Apparatus and method for characterizing particles

Abstract

The present invention provides an apparatus (10) for characterizing particles using nanoparticle tracking analysis (NTA). The apparatus (10) includes a flow cell (101) containing a sample including a plurality of particles suspended in a fluid, a pump (102) configured to provide a flow of the sample through the flow cell (101), a light source (103) configured to illuminate the sample, an imaging system (104) configured to collect light scattered or fluorescently emitted by particles moving within the flow cell (101) and within a detection region of the imaging system (104) and to capture video of the particles moving within the detection region, and a computer (105) configured to process the video. The computer (105) is configured to determine an estimated flow velocity of the sample through the flow cell (101). Determining the estimated flow velocity includes performing one-dimensional particle tracking in a direction orthogonal to the expected direction of flow of the sample.

Description

[Technical Field]

[0001] The present invention relates to an apparatus and method for characterizing particles using nanoparticle tracking analysis. [Background technology]

[0002] Nanoparticle Tracking Analysis (NTA) is a method for characterizing particles suspended in a fluid by tracking the movement of individual particles and determining their size based on Brownian motion. Typically, the method involves illuminating a sample containing multiple particles suspended in the fluid, collecting light scattered and fluorescently emitted by the particles, and capturing video of the particles as they move through the fluid. The fluid may be a liquid (e.g., water or other liquid) or a gas (e.g., air). The video is analyzed frame by frame to track the movement of the particles. The displacement of the particles between frames of the video caused by Brownian motion is determined and used to determine the size of the particles using Fick's second law of diffusion and the Stokes-Einstein equation.

[0003] In NTA, it is desirable to provide a sample flow. This can improve the accuracy of the analysis of measurements, as more sample is moved through the detection region of the device used to perform the analysis, making measurements less susceptible to outliers. Very large or very small particles or contaminants are removed from the video field of view by the sample flow. However, providing a flow can create problems in accurately tracking particles and determining their rate of displacement, some of which are caused by the sample flow and some of which are caused by Brownian motion. These problems become more pronounced as the sample flow rate and concentration increase. As the sample flow rate or concentration increases, it also becomes more difficult to determine when particle tracking accuracy falls below an acceptable level.

[0004] Known NTA processes may include, for example, performing two-dimensional particle tracking in subsequent frames of the video by identifying particles from a current frame of the video as Euclidean nearest neighbor particles in the subsequent frames, estimating a sample flow velocity from the average distance traveled by particles tracked by the two-dimensional particle tracking between subsequent frames of the video, correcting for particle drift based on the estimated flow velocity, determining the distance traveled by the particle due to Brownian motion after correcting for drift, and determining the particle size based on the distance traveled due to Brownian motion. Two-dimensional particle tracking may result in unacceptable errors in identifying particles in subsequent frames of the video, ultimately compromising the accuracy of particle size measurements. Summary of the Invention

[0005] A first aspect of the present invention provides an apparatus for characterizing particles using nanoparticle tracking analysis (NTA). The apparatus includes a flow cell containing a sample including a plurality of particles suspended in a fluid, a pump configured to provide a flow of the sample through the flow cell, a light source configured to illuminate the sample, an imaging system configured to collect light scattered or fluoresced by particles moving within the flow cell and within a detection region of the imaging system and to capture video of the particles moving within the detection region, and a computer configured to process the video to determine an estimated flow velocity of the sample through the flow cell. Determining the estimated flow velocity of the sample through the flow cell includes performing one-dimensional particle tracking in a direction orthogonal to the expected direction of flow of the sample.

[0006] As referred to in the specification, particle tracking is the process of identifying certain particles in subsequent frames of a video. For example, a given particle may be identified in a current frame of a video, and particle tracking may be used to identify certain given particles in subsequent frames of the video. A subsequent frame is a frame that occurs after the current frame. In some embodiments, particle tracking is "frame-by-frame," and a subsequent frame is, but is not limited to, every subsequent frame from the current frame. For example, particles may be tracked every other frame, every third frame, etc.

