Monitoring of milk rennet coagulation: Chemical and physical perspective using Raman spectroscopy
Bibliographic record
Abstract
• Raman spectroscopy and chemometrics are effective in monitoring milk renneting. • Raman signal baseline variation analysis allows the curd cutting time prediction. • Rheological cutting time was modelled through Raman PC scores inflection time. • An R 2 of 0.872 was obtained in cutting time prediction from Raman signal variation. • Baseline correction helps identifying significant components of milk coagulation. The present study applies Raman spectroscopy and chemometrics to monitor the milk renneting process. Raman sensor information (Laser 785 nm) was simultaneously retrieved with rheological measurements to predict the curd cutting time. Principal component analysis was conducted on Raman spectra to collect the relevant information on the coagulating process. Rheological cutting time was modelled using the time at the inflection point of first Principal Component scores, from which an R 2 of 0.901 was obtained for calibration purposes, while 0.872 resulted from a 6-fold cross-validation. For the first time, the response of the Raman sensor was also used to assess the dynamic evolution of signal intensity for every individual Raman shift in order to identify which chemical bonds and molecules are significantly involved in the renneting process. To this concern, the role of tryptophan, P O 4 3 − groups, aspartic acid and phenylalanine results to be important in projecting the original spectra in the principal component subspace. Raman signals can be employed to study the physical behavior of renneting milk and to analyze the changes in chemical bonds, depending on the data pretreatment method applied, specifically in baseline correction. The presented results demonstrate that Raman spectroscopy can be successfully implemented to provide quantitative and qualitative knowledge of the milk coagulation process.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".