Assessing Set‐Style Yogurt Quality After Vibration or Altitude Postproduction Treatment Using Noninvasive Methods
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Bibliographic record
Abstract
ABSTRACT The impact of vibration or altitude on set‐style yogurt after production was assessed on a technology platform simulating the conditions encountered during road and air transportation. Rheological (apparent viscosity, firmness, stress relaxation, frequency dependence of elastic, and viscous moduli) and physicochemical (pH and syneresis) properties of yogurts were evaluated for 22 days. Noninvasive methods (visible near‐infrared reflectance and nuclear magnetic resonance) were also evaluated. The rheological and physicochemical properties were not significantly affected by altitude or vibration compared with control yogurt (no treatment). Apparent viscosity, firmness, and frequency dependence of both moduli significantly increased by 13%, 5%, and 3%, respectively, during storage, probably due to gel restructuring. The transverse relaxation time constant T 21 , measured by nuclear magnetic resonance, significantly decreased by 13% in control and altitude conditions after 22 days. Yogurt with vibration condition showed constant T 21 values, suggesting that vibration affected the restructuring process of yogurt during storage. Both noninvasive techniques were able to significantly differentiate ( p < 0.01) yogurts with postproduction treatments from control (69.2% and 87.0% accuracy) by partial least squares‐discriminant analysis. This was not observed with conventional methods. Predicted correlation between conventional and noninvasive methods by partial least squares regression was found with R 2 cross‐validation of 0.69 for visible near‐infrared reflectance and pH, 0.83 for stress relaxation, 0.79 for firmness, and 0.73 for apparent viscosity with nuclear magnetic resonance. The better sensitivity of the noninvasive methods compared with conventional analysis offers potential for the detection of quality control deviation in set‐style yogurts during transportation.
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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 | 0.000 |
| 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 | 0.001 |
| 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 it