[0007] It is understood that the direction perpendicular to the expected flow direction of the sample is not limited to a direction that is exactly 90 degrees to the expected flow direction, and those skilled in the art will appreciate that in practice there is a latitude in the choice of direction for one-dimensional particle tracking.

[0008] One-dimensional particle tracking may include identifying particles from a current frame of video in subsequent frames of the video. The term one-dimensional particle tracking is used herein to refer to identifying particles from a current frame in subsequent frames of the video based on one-dimensional position (which may be thought of as position along a particular direction). Identifying particles in subsequent frames of the video may include identifying the closest particle in the subsequent frame in a tracking direction (e.g., the direction of expected flow of the sample, regardless of the proximity of particles in other directions). The tracking direction may be orthogonal to the direction of expected flow of the sample.

[0009] One-dimensional particle tracking may be performed on a stored video after the video has been captured, or one-dimensional particle tracking may be performed as the video is being captured.

[0010] Performing one-dimensional particle tracking in a direction perpendicular to the expected flow direction of the sample may reduce errors in identifying particles from the current frame of video in subsequent frames of the video compared to other particle tracking techniques because there is no bulk flow in this direction. Therefore, misidentification of a second particle that moves in a subsequent frame and is very close to the position of the first particle in the previous frame is reduced because the particle moves less perpendicular to the flow. This improved identification may improve estimates of the sample's flow velocity, which can be used to correct for particle drift caused by the flow. This may improve the accuracy of determining the distance traveled by a particle due to Brownian motion and improve the accuracy of determining particle size using the Stokes-Einstein equation.

[0011] In the subsequent frame, identifying the nearest particle may include identifying the nearest particle within a one-dimensional tracking distance limit. The one-dimensional tracking distance limit may be determined from the video. Determining the one-dimensional tracking distance limit may include performing one-dimensional particle tracking in the tracking direction on a subset of frames of the video that do not include the tracking distance limit and determining an average distance traveled by the particle in the tracking direction between successive frames of the video. The average distance traveled by the particle may be used to determine the tracking distance limit. The average may be a mean value. The tracking distance limit may be, for example, two times the median or + / - 3 standard deviations of the median.

[0012] Setting a tracking distance limit minimizes the number of potential nearest neighbor particles in subsequent frames, which requires determining the distance from a particle in the current frame to determine the nearest neighbor particle in the subsequent frame, which advantageously improves processing speed and minimizes consumption of processing resources.

[0013] The estimated flow velocity may be determined from the average distance traveled by one or more particles tracked by one-dimensional particle tracking between subsequent frames of video. The distance traveled by a particle between successive frames of video may be measured in two dimensions (this may accommodate, for example, the case where the camera is not perfectly aligned with the flow cell).

[0014] The estimated flow velocity may be determined from the average distance traveled by particles tracked by the one-dimensional particle tracking between consecutive frames of all frames of the video or a suitable subset of frames of the video, where the suitable subset of frames of the video is a subset of frames of the video that includes fewer than all frames of the video. The flow of the sample may be assumed to be uniform throughout the detection area of ​​the imaging system, in which case the estimated flow velocity may be measured from the average distance traveled by particles tracked by the one-dimensional particle tracking between consecutive frames of a true subset of frames of the video.

[0015] The mean value may be the median value. The median value may be more robust to errors in identifying particles in subsequent frames of video than other mean values. As mentioned above, performing one-dimensional particle tracking in a direction orthogonal to the expected flow direction of the sample may reduce (but not necessarily completely eliminate) errors in identifying particles in subsequent frames of video.

[0016] In other embodiments, the average value may be an arithmetic mean, which may provide a more accurate estimate of flow velocity if the error in identifying particles in subsequent frames of video is known to be below an acceptable limit.

[0017] The computer may be configured to use the estimated flow velocity to determine a corrected position of the particle from which the estimated flow velocity component of the particle's movement is removed, perform two-dimensional particle tracking of the particle's corrected position, and determine a residual flow velocity from an average distance traveled by a particle tracked by the two-dimensional particle tracking of the corrected position between subsequent frames of the video.

