ORIGINAL PAPER

Identification of Vegetable Oil or Biodiesel Added to Diesel UsingFluorescence Spectroscopy and Principal Component Analysis

Giancarlo Tomazzoni • Marilena Meira • Cristina M. Quintella •

Giuliano Fernandes Zagonel • Bill Jorge Costa • Paulo Roberto de Oliveira •

Iuri Muniz Pepe • Pedro Ramos da Costa Neto

Received: 28 May 2013 / Revised: 9 September 2013 / Accepted: 14 September 2013 / Published online: 5 December 2013

� AOCS 2013

Abstract In order to identify possible adulteration of

onroad diesel with vegetable oil, fluorescence spectroscopy

was used as the analytical technique to differentiate

between vegetable oil and biodiesel in diesel blends. Die-

sel/oil and diesel/biodiesel blends made with different

proportions of soy, canola or waste cooking oil were ana-

lyzed. The reduced cost of analysis using fluorescence

spectroscopy together with the reliability of the results

suggest that this technique could be of great use in dif-

ferentiating between diesel, biodiesel and vegetable oil and

could therefore be used for rapid identification or confir-

mation of adulterated diesel. Furthermore, a compact

fluorescence spectrophotometer with an LED excitation

source could be used in gas stations or fuel distributors for

diesel quality control because of its practicality, low cost

and reliability.

Keywords Adulteration � Fuel � Biodiesel � Diesel �Vegetable oil � Fluorescence spectroscopy � Principal

component analysis (PCA)

Introduction

Diesel oil is a complex mixture consisting basically of

paraffinic, olefinic and aromatic hydrocarbons and, in

smaller amounts, molecules containing sulfur, nitrogen,

metals, oxygen, etc. Diesel is produced during petroleum

refining and formulated by mixing various streams such as

gas oils, heavy naphtha, light diesel and heavy diesel from

different stages in the processing of crude oil [1].

Biodiesel is formed by alkyl esters of long-chain fatty

acids derived from vegetable oils or animal fats. It is a

renewable, biodegradable fuel intended for use in com-

pression engines [2] and is produced by the transesterifi-

cation of triglycerides, generally by acid or base catalysis

[3].

Vegetable oils contain 90–98 % triglycerides [4], as

well as fatty acids, phospholipids, sterols, water and other

impurities [5]. Crude vegetable oil is obtained by extrac-

tion with a solvent and/or pressing [6]. Depending on the

use to which they are being put, the oils are subjected to

different stages of the refining process, which involves

removing the solvent, degumming, bleaching, deacidifica-

tion and deodorization [7]. Because fluorophores such as

polycyclic aromatic hydrocarbons (PAH) are found in

diesel [8] and tocopherols, chlorophyll, riboflavin and

vitamins (A, K and D) are present in vegetable oils and

biodiesel [9], fluorescence spectroscopy can be used as an

analytical technique to identify adulteration of diesel with

substances with similar characteristics, such as vegetable

oils.

G. Tomazzoni � P. R. da Costa Neto

Quımica e Biologia, Universidade Tecnologica Federal do

Parana, Curitiba, Brazil

M. Meira � C. M. Quintella

Quımica Geral e Inorganica, Universidade Federal da Bahia,

Salvador, Brazil

G. F. Zagonel � B. J. Costa (&)

Centro de Energias, Instituto de Tecnologia do Parana-Tecpar,

Curitiba, Brazil

e-mail: billcosta@tecpar.br

P. R. de Oliveira

Quımica Geral e Inorganica, Universidade Tecnologica Federal

do Parana, Curitiba, Brazil

I. M. Pepe

Fısica Geral, Universidade Federal da Bahia, Salvador, Brazil

123

J Am Oil Chem Soc (2014) 91:215–227

DOI 10.1007/s11746-013-2354-5

Fuels are adulterated by the addition of low-value-added

products to allow illegal financial gains to be made. This

practice is common in Brazil and causes problems related

to vehicle engine performance and the emission of pollu-

tants into the atmosphere. It also leads to tax evasion and

creates unfair competition, with negative consequences for

the economy. In the case of diesel oil specifically, adulte-

ration with kerosene is a common practice [10]. Another

form of adulteration is the addition of vegetable oil that has

not undergone transesterification or waste oils instead of

biodiesel. Diesel can easily be adulterated in this way

because of the miscibility of oil and diesel. Hence, it is of

fundamental importance in the quality control of this type

of fuel to differentiate between pure diesel oil and blends

with biodiesel or adulterants [9].

In practice, various analytical methods are used to

identify adulterated diesel in terms of the quantity of bio-

diesel added and compliance with legislation on maximum

quantities of foreign substances. Most of these analytical

methods are used in conjunction with chemometric tools,

such as principal component analysis (PCA), which pro-

vide readily understandable information from a given set of

data. These data are organized in matrices (bidimensional

data), where the rows can be samples and the columns

variables. PCA simplifies interpretation by reducing the

number of dimensions of the original data and helping to

model, detect outliers, select important variables in a given

system, classify and forecast [11]. PCA has been used to

analyze data in various fields, such as administration, the

social sciences, engineering, chemistry and biology [12].

Here, fluorescence spectroscopy was used, and an

absorption wavelength and emission band were chosen.

The use of fluorescence techniques together with chemo-

metrics to analyze fuels has been amply reported in the

literature. For example, Meira et al. [9] used total fluo-

rescence spectroscopy with PCA to identify diesel that had

been adulterated by the addition of waste cooking oil

instead of biodiesel. Corgozinho et al. [13] used synchro-

nized fluorescence spectroscopy and partial least squares

(PLS), PCA and linear discriminant analysis (LDA) to

identify and quantify waste oil in diesel with 2 % biodiesel.

