WO2004053468A1 - Image analysis of heterogeneous mixtures - Google Patents

Abstract

A system for analyzing a plurality of samples (202) containing a dispersion of one or more incompletely miscible components in a continuous fluid phase comprises a vial receptacle located at a first location, an image capturing device (102) directed at the first location, a light source (112) directed at the first location, and a programmable processor (124) operatively coupled to the image capturing device and configured to detect a behavior in a captured image of a sample. The programmable processor defines regions of interest in a captured image, generates an intensity profile for each region of interest, and detects the behavior based on the intensity profile. The programmable processor defines the regions of interest by detecting a sample boundary in a captured image and defining a region within the sample boundary. The programmable processor detects behavior by calculating a Laplacian or a derivative of the intensity profile for the region.

Description

IMAGE ANALYSIS OF HETEROGENEOUS MIXTURES

Background [0001] This invention relates to the preparation and analysis of heterogeneous mixtures.

[0002] In general, combinatorial chemistry refers to the approach of creating vast numbers of compounds by reacting a set of starting chemicals in all possible combinations. Since its introduction into the pharmaceutical industry in the late 1980's, it has dramatically sped up the drug discovery process and is now becoming a standard practice in that industry (Chem. Eng. News Feb. 12, 1996). More recently, combinatorial techniques have been successfully applied to the synthesis of inorganic materials (G. Briceno et al, SCIENCE 270, 273-275, 1995 and X. D. Xiang et al., SCIENCE 268, 1738-1740, 1995). Using these techniques, it is now possible to create large libraries of diverse compounds or materials, including biomaterials, organics, inorganics, intermetallics, metal alloys, and ceramics, using a variety of sputtering, ablation, evaporation, and liquid dispensing systems as disclosed in U.S. Patents No. 5,959,297, 6,004,617 and 6,030,917, which are incorporated by reference herein.

[0003] The generation of large numbers of new materials presents a significant challenge for conventional analytical techniques. By applying parallel or rapid serial screening techniques to these libraries of materials, however, combinatorial chemistry accelerates the speed of research, facilitates breakthroughs, and expands the amount of information available to researchers. Furthermore, the ability to observe the relationships between hundreds or thousands of materials in a short period of time enables scientists to make well-informed decisions in the discovery process and to find unexpected trends. High throughput screening techniques have been developed to facilitate this discovery process, as disclosed, for example, in U.S. Patents No. 5,959,297, 6,030,917 and 6,034,775, which are incorporated by reference herein.

[0004] In general, the techniques of combinatorial chemistry have resisted application to the study of variations in emulsified mixtures due to the limitations of known workflows employed to create and analyze emulsions. An emulsion is a heterogeneous system in which one immiscible phase is finely dispersed into another. Emulsions typically consist of a liquid phase dispersed in another liquid phase, with or without added emulsifiers or surfactants that reduce the interfacial tension between the two phases. An emulsion can be characterized by its stability, which represents the homogeneity of the mixture (i.e., the degree to which the components of the emulsion resist bulk separation). Historically, the stability of emulsions has been assessed by visual inspection of the emulsions over time, or images thereof, to identify the presence of free components and banding patterns. Data collection using these manual methods is time-consuming, highly subjective in nature, and requires a trained operator to collect data and make assessments. Because of its reliance on time-consuming and subjective manual observations, this form of data collection has not been readily adaptable to combinatorial methods.

Summary [0005] The invention provides combinatorial techniques for systematically studying variations in dispersed mixtures. Large numbers of heterogeneous mixtures, such as oil- in- water and water-in-oil emulsions, dispersions, and suspensions of particulates in liquid media, are prepared and analyzed using automated and high-throughput techniques.

[0006] In general, in one aspect, the invention provides methods, systems and apparatus, including computer program apparatus, implementing techniques for analyzing a plurality of samples containing a dispersion of one or more incompletely miscible components in a continuous fluid phase. A system for analyzing a plurality of samples containing a dispersion of one or more incompletely miscible components in a continuous fluid phase can comprise a vial receptacle located at a first location, an image capturing device directed at the first location, a light source directed at the first location, and a programmable processor operatively coupled to the image capturing device, the programmable processor being configured to detect a behavior in a captured image of a sample.

[0007] Particular implementations can include one or more of the following features. To detect a behavior, the programmable processor can be configured to define one or more regions of interest in an image of the captured images, then generate an intensity profile for each region of interest, and finally detect a behavior in a sample based on the intensity profile for a corresponding region of interest. The programmable processor can define one or more regions of interest by detecting a sample boundary in a captured image and defining the region of interest as a region within the sample boundary. The programmable processor can then detect a behavior by calculating a Laplacian or a derivative of the intensity profile for the region of interest corresponding to the sample. The system for analyzing a plurality of samples can also include a robotic sample handling system coupled to the programmable processor, where the robotic sample handling system is configured to transport samples to and from the vial receptacle.

[0008] A method for analyzing a plurality of samples containing a dispersion of one or more incompletely miscible components in a continuous fluid phase begins with a system receiving a plurality of samples, each sample including a dispersion of one or more incompletely miscible components in a continuous fluid phase, each sample being contained within a container. Next, the samples are exposed to an external perturbation. Images of the samples are then captured at an orientation that is normal to the direction of the external perturbation. Finally, the system detects a behavior in a captured image of a sample using the techniques described above. The external perturbation can include applying a force selected from the group consisting of a centripetal force, a gravitational force, an electric force, a magnetic force, an optical or acoustic radiation force, and an electromagnetic force.

[0009] The invention can be implemented to provide one or more of the following advantages. Automation of the preparation of sample libraries, the capture of image data for members of the libraries, and analysis of the captured data can provide for integrated, complete workflows that enable the exploration of a wide variety of heterogeneous mixtures. The members of the libraries can also undergo further analysis after the image data is captured, such as particle sizing, long-term stability tests, or other performance tests. Automating these processes provides for the preparation and screening of samples much faster than conventional methods that involve combinations of manual synthesis, manual screening, and/or manual analysis. Multiple sample mixtures can be prepared at one time, and these samples can be screened for desired properties in an automated, high- throughput manner. Liquid and emulsion samples can be prepared on a very small scale.

[0010] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims. Description of the Drawings [0011] Fig. 1 illustrates a screening apparatus in accordance with one embodiment of the invention.

[0012] Fig. 2 illustrates a graphical representation of a library of samples.

[0013] Fig. 3 is a flowchart illustrating one implementation of a method for designing a library of samples according to one implementation of the invention.

[0014] Fig. 4 illustrates an ultrasonic workstation according to one implementation of the invention.

[0015] Fig. 5 is a flowchart illustrating one implementation of a method for screening samples according to one aspect of the invention.

[0016] Fig. 6 is a flowchart outlining a method of analyzing digital images of samples according to one aspect of the invention.

