims 58-68, further comprising providing one or more additional imaging platforms comprising one or more additional optical reference surfaces wherein the one or more additional imaging platforms are positioned such that the one or more additional optical reference surfaces are immersed within the liquid sample.

70. The method of claim 69, wherein the one or more additional imaging platforms are at different depths within the liquid sample.

71. The method of any of claims 58-70, wherein the optical signal is an image.

72. The method of any of claims 58-71, further comprising illuminating the first optical reference surface and the second optical reference surface with a light source.

73. The method of claim 72, wherein the light source comprises sunlight.

74. The method of claim 72, wherein the light source comprises an artificial light

source.

75. The method of any of claims 58-74, wherein the sample characteristic comprises the concentration of the analyte in the liquid sample.

76. The method of any of claims 58-75, wherein the liquid sample comprises a first analyte and a second analyte.

77. The method of claim 76, wherein the sample characteristic comprises the ratio of the first analyte to the second analyte.

78. The method of any of claims 58-75, wherein the analyte comprises a species of algae.

79. The method of claim 78, wherein the species of algae comprises a species of the genus Chlorella.

80. The method of any of claims 58-75, wherein the analyte comprises a mixture of species of algae.

81. The method of any of claims 58-75, wherein the analyte comprises a species of cyanobacteria.

82. The method of claim 81, wherein the species of cyanobacteria comprises Anabaena variabilis.

83. The method of any of the claims 58-75, wherein the analyte comprises a mixture of species of cyanobacteria.

84. The method of claim 76, wherein the first analyte comprises a species of algae and the second analyte comprises a species of cyanobacteria.

85. The method of claim 84, wherein the species of algae comprises a species of the genus Chlorella and the species of cyanobacteria comprises Anabaena variabilis.

86. The method of claim 76, wherein the first analyte comprises a first species of algae and the second analyte comprises a second species of algae.

87. The method of claim 76, wherein the first analyte comprises a first species of

88. The method of claim 76, wherein the first analyte comprises a first mixture of

89. The method of claim 76, wherein the first analyte comprises a first mixture of

90. The method of claim 76, wherein the first analyte comprises a mixture of species of algae and the second analyte comprises a mixture of species of cyanobacteria.

91. The method of any of claims 78-80, 86, or 88, wherein the sample characteristic comprises the biomass concentration of the species of algae.

92. The method of any of claims 81-83, 87 or 89, wherein the sample characteristic comprises the biomass concentration of the species of cyanobacteria

93. The method of any of claims 84-85 or 90, wherein the sample characteristic

comprises the relative biomass concentrations of the species of algae and the species of cyanobacteria.

94. The method of any of claims 78-80, 84-86, 88 or 90, wherein the sample

characteristic comprises an indicator of the health of the species of algae.

95. The method of any of claims 81-85, 87, or 89-90, wherein the sample characteristic comprises an indicator of the health of the species of cyanobacteria.

96. The method of any of the claims 58-75, wherein the analyte comprises cells.

97. The method of claim 96, wherein the cells comprise single suspended cells; a

colony of cells; a flock of cells; absorbing, fluorescing and/or luminescing cells; or combinations thereof.

98. The method of any one of claims 96-97, wherein the cells comprise mammalian cells, insect cells, plant cells, photosynthetic cells, non-photosynthetic cells, or combinations thereof.

99. The method of any of claims 58-98, wherein the sample characteristic comprises an indicator of a contaminant in the liquid sample.

100. A method, comprising:

positioning the first imaging platform and the second imaging platform relative to a liquid sample and an instrument, such that an electromagnetic signal traveling from the first optical reference surface to the instrument does not traverse the liquid sample, and an electromagnetic signal traveling from the second optical reference surface to the instrument traverses the liquid sample;

analyzing the first analysis region to determine a reference parameter; analyzing the second analysis region to determine a sample parameter; comparing the sample parameter and the reference parameter to obtain a sample value;

processing the sample value to obtain a sample characteristic.

101. The method of claim 100, wherein the liquid sample is contained in a sample vessel.

102. The method of claim 101, wherein the sample vessel has an exterior surface and an interior surface, wherein the liquid sample is contained in the volume defined by the interior surface of the sample vessel.

103. The method of any of claims 101-102, wherein the sample vessel comprises a flask comprising:

a plurality of walls, said plurality of walls including a front wall, a back wall, a first side wall, a second side wall, a first end wall, and a second end wall; wherein the front wall and the back wall are joined by the first side wall, the second side wall, the first end wall and the second end wall; and the first end wall has an outwardly-extending open-ended neck that forms a conduit through which the liquid sample has access to the volume defined by the plurality of walls.

104. The method of claim 103, wherein the front wall and the back wall are substantially parallel.

105. The method of any one of claims 103-104, wherein the first side wall and the second side wall are substantially parallel

106. The method of any one of claims 103-105, wherein the second imaging platform is disposed on or in the back wall of the flask.

107. The method of any one of claims 103-106, wherein the first imaging platform is disposed on or in the front wall of the flask.

108. The method of any one of claims 101-102, wherein the sample vessel comprises a dish comprising a base plate and an upstanding wall extending continuously around the periphery of the dish, and the liquid sample is contained within the volume defined by the base plate and the upstanding wall.

109. The method of claim 108, wherein the upstanding wall is formed integrally with the base plate.

1 10. The method of any one of claims 108-109, wherein the second imaging platform is disposed on or in the base plate.

1 11. The method of any one of claims 108-1 10, wherein the sample vessel further

comprises a lid comprising a top plate and a depending wall extending continuously around the periphery of the lid, wherein the lid is sized such that when the lid is placed over the dish, the top plate is located above and substantially parallel to the base plate.

12. The method of claim 11 1, wherein the depending wall is formed integrally with the top plate. 13. The method of any one of claims 11 1-1 12, wherein the first imaging platform is disposed on or in the top plate. 14. The method of any one of claims 100-113, wherein the analyte comprises cells. 15. The method of claim 1 14, wherein the cells comprise single suspended cells; a colony of cells; a flock of cells; absorbing, fluorescing and/or luminescing cells; or combinations thereof. 16. The method of any one of claims 113-1 14, wherein the cells comprise mammalian cells, insect cells, plant cells, photosynthetic cells, non-photosynthetic cells, or combinations thereof.