[0018] Performing two-dimensional particle tracking may include identifying, in the subsequent frame, a particle from the current frame as the particle that is the shortest Euclidean distance from the position of the particle in the current frame.

[0019] The computer may be configured to determine a residual corrected trajectory for each particle, and determining the residual corrected trajectory for each particle may include correcting the two-dimensional particle track of the particle's corrected position to remove the residual flow velocity.

[0020] The computer may be configured to determine the particle size of each particle from the average distance traveled by the particle between subsequent frames of video obtained from the residual correction trajectory.

[0021] The computer may be configured to determine a mean square displacement of each particle from the average distance traveled by the particle between subsequent frames of video obtained from the residual corrected trajectories. The computer may be configured to determine a diffusion coefficient of the particle from the mean square displacement. The computer may be configured to determine particle size from the diffusion coefficient using the Stokes-Einstein equation. The diffusion coefficient of the particle can be obtained from the mean square displacement using Fick's law of diffusion.

[0022] The two-dimensional particle tracking of the corrected position may include identifying particles from the current frame of video in a subsequent frame of video, which may include identifying nearest particles in the subsequent frame, which may be selected from those within the two-dimensional tracking distance limit.

[0023] The two-dimensional tracking distance limit may be determined according to the movement of a particle tracked by one-dimensional particle tracking. For example, the two-dimensional tracking distance limit may be determined based on the mean distance a particle moves while being tracked by one-dimensional tracking. Setting a two-dimensional tracking distance limit provides similar advantages to setting a one-dimensional tracking distance limit described above.

[0024] The computer may be configured to generate a measurement alert if the estimated flow rate and / or, in some cases, the residual flow rate exceeds a predetermined threshold. An estimated flow rate or residual flow rate exceeding a predetermined threshold may be an indication that the accuracy of particle size measurements obtained using the device is below an acceptable level.

[0025] The computer may be configured to determine a particle concentration of the sample. The computer may be configured to generate a measurement alert if the particle concentration exceeds a predetermined threshold. A particle concentration exceeding the predetermined threshold may be an indication that the accuracy of particle size measurements obtained using the device is below an acceptable level.

[0026] A second aspect of this embodiment is a method for determining an estimated flow rate of a sample during nanoparticle tracking analysis, comprising: providing a sample stream including a plurality of particles suspended in a fluid through a flow cell; illuminating the sample with a light source; collecting light scattered or fluoresced by particles moving within the flow cell and within a detection region of an imaging system; taking a video of the particle moving within the detection area; processing the video to determine an estimated flow velocity of the sample through the flow cell, wherein determining the estimated flow velocity of the sample includes performing one-dimensional particle tracking in a direction orthogonal to the expected direction of flow of the sample; Equipped with.

[0027] Performing one-dimensional particle tracking may include identifying particles from a current frame of video in subsequent frames of video. Identifying particles in subsequent frames of video may include identifying nearest particles in the subsequent frames in a tracking direction (regardless of whether they are separated in other directions). The tracking direction may be orthogonal to the direction of expected flow of the sample.

[0028] Identifying the nearest particle in the subsequent frame may include identifying the nearest particle within a one-dimensional tracking distance limit.

[0029] The estimated flow velocity may be determined from the average distance that particles tracked by one-dimensional particle tracking travel between subsequent frames of the video. Distance may be measured in two dimensions.

[0030] The estimated flow velocity may be determined from the average distance traveled by particles tracked by one-dimensional tracking particle tracking between subsequent frames of all frames of the video or a true subset of frames of the video.

[0031] The mean value may be the median value. In other embodiments, the mean value may be the arithmetic mean value.

[0032] The method includes using the estimated flow velocity to determine a corrected position of the particle in which the estimated flow velocity component of the particle's movement is removed, performing two-dimensional particle tracking of the particle's corrected position, and determining a residual flow velocity from the average distance traveled by the particle tracked by the two-dimensional particle tracking of the corrected position between subsequent frames of the video.

[0033] The step of performing two-dimensional particle tracking may include identifying, in the subsequent frame, a particle from the current frame as the particle that is the shortest Euclidean distance from the position of the particle in the current frame.

