Critical Constraints of Image-Based Particle Size Analysis > 자유게시판

The use of imaging for particle analysis is now common in fields from drug formulation to nanomaterials research due to its ability to provide visual confirmation of particle morphology alongside size measurements. However, despite its advantages, this method comes with several inherent limitations that can significantly affect the accuracy, reliability, and applicability of the results. A fundamental limitation stems from the physical resolution cap of the imaging hardware — 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.

Most imaging platforms capture only flat projections, losing depth data, leading to potential inaccuracies in size estimation. Spherical, elongated, and plate-like particles can be indistinguishable based on projection alone, 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.

Sample preparation also presents a major challenge — imaging requires particles to be dispersed in a way that prevents clumping, sedimentation, or overlapping. Creating a non-agglomerated, evenly distributed monolayer is rarely achievable, especially with sticky or irregularly shaped materials. Agglomerates may be mistaken for single large particles, while particles that are partially obscured or in shadow can be missed entirely. These artifacts skew statistical representations and compromise the validity of reported size distributions.

Image processing routines are prone to consistent biases — 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 Human intervention is frequently required, 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. If the sample is heterogeneous or if particles are unevenly distributed, 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.

Despite its strengths in visual characterization, its quantitative reliability is constrained by the physical limits of optics, loss of depth, preparation difficulties, flawed algorithms, biased sampling, and 粒子形状測定 slow analysis. To achieve reliable quantitative data, it should be combined with complementary methods like laser diffraction or FBRM.