Assessing Set‐Style Yogurt Quality After Vibration or Altitude Postproduction Treatment Using Noninvasive Methods
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 T21, measured by nuclear magnetic resonance, significantly decreased by 13% in control and altitude conditions after 22 days. Yogurt with vibration condition showed constant T21 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 R2 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".