[0034] The method includes determining a residual corrected trajectory for each particle, where determining a residual corrected trajectory for each particle includes correcting two-dimensional particle tracking of the particle's corrected position to remove residual flow velocity.

[0035] The method may include determining particle size from the average distance traveled by the particle between subsequent frames of video obtained from the residual corrected trajectory.

[0036] The method may include determining a mean square displacement of the particle from the average distance traveled by the particle between subsequent frames of video obtained from the residual corrected trajectory, determining a diffusion coefficient of the particle from the mean square displacement, and determining a particle size from the diffusion coefficient using the Stokes-Einstein equation.

[0037] Two-dimensional particle tracking of the corrected position may include identifying particles from the current frame of video in subsequent frames of the video, including identifying nearest particles in the subsequent frames within a two-dimensional tracking distance limit.

[0038] The two-dimensional tracking distance may be determined according to the movement of the particle tracked by the one-dimensional tracking.

[0039] The method may include generating a measurement alert if the estimated flow rate and / or possibly the residual flow rate exceeds a predetermined threshold.

[0040] The method may include determining a particle concentration of the sample. The method may include generating a measurement alert if the particle concentration exceeds a predetermined threshold.

[0041] Any feature, alternative or advantage applicable to the first aspect of the invention may also be applicable to the second aspect of the invention (and vice versa).

[0042] Although embodiments of the present invention are described with reference to NTA, it will be appreciated that methods and techniques according to embodiments of the present invention are applicable to any suitable means of characterizing particles that involves single particle tracking.

[0043] An embodiment of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which: [Brief explanation of the drawings]

[0044] [Figure 1] FIG. 1 shows a current and subsequent frame of a video, illustrating how, in a known NTA process, two-dimensional particle tracking is used to determine the distance traveled by particles within a sample due to Brownian motion. [Figure 2a] Figure 2a shows the current and subsequent frames of a video, illustrating how 2D particle tracking is used in NTA to determine the distance particles within a sample travel due to Brownian motion when a flow is introduced into the sample. [Figure 2b] Figure 2b shows the current and subsequent frames of the video, illustrating how 2D particle tracking is used in NTA to determine the distance particles within a sample travel due to Brownian motion when a flow is introduced into the sample. [Figure 3a] FIG. 3a shows the current and subsequent frames of a video, illustrating how errors can occur when 2D particle tracking is used in NTA and flow is introduced into the sample. [Figure 3b] Figure 3b shows the current and subsequent frames of the video, illustrating how errors can occur when 2D particle tracking is used in NTA and flow is introduced into the sample. [Figure 4] FIG. 4 shows an apparatus for characterizing particles using nanoparticle tracking analysis according to this embodiment. [Figure 5a]FIG. 5a shows a current frame and a subsequent frame of a video, illustrating how one-dimensional particle tracking in a direction orthogonal to the expected flow direction of the sample is performed according to an embodiment of the present invention. [Figure 5b] FIG. 5b shows the current and subsequent frames of the video, illustrating how one-dimensional particle tracking in a direction orthogonal to the expected flow direction of the sample is performed according to an embodiment of the present invention. [Figure 6] FIG. 6 shows a current frame and a subsequent frame of video, illustrating how a corrected position of a particle within a frame of video is determined, with the estimated velocity component of the particle's movement removed. [Figure 7] FIG. 7 shows a subsequent frame of FIG. 6, illustrating how two-dimensional particle tracking of the corrected positions of the particles is performed. [Figure 8] Figure 8 shows the residual corrected trajectories of the particles. DETAILED DESCRIPTION OF THE INVENTION

[0045] A typical NTA process involves illuminating a sample, collecting light scattered or fluoresced by particles moving within the sample, and taking a video of the moving particles. The video is processed to identify particles in each frame of the video, which appear as bright spots of light. Individual particles are identified in each frame of the video, and the movement of each particle between frames of the video is tracked. The distance traveled by each particle can be determined from the number of pixels separating the particles between frames of the video and the pixel size, which is derived from the specifications of the camera used to capture the video.

