Effect of Heat Treatments on Microbial Load and Associated Changes to β‐Glucan Physicochemical Properties in Whole Grain Barley
Bibliographic record
Abstract
Health claims for barley β‐glucan (BG) have prompted the development of food products containing barley; however, some new products (such as milled grain used without a cook step, as in a smoothie) do not use any form of heat treatment during processing or prior to consumption, which could affect microbial safety and potential health benefits. The aims of this research were to evaluate current commercial barley products for microbial counts and BG characteristics and to determine the effects of different heat treatments on these attributes in whole grain barley samples. Three heat treatments (micronization, roasting, and conditioning) were performed on three cultivars of barley (CDC Rattan, CDC McGwire, and CDC Fibar). The microbial quality was measured with standard plate count (SPC), mold and yeast count (MYC), and coliforms or Escherichia coli. Only four of the 17 commercial barley products tested met acceptable microbial limits used in this study. All three heat treatments applied to the barley samples in this study reduced SPC, MYC, and coliforms to an acceptable level. BG was extracted with an in vitro digestion method to determine its viscosity, molecular weight (MW), and solubility. All three heat treatments produced BG extracts with high viscosity and MW compared with untreated barley. Overall, heat treatments improved both the safety and the potential health benefits from soluble BG in whole grain barley.
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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 | 0.000 | 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 | 0.000 |
| 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".