Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy

A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning

Adulteration of diesel fuel poses a major concern in most developing countries including Ghana despite the many regulatory schemes adopted. The solvent tracer analysis approach currently used in Ghana has over the years suffered several limitations which affect the overall implementation of the scheme. There is therefore a need for alternative or supplementary tools to help detect adulteration of automotive fuel. Herein we describe a two-level classification method that combines NMR spectroscopy and machine learning algorithms to detect adulteration in diesel fuel. The training sets used in training the machine learning algorithms contained 20-40% w/w adulterant, a level typically found in Ghana. At the first level, a classification model is built to classify diesel samples as neat or adulterated. Adulterated samples are passed on to the second stage where a second classification model identifies the type of adulterant (kerosene, naphtha, or premix) present. Samples were analyzed by 1H NMR spectroscopy and the data obtained were used to build and validate support vector machine (SVM) classification models at both levels. At level 1, the SVM model classified all 200 samples with only 2.5% classification errors after validation. The level 2 classification model developed had no classification errors for kerosene and premix in diesel. However, 2.5% classification error was recorded for samples adulterated with naphtha. Despite the great performance of the proposed schemes, it showed significantly erratic predictions with adulterant levels below 20% w/w as the training sets for both models contained adulterants above 20% w/w. The proposed method, nevertheless, proved to be a potential tool that could serve as an alternative to the marking system in Ghana for the fast detection of adulterants in diesel.

Quality Monitoring of Biodiesel and Diesel/Biodiesel Blends: A Comparison between Benchtop FT-NIR versus a Portable Miniaturized NIR Spectroscopic Analysis

A methodology such as near-infrared (NIR) spectroscopy, which enables in situ and in real-time analysis, is crucial to perform quality control of biodiesel, since it is blended into diesel fuel and the presence of contaminants can hinder its performance. This work aimed to compare the performance of a benchtop Fourier Transform (FT) NIR spectrometer with a prototype of a portable, miniaturized near-infrared spectrometer (miniNIR) to detect and quantify contaminants in biodiesel and biodiesel in diesel. In general, good models based on principal component analysis-linear discriminant analysis (PCA-LDA) of FT-NIR spectra were obtained, predicting with high accuracies biodiesel contaminants and biodiesel in diesel (between 75% to 95%), as well as good partial least square (PLS) regression models to predict contaminants concentration in biodiesel and biodiesel concentration in diesel/biodiesel blends, with high coefficients of determination (between 0.83 and 0.99) and low prediction errors. The miniNIR prototype’s PCA-LDA models enabled the prediction of target contaminants with good accuracies (between 66% and 86%), and a PLS model enabled the prediction of biodiesel concentration in diesel with a reasonable coefficient of determination (0.68), pointing to the device’s potential for preliminary analysis of biodiesel which, associated with its potential low cost and portability, could increase biodiesel quality control.

Interdigitated Electrode for Electrical Characterization of Commercial Pseudo-Binary Biodiesel-Diesel Blends

Non-standard diesel blends can be harmful to the environment and human health. In this context, a simple analytical method to estimate the biodiesel mixture ratio in diesel was developed based on impedance spectroscopy (IS) associated with interdigitated sensors. In this article, four different interdigitated sensors with varied comb spacing (G) were simulated using the COMSOL Multiphysics software. Based on finite element simulations, four interdigitated electrode architectures were manufactured and evaluated. The best geometry was chosen according to theoretical data simulations, and its interdigitated electrodes were manufactured for the compositional evaluation of pseudo-binary biodiesel–diesel mixtures. According to the X-ray powder diffraction technique, the deposition of the conductive layer (Au⁰) over the surface of the dielectric substrate (SiO₂) did not alter its phase composition. In the analysis of AFM and SEM, it was possible to observe irregular edges on the electrodes, possibly related to the manufacturing process of the thin layers and mechanical stability. Another characteristic observed in the AFM images was the height of the step of the gold layer of the sensor. Several cross sections were obtained, and the mean step value was 225.71 ± 0.0032 nm. Although there were differences in the roughness, the whole sensor had nanometric roughness. Based on the finite element method simulation performed, it can be assumed that the geometric parameters more suitable for the manufacturing of the electrode are W = 20 µm, L = 1000 µm, G = 50 µm, and N = 40 digits. The electrical characterization performed by impedance spectroscopy showed that we could differentiate between biodiesel and diesel fuels and their pseudo-binary mixtures in the low-frequency region.