[0046] FIG. 1 illustrates how two-dimensional particle tracking is used in a known NTA process to determine the distance traveled by a particle within a sample due to Brownian motion. FIG. 1 shows a current frame 1a of video and a subsequent frame 1b of video. Particle 2 is shown in current frame 1a and subsequent frame 1b. The position of particle 2 in current frame 1a is shown in subsequent frame 1b using a dashed line. Particle 2 is identified in subsequent frame 1b using two-dimensional particle tracking. Between frames 1a and 1b, particle 2 moves in two dimensions, i.e., the x and y directions as defined by the axes in FIG. 1. Particle 2 is identified in subsequent frame 1b as the Euclidean nearest particle to the position of particle 2 in current frame 1a.

[0047] The Euclidean distance "d" traveled by the particle between frames 1a and 1b is taken to be the distance traveled by the particle due to Brownian motion. The process is repeated for any suitable number of frames of the video to determine the total distance traveled by particle 2 due to Brownian motion. The total distance can be used to determine the size of particle 2 using the Stokes-Einstein equation in a known manner. For particles, this process is performed for each of multiple particles in the sample to determine the particle's size. For polydisperse samples, the size distribution can be determined, and for monodisperse samples, the average particle size (e.g., Dv50) is determined.

[0048] When a flow is established within a sample, the distance traveled by particles within the sample has a first component due to Brownian motion and a second component due to the sample flow. Therefore, in order to accurately determine the distance traveled by particles due to Brownian motion, it is necessary to consider the distance traveled by particles due to the sample flow for purposes of particle size determination. Figures 2a and 2b show that two-dimensional tracking is used in NTA to determine the distance traveled by particles within the sample due to Brownian motion when a flow is established within the sample. In the example of Figures 2a and 2b, the sample flow rate is, for example, relatively low.

[0049] FIG. 2a shows a current frame 1a of a video and a subsequent frame 1b of the video. Particle 2 is shown in the current frame 1a and the subsequent frame 1b. The position of particle 2 in the current frame 1a is shown in the subsequent frame 1b using a dashed line. Particle 2 is identified in the subsequent frame 1b as the particle that is the closest Euclidean neighbor to the position of particle 2 in the current frame 1a within a tracking distance limit 3. The tracking distance limit 3 may be predetermined to maximize the efficiency of identifying particle 2 in the subsequent frame 1b. The Euclidean distance traveled by particle 2 between frames 1a and 1b may be the sum of a Brownian motion component "d" and a flow component "drift" of the distance traveled by particle 2.

[0050] Figure 2b shows the corrected position of particle 2 in subsequent frame 1b, excluding the flow component of the distance traveled by particle 2. The position of particle 2 in the current frame 1a is shown in subsequent frame 1b using a dashed line. The Euclidean distance "d" that particle 2 travels from its position in the current frame 1a to its corrected position in subsequent frame 1b is the distance particle 2 travels due to Brownian motion. The size of particle 2 is determined as described above.

[0051] The flow component of the distance traveled by a particle 2 can be determined from the flow velocity of the sample and the time elapsed between the current frame 1a and the subsequent frame 1b. The time elapsed between the current frame 1a and the subsequent frame 1b may be obtained from the frame rate of the camera used to capture the video. The flow velocity of the sample may be determined from the average distance traveled by a particle within the sample across multiple subsequent frames of the video and the time elapsed between the subsequent frames, as determined by two-dimensional tracking of the particle.

[0052] Figures 3a and 3b show how errors can occur when two-dimensional tracking is used in NTA and a flow is created within the sample. In the example of Figures 3a and 3b, the flow rate of the sample is relatively high (i.e., the flow is fast enough that the flow causes errors in particle tracking).

