Physical and Enzymatic Hydrolysis Modifications of Potato Starch Granules
Bibliographic record
Abstract
In this work, a valorization of the starch stemming from downgraded potatoes was approached through the preparation of starch nanoparticles using different physical methods, namely liquid and supercritical carbon dioxide, high energy ball milling (HEBM), and ultrasonication on the one hand and enzymatic hydrolysis on the other hand. Starch nanoparticles are beneficial as a reinforcement in food packaging technology as they enhance the mechanical and water vapor resistance of polymers. Also, starch nanoparticles are appropriate for medical applications as carriers for the delivery of bioactive or therapeutic agents. The obtained materials were characterized using X-ray diffraction as well as scanning and transmission electron microscopies (SEM and TEM), whereas the hydrolysates were analyzed using size exclusion chromatography coupled with pulsed amperometric detection (SEC-PAD). The acquired results revealed that the physical modification methods led to moderate alterations of the potato starch granules' size and crystallinity. However, enzymatic hydrolysis conducted using Pullulanase enzyme followed by nanoprecipitation of the hydrolysates allowed us to obtain very tiny starch nanoparticles sized between 20 and 50 nm, much smaller than the native starch granules, which have an average size of 10 μm. The effects of enzyme concentration, temperature, and reaction medium pH on the extent of hydrolysis in terms of the polymer carbohydrates' fractions were investigated. The most promising results were obtained with a Pullulanase enzyme concentration of 160 npun/g of starch, at a temperature of 60 °C in a pH 4 phosphate buffer solution resulting in the production of hydrolysates containing starch polymers with low molecular weights corresponding mainly to P-10, P-5, and fractions with molecular weights lower than P-5 Pullulan standards.
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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) | 0.001 | 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".