Visual particle sizing is widely adopted across sectors such as pharmaceuticals, cosmetics, and advanced materials due to its ability to provide visual confirmation of particle morphology alongside size measurements. However, 動的画像解析 despite its advantages, this method comes with several inherent limitations that can significantly affect the accuracy, reliability, and applicability of the results. One of the primary constraints is the resolution limit imposed by the optical system — even high-resolution cameras and microscopes have a physical bound on the smallest feature they can distinguish, which means particles smaller than approximately one micrometer are often not reliably captured or measured. This limitation makes it unsuitable for analyzing sub-micron or ultrafine particulates.

The technique inherently flattens 3D structures into 2D representations, leading to potential inaccuracies in size estimation. Two particles with vastly different 3D geometries may yield identical 2D silhouettes, yet their actual three-dimensional volumes differ substantially. When no 3D reconstruction protocols are applied, these distortions can introduce systematic errors in size distribution analysis.

Preparing samples for imaging is often complex and error-prone — imaging requires particles to be dispersed in a way that prevents clumping, sedimentation, or overlapping. In practice, achieving a uniform, single-particle layer on a substrate is difficult, especially with sticky or irregularly shaped materials. Aggregates are often erroneously interpreted as single large particles, while particles that are partially obscured or in shadow can be missed entirely. These issues compromise the integrity of reported data and compromise the validity of reported size distributions.

Automated particle recognition tools have significant inherent flaws — edge detection, thresholding, and segmentation routines rely on contrast and lighting conditions, which can vary due to changes in illumination, background noise, or particle transparency. Particles with low contrast against their background—such as transparent or translucent materials—are often undercounted or inaccurately sized, and manual correction is sometimes necessary, but this introduces subjectivity and inconsistency, particularly when large datasets are involved.

The statistical representativeness of the results is another concern — imaging systems typically analyze only a small fraction of the total sample, making them vulnerable to sampling bias. In non-uniform suspensions or powders, the images captured may not reflect the true population. The problem intensifies with complex, multi-peaked size profiles, where rare but significant particle types may be overlooked.

Compared to optical scattering techniques, imaging is inherently time-intensive, as the time required to capture, process, and analyze thousands of images can be prohibitive for high-throughput applications or real-time monitoring. Automation has reduced cycle times, they often sacrifice precision for throughput, creating a trade-off that limits their utility in quality-critical environments.

In summary, while imaging-based particle sizing offers valuable visual insight and can be highly informative for qualitative assessment, its quantitative reliability is constrained by optical resolution, dimensional ambiguity, sample preparation challenges, algorithmic limitations, sampling bias, and throughput issues. To achieve reliable quantitative data, it should be combined with complementary methods like laser diffraction or FBRM.