[0053] Figure 3a shows a current frame 1a of a video and a subsequent frame 1b of the video. A first particle 2 and a second particle 4 are shown in the current frame 1a and the subsequent frame 1b. The positions of particles 2 and 4 in the current frame 1a are shown in the subsequent frame using dashed lines. For example, in the subsequent frame 1b, within the tracking distance limit 3, the closest Euclidean particle to the position of the first particle 2 in the current frame 1a is the second particle 4. Therefore, when two-dimensional tracking is used, an incorrect particle is identified as the first particle 2 in the subsequent frame 1b. This causes an error in the calculation of the distance traveled by the first particle 2, which is caused by Brownian motion, as described above. This error in the distance calculation ultimately causes an error in the calculation of the size of the first particle 2, affecting the particle size distribution of the sample. Figure 3b shows the incorrect correction of the position of the second particle 4 in the subsequent frame 1b after the second particle 4 is incorrectly identified as the first particle 2, resulting in an incorrect calculation of the distance traveled by the first particle 2.

[0054] 4 illustrates an apparatus 10 for characterizing particles using nanoparticle tracking analysis (NTA) according to an embodiment of the present invention. The apparatus 10 includes a flow cell 101 containing a sample including a plurality of particles suspended in a fluid, a pump 102 configured to provide a flow of the sample through the flow cell 101, a light source 103 configured to illuminate the sample, an imaging system 104 configured to collect light scattered or fluoresced by particles moving within the flow cell 101 and within a detection region of the imaging system and to capture video of the particles moving within the detection region, and a computer 105 configured to process the video. The computer 105 is configured to determine an estimated flow velocity of the sample through the flow cell 101. Determining the estimated flow velocity of the sample through the flow cell 101 includes performing one-dimensional particle tracking in a direction orthogonal to the expected direction of flow of the sample.

[0055] The imaging device includes a microscope and a camera configured to capture video through the microscope. The microscope may include, for example, 20x magnification, and the camera may include a charge-coupled device. The light source may be a laser. In other embodiments, any suitable alternative imaging system and light source may be used.

[0056] The apparatus further includes a glass chamber 106 and a metal surface 107 disposed between the glass chamber 106 and the flow cell 101. The metal surface is positioned to reflect light scattered or fluoresced from the particles toward the imaging system 104, thereby increasing the contrast of the particles. The glass chamber may include a prism configured to refract light from the light source 103 to form a thin sheet of light substantially parallel to the metal surface 107.

[0057] 5a and 5b illustrate how one-dimensional particle tracking in a direction perpendicular to the direction of expected flow of a sample is performed in accordance with an embodiment of the present invention. Each of FIGS. 5a and 5b shows a current frame 1a of a video and a subsequent frame 1b of the video. A first particle 2 and a second particle 4 are shown in the current frame 1a and the subsequent frame 1b. The direction of expected flow of the sample is indicated by the arrow labeled "F." As indicated by the axes in FIGS. 5a and 5b, the expected flow of the sample is parallel to the x-axis. In FIG. 5b, the position of the first particle 2 relative to the second particle 4 in the current frame 1a is shown in the dimension parallel to the y-axis only.

[0058] In FIG. 5b, the positions of particles 2, 4 in the current frame 1a are indicated in the subsequent frame 1b using dashed lines. Identifying particles 2, 4 in the subsequent frame 1b includes identifying the closest particle in the tracking direction in the subsequent frame 1b. The tracking direction is a direction parallel to the y-axis, e.g., a direction perpendicular to the direction of sample flow. In some embodiments, a tracking distance limit may be used, and a first particle 2 is identified in the subsequent frame 1b as the particle that is within the tracking distance limit 3 and closest to the position of the first particle 2 in the current frame 1a in the direction parallel to the y-axis. The tracking distance limit may be determined with reference to the maximum expected movement distance of the particle and is optional.

[0059] A second particle 4 is identified in the subsequent frame 1b as the particle that is within the tracking distance limit 3 and is the closest particle to the position of the second particle 4 in the current frame 1a in a direction parallel to the y-axis. As shown in Figure 5, particles 2 and 4 are identified in the subsequent frame 1b.

[0060] According to an embodiment of the present invention, an estimated flow velocity of a sample is determined from the average distance traveled by particles within the sample, as tracked by one-dimensional particle tracking as described above with reference to FIG. 5, between subsequent frames of video. Following one-dimensional particle tracking, the movement (in two dimensions) of each particle is measured between subsequent frames of video. The median displacement of all particles and the elapsed time between frames of video are used to estimate the flow velocity of the sample. In other embodiments, a different average distance, such as the arithmetic mean value, may be used.

