Bridging Traditional Sieve Analysis and Modern Image Analysis

October 14, 2025

For many industries – from mining and construction to pharmaceuticals and food – sieve analysis has long been the standard for determining particle size distribution. It’s simple, well-established, and built into countless product specifications and quality control protocols. However, it’s also time-consuming, labor-intensive, and limited in the information it provides. Operator technique, sieve wear, and mesh tolerances can all introduce variability.

Dynamic Image Analysis (DIA) offers a modern alternative. Using high-speed cameras and sophisticated algorithms, DIA measures thousands of particles in seconds, delivering not just size data but also shape information, such as aspect ratio and circularity. This opens the door to faster, richer, and more reproducible measurements.

 

Why the Results Differ

The discrepancies between sieving and image analysis mainly stem from differences in measurement principles:

• Particle orientation: Sieve analysis effectively measures the smallest projection of a particle – its minimum width as it passes through the mesh. Image analysis measures particles in random orientations, often resulting in larger measured sizes, particularly for flaky or angular materials.
• Mesh tolerances: Even nominally identical sieves can vary within allowed manufacturing tolerances, affecting which particles pass through.
• Distribution width: Differences become more pronounced for narrow particle size distributions, where small shape and orientation effects have a bigger impact. For wide distributions, these biases tend to average out.

 

The Correlation Strategy

To align DIA with sieve data, Microtrac recommends focusing on the Xc_min parameter – the minimum particle width. This metric most closely matches how particles behave when passing through a sieve.

A simple three-step procedure is used:

• Run DIA on the full sample to generate size and shape data.
• Perform a high-quality sieve analysis on the same sample, ensuring well-maintained, calibrated sieves and proper technique.
• Use a narrow sieve fraction as a “training set” to derive a correction between Xc_min and sieve-equivalent sizes. This may involve applying an offset, scaling factor, or quantile-based correction.

Once this relationship is established, image analysis results can be transformed to closely match sieve distributions. This correlation is material- and shape-specific, but once set, it allows labs to work confidently with DIA data while maintaining legacy specifications.

Why It Matters

For laboratories, moving to DIA offers clear advantages:

• Faster analysis – results in minutes rather than hours.
• More data – size plus shape information enables deeper material understanding.
• Better reproducibility – reduced operator variability and less dependence on sieve condition.
• Easier documentation and traceability – digital data sets simplify reporting and comparison between sites.

 

Results from sieve analysis and image analysis don’t always match – especially for irregularly shaped particles – making it difficult for labs to switch methods without disrupting established specifications.

Microtrac’s white paper, “Correlation Between Sieve Analysis and Image Analysis Made Easy”, provides a clear framework to correlate both methods, so laboratories can modernize their workflows without abandoning their historical data.

Read the white paper here.