Device for characterizing particles

Abstract

A device (100) for characterizing particles using nanoparticle tracking analysis is provided. The device comprises: a sample cell (101) for containing a sample, said sample comprising a plurality of particles suspended in a fluid; a light source (102) configured to illuminate the sample; an imaging system (103) configured to collect light (112) scattered or fluoresced by particles moving within the sample cell (101) and within a detection zone (111) of the imaging system (103) and to capture a video of particles moving within the detection zone (111); and a computer (104). The computer is configured to process the video to automatically determine the depth of the detection zone (111). Determining the depth of the detection zone (111) comprises: tracking particles moving within the detection zone (111), thereby generating a trajectory for each particle; and analyzing how the measured attribute of the trajectory within the sub-segment of the video varies as the size of the sub-segment varies.

Description

Technical Field

[0001] The present invention relates to nanoparticle tracking analysis, and more particularly to devices and methods for measuring particle properties without instrument calibration. Background Art

[0002] Nanoparticle tracking analysis (NTA) is a method of characterizing particles suspended in a fluid by tracking the movement of individual particles. Generally, the method includes irradiating a sample containing a plurality of particles suspended in a fluid, collecting the light scattered or fluoresced by the particles, and capturing a video of the particles as they move in the fluid. The video is analyzed frame by frame to track the movement of the particles, i.e., the movement caused by Brownian motion and / or bulk flow. The fluid can be a liquid (e.g., water or some other liquid), or it can be a gas (e.g., air).

[0003] It may be necessary to determine the concentration of particles in the fluid. To calculate the concentration, the number of particles within the detection zone of the NTA device (the three-dimensional volume within the sample captured in the video) can be counted. If the volume of the detection zone is known, the concentration can be determined based on the ratio of the number of particles to the detection zone. However, it is difficult to determine the depth of the detection zone. For example, the depth will depend on the imaging system itself, environmental factors, and the different optical properties of the sample.

[0004] Therefore, existing NTA devices require instrument calibration to determine the detection zone. Calibration may involve first using a calibration sample to analyze the brightness of particles at different depths within the detection zone volume. An example of an NTA calibration method is disclosed in EP3071944.

[0005] However, this requires additional steps and time (usually several hours), precise calibration samples, and may need to be performed regularly to maintain accuracy. Therefore, NTA methods and devices that do not require calibration have significant advantages.

[0006] Some progress has been made in developing calibration-free NTA methods. In one calibration-free method, computational modeling of particle movement is combined with prior knowledge of particle size (M et al., Physics Review E84, 031920, September 20, 2011).

[0007] Further improvements to calibration-free NTA technology are needed. Summary of the Invention

[0008] According to a first aspect of the present invention, there is provided a device for characterizing particles using nanoparticle tracking analysis, comprising:

[0009] a sample cell for accommodating a sample comprising a plurality of particles suspended in a fluid, said sample;

[0010] A light source configured to irradiate a sample;

[0011] An imaging system configured to collect light scattered or fluoresced by particles moving within a sample cell and within a detection region of the imaging system and to capture a video of the particles moving within the detection region; and

[0012] A computer configured to process the video to automatically determine the depth of the detection region, wherein determining the depth of the detection region includes:

[0013] Tracking particles moving within the detection region, thereby generating a trajectory for each particle; and

[0014] Analyzing how the measured properties of the trajectories within a sub - segment of the video change as the size of the sub - segment changes.

[0015] The method is computationally inexpensive and can accurately determine the detection region. A trajectory can be defined as a set of vectors that define the change in position of a particle in the video from frame to frame.

[0016] The sub - segment can be a strip intercepted across the video, the strip being defined by a pair of parallel planes. The strip can be in the shape of a parallelogram or a rectangle. The pair of parallel planes can span the width of the video such that the strip spans the width of the video. Optionally, the strip may not span the entire width of the video and thus has a second pair of parallel planes perpendicular to the first pair of parallel planes, and the strip is a rectangular region within the video. The strip can be intercepted in the x or y direction (vertical or horizontal).

[0017] By changing the width of the strip between a pair of planes, the size of the sub - segment can be changed. The computer can also be configured to record the measured properties of the trajectories as a function of the strip width.