[0017] Fig. 7 illustrates a digital image of a vial containing a sample with a template defined.

[0018] Fig. 8 is a graph showing an exemplary intensity profile.

[0019] Fig. 9 is a graph showing an exemplary intensity profile after a LaPlacian calculation has been performed on the data.

[0020] Fig. 10 illustrates an array of intensity profiles taken for an entire library of samples.

[0021] Fig. 11 illustrates an array of intensity profiles after a LaPlacian calculation has been performed on the data taken for an entire library of samples.

[0022] Fig. 12 is a digital image of four samples contained in vials.

[0023] Fig. 13 is an intensity profile of the four samples of Fig. 12.

[0024] Fig. 14 is a LaPlacian analysis of the intensity profile of Fig. 13.

[0025] Fig. 15 is a derivative analysis of the intensity profile of Fig. 13. [0026] Like reference symbols in the various drawings indicate like elements.

Detailed Description

[0027] Fig. 1 illustrates one implementation of a screening apparatus 100 for preparing and screening libraries of samples according to one aspect of the invention. The methods and apparatus described herein are operable to employ rapid liquid handling and high throughput screening techniques to prepare and analyze libraries of samples. The samples are typically heterogeneous mixtures that will tend to be in the liquid phase, although the methods and apparatus described herein can be used to prepare and analyze solid and gaseous mixtures and combinations of different phases, as well as gels and other colloidal systems. As used herein, a continuous fluid phase will refer to all of the above sample types, including liquids and gels. Each sample will typically be made up of a mixture or dispersion of two or more different components (which can include the component constituting the continuous fluid phase) that are combined to form the sample, at least some of the components being incompatible or incompletely miscible, thus having the tendency to exhibit some form of behavior, for instance, separating from one another after being mixed. In some implementations, the exhibited behavior tends to form visible patterns in the sample. If the behavior consists of settling, the separated components tend to form layers or phases, also referred to as bands, banding layers, and banding patterns. Settling behavior can also include crystallizing behavior and ripening behavior. If the behavior consists of flowing, the sample will form a flow pattern. The samples are typically prepared in vials, cuvettes, or other containers, which collectively form the library, where they are also held for analysis. Digital images are captured of each sample in the library, and these digital images are processed using computer implemented image- processing techniques to provide qualitative and quantitative data about the samples. For example, the invention can assess the long-term homogeneity of emulsions by analyzing the banding patterns or line profiles for each emulsion represented in the digital images, and the various features and stages of demixing can be identified and quantified.

[0028] Fig. 1 illustrates screening apparatus 100 configured in accordance with one implementation. Screening apparatus 100 includes an image capturing device 102 such as a digital camera capable of capturing digital images. The digital images captured by digital camera 102 will generally consist of vials containing samples. Many different models of commercially available digital cameras are capable of providing the type of digital images required by screening apparatus 100. For instance, in one implementation, a Canon Power Shot Gl camera, available from Canon U.S.A., Inc. of Lake Success, New York, can be used. Generally, a digital camera having a resolution of 4.0 megapixels or higher will tend to yield more preferable results, although digital cameras having a lower resolution can be used. In one implementation, digital camera 102 will have a resolution that is at least greater than 2.0 megapixels. Digital cameras with higher quality lenses also tend to yield better results. For instance, macro lenses, such as high quality SLR macro lenses, tend to provide digital images that are superior to conventional lenses. Macro lenses are available from many different manufacturers, including Canon U.S.A., Inc. of Lake Success, New York.

[0029] Screening apparatus 100 also includes one or more robotic arms 104 to facilitate high-throughput preparation and screening of the libraries of samples. At least one of robotic arms 104 (referenced as 104A in Fig. 1) can include a vial gripper 106 to transport vials between a vial rack 107 and a location 108 that is within the field of view of digital camera 102. Vial gripper 106 is capable of picking up, holding, and releasing one or more vials when robotic arm 104 is transporting vials between vial rack 107 and location 108. The mechanism of vial gripper 106 may be driven by motorized or pneumatic methods. Another of robotic arms 104 (referenced as 104B in Fig. 1) can be configured to dispense components that are used to prepare the samples. For example, robotic arm 104B can aspirate components from a component rack 109, dispense the components into vials located in vial rack 107, and the components in the vials can then be mixed by a mixing apparatus 111. In the embodiment of Fig. 1 , mixing apparatus 111 is located on robotic arm 104B. In alternative embodiments, mixing apparatus 111 can be located on a separate robotic arm 104, or on another mechanism for moving mixing apparatus 111 to each of the vials in vial rack 107. In yet another embodiment, mixing apparatus 111 can be stationary and robotic arms 104 can move vials to the stationary mixing apparatus 111 for mixing. For example, mixing apparatus 111 can be implemented as a magnetic stirring apparatus configured to drive magnetic stir bars situated in each vial located in vial rack 107. As is explained in more detail below, computer implemented methods can be configured to drive robotic arms 104.

[0030] In Fig. 1, vial rack 107 is designed to hold an array of vials, each vial containing one of the samples that are to be analyzed. Vial rack 107 can be located within screening apparatus 100 to decrease the time required for robotic arm 104 to move vials into and out of location 108. The number of vials located within vial rack 107 will vary based on a number of factors, including the number of samples being analyzed and any size constraints placed on vial rack 107 due to its location within screening apparatus 100.

[0031] As mentioned above, robotic arm 104 positions sample vials at location 108, which can be situated at a focal point of the lens of digital camera 102. As shown in Fig. 1, a back panel 110 can be mounted behind location 108, such that location 108 is positioned between back panel 110 and digital camera 102. The use of back panel 110 can enhance the quality of the digital images captured by digital camera 102. Since screening apparatus 100 can be used to analyze the behavior of the samples, back panel 110 can be used to increase the contrast between different parts of the patterns that often appear. For example, emulsion samples will often separate into opaque bands and clear bands. If the banding layers are light-absorbing, the use of a light-reflecting (e.g., white) back panel 110 will tend to increase the contrast between light-absorbing bands and clear bands. Similarly, if the banding layers are light-reflecting, the use of a light-absorbing (e.g., black) back panel 110 will also tend to increase the contrast between light-reflecting bands and clear bands. If multiple banding layers exist that vary in their light retaining properties, an appropriate back panel 110 can be chosen that aids in band differentiation. In addition, the use of back panel 110 can prevent some of the ambient light present within screening apparatus 100 from reaching the lens of digital camera 102.

