Diesel Blend

Article pubs.acs.org/EF

Fast Detection of Adulterants/Contaminants in Biodiesel/Diesel Blend (B5) Employing Mid-Infrared Spectroscopy and PLS-DA Sarmento Júnior Mazivila,*,†,‡ Lucas Caixeta Gontijo,†,§ Felipe Bachion de Santana,† Hery Mitsutake,† Douglas Queiroz Santos,∥ and Waldomiro Borges Neto† †

Institute of Chemistry, Federal University of Uberlândia, Uberlândia, Minas Gerais, 38408-100 Brazil Josina Machel Secondary School of Belane, Vilankulo-Inhambane, Mozambique § Goiano Federal Institute of Education, Science and Technology, Urutaı ́-Goiaś , 75790-000 Brazil ∥ Technical School of Health, Federal University of Uberlândia, Uberlândia, Minas Gerais, 38408-902 Brazil ‡

ABSTRACT: This work presents the potentiality of partial least squares discriminant analysis (PLS-DA) associated with midinfrared spectroscopy to detect gasoline, residual automotive lubricant oil, soybean oil, and used frying oil, in biodiesel/diesel blend (B5). The samples of biodiesel/diesel blend unadulterated and adulterated were classified correctly in their respective groups; that is, the PLS-DA models showed 100% correct classification in samples of the test set with high levels of sensitivity and specificity to discriminate between adulterated and unadulterated samples. These results indicate that the methodology proposed is a viable alternative to detect these types of adulterants/contaminants in biodiesel/diesel blend (B5), commonly used in Brazil.

1. INTRODUCTION

Table 1. Composition of Sample Sets Used in Built and Validation of the PLS-DA Models

Biodiesel attracts attention by being an ecologically clean fuel and renewable.1 Among the various sources of feedstocks used to produce this fuel, for example, soybean oil is the most used.2 Brazil is the second largest world producer of soybeans,3 due to technology consolidated of production, and consequently, this is the main feedstock used in the production of biodiesel, about 70%.4 In this perspective, biodiesel has been used in its pure form or in binary mixtures with petrodiesel.5 In binary mixtures, some countries use amounts ranging between 2% and 35% (w/w), for example, 5−30% in France, 20% in the United States and Canada, and 30% in the Czech Republic and Slovak.6 In Brazil, the addition of 5% (v/v) of biodiesel to diesel was made mandatory in 2010 by the Brazilian National Agency for Petroleum, Natural Gas and Biofuels (ANP).7 These mixtures can be adulterated with oils, such as residual automotive lubricant oil (RAL), soybean oil, and used frying oil (UFO), among others. Such irregular practice, related to the addition of adulterants to biodiesel/diesel blend, are in most of the times motivated by the high price of biodiesel compared to the low cost of adulterants.8 On the other hand, contamination of biodiesel/diesel blends by gasoline during transportation using a long-range pipeline can occur, as well as unintended contamination by other petroleum products, during the local distribution stage such as tank lorry delivery.9 Thus, the presence of adulterants in biodiesel/diesel blends can cause serious problems of diesel engines.10 However, depending on the concentration and type of adulterant/contaminant, even the physicochemical standardized tests would not be able to identify them. Therefore, quality control in relation to the levels of contamination in the mixture is of paramount importance.11,12 © 2014 American Chemical Society

% (w/w) adulteration in B5 training set

test set

no. samples

gasoline

RAL

soybean oil

UFO

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

1.01 1.91 3.11 4.12 5.10 5.99 6.95 7.97 9.07 11.12 13.34 15.08 17.19 18.67 19.44 1.53 3.72 5.51 7.76 9.44 11.15 13.22 16.61 18.02 19.27

1.00 1.98 3.18 4.02 5.13 6.09 7.22 8.26 9.16 10.13 13.40 15.21 16.32 18.45 19.25 1.45 3.25 5.63 7.54 9.33 11.05 13.51 16.63 18.21 19.21

1.03 2.02 3.09 3.99 5.04 6.15 7.09 8.22 9.32 11.09 13.21 15.70 16.47 19.19 19.74 1.63 3.38 5.58 7.59 9.35 11.27 13.37 16.71 18.42 19.54

