The Effect of Various Extracting Agents on the Physicochemical and Nutritional Properties of Pea Starch
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Organic acids (5% lactic acid and 5% acetic acid, w/v) and biosolvents (5% ethyl lactate, 5% and 10% d ‐limonene, w/v) are used as extraction agents during starch isolation from pea flour. NaOH at 0.4% w/v served as a control. The results show few differences in starch granular morphology, apparent amylose content, and most thermal parameters, and negligible differences in starch crystalline structure. However, noticeable differences are observed in protein and starch damage content, and pasting viscosities among starch isolated by different extraction agents. The starch extracted by 5% ethyl lactate has the highest protein residue (1.1%) compared to others (≤0.4%), while the starch extracted by 5% acetic acid shows the lowest pasting viscosities. d ‐Limonene at 5% seems to be a promising biosolvent to isolate pea starch for its low residual protein, and no solvent residue, yet the highest pasting viscosities in starch. Fourier transform infrared spectroscopy (FTIR) detects bands of residual protein in all samples, and also unique bands at 1564, 1575, and 1720 cm −1 related to acid residue in starches extracted by the two organic acids. Chemometric analysis of FTIR spectra differentiates starches extracted by biosolvents from those extracted by alkali and acidic agents.
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.
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 | 0.000 | 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 it