Ascorbic acid mediated hydrolysis of galactomannans
Bibliographic record
Abstract
Ascorbic acid (AA) is an antioxidant widely used in the food industry to prevent colour fade and spoilage. This study assesses the effect of 0.02 wt% AA on the rheology of common food thickeners – galactomannans (GM). GMs immediately exhibit a significant reduction in solution viscosity upon AA addition: guar gum (−67 % ± 7 %), locust bean gum (−47 % ± 5 %), and cassia gum (−58 % ± 4 %). Other food acids at 0.02 wt% showed no decline in viscosity, nor did another reducing agent, potassium iodide. GMs were then mixed with xanthan gum (XG) ± low acyl gellan gum (LAG) and AA's impact was assessed using small amplitude oscillatory shear rheology. As the temperature decreased, the storage modulus decreased in the presence of AA compared to without. The molecular weight ( MW ) of the GMs ± AA, was assessed using size exclusion chromatography – multi angle light scattering. The reduction in MW , was between 6 and 8 times for each GM, and was supported with analytical ultracentrifugation. This established the hydrolytic decomposition of GMs by AA, leading to a decrease in function due to a reduction in MW . This hydrolytic effect was observed regardless of pH, showing that acid hydrolysis isn't the primary mechanism. This study shows, for the first time, that AA causes extensive degradation of galactomannans, affecting their viscoelastic characteristics. These findings could affect many products; informing decisions on their quality and shelf life, as well as their cost-effectiveness and environmental life cycle. • Ascorbic acid lowered viscosity of galactomannan solutions by up to 60 %. • Ascorbic acid did not appear to affect xanthan or low acyl gellan gums. • SEC-MALS showed ascorbic acid reduced molar mass of galactomannans by almost 90 %. • Anion exchange chromatography recorded only small amounts of free galactose/mannose. • AFM images showed the breakdown of a continuous cassia gum matrix by ascorbic acid.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".