Determining Suspended Liquid Particles Using Dynamic Image Analysis

It is vital to calculate the size of liquid particles in a suspension in several industries. These could be as simple as water droplets suspended in oil, or oil droplets suspended in water. Liquid dosages may be encapsulated inside a harder outer shell for some pharmaceutical applications.

In a lot of these instances, the end user may need to establish the shape, size and even the concentration of these globules. In many cases, the challenge is utilizing an automated method that differentiates between an oil droplet and water droplet in addition to differentiating these particles of interest from other debris to avoid affecting the concentration measurements.

The most common particle measurement methods are only able to differentiate particles based on size. Most of these common methods will also assume that particles are spherical in shape, which, for this globule application, is a correct assumption unless there are non-spherical particles such as debris, which could be measured incorrectly as part of the main population of particles.

Furthermore, some of the more common methods need supplementary information about the particles in addition to the fluid they are suspended in. Parameters such as refractive index may be required to measure particles properly.

As globules are of one, or many, refractive indexes and the liquid they are suspended in are of a different refractive index, undertaking measurements of globules can be a challenge when utilizing some of the more common particle size measurement methods. The differentiation between debris and droplet types can also be challenging with size-only measurement methods.

For this reason, it is difficult to employ the more common methods to measure concentration properly and to even detect globules suspended in liquid. To ensure the particles in question are being identified and measured properly, end users are then limited to use manual microscopy.

Microscopy permits the end user to observe the particles in question and to visually differentiate one type of globule from another. Furthermore, it permits the user to identify debris or other particles, which are not globules, to ignore them or act on the fact that they are present in the sample. The issue is that these manual techniques are usually tedious and time consuming.

Manual microscopy is not a practical method for measuring an adequate population of sample to ensure statistical assurance. As it is put on a microscope slide, microscopy also has a tendency of deforming the sample. Something which does not occur in Dynamic Image Analysis where globules are permitted to flow freely.

Combining the visual abilities of microscopy with the accuracy and speed of the more common techniques have been accomplished using Dynamic Image Analysis. This technique allows users to differentiate not only on size, but also on a number of other shape parameters in a high-speed automated measurement.

Dynamic Image Analysis works on the principle that images are captured and analyzed as particles pass through a detection zone. For every Dynamic Image Analysis instrument available on the market today, ISO 13322-2 is employed as the guideline. The main advantage of utilizing Image Analysis for globule measurement is the identification, quantification and differentiation of different particles through shape, size, and opacity measurements.

In a single analysis, the end user would be able to perform a size/shape measurement, gather a concentration measurement of each kind of particle that is present and generate thumbnail images of every measured particle.

Globules made of different liquids, water, oil and silicone are spherical in nature and can have random sizes, but would all possess different opacity (darkness), which could be detected and utilized as a differentiation discriminator of each type of globule.

Furthermore, the presence of debris (non-globules) would also be gathered and reported. One extra advantage that Dynamic Image Analysis provides is the capability to display particle thumbnails all of the measured particles for visual confirmation and identification.

Experiment One

Size Analyzer and Particle Shape were utilized to measure an oil sample from an airplane engine for the SentinelPro experiment. The engine had significant wear in this instance and resulted in cooling liquid (water-based) present in the oil. Single screen capture of the sample analyzed on the SentinelPro can be seen below.

The PI can analyze the particles on the screen on a real-time basis, and carry out 30 shape measurements in addition to saving the individual particle thumbnail images. As shown here, the SentinelPro can ignore or eliminate unfocused particles. In this example, the measurements are shown for the opacity of each particle.

Furthermore, it is interesting to note that water droplets in oil come in different sizes, yet debris that was also discovered in this sample was distinguishable by their irregular shapes (lower smoothness values, lower circularity values and darker opacity values). Thousands of particles were measured in a matter of minutes.

Yet, as the SentinelPro has no lower limit on concentration detection, even if hardly any particles were present, the recirculating of the sample would even capture 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.

Experiment Two

The second experiment was the identification of oil droplets in water. There are large oil globules plus some air bubbles, as can be observed here. The simple way for the SentinelPro to differentiate between an air bubble and an oil droplet was by utilizing the opacity measurement. As can be observed in the background, smaller debris is present. The instrument settings can be changed to ignore them, or the smaller debris can also be measured in real-time.

The SentinelPro shows all thumbnail images after analyzing tens of thousands of particles in minutes. As can be observed here, darker round particles are air bubbles and darker irregular particles are debris, while lighter round particles are globules. Relative concentration of all three populations can be produced and are important to know.

Experiment Three

The below images are taken from a pharmaceutical time released sample that has an inner globule encapsulated in a hard outer shell. The customer was interested in establishing the thickness of the coating in this sample.

As they are spheres, all particle size instruments are able to measure the inner particle when not coated and report the same size. Where most other particle size instruments have a problem is after the particles have been coated.

As the coating is clear, optical instruments such as laser diffraction and light obscuration are unable to exclude the inner particle from the analysis. In addition, with the clear coating issue, the suspending liquid can cause the outer shell to disappear. All particle size instruments will be able to measure the coating in opaque coatings.

Dynamic Image Analysis has benefits over the few other instruments that can also measure the clear outer shell and has benefits over sizing opaque coatings. As Dynamic Image Analysis provides the ability to alter the dark threshold of image detection, the method can measure both the inner and outer particles in one analysis for clear coatings.

The SentinelPro is able to undertake an automated analysis of the sample analyzing the inner globule particle. After the analysis was finished, the same sample was then analyzed with a different threshold condition where only the outer ring (the shell) was analyzed for shape and size.

Another advantage Dynamic Image Analysis offers for coatings, clear or opaque, is how uniform the coating is. The coating can be tested for circularity, smoothness, uniformity. Finally, for clear coatings, the images saved during the analysis can be viewed to see how centered the spheres are in the coatings. If the coated particles are placed in a solution that dissolves the coating, the images and shape measure will show how evenly the coating dissolves.

Results and Discussions

The suspension liquid was of different opacities in all the above experiments. In the case of the encapsulated globules, the suspension liquid was clear. For the oil samples, the suspension liquid was dark. Regardless of this, Dynamic Image Analysis could detect the suspended globule particles.

In addition, having the particle thumbnails available enabled us to ensure that the proper parameters were set to capture the particles in question. In the oil in water and the water/air bubble in oil analysis, there was a clear differentiation in opacity that enabled us to determine the percentage of water particles present, the percentage of air bubbles present, as well as the percentage of debris present in the sample.

In this case, the end user could determine the health of the equipment and where this fluid came from. It could then be used as a quality control tool to check the stability of the engine from time to time. In the case of the encapsulated globule, the end user could perform the analysis on a single aliquot of sample. This was a very efficient way to perform the test and did not require the end user to break the outer shell to do a size determination of the inner globule.

Conclusion

Liquid or globule particles suspended in other liquids would present difficulties for proper detection using typical size measurement techniques. Dynamic Image Analysis has shown to be a valuable tool in the analysis and differentiation of these particle suspensions. In addition, the ability to have particle thumbnails enables a visual validation of the analysis.

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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