Researchers Automated Phosphate Ore Grade Analysis
A new spectroscopy model provides rapid testing to replace time-intensive laboratory methods for mining.
Updated on Oct. 14, 2026 in Chemistry
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Researchers have developed a laser-induced breakdown spectroscopy framework that predicts phosphate ore grade concentrations. The research-stage model uses machine learning to achieve high accuracy in detecting P2O5 levels.
Why it matters
The approach enables faster ore analysis than conventional methods like ICP-OES, which require hours of prep, helping mining operations maintain product quality. This is critical as the industry faces depletion of high-grade deposits.
The optimized model achieved a coefficient of determination of 0.963 and a root-mean-square error of 0.966 percentage points across P2O5 concentrations ranging from 6.4% to 24.8%.
The players
NIST
The National Institute of Standards and Technology, which provides standardized reference materials used for validating chemical analysis models.
Scientific Reports
A peer-reviewed, open-access journal that publishes research across the natural sciences.
The details
The team created the model using a Q-switched Nd:YAG laser—a crystal-based laser producing high-intensity light pulses—at 1064 nm and 25 mJ energy to analyze samples. Researchers compressed powdered samples into pellets at 300 MPa and applied spectral preprocessing techniques including principal component analysis, Savitzky-Golay smoothing, and standard normal variate normalization. This methodology processed 1008 total spectra from 84 samples to identify phosphate concentrations while filtering eight technical outliers.
Timeline
October 14, 2026: Study results published in Scientific Reports.
The Tech Race
As global phosphate production relies on flotation for 80% of output, mining operations are racing to modernize analytical workflows. This framework marks a shift toward replacing hours-long laboratory preparations with automated, real-time spectroscopy.
This development currently exists at the research stage and does not impact commercial mining operations yet. The researchers plan to test the model on actual production streams and assess instrument durability before industrial deployment can occur.
The takeaway
Mining companies can monitor for future reports on long-term instrument stability tests to see if this method moves toward industrial adoption. Watch for upcoming research that applies this calibration framework to different phosphate deposits and live production streams.
Further reading
Explore more advancements in Chemistry to see how automated spectroscopy is transforming materials science.
More information
View the complete scientific research paper for full technical validation.
Source note: This article includes information reported by AZoMining.
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