[0032] One or more light sources 112 are positioned to illuminate vials positioned at location 108. This illuminating light is then reflected back into digital camera 102, where it is captured in the form of a digital image. In the implementation shown in Fig. 1, three light sources 112 are included. Two of light sources 112 are mounted on either side of digital camera 102, and one light source 112 (not shown) is mounted directly above location 108. In this implementation, light sources 112 provide polarized light to illuminate vials and the samples contained therein. In other implementations, light sources 112 can be oriented such that the images can be captured at an oblique angle relative to the incidence of the light, and non-polarized light can be used. Suitable light sources 112 are available from a variety of manufacturers, such as lamps from StockerYale, Inc. of Salem, New Hampshire, and Edmund Industrial Optics of Barrington, New Jersey. [0033] Screening apparatus 100 can also include a filter 114, such as a polarizing filter, mounted directly in front of the lens of digital camera 102 to eliminate unwanted light from the captured images. This unwanted light includes, for example, specular light that reflects off of the surface of the vial rather than continuing into the sample. This light reflected off the surface of the vial is not desired and can detrimentally affect the analysis. The polarizing filter can also substantially eliminate specular reflection from the interface between the vial and the sample. Polarizers are commercially available from a variety of manufacturers, such as The Tiffen Company, LLC of Hauppauge, New York. By properly orienting polarizer 114 with respect to the polarized light emitted from light sources 112 (e.g., by orienting polarizer 114 orthogonal to the plane of the polarized light emitted from light sources 112), light that is reflected off of the surface of the vial can be substantially or completely blocked from entering the lens of digital camera 102. By contrast, the polarized light that strikes the sample will diffusely reflect as randomly polarized light into digital camera 102, and will pass through filter 114 urihindered.

[0034] Screening apparatus 100 also includes a computer system 120, which can be configured to control the operation of robotic arm 104, light sources 112, and digital camera 102. In one implementation, computer system 120 includes a computer system of conventional construction, including a data store 122 and a programmable processor 124 running a screening program 126 that includes modules 128, 130 and 132 operable to control sample handling, image capture, and image analysis, respectively. Computer system 120 can also include one or more input devices 134, such as a conventional mouse or keyboard, and one or more output devices 136, such as a monitor or display, to enable interactions with a user.

[0035] Screening apparatus 100 includes a housing 116 that is configured to exclude light from the surroundings, such that stray or ambient light cannot affect the digital images captured by digital camera 102. Some or all of the elements described above can be located within housing 116. For example, the sample being analyzed, the light sources, and the camera optics are preferably located within housing 116, where they can be isolated from ambient light. By contrast, elements for which light exclusion is not crucial, such as computer system 120, vial rack 107, and robotic arm 104, can be, but need not necessarily be located outside of housing 116. [0036] Screening apparatus 100 is typically used to analyze a library of samples. As used in this specification, a library is a collection having two or more members, generally containing some variance in chemical or material composition, amount, structures, reaction conditions, and/or processing conditions (including order of process), where a member represents a single sample at a particular location or position, containing one set of chemicals or materials subject to one set of reaction or processing conditions. Libraries can include physical arrays of materials, with different materials located at different regions of a substrate, such as in different vials in a rack of vials or wells of a microtiter plate. Libraries can also include physical arrays of otherwise similar materials, with different regions of the substrate subject to different process conditions or process order or any other physical application that creates diversity. The concept of "library" can also be extended to a plurality of substrates (e.g., vial racks or microtiter plates). In this sense, a library can be defined as any matrix of sites, having two or more members, with parametric diversity between members (or lack thereof, e. g. for error analysis and control purposes), arranged in such a way that physical processes (e.g., synthesis, characterization, or measurement) can be implemented. In one implementation, each library includes two or more members, each of which may be represented as a region in an arrangement (e.g., an array) of one or more regions. A library can include any number of members - for example, two or, more preferably, four, ten, twenty, hundreds or even thousands or more members. Nial rack 107 holds one or more members of the library for analysis within screening apparatus 100.

[0037] While the library may correspond to the geometry of the ultimate physical substrate, it may also represent a collection of library members on a more conceptual level. Libraries can be represented and/or prepared in any convenient shape, such as square, rectangle, circle, triangle or the like, and in zero dimensions (e.g., a point), one dimension (e.g., a linear array of points on a wire), two dimensions (e.g., a surface or plate), or three dimensions (e.g., a block of gel, or other volumetric carrier), depending, for example, on the underlying chemistry or apparatus involved. In one class of substrates, the spatial relationships between the points are or can be predefined and retained during library preparation - in other words, the substrate is spatially addressable. In such substrates, the spatial relationship among the points on the substrate can be used to identify, recognize, or address regions, particularly regions of interest. [0038] Fig. 2 is a graphical representation of a library design for a library of heterogeneous mixtures 200 that includes ninety-five different sample mixtures 202. Each sample 202 in Fig. 2 is graphically represented by a pie chart that displays the different components making up the sample. Thus, for example, for a collection of water- in-oil emulsions, the library design can identify amounts of hydrocarbons, alcohols, water, and additional additives such as other organic materials, acids, nitrates, amines, and salts, as well as surfactants and other suitable stabilizers that are used in some or all of the samples. The library design represented in Fig. 2 can, for example, correspond to a physical collection of ninety-five vials arranged in an array, with each vial containing a liquid mixture having a composition represented by the corresponding pie chart in Fig. 2.

[0039] Fig. 3 is one implementation of a method 300 for generating a library of heterogeneous mixtures 200 according to one aspect of the invention. Although library 200 can include as few as a single sample 202, library 200 will typically take the form of an array including multiple samples 202. Library 200 will generally incorporate some degree of variation from one sample 202 to the next. These variations can include, but are not limited to, changes in the amount of one or more components, differences in processing conditions, and differences in the age of each sample 202. These variations can range from drastic when diverse samples are being studied simultaneously, to subtle when the effects of slight variations to samples of similar composition are studied. The same sample 202 can be used multiple times in order to assess reproducibility of results. The components used in samples 202 will also vary based on the type of samples 202 being studied. After samples 202 have been generated, they can be held for a period of time prior to screening. For some types of samples, this allows the individual components of the heterogeneous mixtures that form samples 202 to exhibit settling behavior by separating and forming banding layers if the components are incompletely miscible. For other types of samples, it allows samples 202 to reorganize or exhibit a flow behavior when exposed to an external perturbation. Digital images of samples 202 are then captured and analyzed, as discussed below with reference to Fig. 5.

[0040] Following the flowchart of Fig. 3, a library design for a library 200 of samples 202 is received (step 302). The library design can be generated using computer- implemented graphical design techniques, such as are embodied in the Library Studio™ library design software available from Symyx Technologies, Inc. of Santa Clara,

California, and described in WO 00/23921, WO 02/14377 A2, and WO 02/14391 A2, all of which are incorporated by reference herein for all purposes. These computer- implemented techniques generally provide a graphical user interface through which a user can design a library of samples 200 conceptually, with the ability to specify the desired composition of multiple samples 202 in terms of a variety of interrelationships between component materials, such as multiple interdependent gradients or ratios of component materials. The software receives a user's conceptual design and performs the detailed calculations necessary to determine the precise composition of each sample 202 in library 200, and can also generate an output data file in a format suitable for manual library preparation or automated preparation using conventional material handling apparatus. The output data file or "recipe file" may include a list of mappings to be performed in preparing library 200, and will contain a "recipe" for each sample 202 to be prepared and analyzed. The library design represented by this output data file can be provided as input to an automated experiment management system implementing synthesis and data handling methods such as those described in WO 00/67086 and/or WO 01/79949, which are also incorporated by reference herein for all purposes.

