Prediction of Li2O and Spodumene by FTIR-PLS in Pegmatitic Samples for Process Control

Abstract

Rapid and reliable analytical methods are required to support quality control and decision-making in lithium-bearing mineral processing. In this study, the application of Fourier Transform Infrared (FTIR) spectroscopy combined with Partial Least Squares (PLS) chemometric modeling is evaluated for the simultaneous prediction of lithium oxide (Li2O) and spodumene contents in pegmatitic samples. Two independent PLS models were developed using FTIR spectra preprocessed with first derivative and/or Standard Normal Variate (SNV). Spectral regions were selected based on the vibrational response of Al–O, Si–O, and OH− groups, which are indirectly influenced by lithium-bearing phases. The spectral datasets were divided into calibration and independent external test sets, and model performance was assessed using statistical metrics and Principal Component Analysis (PCA). The Li2O model achieved an R2 of 0.9934 and an RMSEP of 0.185 in external validation, with a mean absolute error below 0.15%. The spodumene model achieved an R2 of 0.9961, an RMSEP of 1.79, and a mean absolute error of 2.80%. These results demonstrate that the FTIR-PLS approach enables efficient quantitative estimation of lithium-bearing minerals, with reduced analytical time, good predictive accuracy, and suitability for application in process control and mineralogical sorting environments. PCA confirmed the statistical representativeness of the test sets, with no evidence of spectral extrapolation.