1.00 2.11 2.99 4.02 5.11 6.03 7.17 8.07 9.29 11.29 12.77 15.37 17.77 18.04 19.77 1.48 3.35 5.45 7.63 9.37 11.19 13.25 16.54 18.51 19.33

Received: September 19, 2014 Revised: December 13, 2014 Published: December 17, 2014 227

DOI: 10.1021/ef502122w Energy Fuels 2015, 29, 227−232

Article

Energy & Fuels

Figure 1. Spectra of biodiesel/diesel blends (B5): (a) unadulterated and adulterated with (b) gasoline, (c) residual automotive lubricant oil, (d) soybean oil, and (e) used frying oil.

types of vegetable oils and fats present in the adulteration of biodiesel/diesel blend (B5), correctly classifying 17 samples of a total of 18 samples of a validation set, using data of high performance liquid chromatography allies to K-nearest neighbors (KNN). However, the chromatography is a sensitive method but slow if compared to vibration spectroscopy techniques.18 The infrared spectroscopy methods have advantages, such as the use of relatively low cost equipment that allows field analysis, minimal or no sample treatment, expeditious analysis, causing no sample destruction, and demanding no reagents.19 Also, they allow in situ analysis by having portable equipment. In this sense, Pontes et al.20 used partial least squares discriminant analysis (PLS-DA) to detect adulteration in biodiesel/diesel blends using NIR spectroscopy data (10 mm optical path), which achieved a correct prediction rate of 100% in the test set. Thus, this work presents the potentialities of PLS-DA as a methodology of fast detection of adulterants/contaminants in biodiesel/diesel blend (B5) employing MIR spectroscopy data. Notably, this combination has not been previously used to detect adulterants/contaminants in biofuel.

Some studies to determine adulterants of biodiesel/diesel blends using quantitative analysis can be found in the scientific literature.11−13 Souza et al.14 developed multivariate calibration models based on mid-infrared (MIR) spectroscopy to quantify residual automotive lubricant oil in diesel S-10 (biodiesel/diesel blend) where a root-mean-square error of prediction (RMSEP) value of 0.40% (v/v) was obtained. Oliveira et al.15 described the identification of adulterations of B2 and B5 diesel blends with vegetable oils (0−5%, w/w) using near-infrared (NIR) and Raman spectroscopy. For this purpose, the chemometric methods by principal component regression (PCR), partial least squares (PLS), and artificial neural network (ANN) were used. The better accuracy was obtained with ANN/Raman (0.092%, w/w), which was statistically different from PLS/NIR (0.238%, w/w). Although quantitative methods are used, in the detection of adulterants in biodiesel/diesel blends, qualitative methods may also be employed. These methods present an advantage, in relation to quantitative methods. The fact requires less data; therefore, they are more suitable for monitoring automated inline.16 Corgozinho et al.17 identified residual vegetable oil in diesel oil with addition of 2% of biodiesel (B2) employing spectrofluorimetry and principal components analysis (PCA) getting 100% correct classification. Brandão et al.8 identified

2. EXPERIMENTAL SECTION 2.1. Sample Preparation. The biodiesels (B100) were supplied by the Biofuels Laboratory of Chemistry Institute of the Federal 228


