Application of MCR-ALS and correlation constrain for determination

Sociedade Brasileira de Química (SBQ)
Application of MCR-ALS and correlation constrain for determination of
compounds in biodiesel blends using NIR and visible spectroscopy.
Rodrigo R. de Oliveira
1,2
1
3
2
(IC)*, Kássio M. G. de Lima (PQ), Romà Tauler (PQ), Anna de Juan (PQ)
1
Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Av. Senador
Salgado Filho 3000, Natal, CEP 59078-970, Brazil.
2
Chemometrics Group, Dep. of Analytical Chemistry, Universitat de Barcelona, Diagonal 645, Barcelona, 08028, Spain.
3
Institute of Environmental Assessment and Water Studies, IDAEA-CSIC, Jordi Girona 18-26, Barcelona 08034, Spain.
*[email protected]
Key words: Biodiesel, multivariate curve resolution, correlation constraint, multivariate calibration, sample matrix effect.
Results and Discussion
The correlation constraint has been adapted to
handle the presence of batch-to-batch matrix effects
due to ageing effects, which might occur when
different groups of samples were used to build a
calibration model in the first application. Different
data set configurations and diverse modes of
application of the correlation constraint are explored
and guidelines are given to cope with different type
of analytical problems, such as the correction of
matrix effects among biodiesel samples, where
MCR-ALS outperformed PLS reducing the relative
error of prediction RE(%) from 9.82 % to 4.85 % in
the first application, or the determination of minor
compound with overlapped weak spectroscopic
signals, where MCR-ALS gave slightly higher
a
37 Reunião Anual da Sociedade Brasileira de Química
MCR−ALS (Global Model with 2 comp.)
25
x=y
x=y
Calibration
Samples
20
Calibration
Test
samples
20
Validation
15
15
a)
10
10
5 5
0
0
0
15
10
5
MCR−ALS (Global Model with 3 comp.)
25
Predicted biodiesel concentration [%v/v]
20
Predicted biodiesel concentration [%v/v]
Predicted biodiesel concentration [%v/v]
Biodiesel production has been increased mainly
because the environmental concern about fossil
fuels consumptions and the consequent increase of
greenhouse gas emissions. Biodiesel can partially or
completely replace diesel from petroleum and is
used blended with diesel to power vehicles engines.
It consists of a mixture of alkyl esters of long chain
fatty acids susceptible to oxidation. Different
analytical methodologies have been proposed for
analysis of several biodiesel parameters to
overcome this expansion and the quality control
1–4
necessities. This study describes two applications
of a variant of the multivariate curve resolution
alternating least squares (MCR-ALS) method with a
5–7
correlation constraint.
The first application
describes the use of MCR-ALS for the determination
of biodiesel concentrations in biodiesel blends using
near infrared (NIR) spectroscopic data. In the
second application, the proposed method allowed
the determination of the synthetic antioxidant N,N'Di-sec-butyl-p-phenylenediamine (PDA) present in
biodiesel mixtures from different vegetable sources
using visible spectroscopy. Well established
multivariate regression algorithm, partial least
8,9
squares (PLS), were calculated for comparison of
the quantification performance in the models
developed in both applications.
(RE(%) = 3.16 %) for prediction of PDA compared to
PLS (RE(%) = 1.99 %), but with the advantage of
recovering the related pure spectral profile of
analytes and interferences.
Predicted biodiesel concentration [%v/v]
Introduction
x=y
20
Calibration Samples
20
Test samples
x=y
b)
Calibration
Samples
Test samples
15
b)#
15
10
5
0
10
5
0
10
05
5 10 10 15 15
0
2020
55
10 0 1515 20 20 25
Actual
Actual biodiesel concentration
[%v/v]
0
5
10
15 biodiesel
20 concentration [%v/v]
Actual biodiesel concentration [%v/v]
0
25
PLS model with 2 LV
PLS model with 3 LV
Figure
a) before,
25 1 Sample matrix effect correction.
25
b) after. c)
d)
20
20
15
Conclusion
15
The obtained
results show the potential
of the MCR10
10
ALS method to be adapted to diverse data set
5
5
configurations and analytical problems related to the
0
0
determination
of biodiesel mixtures
and added
0
5 therein.
10
15
20
25
0
5
10
15
20
compounds
Actual biodiesel concentration [%v/v]
Aknowledgment
CNPq/CAPES Ciência sem Fronteiras – project
grants 238577/2012-0 and 070/2012 Brazil are
acknowledged. A. de Juan acknowledges support of
Spanish project CTQ 2012-38616.
____________________
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