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

Srata, Loubna; Farres, Sofia; Chikri, Mounim; Addou, Sihame; Fethi, Fouad

doi:10.1007/s10895-022-03108-9

Cited by 8 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Motor oils have been a popular type of samples for the development of recognition and discrimination techniques. They can be recognized by their color [ 30 ], by IR spectroscopy [ 31 ], and by fluorescence [ 32 , 33 ]. Reaction-based fingerprinting techniques have not yet been applied to fat-soluble samples because of incompatibility of the aqueous-solution-based indicator reactions with the hydrophobic nature of fats and oils.…”

Section: Introductionmentioning

confidence: 99%

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

Pypin,

Shik,

Stepanova

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Pypin,

Shik,

Stepanova

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Chemometric Classification of Motor Oils Using ¹H NMR Spectroscopy With Simultaneous Phase and Baseline Optimization

Olejniczak,

Łukaszewicz

2024

Journal of Chemometrics

View full text Add to dashboard Cite

Here, we demonstrate mid‐field 1H NMR spectroscopy combined with chemometrics to be powerful in the classification and authentication of motor oils (MOs). The 1H NMR data were processed with a new algorithm for simultaneous phase and baseline correction, which, for crowded spectra such as those of the refinery products, allowed for more accurate estimation of phase parameters than other literature approaches tested. A principal component analysis (PCA) model based on the unbinned CH3 fingerprint region (0.6–1.0 ppm) enabled the differentiation of hydrocracked and poly‐α‐olefin‐based MOs and was effective in resolving mixtures of these base stocks with conventional base oils. PCA analysis of the 1.0‐ to 1.14‐ppm region enabled the detection of poly (isobutylene) additive and was useful for differentiating between single‐grade and multigrade MOs. Non‐equidistantly binned 1H NMR data were used to detect the addition of esters and to establish discriminant models for classifying MOs by viscosity grade and by major categories of synthetic, semisynthetic, and mineral oils. The performances of four classifiers (linear discriminant analysis [LDA], quadratic discriminant analysis [QDA], naïve Bayes classifier [NBC], and support vector machine [SVM]) with and without PCA dimensionality reduction were compared. In both tasks, SVM showed the best efficiency, with average error rates of ~2.3% and 8.15% for predicting major MO categories and viscosity grades, respectively. The potential to merge spectra collected from different NMR instruments is discussed for models based on spectral binning. It is also shown that small errors in phase parameters are not detrimental to binning‐based PCA models.

show abstract

Predicting Loquat Quality Using Visible, Near Infrared Spectroscopy and Artificial Neural Network

Tmimi,

Srata,

Farres

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

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

Cited by 8 publications

References 42 publications

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

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

Chemometric Classification of Motor Oils Using ¹H NMR Spectroscopy With Simultaneous Phase and Baseline Optimization

Predicting Loquat Quality Using Visible, Near Infrared Spectroscopy and Artificial Neural Network

Contact Info

Product

Resources

About

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

Cited by 8 publications

References 42 publications

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

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

Chemometric Classification of Motor Oils Using 1H NMR Spectroscopy With Simultaneous Phase and Baseline Optimization

Predicting Loquat Quality Using Visible, Near Infrared Spectroscopy and Artificial Neural Network

Contact Info

Product

Resources

About

Chemometric Classification of Motor Oils Using ¹H NMR Spectroscopy With Simultaneous Phase and Baseline Optimization