Self-organizing maps and learning vector quantization networks as tools to identify vegetable oils.
Self-organizing map (SOM) and learning vector quantification network (LVQ) models have been explored for the identification of edible and vegetable oils and to detect adulteration of extra virgin olive oil (EVOO) using the most common chemicals in these oils, viz. saturated fatty (mainly palmitic and stearic acids), oleic and linoleic acids. The optimization and validation processes of the models have been carried out using bibliographical sources, that is, a database for developing learning process and internal validation, and six other different databases to perform their external validation. The model's performances were analyzed by the number of misclassifications. In the worst of the cases, the SOM and LVQ models are able to classify more than the 94% of samples and detect adulterations of EVOO with corn, soya, sunflower, and hazelnut oils when their oil concentrations are higher than 10, 5, 5, and 10%, respectively.
Torrecilla
José S
JS
Departamento de Ingenieria Quimica, Facultad de Ciencias Quimicas, Universidad Complutense de Madrid, 28040-Madrid, Spain. jstorre@quim.ucm.es
Rojo
Ester
E
Oliet
Mercedes
M
Domínguez
Juan C
JC
Rodríguez
Francisco
F
D005227:Fatty Acids / Q000032:analysis; D005506:Food Contamination / Q000032:analysis*; D019787:Linoleic Acid / Q000032:analysis; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D019301:Oleic Acid / Q000032:analysis; D000069463:Olive Oil; D010938:Plant Oils / Q000737:chemistry* / Q000145:classification*; D015203:Reproducibility of Results
D005227:Fatty Acids; D000069463:Olive Oil; D010938:Plant Oils; D019301:Oleic Acid; D019787:Linoleic Acid