[0018] The computer can also be configured to analyze the trajectories within the sub - segment in order to classify each trajectory as:

[0019] (i) an x / y trajectory, where the particle trajectory is observed to cross one of the pair of planes; or

[0020] (ii) a z - trajectory, where the particle trajectory starts and / or ends within the strip but is not observed to cross one of the pair of planes.

[0021] The computer can also be configured to record the measured properties of the x / y trajectories and the z - trajectories separately.

[0022] The computer can also be configured to:

[0023] Compare the measured properties of the x / y trajectories and the z - trajectories for each strip width; and

[0024] The depth of the detection zone is determined by finding the strip width where the measured properties of the x / y trajectory and the z trajectory are equal or most similar, and the determined depth of the detection zone is equal to the strip width.

[0025] The measured properties of the trajectories can be selected from:

[0026] The count of the number of x / y trajectories and the number of z trajectories observed within a predetermined time period;

[0027] The measure of the length of the x / y trajectory and the length of the z trajectory, where the length of each trajectory is the distance the particle moves within the strip; or

[0028] The measure of the number of steps within the x / y trajectory and the number of steps within the z trajectory, where the number of steps in each trajectory is the number of video frames in which the particle moves within the strip.

[0029] The measured property can preferably be the count of the number of x / y trajectories and the number of z trajectories.

[0030] The strip width of each of multiple strips can be analyzed, and the measured trajectory properties of the strip width of each of the multiple strips can be mathematically combined.

[0031] The multiple strips can be configured to:

[0032] Cover most or all of the detection zone; and / or

[0033] Cover multiple regions of the detection zone, where the multiple regions have different detection zone depths.

[0034] Tracking the particles can include identifying the particles from the current frame of the video in subsequent frames of the video.

[0035] Identifying the particles in subsequent frames of the video can include identifying the nearest particles in the subsequent frames.

[0036] Identifying the nearest particles in subsequent frames can include identifying the nearest particles within a tracking distance limit.

[0037] The computer can also be configured to calculate the concentration of the particles suspended in the fluid, and the calculation of the concentration includes:

[0038] Calculating the volume of the detection zone using the determined depth; and

[0039] Measuring the number of particles within the detection zone.

[0040] According to a second embodiment of the present invention, there is provided a computer-implemented method for determining the depth of a detection zone based on video data obtained from nanoparticle tracking analysis, the method including:

[0041] Tracking the particles moving within the detection zone, thereby generating a trajectory for each particle;

[0042] Measure the properties of the trajectories within a video sub-segment;

[0043] Analyze how the measured properties of the trajectories within a sub-segment of a video vary as a function of the size of the sub-segment.

[0044] According to a third aspect of the invention, there is provided a machine-readable non-transitory storage medium comprising instructions for configuring a processor to perform a method, the method comprising:

[0045] Determine the depth of a detection zone based on video data obtained by nanoparticle tracking analysis, the determining of the depth comprising:

[0046] Tracking particles moving within the detection zone, thereby generating a trajectory for each particle;

[0047] Measure the properties of the trajectories within a sub-segment of the video;

[0048] Analyze how the measured properties of the trajectories within a sub-segment of the video vary as a function of the size of the sub-segment.

[0049] The features described with reference to the first aspect can also be applied to any one of the second or third aspects, including optional features. The features of each aspect can be combined with the features of the example embodiments and vice versa.

[0050] Brief Description of the Drawings

[0051] Embodiments of the invention will now be described by way of example only with reference to the drawings:

[0052] Figure 1 Shows an apparatus for characterizing particles using NTA according to an embodiment of the invention;

[0053] Figure 2 Shows a detection zone as observed by the NTA apparatus;

[0054] Figure 3 Shows a current frame and a subsequent frame of a video and illustrates how two-dimensional particle tracking is used in a known NTA process to determine the distance traveled by a particle due to Brownian motion;

[0055] Figure 4 Shows particle trajectories resulting from Brownian motion;

[0056] Figure 5 Shows an example of a particle trajectory moving into or out of the detection zone;

[0057] Figure 6 Shows particles moving within a sub-segment of a video and forming in-plane and out-of-plane trajectories;

[0058] Figure 7Displays a sub - segment of a video that has a variable width;

[0059] Figure 8 Displays an experimental test method for verifying a method according to an embodiment of the present invention;

[0060] Figure 9 Displays Figure 8 the experimental test results shown in;

[0061] Figure 10 Displays Figure 8 the experimental test results shown in;

[0062] Figure 11 Displays the results of a method for calculating particle concentration according to an embodiment of the present invention.

