Polyphenolics and Antioxidant Capacity of White and Blue Corns Processed into Tortillas and Chips
Bibliographic record
Abstract
ABSTRACT White and blue corns of Mexican and American origins were lime‐cooked to obtain nixtamals with optimal moisture (48–50%) for tortillas and chips. Blue kernels had less bulk density, softer endosperm and, consequently, required less cooking time than the white kernels. The optimum cooking regime for the white kernels was 100°C for 20 min, while the optimum for both pigmented genotypes was 90°C for 0 min (until the lime‐cooking solution reached 90°C). Doughs, tortillas, and chips were characterized by total soluble phenolics (TSP), anthocyanins (ACN), and antioxidant capacity (AOX). A dough acidification procedure using fumaric acid (pH 5.2) was assessed as a means to improve TSP, ACN, and AOX retention. The Mexican blue corn had higher AOX (16%) than the American blue genotype, although the latter had a threefold higher TSP content (12.1 g/kg, dwb). Mexican and American blue corns had higher AOX capacity (29.6 and 25.6 μM trolox equivalents [TE]/g dwb), respectively, than the white corn (17.4 μM TE/g). White corns did not have detectable amounts of ACN, while blue Mexican and American kernels contained 342 and 261 mg/kg. Lime cooking had the greatest negative impact on the stability of TSP, ACN, and AOX. However, the acidification reduced ACN, TSP, and AOX losses by 8–23, 3–14, and 4–15%, respectively. Similar ACN losses were observed for both types of blue kernels when processed into nixtamal/dough (47%); however, ACN losses in tortillas and chips manufactured from the American blue genotype were higher (63 and 81%, respectively) than those of Mexican blue corn products (54 and 75%). ACN losses were highly correlated to TSP (r = 0.91) and AOX capacity losses (r = 0.94).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".