Enzymatic synthesis of calcium lactobionate from cheese whey permeate as a value-added ingredient
Bibliographic record
Abstract
<h2>Abstract</h2> 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<sup>−1</sup> lactose) as a substrate, Ca(OH)<sub>2</sub> as a base, and enzyme oxidase (dosage: 400 U·kg<sup>−1</sup> lactose) and catalase (dosage: 168,000 CIU·kg<sup>−1</sup> 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)<sub>2</sub> was used to calculate real-time molar conversion rate (MCR<sub>RT</sub>) 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<sub>RT</sub> rapidly reached a plateau value of ∼470 mmol·h<sup>−1</sup> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| 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 teacher head, 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".