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Energy & Fuels

Figure 2. Estimated class values for training (full symbols) and test sets (empty symbols) for discrimination between adulterated and unadulterated samples of blend of biodiesel/diesel (B5) by PLS-DA model: (a) B5 unadulterated, (b) B5 adulterated with gasoline, (c) B5 adulterated with residual automotive lubricant oil, (d) B5 adulterated with soybean oil, (e) B5 adulterated with used frying oil, where (■) B5 unadulterated samples, (▲) B5 adulterated samples with gasoline, (●) B5 adulterated samples with residual automotive lubricant oil, (⧫) B5 adulterated samples with soybean oil, (◀) B5 adulterated samples with used frying oil, and (- - -) discrimination threshold. University of Uberlândia, Minas Gerais, Brazil. The mineral diesel sample was provided by Transpetro S/A (Uberlândia, State of Minas Gerais, Brazil). In this work, all samples presented the main specifications in accordance with the requirements of Resolutions 14/2012 and 50/2013 of ANP for biodiesel and diesel, respectively. In this study, 25 samples of methyl biodiesels from soybean oil were used. From these biodiesels was prepared 25 samples of biodiesel/ diesel blend with a biodiesel content equal to 5% (v/v); this mixture is called B5. In relation to adulteration, were prepared 25 samples for each type of adulterant (gasoline, RAL, soybean oil, and UFO), added directly into the mixture B5 with concentrations between 1.0% and 20.0% (w/w). Table 1 shows the values of the concentrations of adulterants used in construction (training set) and validation (test set) of PLS-DA models. The training set was completely independent of the test set. 2.2. Acquisition of Infrared Spectra. The MIR spectra were acquired with a PerkinElmer Spectrum Two spectrometer equipped with the attenuated total reflectance (ATR) sample holder and ZnSe crystal. The spectra were record in the range of 4000−600 cm−1, 4 cm−1 resolution, acquired using 16 scans, in quintuplicates. The spectral base lines were corrected in the spectral ranges of 2500−1850 and 4000−3150 cm−1 employing a baseline method. The data set was mean centered and the leave-one-out method was employed. 2.3. Chemometric Analysis. To execute the multivariate procedures, MATLAB Software, version 7.5, and PLS_ Toolbox, version 7.5 (Eigenvector Research), were used.

In this work, for each type of adulterant, a PLS-DA model was built, to classify unadulterated and adulterated samples. The PLS-DA models were developed based on the PLS algorithms, where the variables in the X block (spectral data) are related to classes contained in the y vector.21 The integer values of the class are arranged in a single column, where 1 was used for interest class and 0 for no interest.22 The threshold value is predicted between 0 and 1 based on the Bayes theorem, in order to minimize errors in the prediction of the class through of the appropriate number of latent variables (LV).23 The number of latent variables chosen for the PLS-DA models followed the criterion of lowest prediction error in leave-one-out cross-validation and evaluating of the explained variance in X and y blocks. 2.4. Performance Analysis. The general performance of the PLS-DA models were evaluated through the values of root-meansquare error of calibration (RMSEC) and of prediction (RMSEP).24 The validation of PLS-DA models was performed using the samples of the test set. The parameters of Confusion matrix were adopted to evaluate the performance of PLS-DA models25 such as sensitivity (Sens) and specificity (Spec) in the test sample set. Sensitivity is the number of samples predicted to be in the class divided by the number actually in the class, and specificity is the number of samples predicted not to be in the class divided by the actual number not in the class.26 The sensitivity (Sens) and specificity (Spec) were calculated according to eqs 1 and 2, respectively27 229


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Energy & Fuels

Figure 3. Plots of loadings of latent variables versus wavenumber for the PLS-DA model: (a) (B5) unadulterated and B5 adulterated with (b) gasoline, (c) residual automotive lubricant oil, (d) soybean oil, and (e) used frying oil.

Sens =

TP TP + FN

(1)

Spec =

TN NP + FP

(2)

Therefore, the spectra MIR (Figure 1) presents similarities and, consequently, not possible through simple visual inspection, differentiation of unadulterated and adulterated samples, making it necessary to utilize chemometric methods. Table 2 shows the results of classification parameters obtained for each PLS-DA model for detection of adulteration type in biodiesel/diesel blends (B5). Evaluating the values of mean-squared errors, it is noted that the parameters RMSEC and RMSEP show excellent agreement, meaning that the calibration error is a good estimate of the standard error of prediction observed in samples of the test set.29 We observed that the PLS-DA models developed are efficient in detection of adulterants employing data of MIR spectroscopy. These data were preprocessed using a baseline algorithm for the correction of signal fluctuations in base lines. Thus, with these results, it was not necessary to employ other techniques of preprocessing such as Savitzky−Golay and standard normal variety. Figure 2 presents the values estimated of class, for the training set and test set. Samples that are above the threshold value are classified as belonging to biodiesel/diesel blend (B5) unadulterated and below the threshold value as biodiesel/diesel

where TP, FN, TN, and FP denote the number of True Positives, False Negatives, True Negatives, and False Positives, respectively; for this analysis of biodiesel/diesel blend, unadulterated was considered as “positive” and adulterated was considered as “negative”.