Patra and Mishra [8] studied the contamination of diesel oil

by various common adulterants, such as kerosene, n-hex-

ane, cyclohexane and turpentine, using excitation emission

matrix (EEM) fluorescence and excitation emission matrix

spectral subtraction (EEMSS) fluorescence as basic tech-

niques. They reported that unlike EEMSS, which can easily

detect a minute amount of adulterant in diesel, EEM cannot

be used to analyze adulterated diesel because of the transfer

of energy between the PAH in these systems.

Fourier transform infrared spectroscopy (FTIR) together

with PLS regression has been used to identify B2 diesel

adulterated with 0.5–20 % waste oil [14]. The quality of

biodiesel/diesel blends, particularly with concentrations

greater than 2 %, has been measured by nuclear magnetic

resonance spectroscopy in association with principal

component regression (PCR) and PLS [15]. Soares et al.

[16] used mid-infrared spectroscopy (MIR) and PCA to

identify and quantify adulteration with crude soy oil in

concentrations from 1 to 40 % in biodiesel made from

cotton, castor oil plant or palm.

The simultaneous use of near- and mid-infrared spec-

troscopy in association with PLS regression was used to

quantify the amount of vegetable oil in a diesel/biodiesel

blend [17]. The amount of adulterants in (B2 and B5)

diesel/biodiesel blends was determined by Fourier trans-

form near-infrared and Raman spectroscopy in association

with PLS, PCR and neural network analysis (NNA) [18].

Adulteration of diesel by oils and vegetable fats was

identified and quantified using a methodology based on the

detection, identification and quantification of triglycerides

(the main components of oils and vegetable fats) in diesel

using high performance liquid chromatography (HPLC)

and multivariate analysis methods (PCA, K-nearest

neighbor and PLS) [19].

The aim of the present study was to investigate the

presence of vegetable oils and biodiesel added to onroad

diesel in proportions varying from 2 to 20 % using fluo-

rescence spectroscopy and infrared spectroscopy as ana-

lytical techniques. The results were analyzed by PCA and

classified according to the type of product and the con-

centration of oil or biodiesel added to the diesel.

Methodology

The following substances were used in the experiments:

soy oil, canola oil, waste cooking oil, biodiesels produced

with these oils, onroad diesel (1800 mg kg-1 sulfur con-

tent) and diesel/biodiesel and diesel/oil blends in the pro-

portions 2, 4, 6, 8, 10, 15 and 20 %.

Standard onroad diesel (with zero biodiesel content) was

kindly provided by the Instituto de Tecnologia do Parana

(TECPAR). The soy and canola oils were provided by Bind

Galvao and used in the experiments without any purifica-

tion. The waste cooking oil was supplied by Ambiental

Santos and was a blend of various fractions. No informa-

tion was available about how many times it had been

reused or the type of food that had been fried with it. Both

the soy and canola biodiesel were produced using methyl

transesterification by base catalysis, followed by purifica-

tion by adsorption onto bentonite using the methodology

described by Paula et al. [20]. For the biodiesel made from

waste cooking oil, the same methodology was used but

with an additional earlier acid catalysis stage because of

the high acidity of this oil (over 3 %).

216 J Am Oil Chem Soc (2014) 91:215–227

123

The viscosity of the biofuels was determined using a

Q860M microprocessor-controlled rotational viscometer

from QUIMIS as described by Brock [2], and the relative

density by the pycnometer method as described by Paula

et al. [20]. The percentage conversion of the oil to methyl

esters was determined by 1H-nuclear magnetic resonance

(1H NMR) spectroscopy (200 MHz) using CDCl3, accord-

ing to the methodology described by Gelbard et al. [21]. In

this method the esters are quantified by directly comparing

the areas a1 and a2 of the signals selected at 3.7 and 2.3 ppm

(hydrogens in the methoxy groups in the esters and in the a-

carbonyl methylenes in the oils, respectively). The per-

centage conversion (Y) to methyl esters is given by the

equation Y ¼ 100 2a1=3a2ð Þ½ �.The samples of pure diesel, biodiesel, soy oil, canola oil,

waste cooking oil and diesel/biodiesel and diesel/oil blends

were analyzed in a Varian (model Cary Eclipse) spectro-

fluorimeter and a Quimis (model 798FIL1) spectrofluorim-

eter with dimensions of 31 cm 9 50, 2 cm 9 20 cm, as

well as by Fourier Transform Attenuated Total Reflectance

Infrared Spectroscopy (FTIR-ATR) with an F8001 from

MIDAC. The Quimis fluorescence spectrophotometer was

developed in the Optical Properties Laboratory at the Fed-

eral University of Bahia Institute of Physics in Salvador,

BA, Brazil, with the help of Quimis. It is a compact piece of

equipment and differs from the Varian equipment mainly in

the source of light: LEDs with different wavelengths.

For the measurements taken with the Varian fluores-

cence spectrophotometer, the samples were excited at a

wavelength of 400 nm with an emission range from 410 to

1100 nm and a 5-nm excitation and emission slit. For the

analyses performed on the Quimis fluorescence spectro-

photometer, the following settings were used: excitation

wavelength 400 nm; emission range 300–1100 nm; and

integration time 140 ms. Infrared measurements were

carried out between 650 and 4000 cm-1 with 36 scans per

sample and a resolution of 4 cm-1.

In the principal component analyses of the results

obtained with the Quimis fluorescence spectrophotometer,

the region of the spectra between 400 and 800 nm was

selected, giving matrices with dimensions 17 9 1136 for

samples of soy, canola and waste cooking oil. The matrices

were mean centered and then submitted to multivariate

analysis using MatLab 6.1�. For the analyses carried out

with the Varian fluorescence spectrophotometer, the region

of the spectra between 410 and 800 nm was selected, giving

matrices with dimensions 17 9 196 for soy, canola and

waste cooking oils and for biodiesels made from these oils.

