期刊
FOODS
卷 12, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/foods12010135
关键词
cold-pressed hemp seed oil; differential scanning calorimetry; thermo-oxidative stability; isothermal test; non-isothermal test
Cold-pressed hemp seed oil from stored and fresh hemp seeds of the Henola cultivar were analyzed to determine their differences in acid value, color, peroxide value, p-anisidine value, fatty acid composition, and thermo-oxidative stability. The results showed significant differences in acid value and color between the two groups, while there were no significant differences in peroxide value, p-anisidine value, and fatty acid composition. The thermo-oxidative stability assessment showed no significant differences in oxidation induction time and onset temperature between the two groups of oils.
Cold-pressed hemp (Cannabis Sativa L.) seed oil has become very popular amongst consumers and researchers, due to its manifold application in food and medicine industry. In this study, oils pressed from stored and fresh hemp seeds of the Henola cultivar were analyzed. Determination of the acid value (AV) and color of oil (a* parameter) revealed significant differences between the two groups of oils (fresh and stored seeds) in contrast to the peroxide value (PV), p-anisidine value (p-AV), and fatty acid composition. On the other hand, isothermal and non-isothermal assessments of the thermo-oxidative stability by differential scanning calorimetry (DSC) showed no significant differences in oxidation induction time (OIT) as well as in onset temperature (T-on) between two groups of oils (p > 0.05). The DSC isothermal test (OIT 160) showed significant correlations with mono- and polyunsaturated fatty acids as well as with values of AV and a* (p <= 0.05), in contrast to the non-isothermal test, for which correlations were not significant (p > 0.05). However, the best distinction of both groups of oils was obtained analyzing all results together (DSC, fatty acid and tocochromanols composition, color, and oxidative stability results) by principal component analysis (PCA).
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