[0061] After obtaining the estimated flow velocity of the sample, the corrected position of the particle in each frame of the video, with the estimated flow velocity component of the particle's movement removed, is determined. Figure 6 shows how the corrected position of the particle in a frame of the video, with the estimated flow velocity component of the particle's movement removed, is determined. Figure 6 shows a current frame 1a of the video and a subsequent frame 1b of the video. The positions of three particles, represented by a circle, a square, and a triangle, respectively, are shown in the current frame 1a. As described above, the positions of the three particles as determined by one-dimensional tracking are shown in the subsequent frame 1b. The corrected position of the particle in the subsequent frame 1b is shown using a dashed line. In Figure 6, the predicted flow direction of the sample is parallel to the x-axis. The corrected position of the particle is determined by determining the flow component of the particle's distance traveled from the current frame 1a to the subsequent frame 1b and subtracting this component in the x-direction from the particle's position in the subsequent frame 1b. The flow component of the particle's distance traveled is determined using the sample's estimated flow velocity and the elapsed time between the current frame 1a and the subsequent frame 1b.

[0062] After the corrected positions of the particles in each frame of the video are determined, two-dimensional particle tracking of the corrected positions of the particles is performed. This is shown in FIG. 7. FIG. 7 shows the positions of the particles in subsequent frame 1b of FIG. 6. The positions of the particles in the current frame 1a of FIG. 6 are shown in subsequent frame 1b of FIG. 7 using dashed lines. The particles are identified in subsequent frame 1b as the Euclidean nearest particle to the individual particle's position in the current frame 1a within tracking distance limit 3. Tracking limit 3 may be determined according to the movement of the particles tracked by one-dimensional tracking after correction to remove estimated flow velocity.

[0063] During two-dimensional tracking, multiple particles may be identified within the tracking distance limit. For example, as shown in FIG. 7, two particles are identified in subsequent frame 1b within tracking distance limit 3 from the particle represented by the rectangle. Euclidean distances d1 and d2 are measured between the positions of the particles represented by the rectangle in current frame 1a and the two particles identified in subsequent frame 1b. Because distance d1 is less than distance d2, the particle at distance d1 from the position of the particle represented by the rectangle in current frame 1a is identified as the particle represented by the rectangle in subsequent frame 1b. In the example of FIG. 7, as shown, this results in the correct particle being identified in subsequent frame 1b.

[0064] After the two-dimensional tracking is performed, a residual flow velocity is determined from the average distance traveled by particles tracked by the two-dimensional particle tracking between subsequent frames of the video. The residual flow velocity is determined using the average distance and the elapsed time across subsequent frames of the video. A residual-corrected trajectory for each particle is determined by correcting the two-dimensional particle tracking to remove the residual flow velocity. An example of a particle's residual-corrected trajectory is shown in FIG. 8. The trajectory shows the movement of the particle within the sample from a starting position in the first frame of the video to an ending position in the last frame of the video across subsequent frames of the video. The size of each particle may be determined from the average distance traveled by the particle between subsequent frames of the video, which is obtained from the residual-corrected position using the Stokes-Einstein equation, as described above.

[0065] Table 1 below shows the average movement component of the total distance traveled by particles in the test sample. As previously explained, since Brownian motion is random and the mean of random movement is zero, the average movement of the particles provides an estimate of the flow velocity.

[0066] The first column ("2D Tracking Only") contains the mean motion component (or flow velocity estimate) as determined using the known 2D tracking described above with reference to Figures 2a-3b. The second column ("1D / 2D Tracking") contains the estimated flow velocity determined using 1D tracking (1D) and the sum of the estimated flow velocity from the 1D tracking and the estimated flow velocity determined using 2D tracking (2D) of the corrected particle positions, as described above with reference to Figures 5-8. The test sample contains 100 nm polyethylene latex particles under a flow velocity of approximately -140 pix / fr. This flow velocity is approximately 15 times the particle's typical Brownian displacement.

