Physicochemical, thermal and functional characterisation of protein isolates from Kabuli and Desi chickpea (<i>Cicer arietinum</i> L.): a comparative study with soy (<i>Glycine max</i>) and pea (<i>Pisum sativum</i> L.)
Bibliographic record
Abstract
BACKGROUND: Chickpea (Cicer arietinum L.) seeds are a good source of protein that has potential applications in new product formulation and fortification. The main objectives of this study were to analyse the physicochemical, thermal and functional properties of chickpea protein isolates (CPIs) and compare them with those of soy (SPI) and pea (PPI) protein isolates. RESULTS: Extracted CPIs had mean protein contents of 728-853 g kg(-1) (dry weight basis). Analysis of their deconvoluted Fourier transform infrared spectra gave secondary structure estimates of 25.6-32.7% α-helices, 32.5-40.4% β-sheets, 13.8-18.9% turns and 16.3-19.2% disordered structures. CPIs from CDC Xena, among Kabuli varieties, and Myles, among Desi varieties, as well as SPI had the highest water-holding and oil absorption capacities. The emulsifying properties of Kabuli CPIs were superior to those of PPI and Desi CPIs and as good as those of SPI. The heat-induced gelation properties of CPIs showed a minimum protein concentration required to form a gel structure ranging from 100 to 140 g L(-1) . Denaturation temperatures and enthalpies of CPIs ranged from 89.0 to 92.0 °C and from 2.4 to 4.0 J g(-1) respectively. CONCLUSION: The results suggest that most physicochemical, thermal and functional properties of CPIs compare favourably with those of SPI and are better than those of PPI. Hence CPI may be suitable as a high-quality substitute for SPI in food applications.
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 | 0.001 |
| 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".