Orelysis Geosciences

Orelysis Geosciences We deliver independent high value geologic sample preparation services

We deliver independent high value geologic sample preparation services
Orelysis offers a range of geochemical sample preparation and testing services

Method Validation vs Method Verification: A Critical DistinctionISO/IEC 17025:2017 distinguishes between method validati...
21/07/2026

Method Validation vs Method Verification: A Critical Distinction
ISO/IEC 17025:2017 distinguishes between method validation and method verification, and the distinction determines how much experimental work a laboratory must do before using a method.

Method validation:
– Required when a laboratory develops a new method or significantly modifies a standard method.

– Full validation involves: establishing working range, linearity, detection limits, precision (repeatability + reproducibility), accuracy (bias vs CRM), matrix effects, and ruggedness.

– Result: a documented validation report with performance characteristics for defined matrices and concentration ranges.

Method verification:
– Required when a laboratory adopts an externally published standard method (ISO, ASTM, AOAC, etc.) without modification.

– Verifies that the laboratory can achieve the performance parameters claimed in the standard under its own conditions.

– Typically involves: CRM analysis to confirm accuracy, replicate analysis to confirm precision, and demonstration that DL meets the standard's specification.

– Scope is narrower than full validation — it confirms fitness for purpose, not full characterisation.

A common error is laboratories treating verification as optional when adopting a published method. ISO 17025 clause 7.2.2 is explicit — verification is mandatory. Accreditation bodies will audit for verification records.

Both processes must be documented, reviewed, and updated when conditions change — such as a new instrument, a new reagent lot, a new operator, or a new matrix type.

For technical
questions or further discussions on geoscience applications, Orelysis' expert team at Orelysis.com is available for consultation.

A result without uncertainty is an incomplete result.ISO/IEC 17025:2017 requires accredited laboratories to report measu...
14/07/2026

A result without uncertainty is an incomplete result.

ISO/IEC 17025:2017 requires accredited laboratories to report measurement uncertainty for all analytical results. In geochemical practice, however, uncertainty is seldom included on Certificates of Analysis and is frequently not calculated.

A result reported without measurement uncertainty is considered technically incomplete and non- compliant under ISO/IEC 17025.

Measurement uncertainty expresses the range of values reasonably attributable to a result. It incorporates, but is not limited to, calibration uncertainty, method precision, matrix effects, CRM bias, and sample heterogeneity

For resource reporting, analytical uncertainty is a direct input to resource classification confidence. The absence of documented uncertainty introduces material risk to technical reporting and regulatory compliance.

Are Your Geochemical Duplicates Masking QAQC Issues? Two Plots to Reveal Precision ProblemsAverage relative difference a...
30/06/2026

Are Your Geochemical Duplicates Masking QAQC Issues? Two Plots to Reveal Precision Problems

Average relative difference alone won’t tell you if your duplicate data are fit-for-purpose. For a structured assessment, use two complementary tools: the Thompson-Howarth plot and Half Absolute Relative Difference.

The Thompson-Howarth plot shows the mean vs. absolute difference for each pair, revealing the expected heteroscedastic pattern in geochemical data. Outliers above a 2× median HARD envelope flag problematic pairs, while a shift in scatter can indicate a detection limit or preparation heterogeneity issue. HARD, calculated as $|A - B| / (A + B) \times 100\%$, gives a robust, easy-to-interpret metric — a median

The 3 Duplicate types every Geochemist must understand: Precision  .Duplicate analysis is the primary method for quantif...
23/06/2026

The 3 Duplicate types every Geochemist must understand: Precision .

Duplicate analysis is the primary method for quantifying precision in geochemical programs. Precision, however, is not a single value. It comprises multiple components, each attributable to a distinct stage of the sampling and analytical sequence. Selecting the appropriate duplicate type is therefore essential for making technically sound, data-driven decisions.

Field Duplicate / Twin Sample
Independently collected from the same sampling interval or location.

Variance captured: Total variance, encompassing geological heterogeneity, sampling error, sample preparation error, and analytical error.

Key metric: Half Absolute Relative Difference (HARD%). Elevated variance at this stage typically indicates geological variability, deficiencies in sampling methodology, or both.