[0063] Figure 1 Displays an apparatus 100 for characterizing particles using nanoparticle tracking analysis (NTA) according to an embodiment of the present invention. The apparatus includes: a sample cell 101 for containing a sample, the sample including a plurality of particles suspended in a fluid; a light source 102 configured to irradiate the sample; an imaging system 103 configured to collect light scattered or fluoresced by particles moving within the sample cell and within a detection zone, and to capture a video of the particles moving within the detection zone.

[0064] Apparatus 100 further includes a computer 104 configured to process the video. Processing the video includes tracking particles between subsequent frames of the video, as described in more detail in reference Figure 3 The computer 104 is configured to track each of a plurality of particles within the detection zone and to determine the movement trajectory of each particle through a series of frames. The computer 104 is also configured to determine the depth of the detection zone by analyzing measured properties of the trajectories.

[0065] The imaging system 103 may include a microscope and a camera configured to capture a video through the microscope. For example, the microscope may include an objective lens with a magnification of 20 times, and the camera may include a charge - coupled device (or CMOS device). The light source 102 may be a laser. In other embodiments, any suitable alternative imaging system and / or light source may be used. For example, light may be refracted into the sample through a prism or any other optical device, which may include reflectors, lenses, prisms, etc.

[0066] The apparatus may further include a glass wall 106 and a metallized surface 105 disposed between the glass wall 106 and the sample cell 101. The metallized surface 105 is arranged to reflect light scattered or fluoresced by the particles back to the imaging system 103, thereby increasing particle contrast. The glass wall 106 may include a portion of a prism configured to refract light from the light source 102 to form a thin light sheet substantially parallel to the metallized surface 105.

[0067] Figure 2 shows a sample volume containing particles suspended in a fluid dispersant 110. Although Figure 2 not labeled in, the particle volume in the sample volume 110 may be defined by one or more sample cell walls.

[0068] The detection region 111 may include a sub-region of the sample volume 110. The detection region 111 is a region of volume 110 where the imaging system can detect light 112 scattered or fluoresced by particles moving within the detection region 111. The detection region 111 can be considered the three-dimensional "field of view" of the imaging system. The detection region 111 has a planar area in the x-y plane and a depth z.

[0069] Figure 3 Shows how particle tracking is applied in NTA. Figure 3 Shows the current frame 1a of the video and a subsequent frame 1b of the video. Particle 2 is shown in the current frame 1a. In the subsequent frame 1b, particle 2 has moved from its current position (as indicated by the dashed line) to a new position 2a. Between the current frame 1a and the subsequent frame 1b, particle 2 travels in two dimensions, i.e., in Figure 3 the x direction and the y direction defined by the axes in. The Euclidean distance d that particle 2 moves between frames 1a, 1b is taken as the distance that particle 2 moves due to Brownian motion.

[0070] In one example, two-dimensional particle tracking is used to identify particle 2 in the subsequent frame 1b. In the subsequent frame 1b, particle 2 is identified as the Euclidean particle closest to the position of particle 2 in the current frame 1a. The computer is configured to perform two-dimensional particle tracking on each of a plurality of particles within the detection region.

[0071] The computer can be configured to repeat the tracking process on any suitable number of frames of the video. By tracking each particle across many frames, the computer generates a segmented particle trajectory for each of the plurality of particles.

[0072] The computer can be configured to impose a tracking distance limit when identifying particle 2 in the subsequent frame 1b. The tracking distance limit means that the computer only considers the region within a predetermined distance from the initial particle position 2a when identifying the Euclidean closest particle. This may mean that particle identification and tracking are performed more efficiently.