The matrices were mean centered and then submitted to

multivariate analysis. For the infrared analyses, the

1700–1800 cm-1 region was used, this better represents the

structural characteristics of the compounds being studied.

Three matrices with dimensions 17 9 53 were formed for

the soy, canola and waste cooking oils and biodiesels made

from these oils. As in the previous cases, the matrices were

mean centered and then submitted to multivariate analysis.

The order of the matrices in Matlab was as follows: 1

(diesel), 2 (pure oil), 3 (pure biodiesel), 4 (diesel/oil 2 %),

5 (diesel/oil 4 %), 6 (diesel/oil 6 %), 7 (diesel/oil 8 %), 8

(diesel/oil 10 %), 9 (diesel/oil 15 %), 10 (diesel/oil 20 %),

11 (diesel/biodiesel 2 %), 12 (diesel/biodiesel 4 %), 13

(diesel/biodiesel 6 %), 14 (diesel/biodiesel 8 %), 15 (die-

sel/biodiesel 10 %), 16 (diesel/biodiesel 15 %) and 17

(diesel/biodiesel 20 %).

Results and Discussion

Spectroscopic Analysis of Diesel, Biodiesel, Vegetable

Oils and Blends of These

Table 1 shows the results for the kinematic viscosity and

relative density of pure samples of vegetable oils, diesel

and biodiesels made from soy, canola and waste cooking

oil as well as their respective conversion rates.

Table 1 Physical characteristics of diesel, soy oil, canola oil, waste

cooking oil and their respective monoesters and conversion rates

calculated by H NMR

Samples Kinematic

viscosity

40 �C

(mm2/s)

Density Conversion

rate (%)

(a1) (a2)

Soy oil 26.01 0.9150

Canola oil 27.28 0.9200

Waste

cooking oil

39.41 0.9210

Soy biodiesel 5.37 0.8870 82 0.9398 0.7574

Canola

biodiesel

4.47 0.8780 94 1.2128 0.8583

Waste-

cooking-oil

biodiesel

6.92 0.8910 73 1.0158 0.9250

Diesel 3.15 0.8490

a1 area corresponding to the hydrogen in the methoxy groups, a2 area

corresponding to the hydrogens in the a-carbonyl methylene groups

[6]

1 The Quimis model Q-798FIL fluorescence spectrophotometer used

in this study was a pre-production prototype which was not

commercially available at the time the data reported in this article

were collected although three units had already been installed, tested

and certified in three Brazilian laboratories (two at the Federal

University of Bahia—one in the Optical Properties Laboratory in the

Institute of Physics and one in the Molecular Kinetics and Dynamics

Laboratory in the Institute of Chemistry—and one in the Federal

Technological University of Parana at the Curitiba campus).

J Am Oil Chem Soc (2014) 91:215–227 217

123

The viscosity values for the vegetable oils were signif-

icantly higher than those for the derived monoesters. It can

also be seen that soy and canola biodiesel had much lower

viscosity values than biodiesel made from waste cooking

oil because of the thermal degradation of the oil during the

cooking process and consequent increase in its viscosity.

The conversion rates also followed a similar pattern to the

viscosity and density values, i.e., the greater the conver-

sion, the smaller these parameters (Table 1).

Figure 1 shows the fluorescence and infrared spectra of

diesel, soy, canola and waste cooking oil and their

respective monoesters. The results obtained with each

fluorescence spectrophotometer were similar (Fig. 1a–f).

However, there was a large difference in the 675 nm sig-

nals between the vegetable oils and their monoesters, the

peak for the oil being more intense than that for the bio-

diesel. According to Zandomeneghi et al. [22] and Sikorska

et al. [23], the 675 nm signal is characteristic of chloro-

phyll. In the oil the peak is more intense, possibly because

it has a higher chlorophyll concentration as, unlike bio-

diesel, it did not undergo purification by adsorption onto

clay. Nevertheless, there was no clear evidence of these

signals for waste cooking oil or for biodiesel made from

this oil. For these, the main peaks are in the 520–550 nm

region, and the peak for biodiesel is more intense. This

shift may have occurred because fluorescent compounds

were formed in the waste cooking oil when it was subjected

to high temperatures during the cooking process. Accord-

ing to Poulli et al. [24], the fluorescence spectra of vege-

table oils change significantly when the oils are heated

because of the formation of secondary oxidation products

and the reduction in the quantity of antioxidant phenolic

compounds and chlorophyll. It can be seen in Fig. 1 that

there is a broad high-intensity peak close to 500 nm for

diesel and no peak at 675 nm. It is clear, in any case, that

fluorescence spectroscopy allows differences between pure

diesel, crude vegetable oils and monoesters of these oils to

be detected by observing the position and intensity of the

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Abs

Numero de onda (cm¹)

Diesel Biodiesel Fritura Oleo Fritura

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Abs

Numero de onda (cm¹)

Diesel Biodiesel Canola Oleo Canola

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Abs

Numero de onda (cm¹)

Diesel Biodiesel Fritura Oleo Fritura

0

500

1000

1500

2000

2500

3000

3500

4000

Inte

nsid

ade

Comprimento de onda (nm)

Diesel Biodiesel Fritura Oleo de fritura

0

500

1000

1500

2000

2500

3000

3500

4000

Inte

nsid

ade

Comprimento de onda (nm)

Diesel Biodiesel de Canola Oleo de Canala

500

1000

1500

2000

2500

3000

3500

4000 Diesel Biodiesel Soja Oleo de Soja

Inte

nsid

ade

Comprimento de onda (nm)

