Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.     

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

This paper proposes an analytical method to detect adulteration of diesel/biodiesel blends based on near infrared (NIR) spectrometry and supervised pattern recognition methods. For this purpose, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) coupled with the successive projections algorithm (SPA) have been employed to build screening models using three different optical paths and the following spectra ranges: 1.0 mm (8814-3799 cm⁻¹), 10 mm (11,329-5944 cm⁻¹ and 5531-4490 cm⁻¹) and 20 mm (11,688-5952 cm⁻¹ and 5381-4679 cm⁻¹). The method is validated in a case study involving the classification of 140 diesel/biodiesel blend samples, which were divided into four different classes, namely: diesel free of biodiesel and raw vegetal oil (D), blends containing diesel, biodiesel and raw oils (OBD), blends of diesel and raw oils (OD), and blends containing a fraction of 5% (v/v) of biodiesel in diesel (B5). LDA-SPA models were found to be the best method to classify the spectral data obtained with optical paths of 1.0 and 20 mm. Otherwise, PLS-DA shows the best results for classification of 10 mm cell data, which achieved a correct prediction rate of 100% in the test set.     

Determination of methyl ester contents in biodiesel blends by FTIR-ATR and FTNIR spectroscopies

Partial last square regression (PLS) and artificial neural network (ANN) combined to FTIR-ATR and FTNIR spectroscopies have been used to design calibration models for the determination of methyl ester content (%, w/w) in biodiesel blends (methyl ester + diesel). Methyl esters were obtained by the methanolysis of soybean, babassu, dende, and soybean fried oils. Two sets of samples have been used: Group I, binary mixtures (diesel + one kind of methyl ester), corresponding to 96 biodiesel blends (0–100%, w/w), and Group II, quaternary mixtures (diesel + three types of methyl esters), corresponding to 60 biodiesel blends (0–100%, w/w). The PLS results have shown that the FTNIR model for Group I is more precise and accurate (±0.02 and ±0.06%, w/w). In the case of Group II the PLS models (FTIR-ATR and FTNIR) have shown the same accuracies, while the ANN/FTNIR models has presented better performance than the ANN/FTIR-ATR models. The best accuracy was achieved by the ANN/FTNIR model for diesel determination (0.14%, w/w) while the worthiest was that of dende ANN/FTIR-ATR model (0.6%, w/w). Precisions in Group II analysis ranged from 0.06 to 0.53% (w/w) and coefficients of variation were better than 3% indicating that these models are suitable for the determination of diesel–biodiesel blends composed of methyl esters derived from different vegetable oils.     

Characterization of Biodiesel by Infrared Spectroscopy with Partial Least Square Discriminant Analysis

This paper reports a method to simultaneously classify 7% biodiesel and 93% diesel based on vegetable oil as the source of the biodiesel. Mafurra, moringa, and cotton biodiesel blends were characterized by infrared spectroscopy with partial least square discriminant analysis. The efficiency of model was evaluated based on the sensitivity and specificity. The model showed excellent results as all samples were correctly classified based on the raw material. Hence, the sensitivity and specificity parameters showed values of 1, which means 100% correct characterization of the samples in the calibration and prediction sets. Therefore, infrared spectroscopy with partial least square discriminant analysis is suitable for the characterization of biodiesel/diesel blends.     

Evaluation of biodiesel-diesel blends quality using H NMR and chemometrics

In this work, the use of ¹H NMR spectroscopy and statistical approach to the evaluation of biodiesel-diesel blends quality is described. Forty-six mixtures of oil-diesel, biodiesel-diesel, and oil-biodiesel-diesel were analyzed by ¹H NMR and such data were employed to design four predictive models. Thirty-six mixtures were used in the calibration set and the others in the validation. The PCR and PLS models were evaluated through statistical parameters.Briefly, PLS and PCR models were suitable for the prediction of biodiesel and oil concentration in mineral diesel. Specially, in higher concentration the predicted values were quite similar to the real ones. This fact was evidenced by the low relative errors of high concentrated samples; this means that the prediction of low concentrated samples will probably show high deviation. Therefore, ¹H NMR-PLS and ¹H NMR-PCR methods are fairly useful for the quality control of biodiesel-diesel blends, particularly they are suitable for prediction of concentrations greater than 2%.     

