Whole grain wheat flour: Definitions, production, nutritional, technological and microbiological aspects for application in bakery and pasta products
Bibliographic record
Abstract
© 2018 by Nova Science Publishers, Inc.There has been growing interest in the use of whole grain wheat flour by the bakery and pasta industry, as a high added value ingredient, to improve the nutritional quality of products and bring health benefits to consumers, as it is rich in dietary fiber, vitamins, minerals, and antioxidants. However, the industry faces challenges in the elaboration of the different products that incorporate this flour to partially or totally replace refined wheat flour. These challenges involve the need to change process parameters and to solve end product quality problems, such as volume reduction, and color, appearance, flavor, and texture changes in breads and biscuits; and texture and cooking quality in pasta. This chapter will deal with the definitions of whole grain wheat flour in different countries (e.g., Brazil, Argentina, Canada, and USA), differentiating whole grain wheat flour from reconstituted whole grain wheat flour; obtainment processes (stone mills, roller mills, reconstitution, etc.); nutritional, technological and microbiological aspects. Another relevant aspect when working with whole grain wheat flour is to define its quality for specific applications. This chapter will discuss how traditional evaluation methods, used for refined wheat flour, which use equipment such as the farinograph, the extensograph and the alveograph are altered when analyzing whole grain wheat flour. In addition, the chapter will describe the application of whole grain wheat flour in different bakery and pasta products, with examples of the resources available to enhance product quality, including ingredients such as vital wheat gluten, sourdough, additives such as oxidants and emulsifiers, and processing aids such as enzymes. With this, we intend to give a broad overview of the production and use of this flour.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".