Mining Applications and Chemometrics

 Incorporated 2004  Full line supplier of UV-VIS-NIR spectrometers for lab, inline process & field portable remote sensing  Mfg facility in North Andover, MA  OEM manufacturer  >100 field portable UV-VIS-NIR instruments in field use worldwide

 Field portable full range UV-VIS-NIR spectrometers & spectroradiometers  Laboratory full range UV-VIS-NIR spectrometers & spectroradiometers  Single detector InGaAs photodiode array lab spectrometers  Single detector Si spectrometers, spectroradiometers & spectrophotometers  Light sources & accessories

Spectrometers for mining exploration, mineral identification, and production  oreXpress™ Full range portable spectrometer for mining and mineral identification  oreXpress Platinum Also includes a range of FOV lenses, internal battery, membrane control panel for standalone operation, and on-board storage for 1,000 spectra

oreXpress & oreXpress Platinum  True field portability <7 lbs  Full range UV/VIS/NIR – nm  Fast/High Signal to Noise ratio for better reflectance values  Unmatched stability & performance through SWIR2  DARWin SP Data Acquisition software saves scans as ASCII files for use with 3 rd party software  EZ-ID real-time mineral ID with USGS & SpecMIN libraries

EZ-ID™ Software with Library Builder Module  Real-time mineral identification in the field  USGS and SpecMIN libraries  Select different spectral regions of interest  Compare unknown mineral sample spectra to known library  Best match score quickly and automatically displayed

Qualitative & Quantitative Analysis  Use EZ-ID for mineral identification and qualitative analysis  What is there  Use reflectance spectroscopy and chemometrics for quantitative analysis  How much is there

 Widely used in mining exploration and mineral identification  Identification of key alteration minerals associated with potential economic deposits  Qualitative mineralogy describes the process of using NIR to quickly ID mineral species during exploration

 Usage is typically bound by cost (high) and speed (slow)  Available examples:  Qemscan/MLA  Quantitative X-ray diffraction  Better solution – Quantitative Reflectance Spectroscopy  Analyze a greater number of samples in less time, at an affordable cost

 Use mineralogical and metallurgical information from a representative set of samples and correlated reflectance spectra to develop statistical calibration models  Calibration “trains” the spectrometer to analyze additional unknown samples  Leverage the detailed, more costly analysis of a few samples to analyze a much larger set of related samples

 Useful for mining process optimization  Real-time or near real-time knowledge of mineralogical and metallurgical properties that impact metal recovery, allows for ▪ Intelligent ore sorting ▪ Optimization of ore processing  Useful for gangue minerology to minimize process cost and increase yield  Gangue can affect extractability ▪ Talc and hornblende interfere with flotation ▪ Carbonates increase acid costs ▪ Clays can reduce yield due to loss of heap permeability

oreXpress Mineral Analysis/ Identification

Iron Minerals

Calcite Talc Hornblende

Clays

Advantages of reflectance spectroscopy  High throughput  Hundreds to thousands of samples per day – ideal for rapid blast hole chip analysis  Frequent (<1 minute intervals) measurements for in-process sensors  Non-contact measurements  Simultaneously determine multiple properties

Create Standards Collect Spectra Predict Concentrations Build, Optimize & Test Model Measure Unknown Access Model

Prepare Calibration Set  Samples should reflect the physical properties and diversity that will be encountered in the field  Analyze the properties of interest using appropriate reference analytical methods, such as:  Qemscan  X-ray diffraction  Acid consumption  Other metallurgical tests  Measure the reflectance spectra

Things to consider in measuring spectra  Features can overlap and may not be from a single component  Spectral features in minerals can result from crystal field effects, charge transfer, color centers, and conduction band transitions  Spectral features in organic and industrial samples come primarily from CH, NH, OH, and SH bonds  Multivariate models can consider all, or a substantial portion of the whole spectrum

Develop and validate your calibration  Match each reflectance spectrum you have collected to the corresponding reference analyses  Develop calibration equations using multivariate chemometric techniques like partial least-squares regression  Validate the performance of the calibration by using an independent set of samples

How to select a reference method  NIR is a secondary method – the reference needs to be well controlled with the lowest possible error  The Standard Error of Laboratory (SEL) should be known and documented  If there are changes in the reference method, new reference data may be substantially different from your original data  Submission of known samples is a good idea

Things to consider in collecting spectra  Verify your system performance using wavelength standards  Control particle size, moisture, temperature, and sample packing, or stabilize your model to resist changes in these parameters  Use the same sample preparation as optical geometry can affect your outcome

Now apply your calibration  Prepare unknown samples with the same method used for calibration samples  Measure the reflectance spectrum of the unknown using the same set-up used in building the calibration  Apply the calibration to the unknown reflectance spectrum to predict mineralogical and metallurgical properties

How many samples will I need for calibration and test?  Reserve 20% of samples for an independent test set  samples for a feasibility study  for starting model  >180 for a robust production model

How many samples will I need for calibration and validation?  Cover the anticipated range of composition  Scan in the form that will be analyzed by the model – make them match  Contain a natural combination of minerals - avoid blending as it can cause problems, beware cross correlations

Ensuring that your model retains its integrity  Watch out for samples with high spectral residual and samples that predict at or near the extremes of your model  Establish a consistent monitoring program with reference analysis done frequently  Implement a plan and schedule for improvement of the model including identifying new samples  Establish criteria for revising the model based on time, increased validation error, or similar characteristics

Examples of chemometric analyses using reflectance spectroscopy