Characterization of engine lubricants by fluorescence spectroscopy and chemometrics

A kind of multi-dot ensemble regression AI detector for lubricating oil additive content based on lambert-beer law

In this work, we propose a Multi-dot Ensemble Regression AI detector (MER) based on the Lambert-Beer law. We pre-trained a model using the infrared spectral data of target additives collected in advance to detect the target additives in unknown oil samples. The algorithm's feasibility was validated by assessing the content of additives in a series of simulated commercial oil samples that were not part of the training set. We established models for three common lubricating oil additives (anti-friction, anti-wear, and antioxidant agents), demonstrating their effectiveness in oil sample detection. Additionally, by comparing with other algorithms, we established the superiority of MER in small-sample learning scenarios.

Spectral characterization of the impact of modifiers and different prepare temperatures on snow lotus medicinal residue-biochar and dissolved organic matter

This study involved the production of 20 biochar samples derived from secondary medicinal residues of Snow Lotus Oral Liquid, processed within the temperature range of 200-600 °C. Additionally, four medicinal residues, including dissolved organic matter (DOM), from 24 samples obtained using the shaking method, served as the primary source material. The investigation focused on two key factors: the modifier and preparation temperature. These factors were examined to elucidate the spectral characteristics and chemical properties of the pharmaceutical residues, biochar, and DOM. To analyze the alterations in the spectral attributes of biochar and medicinal residues, we employed near-infrared spectroscopy (NIR) in conjunction with Fourier-infrared one-dimensional and two-dimensional correlation spectroscopy. These findings revealed that modifiers enhanced the aromaticity of biochar, and the influence of preparation temperature on biochar was diminished. This observation indicates the stability of the aromatic functional group structure. Comparative analysis indicated that Na2CO3 had a more pronounced structural effect on biochar, which is consistent with its adsorption properties. Furthermore, we utilized the fluorescence indices from UV-visible spectroscopy and excitation-emission-matrix spectra with the PARAFAC model to elucidate the characteristics of the fluorescence components in the DOM released from the samples. The results demonstrated that the DOM released from biochar primarily originated externally. Aromaticity reduction and increased decay will enhance the ability of the biochar to bind pollutants. Those results confirmed the link between the substantial increase in the adsorption performance of the high-temperature modified charcoal in the previous study and the structural changes in the biochar. We investigated the structural changes of biochar and derivative DOM in the presence of two perturbing factors, modifier and preparation temperature. Suitable modifiers were selected. Preparation for the study of adsorption properties of snow lotus medicinal residues.

Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics

This research investigates the use of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric models to rapidly identify and quantify adulteration in olive oil, a critical concern where sample availability is limited. Adulteration is simulated by blending soybean, peanut, and linseed oils into olive oil, creating diverse adulterated samples. Principal component analysis (PCA) was applied to the EEMF spectral data as an initial exploratory measure to cluster and differentiate adulterated samples. Spatial clustering enabled vivid visualization of the variations and trends in the spectra. The novel application of parallel factor analysis (PARAFAC) for data decomposition in this paper focuses on unraveling correlations between the decomposed components and the actual adulterated components, which offers a novel perspective for accurately quantifying adulteration levels. Additionally, a comparative analysis was conducted between the PCA and PARAFAC methodologies. Our study not only unveils a new avenue for the quantitative analysis of adulterants in olive oil through spectral detection but also highlights the potential for applying these insights in practical, real-world scenarios, thereby enhancing detection capabilities for various edible oil samples. This promises to improve the detection of adulteration across a range of edible oil samples, offering significant contributions to food safety and quality assurance.