Malvern Instruments has released new guidance on using automated image analysis to detect and quantify agglomerates.
Uncontrolled agglomeration can substantially impact the performance and value of powder products, which makes efficient agglomerate detection vital.
‘The identification of agglomerates using automated image analysis’ presents practical strategies to efficiently differentiate agglomerates from primary particles, to support product development, quality control (QC) and process troubleshooting.
Formed through the adhesion or cohesion of smaller primary particles, agglomerates can have serious implications for product performance, value and safety.
Agglomeration must be thoroughly investigated during product development, controlled during manufacturing and checked in final product QC.
Automated imaging is a fast and efficient technique for studying the morphological characteristics of particulate materials.
Our company’s new guidance shows how the combination of size and shape data can be used to securely classify particles as agglomerates. This enables the amount of agglomerated material present in a blend to be quantified.
Shape parameters such as particle convexity and circularity are important in differentiating primary particles and agglomerates.
Malvern’s Sysmex FPIA 3000 and Morphologi G3 image analysis-based particle characterisation systems employ advanced optics to record and analyze images of thousands of particles within suspensions, emulsions and dry powders in a few minutes.
The Morphologi G3-ID extends the capabilities of image analysis by applying the technique of morphologically directed raman spectroscopy.
This enables the chemical identification of multi-component agglomerates that cannot be reliably classified on the basis of size and shape alone. Malvern’s automated imaging systems provide an efficient solution for robust agglomeration detection.