Comparative analysis of physicochemical, nutritional, functional, and sensory properties of rice bran oil from white (Bg 300) and brown rice (At 362)
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Harshaka M. Jans
,
P.A. Buddhika N. Perumpuli
and
A.P. Hashini I. Abeysuriya
Abstract
Rice bran oil (RBO) is one of the healthiest edible oils in the world is locally underutilized. The current study was conducted to examine the physicochemical, nutritional, functional and sensory properties of RBO extracted from two locally grown rice varieties Bg 300 (white rice bran oil: WRBO) and At 362 (brown rice bran oil: BRBO) and to compare those with commercially available coconut oil (CACO). Saponification value in RBOs varied in the range of 175–176 mg KOH/g. BRBO showed a higher peroxide (3.71 ± 0.80 meq O2/kg) and Iodine value (98.38 ± 0.05 g I2/100 g) compared to WRBO. Highest oleic acid percentage (45.34 ± 2.65 %) and smoke point (204.6 ± 8.3 °C) was observed in BRBO. Significantly highest potassium (133.10 ± 1.07 mg/kg) and sodium (76.80 ± 0.10 mg/kg) contents were observed in BRBO and CACO respectively. Further, BRBO showed the highest contents of ɤ-oryzanol (1.49 ± 0.04 %), total flavonoid (1.13 ± 7.66 mg QE/g), total phenolic (1.47 ± 1.46 mg GAE/g) and DPPH radical scavenging capacity (1.04 ± 0.07 mmol Trolox/g). As a whole, BRBO from At362 showed acceptable physicochemical, nutritional and functional properties compared to WRBO and CACO proving it as a promising application in local food industry while utilizing rice bran, a by-product in rice milling.
Acknowledgments
Sincere gratitude is extended to the Department of Food Science and Technology, Faculty of Agriculture, University of Ruhuna, Sri Lanka, for the invaluable support and provision of resources which made this research possible.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Harshaka M.Jans – conducted the research, performed data analysis, and draft the manuscript. P.A.Buddhika N.Perumpuli and A.P.Hashini I.Abeysuriya – provided guidance, supervised the project and critically reviewed the manuscript for important revisions.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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