Molecular Size Distribution and Amylase Resistance of Maize Starch Nanoparticles Prepared by Acid Hydrolysis
Bibliographic record
Abstract
The molecular size distribution of maize starch nanoparticles (SNP) prepared by acid hydrolysis (3.16M H 2 SO 4 ) and their amylase‐resistant counterparts, before and after debranching, was investigated. The weight average molecular weight (M w ) and linear chain length distribution were determined by high‐performance size‐exclusion chromatography (HPSEC) and high‐performance anion‐exchange chromatography (HPAEC), respectively. The objective was to understand the role of amylose involvement in the formation of SNP showing different crystalline structures (A‐ and B‐types). The HPSEC profiles of SNP before debranching from waxy, normal, and high‐amylose maize starches showed broad monomodal peaks. Debranched SNP from waxy maize eluted in a single narrow peak, whereas those from nonwaxy starches showed a multimodal distribution. Similar trends were also observed for the chain length distribution patterns, for which the longest detectable chains (degree of polymerization [DP] 31) in waxy maize were significantly lower than those of nonwaxy maize starches (DP 55–59). This indicated the potential amylose involvement in the SNP structure of normal and high‐amylose starches. Further evidence of amylose involvement was ascribed to the resistance of SNP toward amylolysis (Hylon VII > Hylon V > normal > waxy). The amylase‐resistant residues of SNP from high‐amylose maize starches were composed of both low M w linear and branched chains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".