A study of semihard plant-based imitation cheese.
Bibliographic record
Abstract
Imitation cheese is becoming increasingly popular. Trends of substituting animal ingredients and eating plant-based make studies of imitation cheese properties relevant. There is an increasing interest in creating plant-based products with a functionality that resembles well-known conventional animal counterparts and has a high nutritional quality. To do so, plant proteins can be included in the products. Different functionality properties exist for different plant protein sources and therefore it is necessary to study a range of different plant proteins to determine what has the best functional properties for a given product. The present study focuses on a semi-hard imitation cheese and investigates the effects of plant protein source and concentration on microstructure and functionality. The macrostructural effects in imitation cheese as affected by different protein sources are analyzed by uniaxial compression texture analysis, and sensory analysis. The microstructure is analyzed with Confocal Laser Scanning Microscopy to describe the relations between microstructure to macroscopic functionality. This study aims to combine methods to obtain new knowledge and an insight into the structural distribution of and interactions between the components in imitation cheese with different plant proteins along with determining how plant protein sources affect the sensory profile of the imitation cheese. The perspective is a better prediction of product performance and thereby assisting the production of high-quality foods containing starch and plant proteins.
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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".