Classification of edible vegetable oils using square wave voltammetry with multivariate data analysis.
This paper proposes a simple and non-expensive electroanalytical methodology for classification of edible vegetable oils with respect to type (canola, sunflower, corn and soybean) and conservation state (expired and non-expired shelf life). The proposed methodology employs an alcoholic extraction procedure followed by square wave voltammetry (SWV). Two chemometric methods were compared for classification of the resulting voltammograms, namely Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) with variable selection by the Successive Projections Algorithm (SPA). The results were evaluated in terms of errors in a set of samples not included in the modelling process. The best results were obtained with the SPA-LDA method, which correctly classified all samples in terms of type and conservation state.
Gambarra-Neto
Francisco Fernandes
FF
Universidade Federal da Paraíba, Departamento de Química, João Pessoa, PB, Brazil.
Marino
Glimaldo
G
Araújo
Mário César Ugulino
MC
Galvão
Roberto Kawakami Harrop
RK
Pontes
Márcio José Coelho
MJ
Medeiros
Everaldo Paulo de
EP
Lima
Renato Sousa
RS
D000465:Algorithms; D004563:Electrochemistry / Q000379:methods*; D005504:Food Analysis; D005519:Food Preservation; D015999:Multivariate Analysis; D010936:Plant Extracts; D010938:Plant Oils / Q000032:analysis / Q000145:classification*
D010936:Plant Extracts; D010938:Plant Oils