Determination of biodiesel and used cooking oil in automotive diesel/green diesel fuels through high-performance liquid chromatography

Application of high-performance liquid chromatography in determining saturates and aromatic hydrocarbons along with polars/additives in automotive finished and used lubricating oils

Lubricating oils help an internal combustion engine function effectively by reducing friction and wear on the engine's moving parts. They typically consist of petroleum-derived base oil and various additives to achieve the desired characteristics in automotive engine oils. Determination of aromatics and polar additives in the finished and used lubricating oils is not possible with existing methods hence their development is significant from the perspectives of environment and reuse/re-refining of used lubricating oils. This study reports the development of a new HPLC method to determine additives in the finished lubricating oils and/or polars in the used engine oils. The proposed method is simple, fast (runtime of 13 min), does not require sample pre-treatment, and exhibits high precision and superior limits of detection and quantification. The method demonstrated good linear response ranging from 0.1 to 30 mass for total aromatics and 0.1 to 20 % for additives. The method validation was carried out by analyzing brand-new commercial two and four-wheeler lubricants with used automotive lubricants. Based on the proposed method, the aromatics and additives concentration ranges in the studied finished lubricants were estimated between 0.20-1.70 % (mass) and 0.20-3.50 % (mass), respectively. Similarly, for used lubricants, the aromatics and additives were estimated to be 1.00-6.10 % and 0.60-2.40 % (mass), respectively.

A Reaction-Based Optical Fingerprinting Strategy for the Recognition of Fat-Soluble Samples: Discrimination of Motor Oils

Optical "fingerprints" are widely used for chemometrics-assisted recognition of samples of different types. An emerging trend in this area is the transition from obtaining "static" spectral data to reactions analyzed over time. Indicator reactions are usually carried out in aqueous solutions; in this study, we developed reactions that proceed in an organic solvent, thereby making it possible to recognize fat-soluble samples. In this capacity, we used 5W40, 10W40, and 5W30 motor oils from four manufacturers, with six samples in total. The procedure involved mixing a dye, sample, and reagents (HNO3, HCl, or tert-butyl hydroperoxide) in an ethanolic solution in a 96-well plate and measuring absorbance or near-infrared fluorescence intensity every several minutes for 20-55 min. The obtained photographic images were processed by linear discriminant analysis (LDA) and the k-nearest neighbors algorithm (kNN). Discrimination accuracy was evaluated by a validation procedure. A reaction of oxidation of a dye by nitric acid allowed us to recognize all six samples with 100% accuracy for LDA. Merging of data from the four reactions that did not provide complete discrimination ensured an accuracy of 93% for kNN. The newly developed indicator systems have good prospects for the discrimination of other fat-soluble samples. Overall, the results confirm the viability of the kinetics-based discrimination strategy.

Detection of the Adulteration of Motor Oil by Laser Induced Fluorescence Spectroscopy and Chemometric Techniques

Petroleum products are the target of fraudulent practices due to their high commercial value. The aim of this study is to provide a new analysis system to assess motor oil adulteration. For this purpose, Laser Induced Fluorescence (LIF) spectroscopy was exploited coupled with chemometric tools to detect motor oil adulteration by three types of cheap motor oils. Principal Component Analysis (PCA) was able to distinguish samples in three groups according to the type of adulterant. Besides, Partial Least Squares Regression (PLSR) was exploited to determine the percentage of adulteration. The best model was obtained with a regression coefficient of 0.96, Root Mean Square Error of Prediction (RMSEP) of 2.83, Standard Error of Prediction (SEP) of 2.83 and Bias of 0.40. The main results of this work provide new analysis system using the combination of LIF spectroscopy combined to PCA and PLS as an efficient and fast method for motor oil analysis.