The possibilities are endless when it comes to image analysis. Over the years, the experts at Particle Technology Labs have developed and validated countless particle characterization methods using microscopic image analysis, and the wealth of information from these tests is unmatched for characterizing particle systems..
Why Use Image Analysis?
Most modern particle size analyzers will only tell you one aspect of a particle’s physical dimensions: it’s spherical equivalent diameter. In the real world, however, few particles are perfectly spherical. This convention is borne out of necessity as the detection principle of most instruments will only allow it to “see” a single physical characteristic of a particle, be it its volume, projected area, etc. In addition, it would be impractical to describe every single dimension for potentially millions of particles in a sample — thus the concept of spherical equivalence was developed.
However, by not characterizing other aspects of a material, such as its particle shape, critical aspects of a particle system’s behavior may be missed. Enter image analysis, a powerful particle characterization technique that has grown rapidly in popularity following technical improvements in automation, speed, and precision. Characterizing a new material via microscopic image analysis early on in the development phase can allow one to get a well-rounded picture of a material’s properties and help to identify the parameters that will adequately describe its quality attributes.
What Can Image Analysis Tell Me?
Primary Particles vs. Agglomerates
One key aspect of any particle size method is to first define whether one is interested in the primary particle size or agglomerated particle size (sometimes referred to as the bulk particle size). Primary particles are typically thought of as the smallest discrete particles in a system (think of a single crystal). However, many materials under the right conditions will tend to agglomerate into larger assemblages of these primary particles due to cohesive inter-particle forces, especially as the primary particle size decreases due to the higher surface area and the lessened effect of gravitational settling.
Therefore, it is important to ask which state of the material during analysis will be most representative of the quality attribute attempting to be measured and the real-world conditions it will be subjected to in order to ensure the sample is prepared in an appropriate manner. For instance, if attempting to characterize the particle size distribution for the purposes of monitoring a material’s dissolution rate, that is a function of a particle’s surface area. Thus, measuring the primary particle size free of agglomerates will be of greatest value. However, if one is concerned about the presence of large particles in a topical cream affecting texture/skin feel, then measuring the particles as they are, agglomerates and all, will be the most appropriate approach. In addition, the mere presence of agglomerates may provide valuable information regarding the material’s stability or effectiveness of processing conditions.
Unfortunately, most particle sizing instruments cannot differentiate between primary particles and agglomerates. To a laser diffractor, a 300 µm particle will look just the same as a 300 µm agglomerate composed of smaller particles. That’s where image analysis comes in. Since every particle captured has a corresponding image associated with it, individual particles can be investigated post-analysis to determine if they are primary particles, agglomerates, or even a contaminant such as a fiber or a bubble. Additionally, such particle agglomerates will often exhibit different shape properties relative to primary particles, allowing for differentiation of primary particles vs. agglomerates on the basis of shape parameters such as circularity, convexity, or solidity.
Two particle distributions with identical spherical equivalent diameters may nonetheless exhibit different processing behavior due to the influence of particle shape. For instance, relative to spheres of equivalent volume, rod-like particles may flow much less easily, amorphous particles may dissolve more quickly, and angular particles may be more abrasive. In addition, particle shape information can potentially be used to help identify different species, such as amorphous vs. crystalline particles, solid particles vs. liquid droplets, etc.
Elucidating Optical Properties
One often overlooked aspect of image analysis is that even the amount of light transmitted through or reflected off the particles can provide valuable data for discriminating particles. When working with heterogenous materials, depending on the optical properties of the particles and careful consideration of the illumination conditions, the intensity of light that comprises the particle image can be used to differentiate particles for analysis. For instance, birefrigent particles such as crystals will be illuminated under cross-polarized light whereas an emulsified oil droplet will not, allowing the sizing of a crystalline API within a cream without the need for complicated separations. Alternatively, highly reflective material such as TiO2 in a cosmetic powder (which can be a safety concern if such fine particles are inhaled in significant quantities) will reflect more light under episcopic (top-down) illumination than other particles, allowing for its discrimination. Even under standard brightfield illumination, different particle species can have different levels of transparency that can be used to differentiate them.
Image Analysis as Orthogonal Technique
One other use of image analysis is as an orthogonal technique to more conventional particle sizing methods on the idea that “seeing is believing.” Since microscopy provides an unambiguous look at your particles, the technique is often used to verify that other methods are generating appropriate results. USP <429> specifically recommends that the validation of laser diffraction methods be supported by microscopy to confirm the accuracy of the analysis. Automated image analysis takes that a step further by generating a full-size distribution on a statistically meaningful number of particles for a true quantitative comparison. Image analysis can then be used to confirm or refute the presence of potentially anomalous peaks resulting from improper selection of optical model parameters, or help identify the bias of a particular method.
If you have a challenging analytical problem with your particles, please contact us to see if image analysis can help.
By Michael Vinakos