Enzymatic synthesis of calcium lactobionate from cheese whey permeate as a value-added ingredient
Bibliographic record
Abstract
Lactobionic acid (LBA) and its salt form (e.g., lactobionate [LBN]) have emerged as high-value-added functional ingredients in food and pharmaceutical applications, such as acidulant, antioxidant, metal chelator, and carrier in drug delivery systems. Enzymatic oxidation has been employed as a nontoxic, cost-effective, and environmentally friendly approach for the synthesis of LBA/LBN. The current study investigated, first, the feasibility of producing calcium lactobionate (Ca-LBN) via enzymatic oxidation using cheese whey permeate as a substrate at high concentration, and second, the bioactivity of the resulting Ca-LBN. The production experiment was performed using reconstituted cheese whey permeate solution (300 g·L −1 lactose) as a substrate, Ca(OH) 2 as a base, and enzyme oxidase (dosage: 400 U·kg −1 lactose) and catalase (dosage: 168,000 CIU·kg −1 lactose) in a laboratory bioreactor. Target critical control parameters, such as pH 6.40; dissolved oxygen: 44%; and temperature: 38°C, were defined and monitored using an industrial human-machine interface (HMI) to ensure operational stability. The consumption of Ca(OH) 2 was used to calculate real-time molar conversion rate (MCR RT ) and accumulative molar conversion yield (MCY) according to the pH-stat method. Enzymatic oxidation reaction continued for 7 h, and MCY was observed at nearly 99%. The MCR RT rapidly reached a plateau value of ∼470 mmol·h −1 within 20 min of the process. The critical operational parameters remained controlled by the HMI cascade, suggesting that the process is scalable. The DPPH-radical scavenging and ferrous ion chelating activity of the obtained LBN could not be confirmed based on the colorimetric assays used in the present work; however, characterization processes need to be further optimized. The obtained knowledge may be applied to the scalable production of LBA/LBN, enabling higher yields and an efficient manufacturing 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 | 0.000 | 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 | 0.000 |
| 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".