Preparation Duplicate / Coarse or Pulp Split

A subsample obtained from the same crushed or pulverised material before withdrawal of the final analytical aliquot.

Variance captured: Sample preparation error and analytical error only. Geological and sampling variance are excluded.

Application: Comparison with field duplicate results isolates and quantifies the combined geological and sampling error component.

Analytical Duplicate / Laboratory Pulp Replicate

A second subsample was weighed from the same pulp and subjected to independent digestion and measurement.

Variance captured: Analytical precision attributable to the instrument and method only.

Identifying which variance component is dominant is critical for targeted quality improvement. When field duplicate variance is high but analytical duplicate variance is low, imprecision originates in field sampling and sample preparation rather than in laboratory analysis.

Preparation Blanks vs Reagent Blanks: They Are Not the Same.Blank materials are essential QA/QC tools for detecting cont...
16/06/2026

Preparation Blanks vs Reagent Blanks: They Are Not the Same.

Blank materials are essential QA/QC tools for detecting contamination. However, the type of blank used determines what contamination source it monitors — and using the wrong blank type leads to gaps in the contamination detection system.

Reagent blank (method blank):
– A blank solution carried through the full digestion and analysis procedure using only reagents (no sample matrix).
– Monitors: reagent purity, labware cleanliness, and airborne contamination during digestion.
– Does not capture: contamination introduced during physical sample preparation (crushing, milling).

Preparation blank (coarse blank or pulp blank):
– A low-grade or barren rock material physically processed through the full preparation sequence alongside the samples.
– Monitors: cross-contamination from equipment surfaces, carryover from previous high-grade samples, and dust contamination in the preparation environment.
– This is the critical blank type for geochemical sample preparation.

The classic carryover scenario: a high-grade gold sample is crushed and pulverised on a jaw crusher and ring mill. Residual gold particles remain on equipment surfaces. The next sample — even after cleaning — picks up trace contamination. A preparation blank inserted after the high-grade sample will detect this. A reagent blank will not.

Best practice: insert one preparation blank for every 20 samples, and always insert one immediately after any sample exceeding 10× the typical grade range for that project.

Understanding Certified Reference Materials: Certification vs CharacterisationNot all reference materials carry the same...
09/06/2026

Understanding Certified Reference Materials: Certification vs Characterisation

Not all reference materials carry the same analytical authority. The distinction between a certified reference material (CRM) and a characterised in-house standard is significant — and frequently misunderstood.

Certified Reference Material (CRM):

– Certified values are established through interlaboratory collaboration, typically involving 15–30+ independent laboratories.

– Uncertainty is expressed as a 95% confidence interval derived from statistical analysis of all participant results.

– Metrological traceability is documented — results are traceable to SI units through defined reference methods.

– Homogeneity and stability are tested and documented.

In-house control sample:

– Values are typically assigned by a single laboratory or a small set of laboratories.

– Uncertainty may not be formally quantified.

– Useful for monitoring internal consistency and precision — not for validating accuracy.

In a well-designed QA/QC program, both types are needed. CRMs anchor the accuracy of the system to an externally verified truth. In-house standards track precision and detect within-laboratory drift.

A CRM inserted at 1 per 20 samples provides ~5% coverage for accuracy monitoring. For high-value or high-risk campaigns, 1 per 10 is preferred — particularly where regulatory reporting depends on the data.

Control Charts in Laboratory QA/QC: Reading the Signal Beyond the LimitControl charts (Shewhart charts) are the standard...
02/06/2026

Control Charts in Laboratory QA/QC: Reading the Signal Beyond the Limit

Control charts (Shewhart charts) are the standard tool for monitoring the analytical performance of certified reference materials (CRMs) over time. Most laboratories plot CRM results against the certified value and apply ±2σ warning limits and ±3σ action limits.

But passing a ±2σ threshold is not sufficient evidence of good performance. Control charts must also be interpreted for systematic patterns — the Western Electric Rules define several conditions that indicate process instability even when no individual point exceeds the action limit:

– Rule 1 (Spike): One point beyond ±3σ → investigate contamination or calibration failure.