0100200300400500600700800900

1000

Inte

nsid

ade

Comprimento de onda (nm)

Diesel Biodiesel Fritura Oleo Fritura

0100200300400500600700800900

1000

Inte

nsid

ade

Comprimento de onda (nm)

Diesel Biodiesel Canola Oleo Canola

1700 1720 1740 1760 1780 18001700 1720 1740 1760 1780 18001700 1720 1740 1760 1780 1800

400 450 500 550 600 650 700 750 800 850 400 450 500 550 600 650 700 750 800 400 450 500 550 600 650 700 750 800

400 450 500 550 600 650 700 750 800400 450 500 550 600 650 700 750 800400 450 500 550 600 650 700 750 800

0100200300400500600700800900

1000

Inte

nsid

ade

Comprimento de onda (nm)

Diesel Biodiesel Soja Oleo soja

A B C

D E F

G H I

Fig. 1 Spectra of pure samples of diesel and (soy, canola and waste

cooking oil) biodiesel and vegetable oils. a–c show the spectra

obtained using a Quimis fluorescence spectrophotometer; d–f show

the spectra produced with a Varian fluorescence spectrophotometer;

and g–i show the spectra obtained by infrared spectroscopy

218 J Am Oil Chem Soc (2014) 91:215–227

123

main fluorescence peaks. However, this is not true for

waste cooking oil or biodiesel made from this type of oil

(Fig. 1c, f).

In the results obtained using infrared spectroscopy, the

1700–1800 cm-1 region, which corresponds to the car-

bonyl group, stood out. The spectra for soy, canola and

waste cooking oils and their respective monoesters had

similar profiles, without any differences between the veg-

etable oil and biodiesel. The biodiesel and vegetable oil

had an intense peak at 1745 cm-1 but diesel showed no

signal in such region, indicating that oil or biodiesel are the

sources of these signals.

The results for the analysis of the diesel/biodiesel, die-

sel/soy, diesel/canola and diesel/waste cooking oil blends

obtained with the Quimis fluorescence spectrophotometer

are shown in Fig. 2.

The diesel/soy oil blends (Fig. 2a) and diesel/waste

cooking oil blends (Fig. 2e) had broad signals with maxi-

mum intensity close to 500 nm (the region corresponding

to diesel). It can be seen in this region, that the greater the

0

500

1000

1500

2000

2500

3000

3500

4000

Inte

nsid

ade

Comprimento de onda (nm)

Diesel Biodiesel Fritura BF_2 BF_4 BF_6 BF_8 BF_10 BF_15 BF_20

0

500

1000

1500

2000

2500

3000

3500

4000

Inte

nsid

ade

Diesel Oleo de fritura OF_2 OF_4 OF_6 OF_8 OF_10 OF_15 OF_20

0

500

1000

1500

2000

2500

3000

3500

4000

Inte

nsid

ade

Diesel

Biodiesel de canola

BC_2

BC_4

BC_6

BC_8

BC_10

BC_15

BC_20

0

500

1000

1500

2000

2500

3000

3500

4000

Inte

nsid

ade

Diesel Oleo de canola OC_2 OC_4 OC_6 OC_8 OC_10 OC_15 OC_20

500

1000

1500

2000

2500

3000

3500

4000

Inte

nsid

ade

Diesel Biodiesel Soja BS_2 BS_4 BS_6 BS_8 BS_10 BS_15 BS_20

400 450 500 550 600 650 700 750 800

Comprimento de onda (nm)400 450 500 550 600 650 700 750 800

Comprimento de onda (nm)400 450 500 550 600 650 700 750 800

Comprimento de onda (nm)400 450 500 550 600 650 700 750 800

Comprimento de onda (nm)400 450 500 550 600 650 700 750 800

Comprimento de onda (nm)400 450 500 550 600 650 700 750 800

500

1000

1500

2000

2500

3000

3500

4000In

tens

idad

e Diesel Oleo de Soja OS_2 OS_4 OS_6 OS_8 OS_10 OS_15 OS_20

A B

C D

E F

Fig. 2 Fluorescence spectra of diesel/oil and diesel/biodiesel (2–20 %) blends using soy oil and biodiesel (a and b), canola oil and biodiesel

(c and d) and waste cooking oil and waste-cooking-oil biodiesel (e and f) analyzed in a Quimis fluorescence spectrophotometer (prototype)

J Am Oil Chem Soc (2014) 91:215–227 219

123

concentration of oil in the blends, the less the intensity of

the peak. For the diesel/biodiesel blends, only the soy

samples (Fig. 2b) followed the same behavior. The spectra

for the blends containing canola oil or biodiesel (Fig. 2c, d)

overlapped as a result of broadening of the peaks. In the

case of the soy and canola oil blends, the 675 nm region of

the spectra is also of interest as even at low signal inten-

sities the peak corresponding to chlorophyll could be

Diesel Biodiesel fritura BF_2 BF_4 BF_6 BF_8 BF_10 BF_15 BF_20

0

100

200

300

400

500

600

700

800

900

1000

Inte

nsid

ade

0

100

200

300

400

500

600

700

800

900

1000

Inte

nsid

ade

0

100

200

300

400

500

600

700

800

900

1000

Inte

nsid

ade

0

100

200

300

400

500

600

700

800

900

1000

Inte

nsid

ade

0

100

200

300

400

500

600

700

800

900

1000

Inte

nsid

ade

0

100

200

300

400

500

600

700

800

900

1000

Inte

nsid

ade

Comprimento de onda (nm)

