Most industrial manufacturing processes depend on accurately knowing product particle sizes for quality control. Particle size analysis images and characterizes the shape and size of particles in materials. Manufacturing industries conduct particle analysis to ensure that the products meet the standards required to maintain product quality and consistency.
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Importance of Particle Size
The pharmaceutical industry relies on particle size to control how medicines dissolve and disperse throughout the body.
Failure to know and control the particle size of any given medicine can cause harm to the patient. Knowing particle size is just one of the many precautions needed to ensure pharmaceutical products' specification, quality, and effectiveness.
In industrial plastic extrusion, particle imaging determines the batch size of materials. Batch size data dictates the correct temperature and time to optimize high yields in the manufacturing process. Not knowing the individual particle size can lead to problems in production lines and financial strains for the manufacturers.
Determining accurate particle sizes play an important role in the fracking industry. A mixture of particles of various sizes can lead to uneven distribution of vacuum pressure. As a result, the fracture from which the oil is being drawn can shift. This could stop oil production as the wellbore might close due to the fracture shift.
Image Quality in Particle Imaging
Image quality is critical in imaging particle analysis. It is imperative that the two-dimensional projection of an image captured can accurately resolve the structure of the objects.
Imaging particle analysis is an effective method of deriving information about particles. Such imaging systems can also measure other features of each particle such as shape and grayscale information. Grayscale provides intensity information contained in every pixel of a digital image. Imaging particle analysis can differentiate among non-similar particles in a heterogeneous mixture and classify them into different types.
What is Image Quality?
Image quality is subjective, dependent on individual perception if gauged by humans. The image quality produced by technological means banks on the caliber of the components of the device. For example, if a digital camera is used, the ability and aberrations of the lens, the resolution of the sensor, and the noise and processing properties of the electronics will determine the quality of the image.
Scientifically, the quality of an image is characterized by a qualitative measurement. The result is dependent on the full performance of an imaging system.
Several properties contribute to making a high-quality digital image:
- Resolution – The amount of detail expressed
- Dynamic range - The range of light levels captured
- Distortion – Caused by the optical element in the camera system
- Tonal mapping
- Artifacts – The consequences emanating from the electronics or software processing of the image.
The resolution of an imaging system is the most important factor contributing to the image's quality.
The resolution of a system is its ability to reproduce the spatial detail of the object being imaged. If an image has a low resolution or appears out of focus, other factors such as tonal mapping and dynamic range cannot be applied effectively.
Quantitative resolution measurement is typically calibrated by an imaging system's ability to distinguish closely spaced, high-contrast pairs of objects at a given distance.
Established measurement techniques such as Modulation Transfer Function (MTF) and Point Spread Function (PSF) are applied to quantify the quality of the image produced.
MTF is used to measure the percentage of contrast that is transferred from the original object to the detector. It is used to characterize the spatial frequency content of the object captured in the image. Commonly, the contrast modulation is compared with a sinusoidal target of varying frequency.
While MTF demonstrates the quality of an image in the frequency domain, PSF is used to determine spatial aberrations.
PSF is the diffraction pattern projected onto the image plane of the camera from a point source in the object.
The properties of the optical elements along the optical beam path of the imaging system contribute to the PSF. For example, the numerical aperture (NA) objective lens plays a key role in diffraction. PSF is considered to be the fundamental unit of an image in theoretical models of image formation.
Particle size analysis plays a significant role in many industries as an in-process control tool and analysis during the development procedure. Shape, size, and distribution of particles are highly influential in the manufacturing process's solubility, dissolution, and other essential features.
Imaging and characterization techniques that have been developed and continuously improved enable fast measurement of particle sizes. Machine learning protocols are now being used with imaging methods to simplify and accelerate image quality production.
References and Further Reading
Farkas, Dóra, Lajos Madarász, Zsombor K. Nagy, István Antal, and Nikolett Kállai-Szabó. (2021) Image Analysis: A Versatile Tool in the Manufacturing and Quality Control of Pharmaceutical Dosage Forms" Pharmaceutics 13, no. 5: 685. https://doi.org/10.3390/pharmaceutics13050685
Brown, L. (2020) Imaging Particle Analysis: The Importance of Image Quality. [Online] Fluid Imaging Technologies, Inc. https://info.fluidimaging.com/hs-fs/hub/300163/file-657002000-pdf/white-papers/imaging-particle-analysis-importance-of-image-quality.pdf
Joel P. Zingerman, Surendra C. Mehta, Jeremy M. Salter, Galen W. Radebaugh. (1992) Validation of a computerized image analysis system for particle size determination Pharmaceutical applications, International Journal of Pharmaceutics, Volume 88, Issues 1–3, Pages 303-312, ISSN 0378-5173, https://doi.org/10.1016/0378-5173(92)90328-Y