– Rule 2 (Trend): Eight consecutive points on the same side of the mean → suggests systematic drift, reagent degradation, or instrument baseline shift.

– Rule 3 (Stratification): Fifteen consecutive points within ±1σ → may indicate data rounding or insensitive measurement.

– Rule 4 (Mixture): Eight consecutive points alternating above and below the mean → suggests two alternating analytical populations (e.g., instrument recalibrated mid-batch).

In geochemical QA/QC, drift patterns are particularly significant. A gradual upward trend in a CRM over months can indicate reagent contamination building in the preparation workflow — invisible to single-batch review but detectable in time-series charting.

Effective control chart use requires: long-term data retention, trend analysis beyond single batches, and investigation protocols triggered by pattern detection — not only limit violations.

ICP-MS vs ICP-OES: Choosing the Right Tool for the Right JobBoth ICP-MS (Inductively Coupled Plasma – Mass Spectrometry)...
26/05/2026

ICP-MS vs ICP-OES: Choosing the Right Tool for the Right Job
Both ICP-MS (Inductively Coupled Plasma – Mass Spectrometry) and ICP-OES (Optical Emission Spectrometry) are workhorses of modern geochemical analysis. They are not interchangeable tools — each has defined strengths, limitations, and appropriate applications.

ICP-OES:
– Detection range: typically, mg/kg (ppm) to per cent levels.
– Best suited for major and minor elements: Fe, Al, Ca, Mg, Na, K, Ti, Mn, Cr, Ni, Cu, Zn, Pb.
– Robust against high matrix loads (concentrated acid digestions, brines).
– Spectral interferences are manageable with careful wavelength selection.

ICP-MS:
– Detection range: µg/kg (ppb) to low mg/kg — typically 100–1000× lower than ICP-OES.
– Essential for trace and ultra-trace work: REE, PGE, Tl, Bi, In, Cd, and pathfinder elements at exploration-level concentrations.
– Mass-based detection means isobaric and polyatomic interferences must be managed (e.g., Arc on 75As, MoO on Cd isotopes).
– More sensitive to total dissolved solids (TDS); solution preparation must control the matrix.

Method selection should follow analyte concentration, matrix type, and required uncertainty — not instrument availability alone.

The Sampling Fundamental Error: Why sample mass and size matter. Pierre Gy's Theory of Sampling (TOS) provides the mathe...
19/05/2026

The Sampling Fundamental Error: Why sample mass and size matter.

Pierre Gy's Theory of Sampling (TOS) provides the mathematical framework for understanding why sample mass directly affects analytical error. The Fundamental Sampling Error (FSE) is the minimum error introduced when extracting a small portion from a larger heterogeneous lot.

FSE is governed by:
– Fragment size (d): coarser particles → higher heterogeneity → larger FSE.
– Liberation factor (l): the degree to which the mineral of interest is freed from the host matrix.
– Sample mass (M): FSE decreases as mass increases.
– Constitution heterogeneity (CH): intrinsic variability within the lot.

This is why crushing and pulverisation are not merely mechanical steps — they are variance reduction procedures. Reducing fragment size from d95=2mm to d95=75µm can reduce FSE by two orders of magnitude on the same analytical portion mass.

In practice, a 50g analytical pulp subsample from a coarsely crushed rock will carry higher FSE than the same 50g taken after fine pulverisation. Grade variability observed between field duplicates and preparation duplicates often traces back to insufficient size reduction rather than instrument performance.

For nugget-effect elements like gold, platinum, or coarse native copper, the FSE problem is compounded. Screen fire assay methods and large-mass leach digestions exist precisely to address this.

Address

Nyang'omango
Misungwi
73

Opening Hours

Monday 08:00 - 17:00
Tuesday 08:00 - 17:00
Wednesday 08:00 - 17:00
Thursday 08:00 - 17:00
Friday 08:00 - 17:00
Saturday 09:00 - 16:30

Alerts

Be the first to know and let us send you an email when Orelysis Geosciences posts news and promotions. Your email address will not be used for any other purpose, and you can unsubscribe at any time.

Shortcuts

Share