[0041] After receiving the library design, sample handling module 128 controls automated liquid handing robotics (e.g., robotic arm 104B in Fig. 1) to perform a series of liquid handling steps to dispense liquid components into vials to form library 200 according to the library design. These liquid handling steps can involve a sequence of aspirate and dispense steps to transfer specified amounts of stock solutions and/or neat components from source locations to specified vials for each sample 202 in library 200 (step 304). The components are mixed (e.g., using mixing apparatus 111) to form a sample mixture (step 306).

[0042] The order in which the components are added to the vials and how they are mixed will generally be dependent on the type of sample 202 being generated. Mixing can take place at any point during the addition of components to form sample 202, including, for example, when all or less than all components have been added, and can be carried out for any convenient period of time ranging, for example, from seconds to days or more.

Mixing can also occur multiple times during the addition of components to a sample, for example. Mixing can be carried out using a variety of different techniques, including but not limited to ultrasonic agitation, mechanical mixing, or chemical reaction. Suitable mixing apparatus can include, for example, ultrasonic probes, commercial blenders, mixers, and paint shakers. [0043] For water-in-oil emulsions, ultrasonic probes can provide a suitable mixing result with high emulsion quality, particularly in a small volume environment. Two such ultrasonic probes are the Branson Sonifier 450, and the Branson S-150D, both available from Branson Ultrasonics Corporation of Danbury, Connecticut. Fig. 4 illustrates one implementation of an ultrasonic workstation 400 that includes an ultrasonic probe 402 that can provide mixing functionality for screening apparatus 100. Ultrasonic workstation 400 can also include a washing station 404, and can be configured for automated mixing and tip washing after each mixing.

[0044] In the preparation of water-in-oil emulsions, water can be used as a wash solvent in washing station 404. The use of water ensures compatibility with components used in samples 202. Solvent removal from ultrasonic probe 402 can be performed by sonication of ultrasonic probe 402 for a short duration of time after removing probe 402 from washing station 404. Alternative solvents can also be used in washing station 404 when other samples 202 are being prepared.

[0045] A variety of parameters can be monitored during mixing, including, for example, the power delivered into sample 202 and the duration of the mixing operation. These parameters tend to affect the temperature of sample 202, which can in turn affect the quality of the final mixture. For some water-in-oil emulsions involving volatile organic hydrocarbons, it may be preferable to control mixing so that the temperature of the emulsion does not exceed approximately 35 to 45 °C. In one implementation, delivering approximately 40 to 50 W of power for a duration of 20 to 40 seconds achieves satisfactory mixing without allowing the temperature to rise to a detrimental level.

[0046] The mixed sample is placed into the array of library 200 (step 308). If the sample generation is a serial process, samples 202 are prepared and added to library 200 one at a time. If the process is a parallel process, multiple samples 202 will be added at a time. If the library design includes additional samples to be prepared (the NO branch of step 310), sample handling module 128 returns to step 304 and repeats steps 304-308 to prepare the next sample. When all samples in the library design have been prepared (the YES branch of step 310), the sample preparation phase is complete.

[0047] Samples 202 in library 200 are exposed to an external perturbation prior to screening. This external perturbation induces or causes the behavior that is exhibited by samples 202. In some implementations, the external perturbation can comprise an external vector field. This external vector field can apply a force to the samples, the force including but not limited to any of the following: a centripetal force, a gravitational force, an electric force, a magnetic force, an optical or acoustic radiation force, and an electromagnetic force. In addition to the external perturbation, samples 202 can also be held for a specified time and under specified conditions (e.g., heat, humidity, and other environmental conditions) to enhance the settling, ripening, recrystallizing, or creaming by samples 202, thereby enhancing the exhibited behavior.

[0048] Screening apparatus 100 can include devices to apply an external perturbation, such as an external vector field. These devices include a centrifuge to apply a centripetal force to the samples, a magnetic field generator to apply a magnetic field to the samples, an electric field generator to apply an electric field to the samples, an electromagnetic field generator to apply an electromagnetic field to the samples, or an optical or acoustic radiation field generator to apply an optical or acoustic radiation field to the samples.

[0049] For emulsions subjected to an external vector field that comprises a gravitational or centripetal force, the emulsion samples may exhibit a settling and/or creaming behavior when the individual components separate and form banding layers. The banding layers can then be detected and analyzed, e.g., as a measure of the stability of the emulsion, in the subsequent image capture and image analysis phases. For gels subjected to the same type of external vector field, the gel samples may exhibit flow behavior. The vials containing the gel samples can be tilted or rotated to cause a reorientation of the gel samples with respect to the external vector field. The resulting flow behavior can be analyzed as a measure of gel stability in the subsequent image capture and image analysis phases. The other external vector fields will have similar effects on other types of samples.

[0050] Techniques such as temperature elevation can also be used to enhance the settling behavior of emulsions and the flow behavior of gels. Temperature elevation can also aid in the samples exhibiting some form of behavior. Typically, one aliquot of each sample 202 may be allowed to settle or flow at room temperature, while another aliquot may be allowed to settle or flow at a temperature greater than room temperature. [0051] The duration of time that samples 202 are held for settling or flowing can range from minutes to months, with typical times ranging from 24 hours to seven days. Samples 202 can be divided up into several aliquots, each aliquot being subjected to different times and conditions, or to completely different analyses. For instance, in one implementation, each sample 202 can be divided up into one aliquot that is allowed to settle or flow at room temperature for seven days, another aliquot that is allowed to settle or flow at an elevated temperature for seven days, and other aliquots that are used for alternative analyses including, but not limited to, particle sizing and capacitance measurements. Particle sizing measures the emulsion droplet size, generally by static or dynamic light scattering. Dielectric measurements are used to measure the water content across the vertical profile of the emulsion samples.

[0052] Fig. 5 illustrates a screening method 500 for analyzing a library of samples that can be implemented in a screening apparatus 100 as described above. Screening apparatus 100 provides an automated, high-throughput screening apparatus and process for samples 202, and automatically analyzes samples 202 to yield reliable and standardized data, and to provide additional and more detailed information than can be gathered by relying solely on the subjective assessment of a trained operator. This additional information includes data generated for a full profile of a dispersion that details the exhibited behavior. The high-throughput capability of the screening arises from the automation of the screening apparatus, which substantially reduces the time required to analyze an array of samples 202.

