A standardized method for preparation of potatoes and analysis of their resistant starch content: Variation by cooking method and service temperature
Bibliographic record
Abstract
Consumption of resistant starch (RS) may lead to reduced glycemia, improved satiety, and beneficial changes in gut microbiota due to its unique digestive and absorptive properties. We developed a standardized protocol for preparation of potatoes in order to assess their RS content and modified a commercially available assay for RS to incorporate microscale, high‐throughput processing, quantitative standard curves for amylose and amylopectin and inclusion of a bacteriostatic agent to prevent alteration of carbohydrate profile by microbial contamination. We examined 3 North Dakota potato (Yukon Gold, Red Norland and Russet Burbank) varieties subjected to two methods of preparation (baking at 167° C for 65–80 minutes or boiling at 100° C for 10–11 minutes until tender) and at three service temperatures (hot, 60° C; chilled 4° C for 6 days and chilled followed by reheating to 60° C). RS was analyzed by 3‐way ANOVA. Results show that the RS (g/100g) composition varied by method of preparation (p < 0.0001) and service temperature (p < 0.0001) but not variety (p >; 0.05). Baked preparations of potatoes had higher RS content than boiled preparations, and hot potatoes had less RS than either chilled or reheated potatoes. Knowledge regarding the effects of preparation methods on the RS content of potato products may assist in dietary decision making. Work was supported by USDA 5450–51000‐049–00D and the U.S. Potato Board.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".