Cetane number and thermal properties of vegetable oil,biodiesel, 1-butanol and diesel blends

Vegetable oil derived fuels for diesel engines are becoming important as alternative to petroleum diesel fuels due to their environmental friendliness and availability. Ignition quality in compression ignition (CI) engines is influenced by thermal characteristics and fuel properties. In this study, the effects of vegetable oil transesterification and vegetable oil–1-butanol-diesel blends on fuel properties, cetane number (CN) and thermal characteristics were experimentally investigated. Methyl esters (biodiesel) and 10% vegetable oil–10% 1-butanol–80% diesel blends were prepared from croton oil (CRO), coconut oil (COO) and jatropha oil (JAO). CN was measured in a CFR F-5 engine, and a thermogravimetric analysis (TG), as well as the determination of fuel properties of vegetable oils, biodiesels and blends was carried out. It can be observed for vegetable oils that they possess low volatility characteristics, low CN and high viscosity different from those of biodiesels, blends and diesel fuel. It was observed that biodiesels and blends exhibit similarities with diesel in the fuel characteristics, CN and TG curves.     

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

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.     

Thermal investigation of biodiesel blends derived from rapeseed oil

Over the last few years, the production of biodiesel from vegetable oil has significantly increased in Romania due to its obligatory use in the composition of diesel fuel. In this study, biodiesel from rapeseed oil was produced using methanol and a base catalyst. Four samples of biodiesel/diesel blends were prepared for analysis to determine the main thermal decomposition processes and calorimetric events. The thermal profiles were compared to reference diesel. The data obtained on the Thermogravimetry/Differential thermogravimetry and DTA curves show the quality of biodiesel/diesel blends and the possibility that the fuel be used in diesel engines. It was found that biodiesel blends with higher percentage of biodiesel in their compositions were more thermally stable than diesel fuel.     

Chemometric Techniques Applied for Classification and Quantification of Binary Biodiesel/Diesel Blends

In this paper, three different types of biodiesel, which were synthesized from peanut, corn, and canola oils, were characterized by positive-ion electrospray ionization (ESI) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Different biodiesel/diesel blends containing 2–90% (V/V) of each biodiesel type were prepared and analyzed by near infrared spectroscopy (NIR). In the next step, the chemometric methods of hierarchical clusters analysis (HCA), principal component analysis (PCA), and support vector machines (SVM) were used for exploratory analysis of the different biodiesel samples, and the SVM was able to give the best classification results (correct classification of 50 peanut and 50 corn samples, and only one misclassification out of 49 canola samples). Then, partial least squares (PLS) and multivariate adaptive regression splines (MARS) models were evaluated for biodiesel quantification. Both methods were considered equivalent for quantification purposes based on the values smaller than 5% for the root mean square error of calibration (RMSEC) and root mean square of validation (RMSEP), as well as Pearson correlation coefficients of at least 0.969. The combination of NIR to the chemometric techniques of SVM and PLS/MARS was proven to be appropriate to classify and quantify biodiesel from different origins.     

Olive oil quantification of edible vegetable oil blends using triacylglycerols chromatographic fingerprints and chemometric tools

The present work studies the effectiveness of the use of triacylglycerols (TAGs) for the quantification of olive oil in blends with vegetable oils. The determinations were obtained using high-performance liquid chromatography (HPLC) coupled to a Charged Aerosol Detector (CAD), in combination with Partial Least Squares (PLS) regression and using interval PLS (iPLS) for variable selection.Results revealed that PLS models can predict olive oil concentrations with reasonable errors. Variable selection through iPLS did not improve predictions significantly, but revealed the chemical information important in the chromatogram to quantify olive oil in vegetable oil blends.     

