Liquefied dimethyl ether as alternative extraction solvent for high γ-oryzanol rice bran oil: Systematic HSP theory and experimental evaluation
Source abstract
Abstract
This study aimed to systematically find an alternative solvent to replace hexane for the extraction of bio-oil with high γ-oryzanol content from rice bran (RB). The selection involved predicting solubility through Hansen solubility theory, experimental validation, determination of suitable extraction conditions, and comparison of oil quality with that of conventional hexane. A wide variety of solvents: subcritical water (SCW), supercritical carbon dioxide (SCCO2), bio-based solvents (alcohols and terpenes), and liquefied dimethyl ether (LDME), were initially assessed for rice bran oil (RBO) and γ-oryzanol solubility using Hansen solubility spheres. Solvents demonstrating high solubility for both RBO and γ-oryzanol, including LDME, ethyl acetate, acetone, and others (alcohols and SCCO2) known for effective vegetable oil extraction, were selected/identified for experimental extraction comparison. Among these, LDME performed better overall, affording greater solubility and requiring less solvent, shorter duration, lower pressure, and no additional co-solvents for equivalent extractions. Optimal conditions for LDME extraction were identified as 30 °C with a solvent-to-sample ratio of 10 mL/g and an extraction time of 10 min. Oils extracted with LDME and hexane displayed similar fatty acid compositions and no adverse effects on RB protein and carbohydrate structures after LDME extraction were observed. This study demonstrates LDME as a promising alternative to replace hexane for RBO extraction to further valorize this abundant low-cost RB residue into bio-oil and its γ-oryzanol and de-oil RB co-products.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Subcritical and Supercritical Water Processes · Phase Equilibria and Thermodynamics · Free Radicals and Antioxidants
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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