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Surface contamination can affect NIR readings by changing the spectrum collected from the sample surface. Dust, oil, moisture, residues, coatings, or handling contamination may mask the true material response or introduce signals that are not representative of the bulk sample.
NIR generally cannot analyse pure metals or alloys because metals do not produce the molecular absorption features that NIR relies on. Near-infrared spectroscopy measures bond-related spectral patterns, while pure metals and most alloys are better assessed using elemental techniques such as XRF.
NIR cannot detect elements like XRF because it measures molecular absorption patterns rather than elemental X-ray emissions. It is mainly sensitive to chemical bonds such as C-H, O-H, N-H, and S-H, while XRF directly identifies elements by their characteristic fluorescent X-rays.
Yes, XRF can detect many heavy metals in soil when they are present at measurable concentrations and the method is suitable for the sample. Common target elements include lead, arsenic, chromium, copper, zinc, nickel, cadmium, and mercury, depending on instrument capability, concentration, and testing conditions.
XRF is used in environmental soil testing to rapidly screen soils for elements such as lead, arsenic, chromium, copper, zinc, nickel, and other metals. It supports site assessment by providing fast, non-destructive elemental data that can help guide sampling, investigation, and remediation decisions.
XRF is used in geochemistry to measure the elemental composition of rocks, soils, sediments, ores, and related geological materials. It helps identify major, minor, and trace elements that support field interpretation, sample screening, mapping, and laboratory follow-up decisions.
Homogenisation is important for NIR accuracy because the analyser only measures the portion of the sample seen by the optical system. If the sample is uneven, segregated, or poorly mixed, the spectrum may not represent the true bulk material.
Chemometric calibration is the process that allows NIR spectra to be converted into useful analytical results. It uses mathematical models to relate spectral patterns to known reference values, such as moisture, protein, composition, or material identity.
NIR models drift over time when the relationship between the spectrum and the reference result changes. This can happen because of changes in samples, instruments, operating conditions, reference methods, or the process being monitored.
Learn how XRF is used in mineral exploration for rapid elemental screening, pathfinder analysis, field mapping, and sampling decisions.
XRF can detect gold when it is present at concentrations and conditions suitable for the analyser and method being used. However, detecting gold in ores can be challenging because gold may occur at very low levels, in small particles, or unevenly distributed through the sample.
XRF can be accurate for ore analysis when the analyser is calibrated for the ore type, elements of interest, and expected concentration range. Accuracy depends on sample preparation, particle size, moisture, matrix effects, measurement time, and how representative the sample is.
Sample temperature can influence NIR measurements by changing molecular vibration behaviour, band positions, peak intensities, and baseline response. Temperature differences between calibration and routine testing can reduce accuracy if they are not controlled or included in the model.
Particle size affects NIR spectra by changing how light scatters, reflects, and penetrates the sample. Differences in grind size, surface texture, or particle distribution can alter baseline shape and apparent absorption, which may influence model accuracy.
Moisture can strongly influence NIR results because water has broad absorption features in the near-infrared region. Changes in moisture can affect peak intensity, baseline shape, and the accuracy of both identification and quantitative models.
XRF is used in mining to rapidly identify and measure elements in ores, rocks, soils, drill samples, concentrates, and process materials. It supports field decisions by providing fast, non-destructive elemental data for exploration, grade control, sorting, and process monitoring.
Surface contaminants can impact XRF results by adding unwanted element signals or blocking the signal from the material being tested. Dirt, dust, oil, corrosion, scale, salts, residues, and moisture can all reduce confidence in the reported composition.
Surface coatings can affect XRF analysis by changing the X-ray signal that reaches the analyser. A coating may block, absorb, dilute, or add element signals, which can make the reported composition different from the true base material beneath.
A NIR spectral signature is the unique pattern created when a material interacts with near-infrared light. Different materials absorb and reflect light in different ways, producing a spectrum that can be used to understand or identify the sample.
NIR quantifies chemical composition by measuring how near-infrared light interacts with a sample. Certain molecular bonds absorb NIR light at specific wavelengths, and those absorption patterns can be linked to chemical properties within the material.
NIR accuracy depends on how well the analyser, sample and calibration model suit the material being tested. A strong calibration model built from reliable reference samples is one of the most important factors, as NIR results are calculated by comparing the sample’s spectral response against known data.