[0053] To screen samples 202, robotic arm 104 first obtains a vial 408 containing sample

202 from vial rack 107 and transports it to location 108 (step 502). Nial rack 107 contains one or more members of library 200. As mentioned above, location 108 is within the field of view of digital camera 102. Vial 408 is illuminated by light sources 112 and a digital image of vial 408 is captured by digital camera 102 (step 504). The illumination of the samples can be from one or more directions with respect to the direction of the external perturbation. The images are generally captured at an orientation that is normal to the direction of the external perturbation. The digital images are generated by digital camera 102 capturing light scattered by the samples, and the images of the samples are generally captured at an oblique angle relative to the incidence of the light. In some implementations, robotic arm 104 can transport a plurality of vials 408, e.g. eight vials in parallel, to location 108 and acquire images on all vials 408 simultaneously. [0054] The digital image is typically in the form of an array or matrix of pixel values corresponding to the intensity of light reaching the lens system of camera 102. Alternatively, pixel values other than intensity of visible light can be used. For instance, the digital image can be separated into its primary color components — for example, a red channel, a green channel and a blue channel for an RGB image - and the subsequent image analysis techniques can be separately applied to one or more of these components. Likewise, pixel values can represent the intensity of other forms of radiation, such as infrared, ultraviolet, or the like, captured using an appropriate image capture device. The illumination and image capture can be automated - for example, using an image capture module 130 of a screening program 126 as discussed above. Alternatively, some or all of the illumination and image capture can be carried out based on manual interaction with a user. Thus, for example, some or all of the settings of digital camera 102, including but not limited to aperture, zoom, ISO speed, exposure time, and image quality, can be determined and adjusted by image capture module 130 of screening program 126, or by a user.

[0055] Once an image of vial 408 is captured, vial 408 is returned to vial rack 107 by robotic arm 104. If there are still vials 408 remaining in vial rack 107 that still need to be processed (the NO branch of step 506), image capture module 130 returns to step 502 and another vial 408 is transported to location 108. Otherwise, if the entire vial rack 107 has been processed (the YES branch of step 506), the method proceeds to the image analysis phase, where image analysis module 132 processes the digital images to generate data for each of samples 202 (step 508). In other implementations of the invention, multiple images can be taken of each vial 408. The images of vial 408 can be taken across several points in time, thereby allowing image analysis module 132 to monitor the evolution over time of the disperse systems that make up the samples contained in vials 408. This monitoring over time can include monitoring the flow of the sample in the vial, such as when the sample comprises a gel. For instance, if the gel sample is tilted or rotated, multiple images over several points in time can monitor the effect of this tilt or rotation.

[0056] The digital images can be processed offline, after the entire library 200 has been processed. Alternatively, the image processing can begin while images are being captured of samples 202 from library 200. Image analysis module 132 is configured to receive data from digital camera 102, typically in the form of a sequence of pixel values corresponding to light intensity as discussed above, and provide an analysis of that data. In one implementation, the digital image data received from digital camera 102 can be smoothed either before or after being communicated to image analysis module 132. If the digital images are smoothed, the smoothing will generally be implemented using some form of averaging filter, such as a five pixel circular average filter. Image analysis module 132 uses the digital images to generate qualitative and quantitative descriptions of the behavior observed in each sample 202. These descriptions can include elements such as band heights and band intensities if banding layers are observed. This information can then be used to provide a detailed analysis of samples 202.

[0057] Fig. 6 illustrates one example of a method 600 implemented in image analysis module 132 for analyzing digital images received from digital camera 102. Image analysis module 132 receives a digital image from digital camera 102 (step 602). The digital image can be received directly from digital camera 102 if an electrical connection exists between computer system 120 and digital camera 102. Alternately, the digital image can be retrieved from digital camera 102 by a user and manually input into computer system 120.

[0058] A template can be defined to assist in the analysis of the digital images received from digital camera 102 (step 604). The template defines the boundaries of a region or regions of interest in the digital images, in which image analysis module 132 will focus its analysis. Regions outside of the region(s) of interest will generally be ignored by image analysis module 132. In one implementation, the region of interest defined by the template corresponds substantially to the cross-section of sample 202 in vial 408. The template can be defined based on user input - for example, input corresponding to a user's manipulation of graphical input tools to draw boundaries of the region of interest - or automatically, based, for example, on the application of conventional edge detection techniques to one or more of the digital images. Alternatively, one or more predefined templates, corresponding, for example, to different types of vials 408 that can be used to hold samples 202, can be stored in data store 122 and retrieved by image analysis module 132 for use in the analysis.

[0059] Fig. 7 illustrates the application of a template to define a region of interest 702 in a digital image 700. Digital image 700 includes a representation of a vial 408 held by vial gripper 106 and robotic arm 104. In Fig. 7, sample 202 is an emulsion sample contained within vial 408, and banding layers 706 appear within sample 202. The template defines a region of interest 702 in digital image 700 that generally contains banding layers 706. In Fig. 7, there are five banding layers 706 shown within region of interest 702. The region of interest is the portion of digital image 700 that is subjected to analysis by image analysis module 132. The remainder of digital image 700, which lies outside of region of interest 702 and includes robotic arm 104, vial gripper 106, and the background, is excluded from the analysis.

[0060] Once the template has been applied to define a region of interest 702 in digital image 700, image analysis module 132 samples pixel values in the region of interest to generate an intensity profile for sample 202 (step 606). In one implementation, image analysis module 132 defines one or more vertical lines 704 in region of interest 702, and samples the pixel values (light intensity) for pixels lying along these lines. Typically, image analysis module 132 will define at least one vertical line 704 extending along the length of vial 408 midway between the walls of the vial, and intensity values will be sampled for pixels at a plurality of locations along the vertical line 704. Intensity values can be sampled for all pixels lying on the line 704, or for only a subset of those pixels - for example, pixels lying at regular intervals along the line. In some implementations, image analysis module 132 can also define one or more additional lines 704, also extending along the length of vial 408, that lie between the center and edges of vial 408. Where image analysis module 132 uses multiple lines, the pixel values for corresponding locations along the respective lines can be averaged to define an intensity profile for the sample 202.

[0061] In another implementation, integration widths can be used for averaging pixel values. Integration width refers to averaging over pixels in the horizontal direction (i.e. horizontal relative to the length of the vial). Integration widths can be varied based on the resolution desired, and can range from a few pixels to several hundred pixels. Rectangular shapes can be defined where the short axis is the integration width and the long axis is the length of the vial. Averaging is then performed over the short axis and the intensity profile is taken over the long axis. While it is possible to average pixels over the entire width of the vial (i.e. the defined rectangle can encompass the entire sample), better results are generally obtained when rectangles are defined with smaller integration widths. The use of smaller integration widths permits multiple intensity profiles to be generated for a sample. This allows for the detection of differences between the center of the sample and the outer edges of the sample. For example, water droplets resting on the bottom of the vial can be detected when multiple intensity profiles are generated.

