Nondestructive Measurement of Light-induced Oxidation in Dairy Products by Fluorescence Spectroscopy and Imaging
Bibliographic record
Abstract
The purpose of this paper is to demonstrate the potential of solid-sample fluorescence spectroscopy in nondestructive assessment of light-induced oxidation in different dairy products such as Swiss cheese, cream cheese, and sour cream. Analytical and quantitative spectral properties of fluorescence were elucidated by use of principal component analysis with designed experiments involving different levels of air and light exposure. A significant reduction in fluorescence intensity at approximately 525 nm, and a corresponding increase in the region 415 to 490 nm as a result of illumination was observed on all the products. The effect was ascribed to photodegradation of riboflavin. Variation in two smaller peaks at approximately 620 nm and 630 nm was an interaction effect between exposure to light and air. A pronounced interaction effect between light and air produced intense blue fluorescence and off-flavors on Swiss-like Jarlsberg cheese. High correlations (0.83 to 0.93) between fluorescence spectra and sensory measured off-flavors were obtained for cream cheese. Results indicate that solid-sample fluorescence can be used as a nondestructive and rapid tool to measure the degree of light-induced degradation of riboflavin as well as sensory properties connected to storage of dairy products. Images of fluorescence can be used to visualize the intensity and propagation of this process. The simplicity and rapidity of the method offer rich opportunities for efficient evaluation of factors affecting light-induced oxidation in dairy products, such as packaging materials, light sources, exposure time, and temperature.
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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) | 0.000 | 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) | 0.000 | 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".