Diesel Oleo fritura OF_2 OF_4 OF_6 OF_8 OF_10 OF_15 OF_20

Diesel Biodiesel Canola BC_2 BC_4 BC_6 BC_8 BC_10 BC_15 BC_20

Diesel Oleo_canola OC_2 OC_4 OC_6 OC_8 OC_10 OC_15 OC_20

Diesel Biodiesel Soja BS_2 BS_4 BS_6 BS_8 BS_10 BS_15 BS_20

400 450 500 550 600 650 700 750 800

Comprimento de onda (nm)

400 450 500 550 600 650 700 750 800

Comprimento de onda (nm)

400 450 500 550 600 650 700 750 800

Comprimento de onda (nm)

400 450 500 550 600 650 700 750 800

Comprimento de onda (nm)

400 450 500 550 600 650 700 750 800

Comprimento de onda (nm)

400 450 500 550 600 650 700 750 800

Diesel Oleo_soja OS_2 OS_4 OS_6 OS_8 OS_10 OS_15 OS_20

A B

C D

E F

Fig. 3 Fluorescence spectra of diesel/oil and diesel/biodiesel (2–20 %) blends using soy oil and biodiesel (a and b), canola oil and biodiesel

(c and d) and waste cooking oil and waste-cooking-oil biodiesel (e and f) analyzed in a Varian fluorescence spectrophotometer

220 J Am Oil Chem Soc (2014) 91:215–227

123

Diesel Oleo Fritura OF_2 OF_4 OF_6 OF_8 OF_10 OF_15 OF_20

1700 1720 1740 1760 1780 1800

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Abs

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Abs

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Abs

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Abs

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Abs

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8Ab

s

Numero de onda (cm¹)1700 1720 1740 1760 1780 1800

Numero de onda (cm¹)

1700 1720 1740 1760 1780 1800

Numero de onda (cm¹)

1700 1720 1740 1760 1780 1800

Numero de onda (cm¹)1700 1720 1740 1760 1780 1800

Numero de onda (cm¹)

1700 1720 1740 1760 1780 1800

Numero de onda (cm¹)

Diesel Biodiesel Fritura BF_2 BF_4 BF_6 BF_8 BF_10 BF_15 BF_20

Diesel Biodiesel Canola BC_2 BC_4 BC_6 BC_8 BC_10 BC_15 BC_20

Diesel Oleo Canola OC_2 OC_4 OC_6 OC_8 OC_10 OC_15 OC_20

Diesel Biodiesel soja BS_2 BS_4 BS_6 BS_8 BS_10 BS_15 BS_20

Diesel Oleo soja OS_2 OS_4 OS_6 OS_8 OS_10 OS_15 OS_20

A B

C D

E F

Fig. 4 Infrared spectra in the 1700–1800 cm-1 range for diesel/oil and diesel/biodiesel (2–20 %) blends using soy oil and biodiesel (a and b),

canola oil and biodiesel (c and d) and waste cooking oil and waste-cooking-oil biodiesel (e and f)

J Am Oil Chem Soc (2014) 91:215–227 221

123

observed. This phenomenon is less pronounced for diesel/

biodiesel blends and absent for blends with waste cooking

oil or biodiesel made from this oil. Similar results were

obtained for these blends using the Varian fluorescence

spectrophotometer (Fig. 3). With this equipment the peaks

corresponding to the different concentrations of waste

cooking oil were more clearly separated. For the other

blends, some of the peaks were superimposed, making it

difficult to differentiate between samples. This was most

evident with the canola oil and biodiesel blends (Fig. 3c, d)

but also occurred with the soy oil and biodiesel blends

(Fig. 3a, b). Although the same parameters were used to

carry out the analyses in the two fluorescence spectro-

photometers, the results were different, possibly because

the two spectrophotometers had different sensitivities.

Figure 4 shows the results for the infrared analysis of

diesel, vegetable oils and their monoesters, as well as diesel

blends of up to 20 %. As in Fig. 1, there was an intense

signal at 1745 cm-1 for biodiesel and vegetable oil but not

for diesel. No clear distinction can be made between the

spectra for biodiesel and vegetable oil, as the positions and

intensities of the peaks are similar in both cases. In con-

trast, the diesel/oil and diesel/biodiesel blends differed in

terms of the intensity of the peaks, i.e., the greater the

concentration, the greater the signal intensity, allowing the

addition of oil or biodiesel to be clearly identified visually

even in the lowest concentration used (2 %).

Principal Component Analysis (PCA)

Diesel, Soy Oil, Soy Biodiesel and Blends of These

PCA of the results of the analysis of the fuels in the Quimis

fluorescence spectrophotometer (Fig. 5a) showed that three

main components accounted for 97.92 % of the variance of

the data: PC1 accounted for 78.56 %, PC2 for 16.44 % and

PC3 for 2.92 %. In the PC2 9 PC3 scatter plot the samples

Fig. 5 Results of principal component analysis (PCA) of diesel, soy

biodiesel and soy oil. Scatter plot of PC2 9 PC3 for the samples

analyzed in the Quimis spectrophotometer (a). Scatter plot of

PC1 9 PC3 for the samples analyzed in the Varian spectrophotom-

eter (b). Scatter plot of PC1 9 PC2 for the samples analyzed with the

MIDAC infrared spectrophotometer (c)

222 J Am Oil Chem Soc (2014) 91:215–227

123

separated into five distinct regions according to their

chemical composition: diesel; soy biodiesel; soy oil; diesel/

biodiesel blends; and diesel/oil blends.

The second principal component, PC2, separated the

diesel and soy oil samples from the soy biodiesel sample,

the former having negative and the latter positive scores.

This result can be attributed to the structural differences in

the fluorophores in each sample. The differences between

the fluorescence spectra of biodiesel and soy oil can be

attributed to some compounds in the soy oil that are not

present in biodiesel after transesterification or to chloro-

phyll, which produces a high-intensity peak in the oil but a

much lower-intensity peak in the biodiesel following

purification of the diesel by adsorption with clay.

