Researchers Developed Phosphate Grade Prediction Model
A new spectroscopy calibration framework improves the speed of ore quality analysis for the mining industry.
Updated on Oct. 14, 2026 in Chemistry
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Researchers have developed a laser-induced breakdown spectroscopy calibration framework to predict phosphate grades more efficiently. The model utilizes machine learning and spectral preprocessing to address the limitations of time-intensive conventional testing methods.
Why it matters
As high-grade phosphate deposits become increasingly depleted, mining companies require faster, more precise analysis to maintain production quality. This new system offers an alternative to traditional processes that typically require hours of laboratory preparation.
The optimized model demonstrated a root-mean-square error of 0.966 percentage points of P2O5 based on 84 samples. Testing utilized a Q-switched Nd:YAG laser operating at 1064 nm with 25 mJ of pulse energy.
The players
NIST
The National Institute of Standards and Technology provided the certified reference material used to calibrate the predictive model.
Scientific Reports
This peer-reviewed journal published the study detailing the new spectroscopy framework.
The details
The team created the model by analyzing 1,000 spectra from 84 samples prepared with NIST Standard Reference Material SRM 694. They utilized principal component analysis and Savitzky-Golay smoothing on powdered samples compressed at 300 MPa.
Timeline
The results were published in the journal Scientific Reports on October 14, 2026.
The Big Picture
The research marks a departure from the ICP-OES laboratory analysis protocol by enabling rapid, non-destructive ore testing. This shift could fundamentally change how mining operations manage ore quality as resource grades decline.
This development could lead to faster processing times and reduced chemical waste in mineral refining. Future commercialization may provide industrial operations with more reliable tools for real-time quality control.
The takeaway
This technology highlights the growing role of machine learning in streamlining traditional industrial laboratory workflows. Implementing such models could allow mining firms to optimize resource extraction and maintain quality standards with significantly reduced preparation time.
What happens next
The researchers intend to conduct further testing on diverse phosphate deposits and evaluate the instrument's performance across actual production streams.
Further reading
Learn more about the latest advancements in analytical techniques in the Chemistry section.
More information
View the complete results and methodology in the original scientific research paper.
Source note: This article includes information reported by AZoMining.
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