title: Digital image-based classification of biodiesel.
abstract: This work proposes a simple, rapid, inexpensive, and non-destructive methodology based on digital images and pattern recognition techniques for classification of biodiesel according to oil type (cottonseed, sunflower, corn, or soybean). For this, differing color histograms in RGB (extracted from digital images), HSI, Grayscale channels, and their combinations were used as analytical information, which was then statistically evaluated using Soft Independent Modeling by Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and variable selection using the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). Despite good performances by the SIMCA and PLS-DA classification models, SPA-LDA provided better results (up to 95% for all approaches) in terms of accuracy, sensitivity, and specificity for both the training and test sets. The variables selected Successive Projections Algorithm clearly contained the information necessary for biodiesel type classification. This is important since a product may exhibit different properties, depending on the feedstock used. Such variations directly influence the quality, and consequently the price. Moreover, intrinsic advantages such as quick analysis, requiring no reagents, and a noteworthy reduction (the avoidance of chemical characterization) of waste generation, all contribute towards the primary objective of green chemistry.
authors.affiliation: Programa de Pós-Graduação em Ciências Agrárias, Universidade Estadual da Paraíba, 58.429-500 Campina Grande, PB, Brazil.
authors.forename: Gean Bezerra
authors.initials: GB
authors.lastname: Costa
authors.affiliation: Departamento de Química, Centro de Ciências e Tecnologia, Universidade Estadual da Paraíba, 58.429-500 Campina Grande, PB, Brazil.
authors.forename: David Douglas Sousa
authors.initials: DD
authors.lastname: Fernandes
authors.affiliation: Departamento de Química, Centro de Ciências e Tecnologia, Universidade Estadual da Paraíba, 58.429-500 Campina Grande, PB, Brazil.
authors.forename: Valber Elias
authors.initials: VE
authors.lastname: Almeida
authors.affiliation: Departamento de Química, Centro de Ciências e Tecnologia, Universidade Estadual da Paraíba, 58.429-500 Campina Grande, PB, Brazil.
authors.forename: Thomas Souto Policarpo
authors.initials: TS
authors.lastname: Araújo
authors.affiliation: Departamento de Química, Centro de Ciências e Tecnologia, Universidade Estadual da Paraíba, 58.429-500 Campina Grande, PB, Brazil.
authors.forename: Jessica Priscila
authors.initials: JP
authors.lastname: Melo
authors.affiliation: Programa de Pós-Graduação em Ciências Agrárias, Universidade Estadual da Paraíba, 58.429-500 Campina Grande, PB, Brazil; Departamento de Química, Centro de Ciências e Tecnologia, Universidade Estadual da Paraíba, 58.429-500 Campina Grande, PB, Brazil.
authors.forename: Paulo Henrique Gonçalves Dias
authors.initials: PH
authors.lastname: Diniz
authors.affiliation: Programa de Pós-Graduação em Ciências Agrárias, Universidade Estadual da Paraíba, 58.429-500 Campina Grande, PB, Brazil; Departamento de Química, Centro de Ciências e Tecnologia, Universidade Estadual da Paraíba, 58.429-500 Campina Grande, PB, Brazil. Electronic address: germano.veras@pq.cnpq.br.
authors.forename: Germano
authors.initials: G
authors.lastname: Véras
mesh_terms: D000465:Algorithms; D056804:Biofuels / Q000032:analysis* / Q000145:classification*; D003369:Cottonseed Oil / Q000737:chemistry*; D016002:Discriminant Analysis; D006368:Helianthus / Q000737:chemistry*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D013025:Glycine max / Q000737:chemistry*; D013050:Spectrometry, Fluorescence / Q000379:methods
chemical_list: D056804:Biofuels; D003369:Cottonseed Oil
keywords: Biofuel; Color histograms; Pattern recognition; Successive Projections Algorithm; Webcam