Thermal diesel-like analysis

Biofuel has been obtained by cracking of soybean (Glycine sp.) oil, which is characterized by acidity index, density, cetane index, copper corrosion, carbon residue, fulgor point, and heat of combustion. In order to evaluate the quality of biofuel as well as detect its adulteration with vegetable oil, partial least squares regression calibration models based on thermogravimetric (TG) analysis were used as a precise and an accurate method. Thirty mixtures of biofuel/diesel/vegetable oil standards were prepared. Twenty of them were used for calibration, and ten for validation. The results have shown that the thermogravimetric analysis, PLS/TG, presented the best performance for the detection of vegetable oil contamination with a root mean square error of prediction (RMSEC% w/w) of 0.23, with a relative error of prediction of 3.6%, corroborating with the success of TG analysis application to determine the quality of biofuels and diesel/biofuel blends, showing that the TG analysis is an excellent tool to control quality of biofuels.     

Analysis of biodiesel/petroleum diesel blends with comprehensive two-dimensional gas chromatography

Comprehensive two-dimensional gas chromatography (GCxGC) is used to analyze petroleum diesel, biodiesel, and biodiesel/petroleum diesel blends. The GCxGC instrument is assembled from a conventional gas chromatograph fitted with a simple, in-line fluidic modulator. A 5% phenyl polydimethylsiloxane primary column is coupled to a polyethylene glycol secondary column. This column combination generates chromatograms where the fatty acid methyl esters (FAMEs) found in biodiesel occupy a region that is also populated by numerous cyclic alkanes and monoaromatics found in petroleum. Fortunately, the intensities of the petroleum hydrocarbon peaks are far lower than the intensities of the FAME peaks, even for blends with low biodiesel content. This allows the FAMEs to be accurately quantitated by direct integration. The method is calibrated by analyzing standard mixtures of soybean biodiesel in petroleum diesel with concentrations ranging from 1 to 20% v/v. The resulting calibration curve displays excellent linearity. This curve is used to determine the concentration of a B20 biodiesel/petroleum diesel blend obtained from a local retailer. Excellent precision and accuracy are obtained.     

Multivariate near infrared spectroscopy models for predicting methanol and water content in biodiesel

The transesterification of vegetable oils, animal fats or waste oils with an alcohol (such as methanol) in the presence of a homogeneous catalyst (sodium hydroxide or methoxyde) is commonly used to produce biodiesel. The quality control of the final product is an important issue and near infrared (NIR) spectroscopy recently appears as an appealing alternative to the conventional analytical methods. The use of NIR spectroscopy for this purpose first involves the development of calibration models to relate the near infrared spectrum of biodiesel with the analytical data. The type of pre-processing technique applied to the data prior to the development of calibration may greatly influence the performance of the model. This work analyses the effect of some commonly used pre-processing techniques applied prior to partial least squares (PLS) and principal components regressions (PCR) in the quality of the calibration models developed to relate the near infrared spectrum of biodiesel and its content of methanol and water. The results confirm the importance of testing various pre-processing techniques. For the water content, the smaller validation and prediction errors were obtained by a combination of a second order Savitsky-Golay derivative followed by mean centring prior to PLS and PCR, whereas for methanol calibration the best results were obtained with a first order Savitsky-Golay derivative plus mean centring followed by the orthogonal signal correction.     

