A chemometric approach for the prediction of the aging levels of automatic transmission fluids by mid-infrared spectroscopy

Automatic transmission fluids (ATF) are highly complex multi-component systems with a variety of different additive packages which suffer from manifold aging processes due to interfering factors. This work describes the development of a straightforward approach to model the aging effects by means of Fourier Transform Infrared (FTIR) spectroscopy combined with multivariate data analysis. Therefore, ATF samples were artificially aged under defined conditions by considering effects of product type, temperature, storage time and exposure to metallic materials, yielding 144 samples. For multivariate data analysis, three different approaches have been applied and compared: supervised Fisher's Linear Discriminant Analysis of principal components (PCFDA), regularized FDA (RFDA) of variables, and unsupervised PCA after orthogonalization using Error Removal by Orthogonal Subtraction (EROS + PCA). All methods worked well in reducing unwanted effects and transforming the relevant information to the first components. Combined with k-Nearest-Neighbor (kNN) prediction, RFDA leads to the best model, improving the accuracy ratios by 13%, 41%, and 12% in comparison with direct kNN classification for the target classes storage temperature, additional material and aging level, respectively. These results suggest that RFDA is highly suitable for the reduction of unwanted effects in a dataset with manifold perturbation influences. The model also predicted a correct aging level ranking when applied to unknown field samples.