Texture Analysis of Dried Droplets for the Quality Control of Medicines

The quality control of medicines guarantees the effectiveness of treatments for diseases. We explore the use of texture analysis of patterns in dried droplets as a tool to readily detect both impurities and changes in drug concentration. Four types of medicines associated with different routes of administration were analyzed: Methotrexate, Ciprofloxacin, Clonazepam, and Budesonide. We use NaCl and a hot substrate at 63 ∘C to promote aggregate formation and to reduce droplet drying time. Depending on the medicine, optical microscopy reveals different complex aggregates such as circular to oval splatters, fern-like islands, crown shapes, crown needle-like and bump-like patterns as well as dendritic branched and star-like crystals. We use some physical features of the stains (as the stain diameter and superficial area) and gray level co-occurrence matrix (GLCM) to characterize patterns of dried droplets. Finally, we show that structural analysis of stains can achieve 95% accuracy in identifying medicines with 30% water dilution, while it achieves 99% accuracy in detecting drugs with 10% other substances.

Determination of adulterated gasoline using fluorescence emission-excitation matrices and multivariate calibration

Methanol gasoline, known as a new energy, has a certain degree of damage to automobile. The aim of this work was to identify and quantify the methanol in methanol gasoline using three-dimensional fluorescence spectroscopy technique combined with second order chemometric methods. Parallel factor analysis (PARAFAC) and self-weighted alternating trilinear decomposition (SWATLD) methods were used to analyse artificial samples. However, the obtained results by PARAFAC were not satisfactory. On the other hand, excellent prediction results were obtained when SWATLD model was applied, with recovery rate between 98.7 and 102.8%, and between 97.4 and 101.9% for two and three factor respectively. In order to verify the accuracy of the method, four real samples were predicted using SWATLD model with RMSEP between 0.1 μg/mL and 0.23 μg/mL.

Dual-Mode Encoded Magnetic Composite Microsphere Based on Fluorescence Reporters and Raman Probes as Covert Tag for Anticounterfeiting Applications

Utilizing fluorescence reporters and SERS probes as the security labels, a series of dual-mode encoded magnetic composite microspheres with micrometer size was designed and prepared for anticounterfeiting applications. At first, the micro-meter-sized melamine formaldehyde microspheres with different fluorescence molecules (FMF) were prepared by precipitation polymerization, and then the magnetite composite microspheres (FMF/MNPs) were fabricated by direct immobilization of magnetic nanoparticles (MNPs) onto the surface of FMF microspheres. After deposition of Ag nanoparticles (Ag-NPs) onto FMF/MNPs microspheres, the SERS probes were absorbed onto the surface of Ag-NPs, and then a protection layer of silica was coated on the composite microspheres by Stöber method. The combination of different fluorescence reporters and SERS probes greatly increased the encoding complexity and volume for high-level anticounterfeiting. The structure of the dual-encoded FMF/MNPs/Ag-NPs/SiO2 composite microspheres was characterized by FESEM, TEM, FLS(fluorescence spectrometer), XRD, VSM, UV-vis and EDS. The embedded magnetic nanoparticles enable the composite microspheres to be quickly isolated from the marked latex paint by magnet at the concentration of as low as 1 ppm, and the covert tag information can be read out even from one composite microsphere. In addition, the covert security information in the marked coating film can be also read out in situ and the existence of the composite microspheres does not influence the visible appearance of the coating film. All the above outstanding properties will make these dual-mode encoded composite microspheres as advanced security tags for next-generation anticounterfeiting applications.

A rapid method to authenticate vegetable oils through surface-enhanced Raman scattering

Vegetable oils are essential in our daily diet. Among various vegetable oils, the major difference lies in the composition of fatty acids, including unsaturated fatty acids (USFA) and saturated fatty acids (SFA). USFA include oleic acid (OA), linoleic acid (LA), and α-linolenic acid (ALA), while SFA are mainly palmitic acid (PA). In this study, the most typical and abundant USFA present with PA in vegetable oils were quantified. More importantly, certain proportional relationships between the integrated intensities of peaks centered at 1656 cm⁻¹ (S₁₆₅₆) in the surface-enhanced Raman scattering spectra of different USFA were confirmed. Therefore, the LA or ALA content could be converted into an equivalent virtual OA content enabling the characterization of the USFA content in vegetable oils using the equivalent total OA content. In combination with the S₁₆₅₆ of pure OA and using peanut, sesame, and soybean oils as examples, the ranges of S₁₆₅₆ corresponding to the National Standards of China were established to allow the rapid authentication of vegetable oils. Gas chromatograph-mass spectrometer analyses verified the accuracy of the method, with relative errors of less than 5%. Moreover, this method can be extended to other detection fields, such as diseases.