Multivariate control charts based on NAS and mid‐infrared spectroscopy for quality control of B5 blends of methyl soybean biodiesel in diesel

In this work, mid‐infrared spectroscopy and multivariate control charts based on net analyte signal were applied for quality control of B5 blends of biodiesel/diesel (5% biodiesel/95% diesel). Control charts were constructed using instrumental signal decomposition, generating three charts: the net analyte signal chart for monitoring the analyte of interest (methyl soybean biodiesel); the interference chart, which corresponds to the contribution of all other compounds in the diesel sample (diesel); and the residual chart, which corresponds to non‐systematic variations. Statistical limits were established for each developed chart, using samples inside quality specifications (normal operation conditions). To validate multivariate control charts, new samples were analyzed. The new samples represented samples in‐control and samples out‐of‐control in relation to the content of biodiesel, adulterated biodiesel with severe vegetable oils and adulterated diesel with residual automotive lubricant oil, kerosene, and gasoline. The results obtained show an excellent distinction between the samples inside and out of the quality specifications, with 91% and 100% correctly classified, respectively, which demonstrates that the methodology developed is a viable alternative for quality monitoring of this type of fuel. Copyright © 2015 John Wiley & Sons, Ltd.     

The Obtaining Biodiesel from Used Vegetable Oil and Animal Waste Oil by Two Stages Transesterification Reaction

The biodiesel was obtained from used vegetable oil (UVO) and animal waste oil (AWO) by the two stages transesterification reaction. Also chemical and technical properties of feed and products were determined. Conditions of transesterification reaction for each of the oil samples were determined as a result of several sets of experiments. The suitable conditions of transesterification reaction were the following. Hereto a molar ratio of oil: methanol: catalyst was 1: 6: 1/40, for 30 min, at temperature of 600°C. To obtain biodiesel directly by the one stage transesterification, in case of using UVO sample, when the acidity number of feed oil had to less than 3 mg KOH/g. The biodiesel from UVO and AWO was prepared by mixing 5, 10, 20% of volume in the summer and winter diesel fuel. However, the product from mixture of UVO and winter diesel fuel met the technique requirements both of winter and summer diesel fuel, but the product from mixture of AWO and summer diesel fuel did not satisfy technical requirements of diesel fuel.     

Biofuels–Renewable Energy Sources: A Review

Biodiesel is biodegradable and nontoxic, and it significantly reduces toxic and other emissions when burned as a fuel. The advantages of biodiesel as diesel fuel are its portability, ready availability, renewability, higher combustion efficiency, non-toxicity, higher flash point, and lower sulfur and aromatic content, higher cetane number, and higher biodegradability. The major disadvantages of biodiesel are its higher viscosity, lower energy content, higher cloud point and pour point, higher nitrogen oxide (NO_x) emissions, lower engine speed and power, injector coking, engine compatibility, high price, and greater engine wear. The technical disadvantages of biodiesel/fossil diesel blends include problems with fuel freezing in cold weather, reduced energy density, and degradation of fuel under storage for prolonged periods. The sources of biodiesel are vegetable oils and fats. The direct use of vegetable oils and/or oil blends is generally considered to be unsatisfactory and impractical for both direct injection and indirect type diesel engines because of their high viscosities and low volatilities injector coking and trumpet formation on the injectors, higher level of carbon deposits, oil ring sticking, and thickening and gelling of the engine lubricant oil, acid composition. Biodiesel is obtained by transesterifying triglycerides with methanol. A popular variation of the batch transesterification process which needs high alcohol/acid ratio (several separation problems and high corrosivity and toxicity) is the use of continuous stirred tank reactors in series. This continuous process is heterogeneous and is based on reactive distillation. The key factor is the selection of the right and effective solid catalyst which leads to reduction of energy consumption and investments at all.     

