Automated image analysis systems that provide detailed morphological information are rapidly replacing manual microscopy as the most effective method of characterizing both particle shape and size. These sophisticated new instruments use the latest technology to provide useful and actionable insight.
Until recently, these instruments had been regarded as rather niche, due to their previously limited functionality. However, significant advances in terms of both their capabilities and performance, along with the new insights into particle morphology that these instruments provide, have opened up exciting new avenues for research and development, and helped optimize manufacturing and production techniques to make them a much more generally attractive proposition.
One of the key advances has been improvements in camera technology, enabling more sensitive measurements to be taken. This has been complemented by more powerful image segmentation techniques resulting in much more detailed and accurate particle size and shape measurements.
Another recent development in this field is the integration of Raman spectroscopy with automated imaging systems resulting in a technique known as Morphologically-Directed Raman Spectroscopy (MDRS®). As well as providing morphological data on each of the particles being measured, it also provides chemical identification and differentiation of particles within a mixed sample.
Information provided by automated imaging techniques is invaluable in a range of industry sectors, including pharmaceuticals, forensics, metal powders, additive manufacturing and battery electrode manufacture. It leads to a deeper understanding of manufacturing processes and material characteristics and helps to resolve issues during the development of new formulations and products.
Eight top reasons to upgrade
So, what are the key benefits of migrating from manual microscopy to an automated imaging or MDRS systems?
1. Improved particle characterization for greater insight
Automated imaging systems measure both particle size and shape in a single measurement over a size range from less than a micron to over a millimeter. This provides a much deeper understanding of the entire sample and can enable differentiation of samples that can’t easily be discriminated by size alone. This is important because differences in particle size and shape can impact on
- Powder processing, such as: flowability and blending properties; cohesion and formation of agglomerates; tableting and compaction behavior.
- Final product performance, such as: inhalation drug delivery; dissolution behavior and bioavailability; abrasive efficiency.
By combining measurements such as particle length and width with 2D shape assessments such as circularity, convexity and elongation (Table 1), a more accurate picture of each individual particle is produced, giving a more complete understanding of a sample’s characteristics.
Table 1: Description and examples of circularity, convexity and elongation shape parameters.
2. Better preparation for more robust results
Sophisticated automated imaging systems can analyze both wet and dry samples, providing flexibility when measuring a wide array of sample types including dry powders, suspensions and filters. Appropriate sample preparation prior to analysis is key to accurate, consistent and reliable measurements, which rely good spatial separation and representative sampling of particles. This is ensured by the provision of dedicated sample preparation accessories, including dry powder dispersion units, filter holders and various wet dispersion cells (Table 2).
Table 2: Examples of sample preparation accessories.
3. Enhanced image quality for improved accuracy
Accuracy and reliability are key attributes of automated image analysis systems and are highly dependent on the quality and resolution of particle images. Automated imaging systems, such as the Morphologi 4 range, produce high quality greyscale images of every particle measured. Controlled particle orientation ensures that the camera always sees the largest face of every particle in a sample, eliminating errors associated with 3D particle imaging of randomly orientated particles. Particle images enable qualitative verification of the quantitative size and shape data, and the high-quality intensity information from the greyscale image enables a clearer insight into the relative thickness or density of particles (Figure 1), and an understanding the of their surface homogeneity.
Figure 1: Overlay comparing the Intensity mean distribution (transparency) of two mineral samples along with example particle images.
4. Improved data confidence and decision-making
An automated image analysis system delivers non-subjective, robust and reproducible particle measurements for improved data confidence. Intelligent algorithms, such as Sharp Edge, automatically separate particle images from background making method development easier. Standard operating procedures (SOPs) define all the software and hardware variables from sample dispersion to result reporting and SOP-driven operation ensure that critical processes of the analysis, such as the focusing and illumination, as well as the particle segmentation are operator independent. All critical factors in the analysis are carefully audited providing added confidence in the data produced and the resulting decisions.
5. Seamless integration of Raman spectroscopy for chemical identification
The integration of Raman spectroscopy with automated imaging eliminates the need for two separate analysis techniques, saving valuable time and limiting the complexities associated with interpreting and correlating different data sets. Morphologically-Directed Raman Spectroscopy (MDRS) provides an additional capability over imaging alone, by allowing the physical and chemical characteristics of individual components to be analyzed. Individual chemical classes from within a complex mixture can be identified, delivering valuable insight into the structure and composition of a material, such as a pharmaceutical formulation or a cement blend.
6. Easier to use and quicker to learn
Automated image analysis is easier to use than manual microscopy because, as the name suggests, many of the processes are automated. A sample is loaded into the system and once the start button is pressed, the Standard Operating Procedure (SOP) is activated.
Figure 1 summarizes the workflow of the measurement process for both imaging and MDRS. The imaging instrument automatically disperses the dry sample and analyzes it without the need for any further operator interaction. The particle images are automatically captured, measured, analyzed and classified, with over 20 morphological parameters being measured. With the latest instruments, such as the Morphologi 4 and Morphologi 4-ID, the automated particle segmentation algorithm ‘Sharp Edge’ improves detection of lower contrast particles such as those in nasal sprays, cement and proteins. Once particle imaging is complete, the Morphologi 4-ID system then goes on to perform the chemical identification part of the measurement. Results, which include a detailed description of the characteristics of the individual particles within the sample, are immediately available at the end of the measurement. The software employs an intuitive and flexible workflow that makes Raman spectroscopy suitable for experienced and non-experienced spectroscopists alike.
Figure 2: Workflow for an automated image analysis measurement with and without MDRS
7. Faster analysis for greater productivity
Automated imaging systems provide substantial time savings when compared with manual microscopy, due to the streamlining of various operations as well as the convenience of in-built data analysis. Indeed, automated image analysis can measure tens of thousands of particles in just a few minutes – less time than manual microscopy would take to measure just a handful. This enables operators to generate statistically significant data on a host of particle size and shape parameters much more quickly and easily.
When Raman spectroscopy is included, thousands of particles can be automatically identified and chemically classified without operator input. Morphological information can be used to direct the chemical analysis towards only particles that match specific size and/or shape characteristics, significantly reducing the Raman spectroscopy analysis time.This enables detailed and rapid characterization of individual components in a complex mixture, providing the most detailed insight into the sample in the shortest possible time.
8. In-built data analysis and exploration
With the ability to analyze hundreds of thousands of particles per measurement, statistically meaningful results can be obtained rapidly, and analyzed in a way that allows clear trends and relationships to be identified. Rapid comparison of such large numbers of morphological parameter data sets is achieved using an integrated data comparison tool, shown in Figure 3, that automatically groups similar records together based on a chosen parameter or set of criteria.
Figure 3: Data comparison tool
Particle images can be easily classified according to any combination of particle size and shape parameters, and if available, their chemical composition. An interactive scattergram tool (example shown in Figure 4) helps with the classification process allowing morphological or chemical parameters to be plotted against each other. Regions, or classes, of interest can then be visualized in more detail. Once established, these classes can be added to the SOP and applied to every relevant measurement, enabling quick comparison across different samples and saving valuable time and effort.
Figure 4: Scattergram tool showing the elongation distribution plotted against the circularity distribution along with example particle images from regions of interest.
There is little doubt that organizations and individuals in a wide array of industries could benefit greatly by moving from manual microscopy to automated image analysis. The accurate measurement of particle shape and size – as well as the chemical identification capabilities provided by MDRS – could make a real difference to research, development and manufacturing operations across many different sectors of industry and commerce.