Manyetik rezonans ile gıda sistemlerinde gerçek zamanlı stabilite analizi
Bibliographic record
Abstract
Food stability has always been the interest of the consumers, food producers and regulatory authorities. It is becoming more significant due to the food safety and food quality issues. In this thesis, the initial part of the study focused on the development and optimization of a portable NMR probe which will be used to analyze food systems. The design process included simulations to optimize the magnetic field homogeneity and strength. Then, this custom-built portable NMR probe was tested through a model food system which consists of sucrose, corn syrup, gelatine and water. Correlations done through a linear regression model show its use to monitor the total soluble solid content (TSS) and correlated it with T2eff relaxation times. A linear relationship was observed on the plot of data. Regression analysis was performed accordingly. The lack-of-fit test p-value is found to be 0.210 and has a high coefficient of determination (R² = 0.9113) which shows the model fits well. In the second part, to determine if imaging is necessary to fully understand complex food systems, a benchtop Magnetic Resonance Imaging (MRI) was used to assess the food stability in a model food system which is focusing on pea protein isolate (PPI) mixed with high methoxyl pectin (HMP). Particle size measurements showed that for 2% and 4% PPI containing samples, there is a significant increase (p<0.05). Over the 24-hour observation period, T1 weighted image intensity displayed a statistically significant increasing trend (p<0.05). The T2 weighted image intensity shown a significant decrease (p<0.05) since the water molecules' mobility is increasingly restricted, due to the tighter network of protein aggregates limiting the free movement of water protons. On the other hand, T1 and T2 Map showed that the locational T1 and T2 relaxations time are decreasing over the duration.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".