[0073] Figure 4Shows an example of a particle trajectory 15 formed by a particle as it moves within a fluid. Since this movement is caused by Brownian motion, the particle is seen to perform a "random walk" between a starting position 11 and a subsequent ending position 12. In the first frame, the particle is located at a first position 13. In a subsequent second frame, the particle is located at a second position 14. Thus, the vector between each pair of consecutive vertices of the particle trajectory 15 corresponds to the movement of the particle between two consecutive frames of the video (each vertex corresponding to a position in a frame).

[0074] Figure 5 Shows an example of a particle trajectory in which a particle moves into / out of a detection zone 111 in the z direction. Particle trajectory 3a shows the particle leaving the detection zone 111 through the bottom surface. Particle trajectory 3b shows the particle leaving the detection zone 111 through the top surface. Particle trajectory 3c shows the particle entering the detection zone 111 through the bottom surface and subsequently leaving the detection zone 111 through the same bottom surface. Particle trajectory 3d shows the particle entering the detection zone 111 through the top surface and subsequently leaving the detection zone 111 through the opposite bottom surface.

[0075] However, of course, a video is a two-dimensional image and cannot be used to determine the particle position in the z direction. Thus, when a particle moves into / out of the detection zone 111 in the z direction, Figure 5 the particle trajectories shown in will appear in the video as the particle appearing / disappearing from the video.

[0076] Particles will similarly enter and leave the detection zone in the x and y directions by passing through the sides of the detection zone. The movement of the particles is not limited to purely moving along the x, y, z axes. In fact, the particle movement will be a three-dimensional "random walk" with x, y, and z components for each "step".

[0077] Figure 6 Illustrates how the measured properties of a particle trajectory can be used to determine the depth of a detection zone according to one embodiment of the present invention. Figure 6 Schematically shows a video image 1. Video 1 can include any number of frames to show the movement of the particle.

[0078] The computer is configured to analyze the movement of the particle to determine the particle trajectory, just like traditional NTA. A fourth sub-segment of video 1 is defined. As Figure 6 shown, this sub-segment can be a vertical x-strip 4 intercepted across video 1 with a variable strip width Δx. The x-strip 4 is defined by a pair of parallel vertical planes spanning video 1 in the y direction and a pair of horizontal planes spanning the video in the x direction. In this example, the horizontal planes are located at the edges of the video and the vertical planes are the full height of the video. Other shaped strips can also be used.

[0079] Figure 6Shows an example particle trajectory of two particles moving within strip 4. The first particle is shown moving from a starting position 2 within strip 4 to a subsequent position 2a outside strip 4 at the end time. The first particle of this example moves mainly in the x direction, generating a first particle trajectory 5a. When the first particle moves in the x direction, it remains within the volume of the detection area captured by video 1. Thus, it can be determined by a computer that the first particle trajectory 5a passes through one of the multiple faces of strip 4.

[0080] The computer is configured to classify the first particle trajectory 5a as an "x trajectory". An x trajectory is a particle trajectory that is observed to pass through one of the parallel vertical faces of strip 4, corresponding to a particle entering or leaving strip 4 as observed by the device.

[0081] The second particle is shown moving from a starting position 2b within strip 4. The particle moves along a second particle trajectory 6. The second particle has a z-axis movement component and leaves strip 4 through the top or bottom surface before the end time. Thus, the second particle cannot be seen at the end time. It is observed that the second particle trajectory 6 ends without passing through the surface of strip 4.

[0082] The computer is configured to classify the second particle trajectory 6 as a "z trajectory" or an "unobservable trajectory". A z trajectory is a particle trajectory that passes through the top or bottom of the detection area, corresponding to a particle entering or leaving strip 4, and its trajectory does not pass through one of the multiple faces in strip 4.

[0083] The computer is configured to track multiple particles within strip 4 and classify all particle trajectories as x trajectories and z trajectories.

[0084] In some embodiments of the present invention, a pair of parallel vertical faces of the strip can span the entire height of the video, as Figure 6 shown. In other embodiments, the strip may not span the entire height of the video, but each strip has four faces that are entirely within the video. This can improve the accuracy of determining the depth of the detection area by reducing incorrect trajectory classifications.