A comprehensive review on biodiesel cold flow properties and oxidation stability along with their improvement processes

Biodiesel, which comprises fatty acid esters, is derived from different sources, such as vegetable oils from palm, sunflower, soybean, canola, Jatropha, and cottonseed sources, animal fats, and waste cooking oil.

Stability of biodiesel, its improvement and the effect of antioxidant treated blends on engine performance and emission

Biodiesel consists of long chain fatty acid esters derived from vegetable oils, animal fats, and used oils.

Magnetically recoverable, thermostable, hydrophobic DNA/silica encapsulates and their application as invisible oil tags

A method to encapsulate DNA in heat-resistant and inert magnetic particles was developed. An inexpensive synthesis technique based on co-precipitation was utilized to produce Fe2O3 nanoparticles, which were further functionalized with ammonium groups. DNA was adsorbed on this magnetic support, and the DNA/magnet nanocluster was surface coated with a dense silica layer by sol-gel chemistry. The materials were further surface modified with hexyltrimethoxysilane to achieve particle dispersibility in hydrophobic liquids. The hydrodynamic particle sizes were evaluated by analytical disc centrifugation, and the magnetic properties were investigated by vibrating sample magnetometry. The obtained nanoengineered encapsulates showed good dispersion abilities in various nonaqueous fluids and did not affect the optical properties of the hydrophobic dispersant when present at concentrations lower than 10(3) μg/L. Upon magnetic separation and particle dissolution, the DNA could be recovered unharmed and was analyzed by quantitative real-time PCR and Sanger sequencing. DNA encapsulated within the magnetic particles was stable for 2 years in decalin at room temperature, and the stability was further tested at elevated temperatures. The new magnetic DNA/silica encapsulates were utilized to developed a low-cost platform for the tracing/tagging of oils and oil-derived products, requiring 1 μg/L=1 ppb levels of the taggant and allowing quantification of taggant concentration on a logarithmic scale. The procedure was tested for the barcoding of a fuel (gasoline), a cosmetic oil (bergamot oil), and a food grade oil (extra virgin olive oil), being able to verify the authenticity of the products.

Predicting feedstock and percent composition for blends of biodiesel with conventional diesel using chemometrics and gas chromatography-mass spectrometry

The two main goals of the analytical method described herein were to (1) use principal component analysis (PCA), hierarchical clustering (HCA) and K-nearest neighbors (KNN) to determine the feedstock source of blends of biodiesel and conventional diesel (feedstocks were two sources of soy, two strains of jatropha, and a local feedstock) and (2) use a partial least squares (PLS) model built specifically for each feedstock to determine the percent composition of the blend. The chemometric models were built using training sets composed of total ion current chromatograms from gas chromatography-quadrupole mass spectrometry (GC-qMS) using a polar column. The models were used to semi-automatically determine feedstock and blend percent composition of independent test set samples. The PLS predictions for jatropha blends had RMSEC=0.6, RMSECV=1.2, and RMSEP=1.4. The PLS predictions for soy blends had RMSEC=0.5, RMSECV=0.8, and RMSEP=1.2. The average relative error in predicted test set sample compositions was 5% for jatropha blends and 4% for soy blends.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice.

A comparative study of calibration transfer methods for determination of gasoline quality parameters in three different near infrared spectrometers

This work presents a comparative study of calibration transfer among three near infrared spectrometers for determination of naphthenes and RON (Research Octane Number) in gasoline. Seven transfer methods are compared: direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), reverse standardization (RS), piecewise reverse standardization (PRS), slope and bias correction (SBC) and model updating (MU). Two pre-treatment procedures, namely standard normal variate (SNV) and multiplicative scatter correction (MSC), are also investigated. The choice of an appropriate number of transfer samples for each technique, as well as the effect of window size in PDS/PRS and OSC components, are discussed. A broad set of gasoline samples representative of the Northeastern states of Brazil is employed in the investigation. The results show that the use of calibration transfer yields prediction errors comparable to those obtained with complete recalibration of the secondary instrument. Overall, the results point to RS as the best method for the analytical problem under consideration. When storage and/or physical transportation of transfer samples are impractical, MU is more appropriate. The comprehensive investigation carried out in the present work will be of value for practitioners involved in networks of fuel monitoring.