The diesel/biodiesel blends were differentiated from the

diesel/oil blends by PC2, the former having positive and

the latter negative scores. The third principal component,

PC3, separated the diesel and soy biodiesel from the soy

oil, the former having negative and the latter positive

scores.

PCA of the samples analyzed in the Varian fluorescence

spectrophotometer (Fig. 5b) showed that three principal

components accounted for 96.47 % of the variance of the

data: PC1 accounted for 82.45 %, PC2 for 11.79 % and

PC3 for 2.23 %. In the PC1 9 PC3 scatter plot the samples

separated into four distinct regions according to their

chemical composition: diesel; soy biodiesel; soy oil; and

diesel/biodiesel and diesel/oil blends.

The first principal component, PC1, differentiated soy

biodiesel and soy oil samples from diesel samples, the

former having positive and the latter negative scores, and

PC3 separated the diesel and soy biodiesel from the soy oil,

the scores for the former being negative and for the latter

positive. These results are similar to those obtained by PCA

of the results of the analysis with the prototype (Quimis).

This can be explained by the fact that the parameters used

for the analysis were adjusted to be as close as possible in

both pieces of equipment.

PC1 differentiated diesel/biodiesel and diesel/oil blends

from soy oil and soy biodiesel, the scores for the former

being negative and for the latter positive. Unlike PCA of

the results of the analysis with the Quimis equipment, PCA

of the results obtained with the Varian fluorescence spec-

trophotometer was not effective at distinguishing between

diesel/biodiesel and diesel/oil blends.

PCA of the results of the infrared analysis (Fig. 5c)

showed that two principal components accounted for

99.71 % of the variance of the data: PC1, which accounted

for 97.89 %, and PC2, which accounted for 1.82 %. In the

PC1 9 PC2 scatter plot the samples separated into five

distinct regions according to their chemical composition:

diesel; soy biodiesel; soy oil; diesel/biodiesel blends; and

diesel/oil blends. The first principal component, PC1,

separated the diesel, biodiesel and soy oil, the diesel having

a negative score and the soy biodiesel and soy oil positive

scores. The diesel/biodiesel blends were differentiated

from the diesel/oil blends by PC2, the former having

positive and the latter negative scores. The second princi-

pal component also separated the diesel and soy oil from

the soy biodiesel sample, the scores for the former being

negative and for the latter positive.

PCA of the results of the analyses carried out in each of

the three spectrophotometers was effective in differentiat-

ing between pure diesel, soy biodiesel and soy oil. How-

ever, PCA of the results for the Varian spectrophotometer

was not effective in differentiating between diesel/oil and

diesel/biodiesel blends, as was to be expected in light of the

earlier discussion of the spectra shown in Fig. 3. In con-

trast, PCA of the results of the analyses performed in the

Quimis fluorescence and MIDAC infrared spectropho-

tometers was able to differentiate between these blends

even in small proportions.

Diesel, Waste Cooking Oil, Biodiesel Made

from Waste Cooking Oil and Blends of These

Two principal components accounted for 99.34 % of the

variance of the data analyzed in the Quimis spectropho-

tometer (Fig. 6a): PC1 (96.38 %) and PC2 (2.96 %). In the

PC1 9 PC2 scatter plot the samples separated into five

distinct regions according to their chemical composition:

diesel; biodiesel made from waste cooking oil; waste

cooking oil; diesel/biodiesel blends; and diesel/oil blends.

PC1 separated diesel, waste cooking oil and biodiesel

made from this oil, the diesel having a negative score and

the waste cooking oil and biodiesel positive scores. The

diesel/biodiesel blends were differentiated from the diesel/

oil blends by PC2, the scores for the former being positive

and for the latter negative. The second principal component

also differentiated waste cooking oil from biodiesel made

from this oil, the former having a negative and the latter a

positive score.

PCA of the samples analyzed in the Varian fluorescence

spectrophotometer (Fig. 6b) showed that PC1 and PC2

accounted for 99.59 % of the variance of the data (96.98

and 2.61 %, respectively). The PC1 9 PC2 scatter plot

separated the samples according to their chemical com-

position into the same five regions as described for the

Quimis fluorescence spectrophotometer.

PC1 differentiated waste-cooking-oil biodiesel and

waste cooking oil from diesel, the former having positive

scores and the latter a negative score. PC2 distinguished

between diesel/biodiesel and diesel/oil blends. The former

had positive and the latter negative scores. PC2 also dif-

ferentiated between waste cooking oil and biodiesel made

from this oil, the score for the former being negative and

J Am Oil Chem Soc (2014) 91:215–227 223

123

for the latter positive. With the aid of PCA, the results of

the analysis in either fluorescence spectrophotometer could

be used to differentiate between blends of diesel and waste

cooking oil and blends of diesel and biodiesel made from

this oil. However, the same results were not obtained for

the soy oil and soy biodiesel samples analyzed with the

Varian equipment (Fig. 5b).

PCA of the results of the infrared analysis showed that

99.29 % of the variance of the data was accounted for by

PC1 and PC2 (97.99 and 1.30 %, respectively) (Fig. 6c). In

the PC1 9 PC2 scatter plot, the samples once again sepa-

rated into five distinct regions, as previously described.

PC1 separated diesel from waste cooking oil and biodiesel

made from this oil, the diesel having a negative score and

the waste cooking oil and biodiesel positive scores. The

diesel/biodiesel blends were differentiated from the diesel/

oil blends by PC2, the scores for the former being negative

and for the latter positive. The second principal component

also differentiated waste cooking oil from biodiesel made

from this oil, the former having a positive score and the

latter a negative one. These findings show that PCA of the

results of the analyses in all three spectrophotometers was

effective in differentiating between samples of pure diesel,

waste cooking oil, biodiesel made from this oil and blends

of these.