[0085] For example, consider x strips taken across the entire height of the video, where a particle is very close to the top edge of the strip. If the particle moves in the y direction, it may move out of the top of the image (out of the detection area) and thus not appear in subsequent video frames. Since the computer does not observe the particle leaving the strip but the particle has disappeared, the particle may be misidentified as a z trajectory. However, if the size of the strip is limited within the video image (so there is a "boundary area" around all four sides of the strip), the movement of the particle in the y direction can be observed in subsequent frames. A computer configured to classify x trajectories and z trajectories can also be configured to discard the particle trajectory from the analysis. The size of the boundary area can be selected to correspond to the maximum expected movement of the particle between consecutive frames.

[0086] The computer is also configured to analyze the properties of the x trajectories and z trajectories when the width Δx of strip 4 changes. The property can be the number of x trajectories and z trajectories observed over a defined time period.

[0087] For example, the computer can be configured to analyze a strip width of 100 pixels. The computer will record the number of x trajectories and z trajectories observed over a period of time. Then the process will be repeated with different strip widths, for example in steps of 1 pixel, 10 pixels or 50 pixels.

[0088] By analyzing the change in the recorded number of x trajectories and z trajectories, the computer is configured to automatically determine the depth of the detection area.

[0089] If very narrow strips are used (i.e., width Δx << depth z), most particle trajectories will leave the strip in the x direction before having a chance to leave the detection area in the z direction. Thus, the number of x trajectories is large and the number of z trajectories is small. Conversely, if very wide strips are used (i.e., Δx >> z), very few particles will leave the strip in the x direction before leaving the detection area in the z direction for the first time. Thus, the number of x trajectories is small and the number of z trajectories is large.

[0090] The computer is configured to find the strip width at which the number of x trajectories and z trajectories is equal (or at least most similar). Then the depth z of the detection area is determined to be the same as this intermediate strip width.

[0091] Since the movement of the particles is caused by Brownian motion, the movement of the particles is random (arbitrary), and within a sufficient number of trajectory samples, the movement of the particles tends to be equal on average in the x, y, and z directions. Statistically speaking, when the width Δx of the strip is equal to the depth of the detection area, the number of x trajectories will tend to be equal to the number of z trajectories. This is because when Δx = z, the surface areas of the two perpendicular strip faces and the top and bottom of the strip are equal. Therefore, the "flux" of the particles moving through these faces should be equal, which means that the number of x trajectories and z trajectories is equal.

[0092] In many NTA applications, the sample flows through the sample cell during measurement. This produces a non-Brownian motion component of the sample movement. It is known to correct for such particle drift in NTA analysis. The measurement of the particle trajectories described herein should be understood to have corrected for particle drift. One way to correct for particle drift is to subtract the average velocity of the particles. Since the average Brownian motion of all particles is zero, only the Brownian component of the particle movement remains.

[0093] In other embodiments of the present invention, the measured property of the particle trajectories can be a property other than the number of trajectories. For example, the average lengths of the x and z trajectories of the particles before leaving the strip can be measured and compared. Optionally, the average number of steps in the x and z trajectories of the particles before leaving the strip can be measured. The number of steps can be the number of frames in which the movement of the particles is observed. Like the number of trajectories, the lengths and number of steps of the trajectories are random in three directions, and when the middle strip width is found, the lengths and number of steps of the x and z trajectories are expected to be equal.

[0094] In some embodiments of the present invention, the computer can be configured to analyze the width of each of a plurality of strips. This can improve the accuracy of depth determination. The plurality of strips can be separated from each other, directly adjacent to each other, or can partially or completely overlap.

[0095] The plurality of strips can be configured to surround the entire image area. For example, five strips each 100 pixels wide can be used to analyze an image 500 pixels wide. In another example, by stepping a 100-pixel-wide strip across a 500-pixel-wide image in 1-pixel steps, 400 different 100-pixel-wide strips can be generated. In some embodiments, the edges of the image can be avoided so that particles crossing the x and y edges of the strip can be tracked with greater certainty. For example, an exclusion boundary around the image edge can be defined based on the expected maximum particle movement between consecutive frames.

[0096] Optionally, the strip may not cover the entire detection area, but may instead cover the region of interest in the image. For example, a strip may be taken across the center of the image, and additional strips may be taken near the corners / edges of the image. Many imaging systems have optical aberrations, which means that the depth of the detection area (i.e., the depth of focus) may vary from the center of the image to the corners / edges of the image. By taking strips in different regions of the image, the computer can be configured to account for differences in the depth of the detection area across the image.

