Effect of Roasting on Phenolic Content and Antioxidant Activities of Whole Cashew Nuts, Kernels, and Testa
Bibliographic record
Abstract
The effect of roasting on the content of phenolic compounds and antioxidant properties of cashew nuts and testa was studied. Whole cashew nuts, subjected to low-temperature (LT) and high-temperature (HT) treatments, were used to determine the antioxidant activity of products. Antioxidant activities of cashew nut, kernel, and testa phenolics extracted increased as the roasting temperature increased. The highest activity, as determined by the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging capacity, oxygen radical absorbance capacity (ORAC), hydroxyl radical scavenging capacity, Trolox equivalent antioxidant activity (TEAC), and reducing power, was achieved when nuts were roasted at 130 °C for 33 min. Furthermore, roasting increased the total phenolic content (TPC) in both the soluble and bound extracts from whole nut, kernel, and testa but decreased that of the proanthocyanidins (PC) except for the soluble extract of cashew kernels. In addition, cashew testa afforded a higher extract yield, TPC, and PC in both soluble and bound fractions compared to that in whole nuts and kernels. Phenolic acids, namely, syringic (the predominant one), gallic, and p-coumaric acids, were identified. Flavonoids, namely, (+)-catechin, (-)-epicatechin, and epigallocatechin, were also identified, and their contents increased with increasing temperature. The results so obtained suggest that HT-short time (HTST) roasting effectively enhances the antioxidant activity of cashew nuts and testa.
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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 | 0.001 |
| 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".