Classification of gasoline by octane number and light gas condensate fractions by origin with using dielectric or gas-chromatographic data and chemometrics tools

Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol-Gasoline Blends by Raman Spectroscopy

Methanol-gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol-gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol-gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)-PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol-gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

Paper-Based Optical Nose Made with Bimetallic Nanoparticles for Monitoring Ignitable Liquids in Gasoline

A paper-based optical nose was fabricated by dropping bimetallic silver and gold nanoparticles on a paper substrate. The nanoparticles were synthesized by both natural (lemon, pomegranate, and orange juices) and chemical (citrate, gallic acid, and ascorbic acid) reducing agents. The performance of the assay was evaluated for identifying gasoline and five ignitable liquids such as diesel, ethanol, methanol, kerosene, and thinner. The interaction of the sensor with sample vapors caused aggregation, consequently changing the color of nanoparticles. The color changes, which were captured by a scanner, represented a specified colorimetric map for each analyte, allowing one to identify the studied fuels. The visual results were confirmed using multivariate statistical analysis such as principal component analysis and hierarchical clustering analysis. Also, partial least-squares regression was used to assist the proposed assay for estimating the amount of studied ignitable liquids as counterfeit species in the gasoline sample. The root-mean-square errors for prediction were 3.4, 2.1, 1.9, 2.0, and 1.7% for diesel, thinner, kerosene, ethanol, and methanol, respectively. Finally, the fabricated sensor indicated high efficiency for the on-site detection of pure industrial gasoline samples from adulterated ones.

Chemometrics in forensic science: approaches and applications

Forensic investigations are often reliant on physical evidence to reconstruct events surrounding a crime. However, there remains a need for more objective approaches to evidential interpretation, along with rigorously validated procedures for handling, storage and analysis. Chemometrics has been recognised as a powerful tool within forensic science for interpretation and optimisation of analytical procedures. However, careful consideration must be given to factors such as sampling, validation and underpinning study design. This tutorial review aims to provide an accessible overview of chemometric methods within the context of forensic science. The review begins with an overview of selected chemometric techniques, followed by a broad review of studies demonstrating the utility of chemometrics across various forensic disciplines. The tutorial review ends with the discussion of the challenges and emerging trends in this rapidly growing field.

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC-MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC-MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC-MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC-MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC-MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC-MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.

Gasoline sensor based on piezoelectric lateral electric field excited resonator

It has been shown that by using piezoelectric lateral electric field excited resonators based on X - cut LiNbO₃, one can determine the octane number of gasoline. The measured dependence of gasoline permittivity on its octane number has shown that there is an ambiguous connection between pointed parameters. We have demonstrated both theoretically and experimentally that the value of the real part of the electrical impedance on the frequency of parallel resonance uniquely associates with the octane number of gasoline contacting the free side of the resonator. At that the frequency of parallel resonance does not depend on permittivity/octane number of gasoline. An example of determination of the octane number of a mixture of two different samples of gasoline is given.

Chemical characteristics combined with bioactivity for comprehensive evaluation of Panax ginseng C.A. Meyer in different ages and seasons based on HPLC-DAD and chemometric methods

Panax ginseng C.A. Meyer has been known as a valuable traditional Chinese medicines for thousands years of history. Ginsenosides, the main active constituents, exhibit prominent immunoregulation effect. The present study first describes a holistic method based on chemical characteristic and lymphocyte proliferative capacity to evaluate systematically the quality of P. ginseng in thirty samples from different seasons during 2-6 years. The HPLC fingerprints were evaluated using principle component analysis (PCA) and hierarchical clustering analysis (HCA). The spectrum-efficacy model between HPLC fingerprints and T-lymphocyte proliferative activities was investigated by principal component regression (PCR) and partial least squares (PLS). The results indicated that the growth of the ginsenosides could be grouped into three periods and from August of the fifth year, P. ginseng appeared significant lymphocyte proliferative capacity. Close correlation existed between the spectrum-efficacy relationship and ginsenosides Rb1, Ro, Rc, Rb2 and Re were the main contributive components to the lymphocyte proliferative capacity. This comprehensive strategy, providing reliable and adequate scientific evidence, could be applied to other TCMs to ameliorate their quality control.