[0062] In yet another implementation, pixel value detection and averaging can be performed by defining squares instead of rectangles to analyze the digital image. Squares can be sampled over some or the entire digital image, and the squares can then be compared in terms of their average value, standard deviation, and minimum and maximum pixel intensity values. This type of analysis provides information concerning the homogeneity or graininess of the sample, and can indicate the presence of large, flocculated particles. This type of analysis is suitable to assess the quality of many types of samples, including gel samples.

[0063] The light intensity at a point is dependent on the quantity of light that was scattered by banding layers 706 (or back panel 110) at that point and reflected back to digital camera 102. Accordingly, for banding layers 706 that are light reflecting, the light intensity captured by digital camera 102 will be relatively high. Likewise, for banding layers 706 that are light absorbing, the light intensity captured by digital camera 102 will be relatively low. For banding layers 706 that are clear, the light intensity measured by processing unit 118 will depend on the type of back panel 110 that is used. In an alternative embodiment, light source 112 can be reoriented so that light is transmitted through banding layers 706 to digital camera 102 rather than reflected, in which case the light intensity can be measured based on the light transmitting or light obstructing properties of banding layers 706. In yet another embodiment, if banding layers 706 are composed of a light-emitting material, such as a glow-in-the-dark or fluorescent material, light intensity can be measured without the need for simultaneous illumination by light source 112.

[0064] Image analysis module 132 can save the resulting intensity profile data to data store 122 - for example, as a text file compatible with conventional text processing or spreadsheet programs - and can be configured to perform additional processing on the intensity profile data to facilitate further analysis. Thus, for example, image analysis module 132 can be configured to generate an intensity profile graph that plots relative light intensity versus location (e.g., height) within the region of interest (step 608). Fig. 8 illustrates an exemplary intensity profile graph for digital image 700 of Fig. 7. As shown in Fig. 8, at 0% height within region of interest 702 the observed light intensity is relatively low, corresponding to the light-absorbing banding layer 706 located at the bottom of vial 408 in Fig. 7, which reflects only a relatively small amount of light back to digital camera 102. At 10% height within region of interest 702, the observed light intensity rises a small amount, corresponding to a slightly more light-reflecting banding layer 706. The change in observed light intensity signals to image analysis module 132 that a new banding layer 706 has been reached. At 20% height, the observed light intensity rises steeply, again signaling the start of another banding layer 706, this time a layer that is highly light-reflecting. Similarly, additional banding layers begin at 65% height within region of interest 702 (a relatively light-absorbing layer) and 95% height (a light-reflecting region indicated by a high observed light intensity). Optionally, if image analysis module 132 uses pixel values for multiple vertical lines, an intensity profile graph can be generated for each of these lines, and the results combined (e.g., averaged) to provide a final graph. Alternatively, the pixel values can be combined before the generation of the intensity profile graph.

[0065] It should be noted that in Fig. 8, the measured light intensity is provided as either relatively high or relatively low for simplification purposes. In other implementations, the light intensity measurements can be quantitative amounts, for example, in foot- candles or lux, and this quantitative data will appear in the intensity profile graph.

[0066] Image analysis module 132 can be configured to transform the intensity profile data to facilitate display and interpretation of the data. For example, in one implementation image analysis module 132 calculates the LaPlacian of the intensity profile to yield a graph displaying the transitions between banding layers as more readily identifiable peaks and valleys. Image analysis module 132 can then identify the transitions using conventional peak detection techniques. Fig. 9 illustrates one such plot, in which the intensity profile data illustrated in Figs. 7 and 8 has been subjected to a LaPlacian calculation. As shown in Fig. 9, peaks and valleys appear in the graph that correspond to boundaries between banding layers 706. The heights of the peaks and valleys correspond to the measured light intensity levels of banding layers 706.

[0067] The transitions and/or peaks and valleys in the intensity profile data mark the edges of the different banding layers in sample 202. Based on the location and heights of these transitions, banding layers can be identified by image analysis module 132 and banding heights can be calculated. The location and height of these layers can be used to identify features and stages of demixing present in sample 202 ~ for example, oil layers, oily emulsion layers, emulsion layers, water bands, or free water in a sample that is a water-in-oil emulsion. The distance between transitions corresponds to banding height, which provides information relevant to whether a mixture, such as an emulsion, is acceptable. The banding height indicates how well a system, such as an emulsion, can withstand demixing.

[0068] In another implementation, banding layers can be identified using their measured intensities. Brightness thresholds can be set within image analysis module 132 to define regions. For example, if it is known that a particular banding layer has a measured intensity of 80 +/- 3 units of brightness, image analysis module 132 can be provided with that threshold information. When the intensity profile is generated for a sample, any banding layers that have an intensity falling within the threshold bounds will be identified by image analysis module 132.

[0069] Since emulsions are not thermodynamically stable, they will tend to separate macroscopically over time. For emulsions, as well as other types of samples, smaller banding heights, or an absence of banding layers altogether, may be indicative of better performance and long-term stability of the emulsion. The sizes of the transitions (e.g., the height of the peaks and the depth of the valleys) provide clues as to the characteristics of the mixture that constitutes the layer. For example, emulsion layers tend to be the most light reflecting in water-in-oil emulsions, so the highest peak will likely correspond to the transition to an emulsion layer. Optionally, image analysis module 132 can be configured to identify the various layers of sample 202 using data from data store 122 that reflects the relationship between light intensity and specific types of banding layers; alternatively, this analysis can be performed by a user.

[0070] In an alternative implementation, a derivative can be calculated for the intensity profile graph rather than a LaPlacian calculation, and this will similarly provide a graph with peaks and valleys that can be used to identify banding layers and banding heights.

[0071] Fig. 10 shows an array of intensity profiles taken for a library such as library 200 shown in Fig. 2. Fig. 11 shows an array of intensity data corresponding to that of Fig. 12 in which the data has been transformed according to a LaPlacian calculation. [0072] Figs. 12 through 15 illustrate one example of the application of the techniques described above to the analysis of a collection of water-in-oil emulsions. Fig. 12 is a digital image 1200 of four vials 1302 to 1308 containing four different water-in-oil emulsion samples. The layers visible in the four samples include an oil banding layer, an emulsion layer, a water banding layer, and a free water layer. The oil banding layer is composed almost exclusively of oil and appears clear (i.e. black) in the image. The emulsion layer is approximately eighty percent oil, twenty percent water, and about one to two percent surfactants. The emulsion layer appears dingy-white in the image. The water banding layer is composed primarily of water with some oil mixed into it and appears milky white. The free water is primarily water and appears gray.

