Recent progress in the thermal treatment of oilseeds and oil oxidative stability: A review
Bibliographic record
Abstract
Oxidative deterioration of vegetable oils is of great importance in the food industry. In China, vegetable oils produced via thermal pretreatment are popular owing to their strong oil flavor and enhanced yield. Here, we review: (i) the currently employed thermal treatment methods of oilseeds before oil extraction; (ii) effects of thermal treatments on the physicochemical properties, contents of minor lipid components, and oxidative stability of vegetable oils; and (iii) Maillard model systems that are related to oil and oilseed chemistry. Among the thermal pretreatment technologies, microwave and infrared radiations are promising, but these are not performed on the same large production scales as roasting. For most oilseeds, thermal treatments increase the yield of extracted oil and content of minor lipid compounds in the oil, such as polyphenols, tocopherols, and phytosterols. In addition, some Maillard reaction products (MRPs) generated by heating oilseeds have been extracted. The presence of both minor lipids and MRPs in the oil confers improved oxidative stability. However, the mechanism or relationship between thermal treatment and oxidative stability is yet to be clearly elucidated because vegetable oil oxidation is dependent on variables such as unsaturation, concentration and types of minor lipid components, MRPs, and the potential synergistic effects of these components. Recently, several Maillard reaction models related to thermally treated oilseeds have been established, suggesting that MRPs play a critical role during oxidation. However, comprehensive identification of antioxidants and the mechanism by which they inhibit oxidation are lacking. Future research can be performed to establish models that would help elucidate the antioxidative mechanisms of MRPs for more oilseeds. Using these models, it will be possible to predict the oil quality after processing, based on the presence of MRPs and oil chemistry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".