[0097] The results of the measured properties for each width of multiple strips can be mathematically combined. For example, the number of tracks across each width of multiple strips (where each width uses the same number of strips) can be added together, or the average or median track length can be calculated for each strip width. Optionally, the results of strips taken in different regions of the image can remain separate, such that the depth is calculated for multiple regions of the detection area. This can allow for the calculation of differences in the depth across the detection area (e.g., differences caused by optical aberrations).

[0098] Figure 7 Shows Figure 6 how the process described above applies equally to the y direction. As described above, when the width Δx varies, the vertical x-strip 4a of video 1 can be analyzed. However, similarly, when the width Δy varies, the horizontal y-strip 4b can be analyzed. In this case, the y tracks 5b are classified by the computer instead of the x tracks 5a, but all of the processing discussed for the x-direction case applies equally. In some embodiments, the computer can be configured to obtain a combination of x-strips and y-strips and combine the results to determine the depth of the detection area.

[0099] Figure 8 Shows Figure 7 an improved version of the process described above. This improved process is used in experimental testing to verify the effectiveness of the depth determination technique. The computer is configured to analyze video 1 of particles moving in the detection area. However, instead of attempting to determine the depth (in the unobservable z direction), the analysis is performed in the observable x and y directions.

[0100] The width of strip 4a is set to a known value x. Then, sub-strip 4b is sampled across strip 4a, and the sub-strip has a variable width Δy. The computer is configured to count the number of x-trajectories 5a and the number of y-trajectories 5b observed in each of a plurality of sub-strips 4b sampled from strip 4a as Δy varies. When the width of the y-strip is less than the total height of the x-strip, a plurality of y-strips within the x-strip can be defined (e.g., the y-strips can be tiled so that they do not overlap, or if they overlap, they can step across the x-strip). For a small height Δy, more sub-strips 4b are sampled from strip 4a. Using the same logic as discussed previously, when Δy = x, the number of x-trajectories and y-trajectories should be equal, thus allowing x to be determined experimentally.

[0101] Figure 9 A graph showing the number of x-trajectories and y-trajectories as a function of Δy is shown. In this test, the width x of strip 4a was set to 200 pixels, and for each sub-strip width Δy, all possible sub-strips 4b were sampled from strip 4a. Figure 9 It is shown that the number of x-trajectories and y-trajectories is equal at approximately 200 pixels, as expected.

[0102] As expected, when Δy is small, the number of y-trajectories is very high and decreases as Δy increases. As expected, the number of x-trajectories initially increases as Δy increases but then decreases at higher Δy values. This is because Figure 9 the number of trajectories shown in is the cumulative total across all possible strips of each Δy width. Using wider strips, fewer sub-strips 4b can be sampled from strip 4a, resulting in a decrease in the total number of trajectories at high Δy values.

[0103] Figure 10 A graph is shown in which Figure 9 the process described in is repeated at many different x-widths. Figure 10 It is shown that the calculated value of x is close to the known values of x = 100, 200, 300, and 400 pixels. The maximum error between the estimated x-width and the expected value was found to be only 6.5%.

[0104] According to another embodiment of the present invention, the computer can also be configured to calculate the concentration of particles in a fluid. The computer can be configured to count the number of particles N in a video (corresponding to the number of particles in the detection zone). The volume V of the detection zone can be calculated based on the planar area of the detection zone (the area of the sample captured by the imaging system is known) and the determined depth. Using the known properties of the device, the measurements in pixels can be converted to physical dimensions. Then, the concentration C can be calculated as N / V.

[0105] Figure 11Shows the experimental concentrations obtained using several samples of different particle sizes with the device according to an embodiment of the present invention and following the above method. The concentrations are expressed in parts per million (ppml). Figure 11 Shows that the estimated concentrations obtained using the device of the present invention are very similar to the known concentrations of the samples.

[0106] Although example embodiments have been described, these embodiments are not intended to limit the scope of the present invention, which should be determined with reference to the appended claims.

