Cluster analysis for some different types of vegetable oils by the physicochemical characteristics

Antoanela Cozma, Ariana Velciov, Daniela Stoin, Diana Moigrădean, Dacian Lalescu, Mihaela Petcu, Iuliana Crețescu, Casiana Mihuț, Adrian Riviș

Volume 25, Issue 3;
Pages: 121-126; 2019
ISSN: 2069-0053 (print) (former ISSN: 1453-1399), Agroprint;
ISSN (online): 2068-9551
Vegetable oils are a class of natural products commonly used and appreciated due to their nutritional, sensorial, technological principles. Vegetable oils constitutes an important component of human diet often used in daily consumption with beneficial effects for the body balance. The aim of this study was to evaluate and compare some of the physicochemical characteristics (dynamic viscosity, refractive index, surface tension, relative density and acidity index) in case of six food consumption vegetable oils, cold pressed. The analysed oil assortments, in these work were soybean, rapeseed, corn oil, almond, sesame and pumpkin seed oil purchased from the speciality stores with natural products, having different origins. The refractive index was measured using the refractometry method, and the dynamic viscosity using the Ostwald-type viscometer. From the physicochemical analyzed characteristics, it was observed that the values differ from one category of oil to another. Oils density varies from species to species and at the same oil with the conservation conditions (conservation period, climatic conditions in which the plant has developed). Viscosity gives relevant indication of the degree of oil fluidity. The experimental results showed that the highest value for viscosity was registered in rapeseed oil (38,7088cP) and the smallest in soybean oil (34,0174cP). All the data was statistically analyzed using Statistica10. The results showed that there is statistically significant correlation between the physicochemical characteristics for the analyzed types of oils. The purpose of the statistical analysis was to highlight the analysed parameters expressed by cluster analysis.
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