[0067] [Table 1]

[0068] As shown in Table 1, the mean shift component obtained using the technique according to the present invention is much more accurate than the mean shift component obtained using known techniques.

Claims

1. 1. An apparatus for characterizing particles using nanoparticle tracking analysis, comprising: a flow cell for containing a sample including a plurality of particles suspended in a fluid; a pump configured to provide a flow of the sample through the flow cell; a light source configured to illuminate the sample; an imaging system configured to collect light scattered or fluorescently emitted by particles moving within the flow cell and within a detection region of the imaging system and to capture video of the particles moving within the detection region; a computer configured to process the video to determine an estimated flow rate of the sample through the flow cell, wherein determining the estimated flow rate of the sample includes performing one-dimensional particle tracking in a direction orthogonal to a direction of expected flow of the sample; and Equipped with Device.

2. the one-dimensional particle tracking includes identifying particles from a current frame of the video in a subsequent frame of the video; 10. The apparatus of claim 1.

3. and identifying the particle in the subsequent frame of the video includes identifying a closest particle in the subsequent frame in a tracking direction.

3. The apparatus of claim 2.

4. and identifying the nearest particle in the subsequent frame includes identifying the nearest particle within a one-dimensional tracking distance limit.

4. The apparatus of claim 3.

5. the estimated flow velocity is determined from an average distance traveled by one or more particles tracked by the one-dimensional particle tracking between subsequent frames of the video.

5. An apparatus according to any one of claims 1 to 4.

6. The average distance is the median distance.

6. The apparatus of claim 5.

7. The computer using the estimated flow velocity to determine a corrected position of the particle in which the estimated flow velocity component of the particle's movement is removed; performing two-dimensional particle tracking of the corrected positions of the particles; determining a residual flow velocity from an average distance traveled by particles tracked by the two-dimensional particle tracking at the corrected positions between subsequent frames of the video; It is configured as follows:

7. An apparatus according to any one of claims 1 to 6.

8. the computer is configured to determine a residual corrected trajectory for each particle; determining the residual corrected trajectory of each particle includes correcting the two-dimensional particle track of the corrected position of the particle to exclude the residual flow velocity; 8. The apparatus of claim 7.

9. the computer is configured to determine a particle size for each particle from an average distance traveled by the particle between subsequent frames of the video obtained from the residual corrected trajectory.

9. The apparatus of claim 8.

10. the computer is configured to determine a mean square displacement of each particle from the average distance traveled by the particle between subsequent frames of the video obtained from the residual corrected trajectories, determine a diffusion coefficient of each particle from the mean square displacement, and determine a particle size of each particle from the diffusion coefficient using the Stokes-Einstein equation.

10. The apparatus of claim 9.

11. the two-dimensional particle tracking of the corrected position includes identifying, in a subsequent frame of the video, a particle from the current frame of the video, wherein identifying the particle in the subsequent frame includes identifying a nearest particle in the subsequent frame within a two-dimensional tracking distance limit.

11. Apparatus according to any one of claims 7 to 10.

12. the two-dimensional tracking distance limit is determined according to the movement of the particle tracked by the one-dimensional particle tracking; 12. The apparatus of claim 11.

13. the computer is configured to generate a measurement alert if the estimated flow rate exceeds a predetermined threshold, and / or the computer is configured to determine a particle concentration of the sample and generate a measurement alert if the particle concentration exceeds a predetermined threshold.

13. An apparatus according to any one of claims 1 to 12.

14. the one-dimensional particle tracking is performed on the stored video after the video is taken or as the video is being taken; 14. Apparatus according to any one of claims 1 to 13.

15. 1. A method for determining an estimated flow rate of a sample during nanoparticle tracking analysis, comprising: providing a flow of the sample, the flow including a plurality of particles suspended in a fluid, through a flow cell; illuminating the sample with a light source; collecting light scattered or fluoresced by particles moving within the flow cell and within a detection area of ​​an imaging system; taking a video of the particle moving within the detection area; processing the video to determine an estimated flow velocity of the sample through the flow cell, the processing comprising performing one-dimensional particle tracking in a direction orthogonal to the expected direction of flow of the sample; Equipped with method.

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