Fourier transform infrared (FTIR) spectroscopy has been advocating a promising alternative for Karl Fischer titration method for quantification of moisture in oil. This study aims to integrate partial least squares regression (PLSR) approach on FTIR spectra for prediction of moisture in locally accessible transformer oil and lubricating oil. The oil samples spiked with known moisture concentrations were extracted with acetonitrile and subjected to analysis with an FTIR spectrophotometer. The PLSR model was built based on 100 training/test splits, and the prediction performance was measured with the percentage root mean squares error (% RMSE). The range of concentration studied was between 0 and 5000 ppm. The marker region of moisture was found at 3750–3400 and 1700–1600 cm−1 with the latter demonstrating a better predictive ability in both lubricating oil and transformer oil. The prediction of moisture in lubricating oil was characterized with lower % RMSE. At concentration less than 700 ppm, the prediction accuracy deteriorates suggesting poor sensitivity. The PLSR was implemented on IR spectra of a set of blind samples, verified with Karl Fischer (for transformer oil) method and Kittiwake (for lubricating oil) method. The prediction was encouraging at concentrations above 1000 ppm; at lower concentrations, the prediction was characterized with high percent error. The algorithm, validated with 100 training/test splits, was converted into an executable program for prediction of moisture based on FTIR spectra. This program can be used for prediction of other substances given that the marker region is identified. FTIR can be used for prediction of moisture in oil nevertheless the sensitivity and precision is low for samples with low moisture concentration.
Partial Least Squares (PLS) Integrated Fourier Transform Infrared (FTIR) Approach for Prediction of Moisture in Transformer Oil and Lubricating Oil