Study of the color development during electro-activation of lactose solution for lactulose production
Bibliographic record
Abstract
Electro-activation (EA) for lactose isomerization into lactulose, a recognized prebiotic, has emerged as a promising and effective technology. However, despite its advantages, the yellowish colored electro-activated solution must be investigated to optimize lactulose production and new knowledge development. In this study, the color development of EA-lactose solution was studied as a function of electro-activation time (0–60 min), relaxation duration (0, 24, 48, and 72 h), and storage temperature for relaxation (5, 25, and 45 °C). The colorization of EA-lactose solution was compared with a chemically alkalinized lactose solution using KOH under equivalent alkalinity as that formed during electro-activation. The colorization was monitored through CIE L*a*b* color space and UV–vis absorption. The gradual increase of b* showed that the color was intense with the duration of electro-activation and the relaxation time. EA-lactose solutions absorbed light mostly in the UV-B region. To understand the color development, the kinetics were analyzed and were found to follow a zero-order reaction. The relatively lower activation energy showed better performance of the EA isomerization compared to chemical isomerization process. HPLC analyses showed that EA yielded higher lactulose (34.13 %) after 72 h relaxation and 19.44 % galactose, but with an intense yellowish color. Relaxation of 10 h was selected as the optimum, corresponding to lactulose and galactose of 30.39 % and 9.94 %, respectively. A similar result was obtained after 31 h for the chemical isomerization process. Moreover, the presence of oligosaccharides and unknown sugars was observed in HPLC, which may open future research on lactose electro-activation.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".