Effect of Cooking and Reconstitution Methods on the Loss of Bioactive Compounds in Pigmented and Unpigmented Potatoes
Bibliographic record
Abstract
Total phenolics, anthocyanins and antioxidant capacity of five coloured/pigmented (AR2009-10, Adirondack Red, Adirondack Blue, Congo, and POROIPG22-1) and two unpigmented potato genotypes (“Anuschka” and “Russet Burbank”) were assessed in fresh (with and without skin) and commercially processed/cooked/reconstituted products. Ascorbate profiles of the seven genotypes also were investigated using fresh tuber/tissue only. The results showed that genotypes greatly varied in their contents of bioactive compounds. Ascorbate profiles of the genotypes were not associated to any particular flesh colour/pigment. However, the pigmented potatoes had 1.5 to 2.5 times more the phenolics, 2 to 3 times more antioxidant capacity and higher levels of anthocyanins (13.98 to 38.57 mg C3GE-100g FW) compared to unpigmented genotypes. No anthocyanins were detected in the unpigmented potatoes. Significant losses of total phenolics, anthocyanins and total antioxidant capacity were found during peeling (18% - 23%), blanching process (40% - 60%) and further cooking/ reconstitution (7% - 12%) with no prominent genotype differences. Together, 65 to 90% of these bioactive compounds were lost during processing. The results suggested that pigmented potatoes contained higher amounts of total phenolics and anthocyanins and blanching step took away the most of the original bioactive compounds.
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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".