Diesel, Canola Oil, Canola Biodiesel and Blends

of These

PCA of the results of the analysis of diesel, canola oil,

canola biodiesel and blends of these carried out with the

Quimis fluorescence spectrophotometer (Fig. 7a) revealed

that PC1 and PC2 accounted for 99.99 % of the variance

(99.84 and 0.16 %, respectively). In the PC1 9 PC3 scatter

plot the samples separated into four distinct regions

according to their chemical composition: canola biodiesel;

canola oil; diesel/biodiesel blends; and diesel and diesel/oil

blends. PC1 differentiated between diesel/biodiesel blends

and the other samples, the diesel/biodiesel blends having a

positive score and the other samples negative scores.

Fig. 6 Results of principal component analysis (PCA) of waste

cooking oil and biodiesel made from this oil. Scatter plot of

PC1 9 PC2 for the samples analyzed in the Quimis spectrophotometer

(a). Scatter plot of PC1 9 PC2 for the samples analyzed in the Varian

spectrophotometer (b). Scatter plot of PC1 9 PC2 for the samples

analyzed with the MIDAC infrared spectrophotometer (c)

224 J Am Oil Chem Soc (2014) 91:215–227

123

Importantly, PC2 also differentiated between canola oil and

canola biodiesel, the score for the former being more

negative than the score for the latter. The results for the

diesel/oil blends are superimposed on those for the diesel

sample and so cannot be differentiated (Fig. 7a).

PCA of the results obtained with the Varian fluorescence

spectrophotometer show that three principal components

accounted for 99.87 % of the variance of the data (Fig. 7b):

PC1 accounted for 95.13 %, PC2 for 3.68 % and PC3 for

1.06 %. In the PC1 9 PC3 scatter plot the samples sepa-

rated into four regions according to their chemical com-

position: diesel; canola biodiesel; canola oil; and diesel/oil

and diesel/biodiesel blends.

PC1 differentiated canola biodiesel and canola oil from

diesel, the former having positive scores and the latter a

negative score. PC2 differentiated between canola oil and

canola biodiesel, the score for the former being negative

and for the latter positive. It was not possible to differ-

entiate between the diesel and canola oil or canola bio-

diesel blends because the results could not be clearly

divided into distinct regions. This difficulty separating the

samples was already observed for the fluorescence spectra

shown in Fig. 3c and d. The PCA results for canola

biodiesel and canola oil analyzed in the Varian and

Quimis spectrophotometers were different from those for

soy and waste cooking oil and their respective biodiesels.

With the Quimis spectrophotometer, PCA was able to

differentiate clearly between canola oil, canola biodiesel

and diesel/biodiesel blends but could not differentiate

diesel and diesel/oil blends (Fig. 7a). With the Varian

spectrophotometer, PCA differentiated between canola oil

and canola biodiesel but was unable to differentiate

between diesel blends with canola oil or canola biodiesel

(Fig. 7b).

Fig. 7 Results of principal component analysis (PCA) of diesel,

canola biodiesel and canola oil. Scatter plot of PC1 9 PC3 for the

samples analyzed in the Quimis spectrophotometer (a). Scatter plot of

PC1 9 PC3 for the samples analyzed in the Varian spectrophotom-

eter (b). Scatter plot of PC2 9 PC3 for the samples analyzed with the

MIDAC infrared spectrophotometer (c)

J Am Oil Chem Soc (2014) 91:215–227 225

123

PCA of the results of the analysis with the infrared spec-

trophotometer (Fig. 7c) showed that 99.90 % of the variance

of the data was accounted for by three principal components:

PC1 98.03 %, PC2 1.75 % and PC3 0.12 %. The

PC2 9 PC3 scatter plot separated the samples into five

distinct regions: diesel; canola oil; canola biodiesel; diesel/

oil blends; and diesel/biodiesel blends. PC2 separated canola

oil from canola biodiesel, the oil having a negative and the

biodiesel a positive score. The diesel/biodiesel blends were

differentiated from the diesel/oil blends by PC2, the former

having positive and the latter negative scores. The third

principal component differentiated diesel from canola oil

and canola biodiesel, the diesel having a negative score and

the oil and biodiesel positive scores. PCA of the data

obtained using infrared analysis proved the most effective

method for differentiating between pure samples of diesel,

waste cooking oil and biodiesel made from this oil, as well as

between diesel/oil and diesel/biodiesel blends.

Conclusion

The reduced cost of analysis can reach up to 50 % of the

cost of a HPLC analysis, for example. In addition, the

reliability of the results make this technique of great value

in differentiating among diesel, biodiesel and vegetable oil

and allow it to be used for rapid characterization or iden-

tification of adulterated diesel.

A compact fluorescence spectrophotometer with an LED

excitation source could be used in gas stations or fuel

distributors for diesel quality control because of its prac-

ticality, low cost and reliability.

The fluorescence spectra of pure substances and blends

of diesel, vegetable oils and their monoesters obtained with

the prototype (Quimis) and with the Varian fluorescence

spectrophotometer were similar. Vegetable oil and bio-

diesel can be most clearly differentiated by the fluores-

cence peak centered on 675 nm, which is more intense for

vegetable oil. Diesel does not produce any signal at this

wavelength. However, this distinction is not so clear for

diesel with waste cooking oil or biodiesel made from this

oil added to it, making it necessary the use of PCA, an

effective technique for differentiation of pure samples of

diesel, biodiesel, waste oil and their blends. PCA was also

essential to discriminate between biodiesel and vegetable

oil added to diesel from the results of infrared analysis.