[0073] Digital image 1200 is received and analyzed by image analysis module 132. Image analysis module 132 can then define a region or regions of interest in digital image 1200 to focus its analysis, either based on user input or automatically. Next, image analysis module 132 samples pixel values in the region of interest to generate an intensity profile for the samples shown in digital image 1200. Fig. 13 illustrates an intensity profile generated by image analysis module 132 based on digital image 1200. There is one intensity profile corresponding to each vial 1302 to 1308. Image analysis module 132 can save the resulting intensity profile data to data store 122.

[0074] As seen in Fig. 12, finding the transitions between layers can sometimes be difficult to the human eye. The use of screening apparatus 100 can discern transitions between layers that are otherwise difficult to see. For instance, what appears as a homogenous white layer in sample 1306 is actually comprised of two layers. One layer is the emulsion layer and the other layer is the water banding layer. Although the human eye cannot easily distinguish these layers, they are identified by image analysis module 132 in the intensity profile. There is a small peak 1310 in the intensity profile that corresponds to the transition between the emulsion layer and the water banding layer.

[0075] Image analysis module 132 can also perform additional processing on the intensity profile data to facilitate further analysis. This processing can include calculating a LaPlacian or a derivative of the intensity profile. For instance, Figs. 14 and 15 respectively show a LaPlacian and a derivative calculated for the intensity profile illustrated in Fig. 13. Figs. 14 and 15 have peaks and valleys that identify transitions between banding layers in the samples shown in digital image 1200. Image analysis module 132 can identify the transitions using conventional peak detection techniques. These techniques, coupled with rules set up in image analysis module 132, can ascertain the identity of each banding layer and provide this information to a user.

[0076] For the water-in-oil samples shown in digital image 1200, the rules can be set as follows:

oil banding layer = difference between first positive and second positive peak, if brightness in line profile is less than 60;

water banding layer = difference between small positive peak and first negative peak in the region of the graph that is greater than 500 pixels;

free water = apparent water minus 2.6%, where the apparent water is the difference between the first negative peak and last positive peak for Laplace analysis (first positive peak for derivative analysis), and the 2.6% constitutes the thickness of the glass bottom of the vial; and

emulsion layer = the remaining portion of the sample.

[0077] This analysis of digital image 1200 yields the following results:

[0078] An absence of banding layers altogether can be indicative of better performance and long-term stability of the emulsion. Here, the absence of banding layers in sample 1308 indicates that the entire sample consists of a water-in-oil emulsion that has not yet separated on a macroscopic level. [0079] The computer-implemented aspects of the invention can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. These aspects of the invention can be implemented as one or more computer program products, i.e., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable storage device or in a propagated signal, for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

[0080] Method steps of the invention can be performed by one or more programmable processors executing a computer program to perform functions of the invention by operating on input data and generating output. Method steps can also be performed by, and apparatus of the invention can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0081] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. Information carriers suitable for embodying computer program instructions and data include all forms of non- volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and

CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in special purpose logic circuitry. [0082] The invention can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the invention, or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network ("LAN") and a wide area network ("WAN"), e.g., the Internet.

[0083] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0084] A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. For example, ultra-violet or infrared radiation can be used to illuminate vials 408 in conjunction with digital cameras 102 that are capable of capturing either ultraviolet or infrared images. Similarly, laser radiation can be used to analyze vials 408 when used with a digital camera that can capture reflected laser light. In another embodiment, robotic arm 104B can be used to dispense a non-solvent into solution, and digital camera 102 can be used to capture a series of images to monitor the onset of precipitation. Accordingly, other embodiments are within the scope of the following claims.

Claims

ClaimsWhat is claimed is:

1. A method for analyzing a plurality of samples, the method comprising: receiving a plurality of samples, each sample including a dispersion of one or more incompletely miscible components in a continuous fluid phase, each sample being contained within a container; exposing the samples to an external perturbation; capturing images of the samples at an orientation that is normal to the direction of the external perturbation; and detecting in a captured image a behavior of the corresponding sample.

2. The method of claim 1 , wherein the behavior comprises a flow behavior in response to the external perturbation.

3. The method of claim 1 , wherein the behavior comprises a settling behavior in response to the external perturbation.

4. The method of claim 3, wherein detecting a behavior comprises detecting two or more phases in the captured image.

5. The method of claim 1, wherein exposing the samples to an external perturbation comprises exposing the samples to an external vector field.

6. The method of claim 5, further comprising: reorienting the samples with respect to the external vector field; wherein detecting a behavior comprises detecting in a captured image a flow behavior in the corresponding sample.

7. The method of claim 1, wherein capturing images of the samples comprises capturing images of individual samples.

8. The method of claim 1, wherein capturing images of the samples comprises capturing images with multiple samples per image.

9. The method of claim 5, wherein: exposing the samples to an external vector field includes applying a force selected from the group consisting of a centripetal force, a gravitational force, an electric force, a magnetic force, an optical or acoustic radiation force, and an electromagnetic force.

10. The method of claim 1, wherein capturing images of the samples includes: illuminating the samples from one or more directions with respect to the direction of the external perturbation; and capturing light scattered by the samples.

11. The method of claim 10, wherein: illuminating the samples includes illuminating the samples with light selected from the group consisting of visible light, ultraviolet light, and infrared light.

12. The method of claim 10, wherein: capturing light scattered by the samples includes capturing images of the samples at an oblique angle relative to the incidence of the light.

13. The method of claim 10, wherein: illuminating the samples includes illuminating the samples with polarized light; and capturing light scattered by the samples includes capturing light through a polarizing filter.

14. The method of claim 13, wherein the polarizing filter substantially eliminates specular reflection from the container and from an interface between the container and the sample.

15. The method of claim 1 , wherein capturing images of the samples includes capturing a plurality of images of each sample over a period of time.

16. The method of claim 1 , wherein detecting a behavior includes: defining one or more regions of interest in an image of the captured images; generating an intensity profile for each region of interest; and detecting a behavior in a sample based on the intensity profile for a corresponding region of interest.

17. The method of claim 16, wherein: defining a region of interest includes detecting a sample boundary in a captured image and defining the region of interest as a region within the sample boundary.

18. The method of claim 17, wherein: the sample boundary represents an edge of the container containing a sample.

19. The method of claim 16, wherein: detecting a behavior includes calculating a Laplacian of the intensity profile for the region of interest corresponding to the sample.

20. The method of claim 16, wherein: detecting a behavior includes calculating a derivative of the intensity profile for the region of interest corresponding to the sample.

21. A computer program product embodied in an information carrier for analyzing a plurality of samples, the product comprising instructions operable to cause a programmable processor and one or more robotic devices to: receive a plurality of samples, each sample including a dispersion of one or more incompletely miscible components in a continuous fluid phase, each sample being contained within a container; expose the samples to an external perturbation; capture images of the samples at an orientation that is normal to the direction of the external perturbation; and detect in a captured image a behavior of the corresponding sample.

