In the mining industry, science isn’t a passive instrument. Its findings drive commercial decision-making, explains Greg Rankin.

With commodity prices rising across the mining sector, from gold to strategically critical rare earth elements, more material has moved back into economic consideration. This has placed greater emphasis on reliable and defensible analytical results.

When gold reached record levels earlier this year, material long considered marginal was suddenly worth another look. Stockpiles were revisited, lower-grade zones returned to the model, and economic assumptions were recalculated in light of a new price reality.

That recalibration narrows the margin for analytical uncertainty. As projects operate closer to economic thresholds, smalldifferences in assay results begin to carry greater weight across exploration programs, grade control and processing decisions.

In many operations, the line between sending material to the mill or to the dump hinges on fractions of a gram per tonne. This is true in gold projects adjusting cutoff gr ades or in rare earth deposits managing complex elemental distributions. At that scale, analytical data does more than confirm grade; it shapes classification, recovery modelling and longterm resource estimates.

No reserve is declared without defensible analytical data supporting the underlying resource model. In projects operating near cutoff, those laboratory results directly influence economic classification decisions.

“The difference between a viable project and a marginal one can come down to analytical precision,” says William R Sattlegger, professional geoscientist, CEO of i2iVestcom Advisors Corporation and executive director of the Critical Minerals Conference.

“Those numbers drive metallurgy, recovery and valuation models. If you lose confidence in the data, you lose confidence in the project.”

Consider a deposit processing 20m tonnes annually at a cutoff near 0.5 g/t. A shift of just 0.05 g/t in reported grade, whether from sampling variability or calibration bias, can alter how large volumes of material are classified. Across that scale, even small analytical differences can translate into meaningful economic consequences.

Sampling, the first point of failure

Before instrumentation or calibration is evaluated, a more fundamental question must be answered: Is the sample representative?

“In mining, it’s really the sampling,” Sattlegger explains. “A poor sample can lead to swings of over 50% in reported values.”

Such variability can distort block models, misclassify material and undermine reconciliation between predicted and actual production. Even the most advanced ICP-MS cannot compensate for a nonrepresentative sample.

QA/QC shifts from procedural discipline to active risk management. Investors, regulators and financing partners increasingly expect clear evidence that sampling and verification protocols are robust, repeatable and well documented

For that reason, serious operations build layered QA/QC systems around sampling integrity. Blanks monitor contamination. Duplicates assess precision. Certified reference materials verify accuracy. In exploration programmes, these controls support resource estimates used in prefeasibility and feasibility studies. In producing mines, they safeguard grade control and metal accounting.

As projects move closer to economic thresholds, QA/QC shifts from procedural discipline to active risk management. Investors, regulators and financing partners increasingly expect clear evidence that sampling and verification protocols are robust, repeatable and well documented.

Without confidence in sampling integrity, the rest of the analytical workflow loses credibility.

Instrumentation, calibration and the matrix challenge

Even when sampling is disciplined, analytical chemistry must withstand challenging matrices.

Gold operations frequently involve cyanide leach solutions, high total dissolved solids digests and complex metallurgical streams that can interfere with signal stability. Rare earth deposits introduce a different challenge: chemically similar elements across the lanthanide series, often occurring at low concentrations, where small analytical biases can distort distribution modelling and downstream separation economics.

Accurate results depend not only on instrument performance but on calibration alignment. In high-TDS systems, mismatches between sample matrices and calibration standards can introduce systematic bias. Dilution steps intended to protect instrumentation create additional exposure to contamination, signal suppression, or drift. In rare earth analysis, interference correction and method validation become central to defensible reporting.

As projects advance toward bankable feasibility studies or reserve disclosures, defensibility carries as much weight as detection limits. Technical reports must demonstrate repeatability, traceability and robust calibration practices capable of withstanding external scrutiny.

At that stage, the calibration point behind the instrument is no longer a laboratory detail. It becomes part of the project’s economic foundation.

Closer to the rock face

These pressures are reshaping expectations across the analytical supply chain.

“As grades decline and deposits become more complex, the margin for analytical error narrows,” says Brian Alexander, chief technical officer of Inorganic Ventures, a manufacturer of inorganic certified reference materials. “In mining, you’re often working at trace levels in difficult matrices. If calibration doesn’t reflect the chemistry you’re measuring, small biases can compound.”

In one gold operation working near its cutoff threshold, reconciliation differences emerged between process samples and laboratory assays. Instrumentation performed as expected. The issue traced back to calibration alignment within a cyanide matrix. After shifting to matrix-matched standards and strengthening documentation protocols, variability decreased and confidence in reported values improved.

The trend extends beyond centralised laboratories. More analytical work is moving closer to the mine site, where faster decision cycles and leaner staffing increase reliance on stable, predictable calibration behaviour.

“When testing moves closer to operations, you’re making decisions in near real time,” Alexander notes. “That increases the importance of well-characterised standards and clear traceability.”

In this environment, suppliers that understand matrix effects, documentation rigour and method alignment increasingly influence analytical reliability, even if their role remains largely behind the scenes.

Companies such as Inorganic Ventures have responded by developing matrix-matched certified reference materials, extended shelflife formulations, and technical support models designed specifically for highthroughput mining environments.

The shrinking margin for uncertainty

Rare earth development, accelerated by strategic mineral policy and supply chain realignment, introduces additional analytical complexity. Multiple elements at low concentrations require careful interference control and stable calibration across analytes. Even a small bias can distort the modelled distribution and downstream separation economics.

Gold operations face parallel pressure. With sustained high prices, marginal zones and stockpiles move back into economic consideration. When cutoff grades shift by fractions of a gram per tonne, assay precision influences how large volumes of material are classified and valued.

The trend extends beyond centralised laboratories. More analytical work is moving closer to the mine site, where faster decision cycles and leaner staffing increase reliance on stable, predictable calibration behaviour

In both cases, the implications extend well beyond daily production metrics. Resource estimates, environmental disclosures, technical reports and financing models all depend on defensible analytical foundations.

“What’s changed isn’t that QA/QC exists. It always has,” Alexander says. “What’s changed is how closely projects operate to their economic limits. As margins narrow or mineralogy becomes more complex, the tolerance for uncertainty shrinks. Data that might once have been acceptable is no longer sufficient.”

For geoscientists, metallurgists and laboratory managers, analytical testing is no longer a background function. It is a control point within the economic model itself.

Heavy equipment may move the rock. But in modern mining, it is the integrity of the assay, along with the systems that support it, that ultimately determines whether that rock creates value.

• Greg Rankin is a US-based geoscience expert with more than 20 years of experience writing in the laboratory science sector