3. RESULTS AND DISCUSSION Figure 1 shows the spectra of biodiesel/diesel blend (B5) unadulterated and adulterated with gasoline, RAL, soybean oil, and UFO. We observed that, in all spectra, there is a prominent band at 2920 cm−1 assigned to the symmetric stretching of CH3; 2850 and 2950 cm−1 are associated with the symmetric and asymmetric stretching of CH2, respectively;28 1750−1730 cm−1 corresponds to the vibrations of CO;11 1600 cm−1 is assigned to CC; and 1450 and 1380 cm−1 is associated with the asymmetric and symmetric deformation of CH3, respectively.28 Finally, the vibration observed next of 1100−1350 cm−1 is probably the stretching of CO−(OR) and in the region of 700−900 cm−1 assigned to the stretching vibration of CH.11 230


Article

Energy & Fuels Table 2. Classification Parameters Obtained by PLS-DA for Detection of Type of Adulteration unadulterated

adulterated with

parameter

B5 pure

gasoline

RAL

soybean oil

UFO

latent variables variance explained % (X/y) RMSEC % (w/w) RMSEP % (w/w) sensitivity (%) specificity (%) threshold value

4 99.37/92.78 0.11 0.11 100 100 0.56

3 97.85/72.06 0.21 0.24 100 100 0.30

8 99.84/87.86 0.14 0.20 100 100 0.34

7 99.82/86.70 0.14 0.14 100 100 0.32

6 99.77/82.58 0.17 0.18 100 100 0.33

■

REFERENCES

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4. CONCLUSION This study presented 100% correct classification, which shows that MIR spectroscopy and PLS-DA can be applied with a safe discrimination between adulterated and unadulterated samples of biodiesel/diesel blend (B5). The correct classification of adulterated samples in relation to the unadulterated samples in the test set indicates that the developed models can be used in quality control of fuels. Therefore, the results of this investigation suggest that the method proposed is a promising alternative for use in the detection of adulterants/contaminants in biodiesel/diesel blends. The procedure is fast, nondestructive, and can be used in situ analysis. The differences between adulterated and unadulterated samples of biodiesel/ diesel blends are important for supervisory organs to detect adulterants/contaminants.

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ACKNOWLEDGMENTS The authors thank the Covenant CNPq/MCT-Mz, process number: 190802/2012-0, for financial support.

blend (B5) adulterated. Threshold is the value used for separating the classes and calculates according to the Bayes theorem.30 It is interesting to note that the vector y contains the numbers 1 and 0 to relate the classes; in this perspective, it was expected that the threshold value was 0.5.23 However, in Figure 2b−e, it is observed that the threshold value is not exactly 0.5. This asymmetry is connected to the fact that the PLS-DA models developed show that the profiles of the MIR spectra have variability from one class to another, in this case, of the different types of adulterants. However, this variability between profiles of MIR spectra makes the estimated threshold value for the PLS-DA model different for each type of adulterant.30 In the five models built, it was observed that samples of the test set were classified as belonging to their respective classes (Figure 2). Consequently, all models showed excellent levels of sensitivity and specificity (Table 2), in which 100% of the samples of the test set were correctly classified. The discriminant analysis was based on the profile of the MIR spectra of the adulterated and unadulterated biodiesel/ diesel blends. The loading plot, presented in Figure 3, shows the MIR bands that contribute to the differentiation between classes. The regions that influenced in discrimination were 2950−2850 cm−1 attributed to the stretching modes of CH2 and CH3; 1750−1730 cm−1 corresponds to vibrations of CO, and the region of 1100−1350 cm−1 is attributed to CO−(OR).

AUTHOR INFORMATION

Corresponding Author

*Phone: +55-34-92354883. E-mail: mazivilasarmentojunior@ yahoo.com.br (S.J.M.). Notes

The authors declare no competing financial interest. 231


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