22. The product of claim 21 , wherein the behavior comprises a flow behavior in response to the external perturbation.

23. The product of claim 21 , wherein the behavior comprises a settling behavior in response to the external perturbation.

24. The product of claim 23, wherein the instruction to detect a behavior includes instructions operable to cause a programmable processor to: detect two or more phases in the captured image.

25. The product of claim 21 , wherein the instruction to expose the samples to an external perturbation includes instructions operable to cause a programmable processor and one or more robotic devices to: expose the samples to an external vector field.

26. The product of claim 25, further comprising instructions operable to cause a programmable processor and one or more robotic devices to: reorient the samples with respect to the external vector field; wherein detecting a behavior comprises detecting in a captured image a flow behavior in the corresponding sample.

27. The product of claim 21 , wherein the instruction to capture images of the samples includes instructions operable to cause a programmable processor and one or more robotic devices to: capture images of individual samples.

28. The product of claim 21, wherein the instruction to capture images of the samples includes instructions operable to cause a programmable processor and one or more robotic devices to: capture images with multiple samples per image.

29. The product of claim 25, wherein the instruction to expose the samples to an external vector field includes instructions operable to cause a programmable processor and one or more robotic devices to: apply a force selected from the group consisting of a centripetal force, a gravitational force, an electric force, a magnetic force, an optical or acoustic radiation force, and an electromagnetic force.

30. The product of claim 21, wherein the instruction to capture images of the samples includes instructions operable to cause a programmable processor to: illuminate the samples from one or more directions with respect to the direction of the external perturbation; and capture light scattered by the samples.

31. The product of claim 30, wherein the instruction to illuminate the samples includes instructions operable to cause a programmable processor to: illuminate the samples with light selected from the group consisting of visible light, ultraviolet light, and infrared light.

32. The product of claim 30, wherein the instruction to capture light scattered by the samples includes instructions operable to cause a programmable processor to: capture images of the samples at an oblique angle relative to the incidence of the light.

33. The product of claim 30, wherein the instruction to illuminate the samples includes instructions operable to cause a programmable processor to: illuminate the samples with polarized light; and capture light scattered by the samples includes capturing light through a polarizing filter.

34. The product of claim 33, wherein the polarizing filter substantially eliminates specular reflection from the container and from an interface between the container and the sample.

35. The product of claim 21, wherein the instruction to capture images of the samples includes instructions operable to cause a programmable processor to: capture a plurality of images of each sample over a period of time.

36. The product of claim 21 , wherein the instruction to detect a behavior includes instructions operable to cause a programmable processor to: define one or more regions of interest in an image of the captured images; generate an intensity profile for each region of interest; and detect a behavior in a sample based on the intensity profile for a corresponding region of interest.

37. The product of claim 36, wherein the instruction to define a region of interest includes instructions operable to cause a programmable processor to: detect a sample boundary in a captured image; and define the region of interest as a region within the sample boundary.

38. The product of claim 37, wherein: the sample boundary represents an edge of the container containing a sample.

39. The product of claim 36, wherein the instruction to detect a behavior includes instructions operable to cause a programmable processor to: calculate a Laplacian of the intensity profile for the region of interest corresponding to the sample.

40. The product of claim 36, wherein the instruction to detect a behavior includes instructions operable to cause a programmable processor to: calculate a derivative of the intensity profile for the region of interest corresponding to the sample.

41. A system for analyzing samples containing a dispersion of two or more incompletely miscible liquid components, the system comprising: a vial receptacle located at a first location; an image capturing device directed at the first location; a light source directed at the first location; and a programmable processor operatively coupled to the image capturing device, the programmable processor being configured to detect a behavior in a captured image of a sample.

42. The system of claim 41 , the programmable processor being configured further to : define one or more regions of interest in an image of the captured images; generate an intensity profile for each region of interest; and detect a behavior in a sample based on the intensity profile for a corresponding region of interest.

43. The system of claim 42, wherein the programmable processor defines one or more regions of interest by detecting a sample boundary in a captured image and defining the region of interest as a region within the sample boundary.

44. The system of claim 42, wherein the programmable processor detects a behavior by calculating a Laplacian of the intensity profile for the region of interest corresponding to the sample.

45. The system of claim 42, wherein the programmable processor detects a behavior by calculating a derivative of the intensity profile for the region of interest corresponding to the sample.

46. The system of claim 41 , further comprising : a robotic sample handling system coupled to the programmable processor, the robotic sample handling system being configured to transport samples to and from the vial receptacle.

47. The system of claim 41 , wherein: the programmable processor is configured to identify one or more features of the sample based on the detected behavior.

48. The system of claim 47, wherein: the dispersion of two or more incompletely miscible liquid components is a water- in-oil emulsion; and the one or more features include features selected from the group consisting of an oily layer, an oily emulsion layer, a homogenous emulsion layer, a water-rich layer, and a water layer.

49. The system of claim 41 , wherein the programmable processor detects a behavior by: detecting two or more phases in the captured image of a sample.

50. The system of claim 42, wherein: the programmable processor is configured to detect a behavior by calculating a Laplacian of the profile for a region of interest corresponding to the sample.

51. The system of claim 42, wherein: the programmable processor is configured to detect a behavior by calculating a derivative of the profile for a region of interest corresponding to the sample.

52. The system of claim 41, wherein: the light source includes one or more lamps configured to illuminate the positioned samples with polarized light at an angle relative to the image capturing device.

53. The system of claim 41, wherein: the light source includes one or more lamps configured to illuminate the positioned samples with infrared light at an angle relative to the image capturing device.

54. The system of claim 41 , wherein: the light source includes one or more lamps configured to illuminate the positioned samples with ultraviolet light at an angle relative to the image capturing device.

55. The system of claim 46, wherein: the robotic sample handling system is configured to sequentially position groups of samples at the location with a single robotic action.

56. The system of claim 42, wherein: defining a region of interest includes detecting a sample boundary in a captured image and defining the region of interest as a region within the sample boundary.

57. The system of claim 56, wherein: the sample boundary represents the edge of a vial containing the sample.

58. The system of claim 41 , further comprising : a centrifuge coupled to the programmable processor, the centrifuge being configured to apply a centripetal force to the samples.

59. The system of claim 41 , further comprising: a magnetic field generator coupled to the programmable processor, the magnetic field generator being configured to apply a magnetic field to the samples.

60. The system of claim 41, further comprising: an electric field generator coupled to the programmable processor, the electric field generator being configured to apply an electric field to the samples.

61. The system of claim 41 , further comprising : an electromagnetic field generator coupled to the programmable processor, the electromagnetic field generator being configured to apply an electromagnetic field to the samples.

62. The system of claim 41 , further comprising : an optical or acoustic radiation force field generator coupled to the programmable processor, the optical or acoustic radiation force field generator being configured to apply an optical or acoustic radiation field to the samples.