Particle characterization is the process of measuring key particle properties such as size, shape, surface area, and other physical or chemical attributes. A wide range of analytical techniques is available to characterize particulate materials, each based on different measurement principles. Because no single method is universally applicable to all sample types and conditions, understanding the strengths and limitations of each technique is essential for selecting the most appropriate approach.
Particle characterization is most commonly introduced during the early research and development phase of projects involving raw particulate materials. During this stage, investigators use analytical techniques to evaluate key particle properties that may impact product performance and processing behavior.
As the project advances, these methods are typically implemented in the manufacturing process to monitor and control critical parameters. This ensures consistency, manufacturability, and the efficacy of the final product.
For many years, particle size has been a fundamental method of particle characterization. However, most established particle sizing techniques are indirect and report results based on the assumption that all particles are spherical. In reality, most industrial particles are irregular in shape, which introduces uncertainty when relying on size-only measurements.
Particle shape plays a significant role in how particles interact, flow, and compact, ultimately affecting product performance and efficacy. Recognizing this, scientists began incorporating shape into particle characterization by using direct measurement techniques such as microscopy. While often slow and labor-intensive, microscopy provides valuable qualitative insight into particle morphology and structure that cannot be obtained from size-only methods.
The importance of particle shape has been recognized for decades. In 1963, the Krumbein Scale was introduced as a standardized method for geologists to assess particle roundness and sphericity. This visual scale used reference images to compare particle shapes, but the evaluation was inherently subjective.
Even at this early stage, it was clear that understanding particle shape was just as important as measuring particle size. As technology advanced, scientists began using microscopy to examine small sample populations, gaining deeper insight into particle morphology.
The field of particle shape analysis advanced significantly in the early 1990s with the introduction of improved machine vision cameras and more powerful computing systems. These advancements enabled the analysis of much larger particle populations in a more standardized and quantitative manner.
Image analysis—whether performed on static samples or particles in motion—relies on rapid image capture and processing, allowing for high-throughput measurement of both particle size and shape.
Commercial particle shape analysis systems began emerging in the 1990s and early 2000s. These systems, including ours, offered not only particle size but also a wide range of shape parameters—often exceeding 32 distinct measurements. Despite these capabilities, shape analysis was initially considered a novel approach, competing with well-established size-only techniques such as laser diffraction.
As with any new technology, widespread adoption required significant industry education. Over time, awareness grew, and users began to recognize the value of incorporating shape into particle characterization.
Around 2014, we introduced a system designed to function as an accessory to existing laser diffraction instruments. This development helped position shape analysis as a complementary technique to traditional size-only methods. Its high resolution and flexibility have since enabled its use across a wide range of applications, including real-time and online particle analysis.
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Today, particle shape analysis is widely used not only for characterization but also for particle identification. In characterization applications, shape metrics can be used to quantify the composition of mixed samples.
For example, users can determine the percentage of excipients versus active pharmaceutical ingredients in a blend, both before and after processing steps such as tableting. This enables more precise control over formulation and manufacturing processes.
Changes in the composition of a particle mixture can have significant consequences for the end product. Even small variations in component percentages can negatively impact performance, but these differences can be readily detected through particle shape analysis. With this insight, users can make informed adjustments to minimize separation and maintain a consistent blend, particularly when particles of differing shapes tend to segregate.
In addition to characterization, particle shape analysis serves as a powerful identification tool. As a number-based measurement technique, it enables the detection of specific particle types, such as agglomerated proteins. Advanced software tools support this analysis, providing quantitative data along with thumbnail images of individual particles, offering clear and objective visual confirmation.
Particle shape analysis is increasingly recognized as a critical component of material characterization. Most scientists understand the important role that particle shape plays in determining product performance. However, compared to traditional size-only techniques, shape analysis is still a relatively newer approach.
As a result, it continues to be a rapidly growing field. Over time, particle shape analysis has gained acceptance as a valuable complement to particle size, providing deeper insight into particle behavior. Advances in Dynamic Image Analysis have further accelerated this growth by enabling faster, more detailed measurements. While adoption is still expanding, awareness of particle shape analysis is now widespread among professionals involved in particle characterization.
Particle shape analysis provides far more than simple visual inspection. While particle images offer valuable qualitative insight, modern systems can generate over 30 quantitative shape parameters for every particle, along with concentration measurements.
This combination of data enables users to detect and diagnose issues that size-only techniques may miss. In many cases, differences in particle shape—rather than size—have been used to distinguish between raw material lots. Additionally, particle thumbnail images allow users to quickly identify anomalies such as bubbles, agglomerates, or contamination, providing both quantitative data and objective visual evidence.
Dynamic Image Analysis is a number-based technique, meaning it measures particles individually rather than as a bulk population. This enables the collection of detailed information for each particle, including size, shape, count, and concentration—parameters that are critical across many industries.
In addition, Dynamic Image Analysis is a direct measurement technique. It captures images of actual particles and performs measurements based on their true geometry. In contrast, many widely used techniques are indirect, measuring physical phenomena related to particle size and then inferring size based on the assumption that particles are spherical.
This distinction is a key reason why Dynamic Image Analysis continues to gain popularity. By measuring individual particles directly and calculating dozens of shape parameters for each one, users gain access to a much richer dataset. This data can then be used for advanced classification, identification, and post-analysis processing.
Particle shape analysis will continue to advance alongside improvements in imaging technology, computing power, and data processing capabilities. At Vision Analytical, we continuously work to enhance our image analysis systems—pushing the limits of detection speed, accuracy, and data processing.
As technology evolves, these advancements are rapidly integrated into our systems, enabling users to obtain more detailed and meaningful information without relying on assumptions. The result is ongoing improvement in both measurement capability and user confidence.
Ongoing technological improvements will continue to expand the role of particle shape analysis across a broader range of applications. Modern systems are no longer limited to laboratory use; they now include benchtop, portable, and fully online solutions.
Online systems, in particular, provide real-time feedback during manufacturing, allowing users to detect and respond to raw material or process variations immediately. This capability significantly enhances process control and product consistency.
Particle shape analysis systems are based on image analysis and are typically categorized as either static or dynamic.
Dynamic systems also offer the ability to detect rare events—such as agglomerates or low-level contamination—and provide thumbnail images as objective visual evidence. In addition, many systems can be integrated as complementary tools alongside other techniques, such as laser diffraction, enhancing overall particle characterization.
Users considering particle shape analysis typically already understand particle size but recognize that size alone does not provide a complete picture. They are looking for deeper insight into their materials to better understand performance and variability.
While particle images provide valuable visual confirmation, the real value lies in the quantitative data. Modern image analysis systems can generate over 30 shape parameters per particle, enabling detailed comparison between materials, batches, and processes.
Importantly, it is not the user’s responsibility to interpret this complexity alone. Advanced software tools are essential to extract meaningful insights from the data. Features such as particle classification, correlation analysis, concentration measurement, and shape overlays allow users to quickly identify trends and differences.
High-quality images remain important, but it is the combination of robust data and powerful post-processing tools that delivers the greatest value in particle shape analysis.