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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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Publication details

The article was received on 18 Apr 2016, accepted on 20 May 2016 and first published on 20 May 2016


Article type: Technical Note
DOI: 10.1039/C6AY01158F
Citation: Anal. Methods, 2016,8, 4949-4954
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    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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