The Role of Food Physics in Fulfilment the Expectations of Up-to-Date Food Technologies and Biotechnologies to Use a Well Balanced Nutrition
Bibliographic record
Abstract
The paper deals with some topics of important aspects of food safety and application of principles of food physics in the sector of agriculture, food technology, biotechnology, including questions of well balanced nutrition, as well. One of the most important and widely applied field of biotechnology is the food technology. Food production and processing of quality food and safe food are today of primary importance. Food production is based on the principles of GAP, GMP and GHP. Recently the industrial food processing is focused dominantly on the quality, and one of the basic requirements of the quality is the safety. There are various methods and techniques to produce safe food. The modern food technologies and quality measurements (quality control, quality assurance) involve the application of different physical methods – e.g. high pressure, pulsing electrical field, nondestructive techniques (e.g. NMR, NIR-NIT, PAS, INAA) for chemical composition determination, radiation techniques, nanofiltration and reverse osmosis (RO) – as well. Using e.g. ionizing radiation (nuclear methods) and non-ionizing radiation technologies it is possible to fulfil a lot of important expectations: decrease of microbial contamination, improve of sensory properties, increase of storability, etc. The paper deals with questions of up-to-date, with sport motion combined diet, helping in keeping the health, as well. In the last decades a lot of information were distributed concerning several viewpoints of bodyweight reduction and optimation of bodymaa. How should we know that it is useful and not unhealthy? A suitable Nutrition software (AOPNEI, Analysing and Optimation Program for Nourishment and Energy Intake) – developed at the Department of Food Chemistry and Nutrition of the Faculty of Food Science, Corvinus University – can help.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".