Fast quantitative detection of sesame oil adulteration by near-infrared spectroscopy and chemometric models

Adulteration of foods has been known to exist for a long time and various analytical tests have been reported to address this problem. Among them, authenticity of sesame oil has attracted much attention. Near-infrared (NIR) spectral quantitative detection models of sesame oil adulterated with other oils are constructed by chemometric methods, i.e., competitive adaptive reweighted sampling (CARS), elastic component regression (ECR) and partial least squares (PLS). Sixty samples adulterated with different proportions of five kinds of other oils of lower price were scanned by a Fourier-transform-NIR spectrometer and the NIR spectra were collected in 4500–10000 cm⁻¹ region by transmission mode. All samples were divided into the training set and an independent test set. Model population analysis has also been carried out and confirms the importance of selecting representative samples. The experimental results indicate that the PLS model using only 10 variables from CARS and the ECR model show similar performance and both are superior to the full-spectrum PLS model. CARS focuses on selecting variables and ECR focuses on optimizing the parameters, implying that both roads lead to the same destination. It seems that NIR technique combined with CARS or ECR is feasible for rapidly detecting sesame oil adulterated with other vegetable oils.     

Production of Tung Oil Biodiesel and Variation of Fuel Properties During Storage

The crude Tung oil with 4.72?mg KOH/g of acid value (AV) was converted by direct transesterification, and the reaction mixture was quantified. The phase distribution data showed that 38.24% of excess methanol, 11.76% of KOH, 10.13% of soap and 4.36% of glycerol were in the biodiesel phase; 0.35% of biodiesel dissolved in the glycerol phase. Tung oil biodiesel as well as its blends with 0(#) diesel was investigated under different storage conditions. The results indicated that higher temperature greatly influenced the storage stability, especially when the volume fraction of Tung oil biodiesel is increased in the blends.     

Determination of Ni2+ using an equilibrium ion exchange technique: Important chemical factors and applicability to environmental samples

The main purpose of this study was to investigate the relationship between some coffee roasting variables (weight loss, density and moisture) with near infrared (NIR) spectra of original green (i.e. raw) and differently roasted coffee samples, in order to test the availability of non-destructive NIR technique to predict coffee roasting degree. Separate calibration and validation models, based on partial least square (PLS) regression, correlating NIR spectral data of 168 representatives and suitable green and roasted coffee samples with each roasting variable, were developed. Using PLS regression, a prediction of the three modelled roasting responses was performed. High accuracy results were obtained, whose root mean square errors of the residuals in prediction (RMSEP) ranged from 0.02 to 1.23%. Obtained data allowed to construct robust and reliable models for the prediction of roasting variables of unknown roasted coffee samples, considering that measured vs. predicted values showed high correlation coefficients (r from 0.92 to 0.98). Results provided by calibration models proposed were comparable in terms of accuracy to the conventional analyses, revealing a promising feasibility of NIR methodology for on-line or routine applications to predict and/or control coffee roasting degree via NIR spectra.     

Application of Fourier transform infrared (FT-IR) spectroscopy combined with chemometrics for authentication of cod-liver oil

Some vegetable oils such as canola (CaO), corn (CO), soybean (SO), and walnut (WO) oils have similar color with cod liver oil (CLO), therefore, the presence of these oils was difficult to detect using naked eye. For this reason, Fourier transform infrared (FTIR) spectroscopy using horizontal attenuated total reflectance (HATR) as sampling accessory and in the combination with chemometrics was developed for detection and quantification of these vegetable oils as adulterants in CLO. The quantification of vegetable oils was carried out by using multivariate calibrations of partial least squares (PLS) and principle component regression (PCR), while the classification between pure CLO and CLOs adulterated with CaO, CO, SO, and WO was performed using discriminant analysis (DA). PLS with FTIR normal spectra was more suitable compared with PCR for quantification purposes with coefficient of determination (R²) higher than 0.99 and root mean square error of calibration (RMSEC) in the range of 0.04-0.82% (v/v). The PLS model was further used to predict the levels of these vegetable oils in independent samples for validation/prediction purpose. The root mean square error of prediction (RMSEP) values obtained were of 1.75% (v/v) (CaO), 1.39% (v/v) (CO), 1.35% (v/v) (SO), and 1.37% (v/v) (WO), respectively. The classification using DA revealed that the developed method can classify CLO and that mixed with these vegetable oils using 9 principal components.