Effect of heat and pectinase maceration on phenolic compounds and physicochemical quality of Strychnos cocculoides juice
Bibliographic record
Abstract
Strychnos cocculoides fruit is an important food source for rural populations in Zimbabwe in times of scarcity. Its thick pulp tightly adheres to its seeds, causing pulp extraction constraints and waste during processing, leading to underutilisation. Therefore, pectinase maceration combined with heat treatments was studied to improve juice yield and juice quality. Metabolite profiling according to the heat map, FancyTile chromatic scale approach and phenolic compound content were used to compare the identified compounds. Prior to treatments, 16 known phenolic compounds, predominantly belonging to the phenolic acids, flavonoids and iridoid glucoside classes, were tentatively characterized for the first time in S. cocculoides using High Resolution Mass Spectrometry and LC/MS/MS. Overall, results showed that enzymatic treatments increased pulp yield (by 26%), physicochemical quality (38% increase in juice clarity), content of phenolic compounds (predominantly kaempferol, quercetin, caffeic acid, protocatechuic acid, iridoids) and antioxidant activity.The improved extraction of S. cocculoides pulp increases juice yield as well as juice quality by supplying larger amounts of phenolic compounds that have potential health benefits and act as dietary sources of antioxidants for the prevention of diseases caused by oxidative stress.
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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) | 0.001 | 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 | 0.001 |
| 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".