Where do sampling errors come from in mineral processing? Sampling errors field vs laboratory uncertainty

In mineral processing operations, the largest source of measurement uncertainty is field sampling, not the laboratory. CASPEO’s audits of mining operations across 70 countries show a consistent pattern: laboratories manage accuracy well, but errors introduced during sample collection, division, and preparation in the field account for the majority of total uncertainty. This finding aligns with Pierre Gy’s Theory of Sampling, which links sampling errors to two factors: the heterogeneity of the material and the practice of sampling itself.

Key takeaways

• Laboratory analysis contributes roughly 5% of total measurement uncertainty. Field sampling contributes the rest.
• Sampling errors are not random. They are systematic, quantifiable, and reducible through proper equipment and consistent practice.
• Heterogeneity is a property of the material. It cannot be eliminated. It can only be accounted for through correct sampling design.
• Better on-line sensors (XRF, NIR) do not replace sampling theory. They change the sampler, not the Fundamental Sampling Error.

Process engineer teamWhat CASPEO finds on mining audits

When CASPEO performs sampling audits, the same pattern appears. The lab results are accurate. The technicians follow protocol. The problem is upstream.

In the field, samples are often collected without sufficient attention to division and preparation. Sample mass is wrong for the particle size. Collection is inconsistent across shifts. Blastholes are sampled without a defined protocol.

These are not expensive problems to fix. They don’t require new equipment. They require good practices applied consistently.

As Stéphane Brochot, CASPEO’s scientific and technical director, explains in a recent article published by E&MJ (Engineering & Mining Journal, August 2025): the sampling procedure must be evaluated from primary sample to final sample for analysis. Every step matters. Same quality throughout the process.

Why this matters for metallurgical accounting

Sampling errors propagate. A biased sample at the feed produces a biased mass balance, which produces a biased recovery estimate, which produces inaccurate financial reporting.

Metallurgical accounting systems depend on accurate measurements. If the underlying sampling is flawed, the accounting inherits the error. Improving the sampling system is the first step toward reliable metallurgical accounting results.

Pierre Gy’s Theory of Sampling

Pierre Gy identified that sampling errors come from two sources: constitution heterogeneity (variations in composition, size, and density between particles) and distribution heterogeneity (spatial variations across the lot or over time).

With complex constitution heterogeneity, a sample will never have the exact same characteristics as the lot from which it was taken. The only way to estimate this error is to understand the material well enough to calculate the minimum sample mass required.

Gy’s theory provides the math. CASPEO transforms that math into practical audit recommendations and sampling protocols that mining operations can apply on site.

Read the full article

This page summarizes findings from the article “Consistent Practices Reduce Sampling Errors” by Steve Fiscor, Editor-in-Chief, published in E&MJ (Engineering & Mining Journal), August 2025. The article features an interview with Stéphane Brochot, Ph.D., CASPEO’s scientific and technical director.