Optimization of Aqueous Enzymatic Microwave Assisted Extraction of Macadamia Oil And Evaluation of Its Chemical Composition, Physicochemical Properties, and Antioxidant Activities
Bibliographic record
Abstract
Abstract This study presents the green and effective aqueous enzymatic process assisted microwave extraction (AEME) to preparate macadamia nut oil (MNO). The conditions of the extraction process are optimized (extraction temperature 50 °C, extraction time 64 min, enzyme concentration 1.60% (w/w), and irradiation power 450 W). An oil yield of 58.09 ± 0.63% is achieved under these optimal conditions. The scanning electron micrograph (SEM) analysis of nuts before and after extraction illustrates that AEME promotes the emancipation of oil stored within the organelles. Gas chromatography‐flame ionization detector (GC‐FID) analysis reveals the fatty acid compositions of MNOs obtained by AEME and the Soxhlet extraction (SE) are similar and mainly dominated by monounsaturated fatty acids beneficial to human health which is higher in MNO than in any other known food. Moreover, gas chromatography‐mass spectrometry (GC‐MS) analysis reveals higher amounts of more odoriferous oxygenated terpenes is present in the MNO extracted by AEME in comparison with SE. The physicochemical properties of AEME oil are more excellent than those of SE oil. Moreover, AEME oil exhibits superior antioxidant capacities. In conclusion, green AEME gives relatively satisfactory yield and better retains the fragrance and functionality of MNO. Practical Applications : The present study provides a green extraction method and valuable data for the process design as well as industrial scale‐up applications. In addition, compared to the nonsustainable and environmentally nonfriendly traditional method, AEME preserves the initial composition of the flavor substances and enhances the extraction of healthy beneficial compounds in MNO. Therefore, AEME oil can be used to develop functional edible oils or even in medicinal, cosmetic, and pharmaceutical preparations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".