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Improved process control

Particle shape analysis enables evaluation of product behaviour
Improved process control

Particle shape can directly influence product performance and its measurement can lead to improved process efficiency and product quality. Image analysis systems like Morphologi G2 are particularly suited to analysing size and shape and are a valuable tool for the sector. With particle shape and size data readily available, it becomes possible to more effectively define the process endpoint and rationalise differences in the behaviour of different batches.

Carl Crompton

Manufacturers producing a particulate product often need to identify and understand the differences between batches, either for product development reasons or for quality control purposes. With some applications, particle size analysis generates enough data for sample differences to be fully rationalised, but for applications where the samples are very close in size, measurement of subtle variations in shape may be necessary. Figure 1 shows two different samples. The particle size distributions for each material could be the same, but they are clearly not identical. It is likely that these two materials would behave differently during processing, or in their final product form. For example, their flow and abrasion characteristics would be dramatically different. Particle size data alone would not be sufficient to differentiate them.
Particle analysis technologies
The FDA’s PAT initiative, an effort to improve cGMP by providing a regulatory framework for the introduction of new manufacturing technologies for the pharmaceutical industry, is ultimately designed to improve process control in the sector. Improved process control delivers greater efficiency, less waste and lower production costs. It will therefore allow the industry to respond more effectively to environmental and economic challenges.
Currently, many manufacturing operations are based on time-defined endpoints, for example blend for 10 min or mill for 1 h. The spirit of the PAT initiative is to move away from this approach, to one where the endpoint is defined in relation to a property that is closely linked to product quality – granule size, morphic form or blend uniformity, for instance. Material with the desired properties is then produced more consistently and waste is minimised. This approach requires the identification of an appropriate variable, with effective monitoring and control of the selected parameter.
Particle shape and size data can be generated using automated image analysis techniques, complementing both microscopy and laser diffraction for particle characterisation. In contrast to manual microscopy, image analysis generates statistically relevant data with no subjective bias, allowing shape and its effects to be studied systematically. Image analysis generates number-based distributions and is therefore extremely sensitive to the presence of fines or small numbers of foreign particles. In addition, individual particle images are recorded, allowing visual detection and verification of agglomerates or contaminants. Image analysis procedures involve the capture of images using transmitted or reflected light, a lens system and a CCD. Movement between the sample and the magnification lens allows a large number of particles to be scanned to produce statistically relevant data; typically, several thousand particles are measured per min-ute. Multiple shape parameters are calculated for each individual particle and collated into distributions with all the associated distribution parameters.
Particle orientation
Particle orientation is critically important for effective characteris-ation of particle shape by image analysis. Figure 2, which shows an analysis of a sample of monodisperse, needle-shaped particles, clearly illustrates the problem associated with random orientation. The shape and particle size data produced shows a polydisperse sample. The bank of images illustrates why. The camera and software are seeing a selection of different 2D views of similar particles – the random orientation is hiding the genuine primary morphology of the sample. Consistent orientation is critical for the identification of real morphological differences. Particles may be presented showing their largest surface area, their smallest surface area or something in-between. Which area is analysed is less important than the consistency of presentation. However, as the largest area orientation is more closely correlated with surface area and volume-based data – and easier to achieve – this approach tends to be adopted.
Defining particle shape
Several different aspects of particle shape are of interest and a range of descriptors has therefore been devised to allow particle shape to be quantifiably described. No single shape descriptor is suitable for all applications. The following three parameters, which are all normalised (defined to have values in the range from 0 to 1), are frequently used to quantify different aspects of particle shape. Elongation provides an indication of the length/width ratio of the particle and is defined as (1-[width/length]). Shapes symmetrical in all axes, such as circles or squares, will tend to have an elongation close to 0 whereas needle-shaped particles will have values closer to 1. Elongation is more an indication of overall form than surface roughness (see figure 3) – a smooth ellipse has a similar elongation to a spiky ellipse of similar aspect ratio. Convexity is a measure of the surface roughness of a particle and is calculated by dividing the particle area by a total area, best visualised as the area enclosed by an imaginary elastic band placed around the particle. A smooth shape, regardless of form, has a convexity of 1 while a very spiky or irregular object has a convexity closer to 0 (see figure 3). Circularity describes the ratio of the actual perimeter of a particle to the perimeter of a circle of the same area. A perfect circle has a circularity of 1 while a very spiky or irregular object has a circularity closer to 0. Intuitively, circularity is a measure of irregularity or the deviation from a perfect circle. Figure 3 shows how circularity is sensitive to both overall form (like elongation) and surface roughness (like convexity). This shape factor is particularly useful for applications where perfectly spherical particles are the desired end product.
A further parameter frequently used in particle characterisation is the circle equivalent diameter. It is calculated by measuring the area of a 2D image of a particle and back-calculating the diameter of a circle with the same area. It is one of many equivalent values used to define particle size and is calculated easily from image analysis data. The circle equivalent diameter calculation depends upon which 2D view is captured and hence may not be directly comparable with alternative particle size measuring techniques, particularly if the particles are not spherical.
Practical example
The sensitivity of one pharmaceutical process to particle shape is exemplified in the following. One of four batches of a pharmaceutical excipient was continuously failing at the tabletting stage of a manufacturing process. This was proving to be highly expensive because the tabletting process was at the very end of the manufacturing chain, where all the value has been locked into the product. The tablet producer wanted some way of identifying the failed batch much earlier – ideally as a raw material. Traditional microscopy or ensemble sizing methods could not distinguish between the four batches in question. Automated image analysis was used to evaluate the average convexity of each of these four batches. Convexity is a measure of the surface roughness or spikiness of the particle surface, and the failed batch was found to consistently exhibit a lower average convexity than the other three good batches (figure 4).
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Process Analytical Technologies
Malvern Products

Morphologi G2 in detail
The Morphologi G2 high-sensitivity particle analyser provides repeatable and routine characterisation of particle size, shape and count. Based upon digital image analysis, the Morphologi G2 uses the latest Nikon CFI 60 optics and a high-resolution, high pixel-density digital FireWire camera to provide aberration-free images. This enables microscope-quality images and statistically significant histograms. Further advantages are:
  • Particle size measurement from 0.5 to 1000 µm
  • Particle shape characterisation by a range of parameters such as width, length, aspect ratio (length/width ratio), elongation, circularity and convexity
  • Storage of high quality images
  • Particle viewer for selection of images
  • Software und hardware settings (focus, magnification, light intensity) controlled by standard operating procedures (SOPs)
  • Automatic calibration, conformance with 21CFR Part 11 requirements and availability of full IQ/OQ documentation
  • Sample preparation accessories for controlled dispersion
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