Real-World Quality Assessment of a Medium- and Long-Chain Triglyceride Oil for Deep Fried Japanese Rice Cakes (Agemochi)
Bibliographic record
Abstract
Background: Edible oil consisting of medium-chain triglycerides (MCTs) has potential health benefits; however, the low smoking temperature of this oil prevents its use in deep-frying. Therefore, this study aimed to evaluate the thermal stability of a synthetic medium- and long-chain triglyceride (MLCT) oil, as well as its acceptability for cooking traditional Japanese deep-fried rice cakes (Agemochi). Methods: The content of total polar materials (TPMs) was measured after continuously heating the MLCT and control (non-MCT) cooking oils at 160 °C or 180 °C for 8 h, or after a frying cycle of 1 h performed twice daily for 4 consecutive days. The TPM content was also measured after deep-frying Agemochi without breading or butter (twice daily for 1 h over 4 days). Results: The TPM of the MLCT oil remained below the rejection threshold (25%) under all conditions, whereas that of the control oil increased with temperature and heating time. The TPM content during deep-frying of Agemochi did not reach the rejection limit with either oil type. However, the TPM values after the fourth cooking day were significantly lower in the MLCT oil than in the control, with no apparent differences in sensory evaluation scores. Conclusion: The MLCT oil demonstrated improved thermal stability for deep-frying compared to the non-MLCT oil, as evidenced by the cooking of the Japanese rice cake, Agemochi. Additional studies are required with other foods; however, the results of this study illustrate that an MLCT oil could be a viable option for cooking oil in domestic deep-fat frying.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".