An Introduction to Dynamic Image Analysis of Suspended Liquid Particles

It is important to measure the size of liquid particles in a suspension in a variety of industries. These could simply be water droplets suspended in oil or oil droplets suspended in water.

In pharmaceutical applications, there are other times at which liquid dosages may be encapsulated inside a harder outer shell. In a number of these cases, the size, shape, and possibly the concentration of these globules may need to be determined by the end user.

The main challenge faced by users is operating an automated technique which is able to differentiate between an oil droplet and a water droplet. It also needs to be able to differentiate these particles of interest from other debris so that the concentration measurements are not influenced.

The particle measurement techniques which are most frequently used can only differentiate particles on a size basis. The majority of these techniques also assume that all particles are spherical. For this globule application, this is an accurate assumption unless non-spherical particles such as debris are present. These may be incorrectly measured as a portion of the main particle population.

Furthermore, supplementary information about the particles and the fluid in which they are suspended is required for several of these more common techniques. In order to properly measure particles, parameters such as the refractive index may be required.

The refractive indexes of globules and the liquids in which they are suspended differ. This makes it difficult to measure globules using some of the more frequently used particle size measurement techniques.

It goes without saying that it is difficult to differentiate between debris and droplet types using size-only measurement techniques. Consequently, properly measuring concentrations and detecting globules suspended in liquids is challenging when using common measurement techniques.

As a means of guaranteeing that the relevant particles are identified and measured correctly, end users are limited to using manual microscopy. The relevant particles can be viewed through a microscope by the end user, allowing them to differentiate different types of globule from one another. Users can also identify debris and other particles which are not globules, allowing them to either ignore their presence or deal with them appropriately.

Such manual methods take a lot of time and are laborious. Additionally, when ensuring statistical assurance, manual microscopy is not a practical technique for measuring an adequate population of a sample. There is also a tendency for the sample to be deformed when it is placed on a microscope slide.

These problems are not encountered during Dynamic Image Analysis, in which globules are able to flow freely. Dynamic Image Analysis has succeeded in combining the accuracy and speed of more common particle measurement techniques with microscopy’s visual capabilities. Users are able to differentiate particles based not just on size, but also on a number of other shape parameters with this high-speed automated measurement.

The fundamental principle upon which Dynamic Image Analysis operates is that as particles pass through a detection zone, images are taken and subsequently analyzed. The guideline for every Dynamic Image instrument which is currently available is ISO 13322-2.

The main purposes of using Image Analysis for measuring globules is that they allow users to identify, quantify, and differentiate particles with Opacity, Shape, and Size measurements. In just one analysis, the ender user is able to perform a size/shape measurement, perform a concentration measurement of each kind of particle present, and collect thumbnail images of each of the measured particles.

All globules comprised of different liquids such as silicone, oil, and water, tend to be spherically shaped with random sizes. However, they each have different opacities (darkness) which are able to be detected and used to differentiate between each type of globule.

Dynamic Image Analysis is able to detect non-globules such as debris and report them. Thumbnails of each of the measured particles are displayed for visual identification and confirmation.  

Experimental #1

The Particle Insight, Particle Shape, and Size Analyzer were used in this experiment in order to measure an oil sample from the engine of an airplane. There was significant wear to the engine in this case, which resulted in a cooling liquid (water-based) being present in the oil. A single screen capture of the sample analyzed with the Particle Insight (PI) is shown below.

The PI is able to analyze the particles on the screen in addition to performing 30 shape measurements in real-time. It is also able to save the individual particle thumbnail images. As shown here, particles which are not in focus can be ignored or eliminated by the PI. This case displays the measurements for each particle’s opacity.

It is noteworthy that water droplets in oil can be different sizes, yet debris which was located in this sample was able to be distinguished by its irregular shapes (darker Opacity values, and lower Smoothness and Circularity values). In just a few minutes, thousands of particles were measured.

As there is no lower limit on concentration detection in the PI, the recirculation of the sample would capture any rare-event particles even if very few particles were present.

It is also interesting to point out that water droplets in oil come in different sizes however debris that was also found in this sample was distinguishable by their irregular shapes (lower Circularity values, lower Smoothness values and darker Opacity values). Thousands of particles were measured in a matter of a few minutes. However, because the Particle Insight has no lower limit on concentration detection, even if very few particles were present, the recirculating of the sample would capture any rare event particles.

