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Issue 24, 2016
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Identification of biodiesel feedstock in biodiesel/diesel blends using digital images and chemometric methods

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Abstract

This study aims to identify the biodiesel feedstock (cottonseed, sunflower, corn or soybean oil) in biodiesel/diesel blends using digital images and chemometric methods. For this purpose, colour histograms (extracted from digital images) coupled with supervised pattern recognition techniques: Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA) and the Successive Projections Algorithm for variable selection associated with Linear Discriminant Analysis (SPA-LDA) were used. SPA-LDA coupled with intensity histograms provided better results by selecting 12 variables alone, achieving only one error of classification in the external validation (test) set. Thus, the proposed methodology presents a noteworthy eco-friendly approach for identifying the biodiesel feedstock in biodiesel/diesel blends using a simple, fast, inexpensive and non-destructive analytical tool.

Graphical abstract: Identification of biodiesel feedstock in biodiesel/diesel blends using digital images and chemometric methods

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Article information


Submitted
18 Apr 2016
Accepted
20 May 2016
First published
20 May 2016

Anal. Methods, 2016,8, 4949-4954
Article type
Technical Note

Identification of biodiesel feedstock in biodiesel/diesel blends using digital images and chemometric methods

G. B. Costa, D. D. S. Fernandes, V. E. Almeida, M. S. Maia, M. C. U. Araújo, G. Véras and P. H. G. D. Diniz, Anal. Methods, 2016, 8, 4949
DOI: 10.1039/C6AY01158F

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