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<p>Lubricants are vital in mechanical systems, particularly automobiles. Regular analysis of engine oils is essential because degraded oils can damage engines and harm the environment, impacting sustainability. However, research on detecting adulterants in lube oil is limited. The objective of the study is to detect the presence of liquid adulterants in lube oil by measuring its refractive index using an optical sensor system with white-light light emitting diode (LED) and light dependent resistor (LDR)-based sensor. Test samples included fresh lube oil mixed with various concentrations of contaminated oil. Beer’s Law and Lorentz-Lorenz formulas were applied to correlate output potential of mixed samples with their refractive index, molar concentration, mean polarizability, and molar mass at constant temperature and pressure. The optical signal through the cuvette is attenuated by sample’s refractive index, changing output potential as concentrations vary, confirming mathematical analysis from Beer’s Law and Lorentz-Lorenz formula. The absorbance values of the samples show a steady rise as the concentration of the samples vary. The experimental results were validated using machine learning algorithms like support vector machine (SVM) and decision tree. The overall accuracy of the machine learning model was 90.476%. The findings propose a methodology for identifying contaminants and implementing measures to address adulteration during lubricating oil verification.</p>

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Beer’s Law and Lorentz-Lorenz Formula Based Optical Sensor System for Adulterant Detection in Lubricants Using Support Vector Machine (SVM) Model and Decision Tree

  • 期刊论文

  • 发表日期：

    2025年12月9日

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  • Rashmi Rekha Roy,
  • Sandip Bordoloi

摘要

Lubricants are vital in mechanical systems, particularly automobiles. Regular analysis of engine oils is essential because degraded oils can damage engines and harm the environment, impacting sustainability. However, research on detecting adulterants in lube oil is limited. The objective of the study is to detect the presence of liquid adulterants in lube oil by measuring its refractive index using an optical sensor system with white-light light emitting diode (LED) and light dependent resistor (LDR)-based sensor. Test samples included fresh lube oil mixed with various concentrations of contaminated oil. Beer’s Law and Lorentz-Lorenz formulas were applied to correlate output potential of mixed samples with their refractive index, molar concentration, mean polarizability, and molar mass at constant temperature and pressure. The optical signal through the cuvette is attenuated by sample’s refractive index, changing output potential as concentrations vary, confirming mathematical analysis from Beer’s Law and Lorentz-Lorenz formula. The absorbance values of the samples show a steady rise as the concentration of the samples vary. The experimental results were validated using machine learning algorithms like support vector machine (SVM) and decision tree. The overall accuracy of the machine learning model was 90.476%. The findings propose a methodology for identifying contaminants and implementing measures to address adulteration during lubricating oil verification.