Sustainable Electroisomerization of Lactose into Lactulose and Comparison with the Chemical Isomerization at Equivalent Solution Alkalinity
Bibliographic record
Abstract
The demand of lactulose production is increasing tremendously because of its bifidogenic (prebiotic) functionality. Therefore, the isomerization of lactose to synthesize lactulose through electroactivation (EA) technology is of great interest nowadays. However, lactulose production through electroisomerization is affected by several operational and experimental conditions, and the process needs to be optimized. In this context, the EA technique was applied to isomerize lactose into lactulose in an EA reactor modulated by anion and cation exchange membranes. The effect of lactose concentrations (5, 10, 15, and 20%), applied electric fields (300, 600, and 900 mA), and processing time (0-60 min) on lactose electroisomerization rate (lactulose formation) and coproduct (glucose, galactose, and fructose) formation has been investigated. The effect of different physicochemical parameters such as pH, alkalinity, temperature, ion migration, and oxidation-reduction potential (ORP) on the conversion of lactose into lactulose was correlated with the lactulose formation to understand the involved process mechanism of action. The conversion of lactose into lactulose was lactose-concentration-, electric-current-, and EA-time-dependent and reached the highest lactulose yield of 38% at 40 min using a 900 mA current intensity in a 10% lactose solution. The results were then compared to conventional chemical isomerization maintaining similar alkaline conditions at ambient temperature (22 ± 2 °C). A higher yield of lactulose was achieved in the EA process within a short reaction time compared to that of the chemical isomerization. The outcome of this study suggests that EA is a promising technique for the enhanced production of lactulose from lactose.
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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 | 0.001 |
| 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".