References

1. Vieira PA, Vieira RB, De Franca FP, Cardoso VL (2007) Bio-

degradation of effluent contaminated with diesel fuel and gaso-

line. J Hazard Mater 140:52–59

2. Brock J, Noguerira MR, Zakrzevski C, Corazza FC, Corazza ML,

Oliveira JV (2008) Determinacao experimental da viscosidade e

condutividade termica de oleos vegetais. Ciencia e Tecnologia de

Alimentos 28:564–570

3. Silva PRF, Freias TFS (2008) Biodiesel: o onus e o bonus de

produzir combustıvel. Ciencia Rural 38:843–851

4. Srivastava A, Prasad R (2000) Triglycerides-based diesel fuels.

Renew Sustain Energy Rev 4:111–133

5. Ma F, Hanna MA (1999) Biodiesel production: a review. Bio-

resource Technol 70:1–15

6. Tasan M, Gecgel U, Demirci M (2011) Effects of storage and

industrial oilseed extraction methods on the quality and stability

characteristics of crude sunflower oil (Helianthus annuus L.).

Grasas y aceites 62:389–398

7. Reda SY, Carneiro PIB (2007) Oleos e gorduras: aplicacoes e

implicacoes. Revista Analytica 8:60–66

8. Patra D, Mishra AK (2002) Study of diesel fuel contamination by

excitation emission matrix spectral subtraction fluorescence. Anal

Chim Acta 454:209–215

9. Meira M, Quintella CM, Ferrer TM, Goncalves HR, Kamei AG,

Santos MA, Costa Neto PR, Pepe IM (2011) Identificacao de

Adulteracao de Biocombustıvel por Adicao de Oleo Residual ao

Diesel por Espectrofluorimetria Total 3D e Analise das Compo-

nentes Principais. Quım Nova 34:621–624

10. Yadav SHR, Murthy VK, Mishra D, Baral B (2005) Estimation of

petrol and diesel adulteration with kerosene and assessment of

usefulness of selected automobile fuel quality test parameters. Int

J Environ Sci Technol 1:253–255

11. Matos GD, Pereira Filho ER, Poppi RJ, Arruda MAS (2003)

Analise exploratoria em quımica analıtica com emprego de quim-

iometria: PCA e PCA de imagens. Analytica Sao Paulo 6:38–50

12. Faro AC Jr, Rodrigues VO, Eon JG, Rocha AS (2010) Analise

por componentes principais de espectros nexafs na especiacao do

molibdenio em catalisadores de hidrotratamento. Quim Nova

33:1342–1347

13. Corgozinho CNC, Barbeira PJS, Pasa VMD (2008) Determina-

tion of residual oil in diesel oil by spectrofluorimetric and che-

mometric analysis. Talanta 76:479–484

14. Soares IP, Rezende TF, Fortes ICP (2009) Study of the behavior

changes in physical-chemistry properties of diesel/biodiesel (B2)

mixtures with residual oil and its quantification by partial least-

squares attenuated total reflection-Fourier transformed infrared

spectroscopy (PLS/ATR-FTIR). Energy Fuels 23:4143–4148

15. Monteiro MR, Ambrozin ARP, Santos MS, Boffo EF, Pereira-

Filho ER, Liao LM, Ferreira AG (2009) Evaluation of biodiesel–

diesel blends quality using 1H NMR and chemometrics. Talanta

78:660–664

16. Soares IP, Rezende TF, Pereira RCC, Santos CG, Fortes ICP (2011)

Biodiesel adulteration with raw vegetable oil from ATR-FTIR data

using chemometric tools. J Brazilian Chem Soc 22:1229–1235

17. Gaydou V, Kister J, Dupuy N (2011) Evaluation of multiblock

NIR/MIR PLS predictive models to detect adulteration of diesel/

biodiesel blends by vegetal oil. Chemom Intell Lab Syst

106:190–197

18. Oliveira FCC, Brandao CRR, Ramalho HF, Costa LAF, Suarez

PAZ, Rubim JC (2007) Adulteration of diesel/biodiesel blends by

vegetable oil as determined by Fourier transform (FT) near

infrared spectrometry and FT-Raman spectroscopy. Anal Chim

Acta 587:194–199

19. Brandao LFP, Braga JWB, Suarez PAZ (2012) Determination of

vegetable oils and fats adulterants in diesel oil by high perfor-

mance liquid chromatography and multivariate methods. J Chro-

matogr 1225:150–157

20. Paula AJA, Krugel M, Miranda JP, Rossi LFS, Costa neto PR

(2011) Utilizacao de argilas para purificacao de biodiesel. Quım

Nova 34:91–95

226 J Am Oil Chem Soc (2014) 91:215–227

123

21. Gelbard G, Bres O, Vargas RM, Vielfaure F, Schuchardt U

(1995) Nuclear magnetic resonance determination of the yield of

the transesterification of rapeseed oil with methanol. J Am Oil

Chem Soc 72:1239–1241

22. Zandomeneghi M, Carbonaro L, Caffarata C (2005) Fluorescence

of vegetable oils: olive oils. J Agric Food Chem 53:759–766

23. Sikorska E, Gorecki T, Khmelinskii IV, Sikorski M, Kozio J

(2005) Classification of edible oils using synchronous scanning

fluorescence spectroscopy. Food Chem 89:217–225

24. Poulli KI, Chantzos NV, Mousdis GA, Georgiou CA (2009)

Synchronous fluorescence spectroscopy: tool for monitoring

thermally stressed edible oils. J Agric Food Chem 57:8194–8201

J Am Oil Chem Soc (2014) 91:215–227 227

123