This study aims to develop an innovative technique for identifying and predicting lubricant components using Fourier-transform infrared spectroscopy (FTIR) spectroscopy data with Principal Component Analysis (PCA) and a Stacked Ensemble Regression Model Extreme Gradient Boosting (SER_XGBRegressor). This technique is especially applicable to sectors such as wind energy, power generation, automotive and other similar industries, where the exact composition of lubricants plays a vital role in enhancing the performance and durability of mechanical components.
This study analyzed 35 lubricant samples comprising molybdenum dialkyl dithiocarbamate (MoDTC), zinc dialkyl dithiophosphate (ZnDDP), isooctyl acid phosphorodithioate amine salt (T308) and thiophosphoric ester amine salt (T310A). FTIR spectroscopy yielded extensive spectral data, in which PCA decreased the dimensionality while preserving more than 90% of data variability. Subsequently, an XGBRegressor-stacked ensemble regression model was used to accurately predict the lubricant components.
The PCA-SER Model attained high accuracy in predicting lubricant components (R²: 0.983–0.996) for additives such as MoDTC, ZnDDP, T308 and T310A. PCA reduced dimensionality, preserving over 90% variance, while reducing errors to a mean absolute error of 9.9 × 10–5 and a mean squared error of 1.6 × 10–8. These scores illustrate the ability of the model to precisely predict and classify lubricant components, even in complex and high-dimensional FTIR data sets.
This study presents a novel PCA and stacked ensemble learning framework for analyzing high-dimensional FTIR data, enhancing lubricant classification and prediction, optimizing formulation processes and ensuring quality control of lubricants.
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