Non-destructive assessment of milk quality using pulsed UV photoacoustic, fluorescence and near FTIR spectroscopy
Bibliographic record
Abstract
Abstract The work describes the application of photoacoustic (PA), fluorescence spectroscopy, and Fourier transform near-infrared spectroscopy as non-destructive optical techniques to examine the quality of milk. The amplitude of the acoustic wave was linearly proportional to the absorbed fluence. The acoustic velocity and the fluence threshold for onset of non-linearity were decreased as the fat % increased. Initially, the PA pressure was increased with fluence but it exhibited non-linearity and occurred earlier i.e. faster as the fat % was increased. The peak pressures of 120, 160, and 180 kPa were determined for 1%, 2%, and 3.5% respectively. The corresponding acoustic transient times of 0.5, 0.44, and 0.36 µ s were calculated for 1%, 2%, and 3.5% milk respectively. The absorption coefficient of milk samples was determined using the pressure-fluence slope and Grüneisen constant, which increased with fat %. The bandwidths between 350–450 nm and 450–550 nm correspond to tryptophan or valine, and Methionine amino acids respectively, and the peak at ≈315 nm is thought to be due to tyrosine. The fluorescence intensity of the sample day 1 (D1-open) decreased with time more significantly due to variations in the environmental condition. The bands between 4000 and 4500 cm −1 correspond to CH-stretch, and day 4 (D4-closed) showed the highest peak amplitudes compared to the others. Combination of N–H and O–H stretch was mainly observed between 4500 and 5000 cm −1 , and the bands at 4581, 4655 cm −1 in fresh sample disappeared in D1-open and D1-closed. New bands of 4717, 4792, and 4829 cm −1 were observed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".