Screening analysis to detect adulteration in diesel/biodiesel blends using near infrared spectrometry and multivariate classification

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.

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

This study reports a simple and convenient analytical method for the simultaneous determination of biodiesel and vegetable oils or used cooking oils in petrodiesel and green diesel (hydrotreated vegetable oils or paraffinic diesel). The approach is based on normal-phase high-performance liquid chromatography with refractive index detection. It employed silica stationary phase, n-hexane mobile phase with isopropanol modifier to achieve optimum separation between hydrocarbons (petrodiesel or green diesel), fatty acid methyl esters (biodiesel) and triglycerides (vegetable oils and used cooking oil). In addition to determining vegetable oils or used cooking oils as adulterants in diesel, this method is also proposed as a better alternative to the standard method ASTM D7371, which is currently recommended for determining fatty acid methyl esters in petrodiesel. The method development involved screening of various stationary and mobile phases, with and without modifiers, to achieve acceptable chromatographic resolutions between analytes. Under the optimized method conditions, silica column, and n-hexane containing 0.6% isopropanol as the mobile phase provided the best results. The real-world scenario was simulated for the method validation carried out by fortifying Jatropha seed oil, soybean oil, and used cooking oil in the biodiesel blended petrodiesel and green diesel. Measurement of all analytes was accompanied by high precision, low limit of detection/quantification and linear response range of 0.05 to 50% for biodiesel, and 0.05 to 30% for vegetable oils. The proposed method is simple, fast (runtime 7 min), and does not require sample pre-treatment and backflushing.

Infrared optical constants of liquid palm oil and palm oil biodiesel determined by the combined ellipsometry-transmission method

The optical constants of vegetable oils and biodiesels are the basic input parameters in the study of the thermal radiation transfer and monitoring the productivity of vegetable oils converting to biodiesels. In this work, a combined ellipsometry-transmission method is presented to obtain the optical constants of palm oil and palm oil biodiesel between 20°C and 150°C in the spectral range 600-4100  cm^-1 and to study the temperature effect on the optical constants. In the combined method, a modified ellipsometry method is used to measure the optical constants of palm oil and palm oil biodiesel for the whole researched wave bands. For the weak absorption regions in which the ellipsometry method cannot give precise absorption indices, the transmission method is conducted to get the absorption indices using the refractive indices obtained by the proposed ellipsometry method. Deionized water and methanol are taken as examples to verify the combined ellipsometry-transmission method. It is shown that the combined method can overcome the deficiencies of the traditional ellipsometry and transmission method, which can be used for the measurements of both strong and weak absorption wave bands. The experimental analyses indicate that temperature exerts a noticeable influence on the infrared optical constants of palm oil and palm oil biodiesel. With the increase of temperature, the refractive indices at certain wavenumbers decrease nearly linearly, and the amplitudes of dominant absorption peaks show a decreasing trend. The absorption peaks located around 3550  cm^-1 show blueshift trends as temperature increases. Comparing these two kinds of oils, palm oil presents larger values in refractive indices and dominant absorption peaks.

A novel combined approach of diffuse reflectance UV-Vis-NIR spectroscopy and multivariate analysis for non-destructive examination of blue ballpoint pen inks in forensic application

The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%).

Quantification of animal fat biodiesel in soybean biodiesel and B20 diesel blends using near infrared spectroscopy and synergy interval support vector regression

In this work, multivariate calibration based on partial least squares (PLS) and support vector regression (SVR) using the whole spectrum and variable selection by synergy interval (siPLS and siSVR) were applied to NIR spectra for the determination of animal fat biodiesel content in soybean biodiesel and B20 diesel blends. For all models, prediction errors, bias test for systematic errors and permutation test for trends in the residuals were calculated. The siSVR produced significantly lower prediction errors compared to the full spectrum methods and siPLS, with a root mean squares error (RMSEP) of 0.18%(w/w) (concentration range: 0.00%-69.00%(w/w)) in the soybean biodiesel blend and 0.10%(w/w) in the B20 diesel (concentration range: 0.00%-13.80%(w/w)). Additionally, in the models for the determination of animal fat biodiesel in blends with soybean diesel, PLS and SVR showed evidence of systematic errors, and PLS/siPLS presented trends in residuals based on the permutation test. For the B20 diesel, PLS presented evidence of systematic errors, and siPLS presented trends in the residuals.

Detection of adulteration in hydrated ethyl alcohol fuel using infrared spectroscopy and supervised pattern recognition methods

This paper proposes an analytical method to detect adulteration of hydrated ethyl alcohol fuel based on near infrared (NIR) and middle infrared (MIR) spectroscopies associated with supervised pattern recognition methods. For this purpose, linear discriminant analysis (LDA) was employed to build a classification model on the basis of a reduced subset of wavenumbers. For variable selection, three techniques are considered, namely the successive projection algorithm (SPA), the genetic algorithm (GA) and a stepwise formulation (SW). For comparison, models based on partial least squares discriminant analysis (PLS-DA) were also employed using full-spectrum. The method was validated in a case study involving the classification of 181 hydrated ethyl alcohol fuel samples, which were divided into three different classes: (1) authentic samples; (2) samples adulterated with water and (3) samples contaminated with methanol. LDA/GA and PLS-DA models were found to be the best methods for classifying the spectral data obtained in NIR region, which achieved a correct prediction rate of 100% in the test set, while the LDA/SPA and LDA/SW were correctly classified at 84.4% and 97.8%, respectively. For MIR data, all models (PLS-DA and LDA coupled with the SW, SPA and GA) employed in this study correctly classified all samples in the test set.