Claims

1. An apparatus for characterizing particles using nanoparticle tracking analysis, which comprises: a sample cell for accommodating a sample comprising a plurality of particles suspended in a fluid; a light source configured to irradiate the sample; an imaging system configured to collect light scattered or fluoresced by particles moving within the sample cell and within a detection zone of the imaging system and to capture a video of the particles moving within the detection zone; and a computer configured to process the video to automatically determine the depth of the detection zone, determining the depth of the detection zone comprising: tracking particles moving within the detection zone, thereby generating a trajectory for each said particle; and analyzing how measurement attributes of the trajectories within a sub - segment of the video change as the size of the sub - segment changes.

2. The apparatus according to claim 1, wherein the sub - segment is a parallelogram strip transversely intercepted along the video, the strip being defined by a pair of parallel faces.

3. The apparatus according to claim 2, wherein, the size of the sub - segment is changed by varying the width of the strip between the pair of faces; and wherein the computer is further configured to record the measurement attributes of the trajectories as a function of the strip width.

4. The apparatus according to claim 3, wherein the computer is further configured to analyze the trajectories within the sub - segment so as to classify each said trajectory as: (i) an x / y trajectory, where it is observed that the particle trajectory crosses one of the pair of faces; or (ii) a z trajectory, where the particle trajectory starts and / or ends within the strip but is not observed to cross one of the pair of faces; and wherein the computer is further configured to record the measurement attributes of the x / y trajectories and the z trajectories separately.

5. The apparatus according to claim 4, wherein: the computer is further configured to compare the measurement attributes of the x / y trajectories and the z trajectories for each strip width; and the depth of the detection zone is determined by finding the strip width at which the measurement attributes of the x / y trajectories and the z trajectories are equal or most similar, and the determined depth of the detection zone is equal to the strip width.

6. The apparatus according to claim 4 or claim 5, wherein the measurement attributes of the trajectories are selected from: counts of the number of x / y trajectories and the number of z trajectories observed over a predetermined time period; measures of the length of the x / y trajectories and the length of the z trajectories, the length of each trajectory being the distance the particle moves within the strip; or measures of the number of steps within the x / y trajectories and the number of steps within the z trajectories, the number of steps in each trajectory being the number of video frames in which the particle moves within the strip.

7. The apparatus according to claim 6, wherein, the measurement attribute is the count of the number of x / y trajectories and the number of z trajectories.

8. The apparatus according to any one of claims 3 to 7; wherein, each strip width of a plurality of strips is analyzed and the measured trajectory attributes of each strip width of the plurality of strips are mathematically combined.

9. The apparatus according to claim 8, wherein the plurality of strips are configured to: Cover most or the entire area of the detection zone; and / or Cover multiple regions of the detection zone, the multiple regions having different detection zone depths.

10. The apparatus according to any one of the preceding claims, wherein tracking the particles comprises identifying, in subsequent frames of the video, particles from a current frame of the video.

11. The apparatus according to claim 10, wherein, identifying the particles in subsequent frames of the video comprises identifying the nearest particles in the subsequent frames.

12. The apparatus according to claim 12, wherein, identifying the nearest particles in the subsequent frames comprises identifying the nearest particles within a tracking distance limit.

13. The apparatus according to any one of the preceding claims, wherein the computer is further configured to calculate the concentration of the particles suspended in the fluid, the calculation of the concentration comprising: calculating the volume of the detection zone using the determined depth; and measuring the number of particles within the detection zone.

14. A computer-implemented method for determining the depth of a detection zone from video data obtained by nanoparticle tracking analysis, the method comprising: tracking particles moving within the detection zone, thereby generating a trajectory for each of the particles; measuring properties of the trajectories within a sub-segment of the video; analyzing how the measured properties of the trajectories within a sub-segment of the video change as the size of the sub-segment changes.

15. A machine-readable non-transitory storage medium comprising instructions for configuring a processor to execute a method, the method comprising: determining the depth of a detection zone from video data obtained by nanoparticle tracking analysis, the determining of the depth comprising: tracking particles moving within the detection zone, thereby generating a trajectory for each of the particles; measuring properties of the trajectories within a sub-segment of the video; analyzing how the measured properties of the trajectories within a sub-segment of the video change as the size of the sub-segment changes.

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