Typical Opacity histogram showing distribution of particles based on how dark they are. Air bubbles and debris tend to have a higher Opacity value than liquid globules.

Typical Opacity histogram showing distribution of particles based on how dark they are. Air bubbles and debris tend to have a higher Opacity value than liquid globules.

Typical Opacity histogram showing distribution of particles based on how dark they are. Air bubbles and debris tend to have a higher Opacity value than liquid globules.

Experimental #2

During this experiment, oil droplets in water were detected. As displayed here, there are both some air bubbles and large oil globules. An opacity measurement constituted a straightforward way for the PI to differentiate between air bubbles and oil droplets. As is visible in the background, smaller debris is present. It is possible to adjust the instrument’s settings in order to ignore this debris or they can be measured in real-time.

After analyzing tens of thousands of particles in minutes, the Particle Insight shows all thumbnail images. As can be seen here, lighter round particles are globules while darker round particles are air bubbles and darker irregular particles are debris. Relative concentration of all three populations can be given and are important to know.

After analyzing tens of thousands of particles in minutes, the Particle Insight shows all thumbnail images. As can be seen here, lighter round particles are globules while darker round particles are air bubbles and darker irregular particles are debris. Relative concentration of all three populations can be given and are important to know.

After analyzing tens of thousands of particles in minutes, the Particle Insight shows all thumbnail images. As can be seen here, lighter round particles are globules while darker round particles are air bubbles and darker irregular particles are debris. Relative concentration of all three populations can be given and are important to know.

Experimental #3

Shown below are the images obtained from a pharmaceutical time-released sample which has an inner globule encapsulated in a hard-outer shell. The customer in this sample wanted to determine the coating’s thickness.

The inner particle is able to be measured by all particle size instruments when it is not coated. The same size is then reported as they are spheres. Most particle size instruments encounter difficulties after the particles have been coated.

This is because the coating is often clear, and optical instruments such as laser diffraction or light obscuration are unable to exclude the inner particle from the analysis. The clear coating issue with the suspending liquid is liable to causing the outer shell to disappear.  In the case of opaque coatings, every particle size instrument is able to measure the coating.

There are advantages inherent to Dynamic Image Analysis as compared to other instruments which are able to measure clear outer shells, as well as those which measure using opaque coating. Dynamic Image Analysis is able to measure both outer and inner particles in just one analysis for clear coatings. This is because the dark threshold of image detection is able to be changed using Dynamic Image Analysis.

The PI was able to perform an automated analysis of the sample by analyzing the inner globule particle. When the analysis was finished, a different threshold condition was used to analyze the sample, in which just the outer ring (the shell) was analyzed for shape and size.

The uniformity of the coating, whether opaque or clear, is another benefit offered by Dynamic Image Analysis. It is possible to test the coating for uniformity, smoothness, and circularity.

Lastly, it is possible to view the images of clear coatings saved during the analysis in order to see the degree to which the spheres are centered in the coatings. Furthermore, if the coated particles are placed in a solution which dissolves the coating, the shape measurements and images will demonstrate how evenly the coating dissolves.

Results and Discussions

In all of the experiments above, there were differing degrees of opacity for the suspension liquids used. The suspension liquid was clear for the encapsulated globules, while for the oil samples the suspension liquid was dark. Even so, Dynamic Image Analysis was able to detect the suspended globule particles in all cases. The availability of the particle thumbnails also ensured that the proper parameters were established in order to capture the relevant particles.

During the water/air bubble in oil and oil in water analyses, the obvious differentiation in Opacity allowed the users to determine the percentages of air bubbles, water particles, and debris which were present in the sample. In this example, the end user was able to gauge the health of the equipment at the point from which the fluid was derived. This facilitated quality control checks on the engine’s stability.

The end user was able to perform the analysis on a single aliquot of a sample in the case of the encapsulated globule. This method did not require the outer shell to be broken by the end user when determining the inner globule’s size, and was a particularly efficient manner of performing the test.

Conclusions

Typical size measurement techniques find it challenging when measuring globule or liquid particles suspended in other liquids. Dynamic Image Analysis has proved itself to be a useful tool in the differentiation and analysis of such particle suspensions. Furthermore, a visual validation of the analysis is enabled by the availability of particle thumbnails.

This information has been sourced, reviewed and adapted from materials provided by Particulate Systems.

For more information on this source, please visit Particulate Systems.

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