Impact of Structure Modification on Texture of a Soymilk and Cow's Milk Gel Assessed Using the <scp>N</scp>apping Procedure
Bibliographic record
Abstract
Abstract It was hypothesized that with careful control of the structure of a mixed protein matrix, it is possible to obtain different textures without changing ingredients or their concentrations. A model system containing soymilk, cow's milk and cream was used. To modify the texture of the final matrix, the mode of protein gelation and the order of homogenization of the cream (with soymilk or skim milk alone or with both mixed together) were investigated. Using a partial Napping and ultra‐flash profiling procedure, it was demonstrated that as long as milk was homogenized with cream, the mixed protein gels had higher thickness and mouthcoating compared with gels made from unhomogenized samples or samples made by homogenizing the cream with soymilk. It was found that aggregation of milk proteins before soy proteins resulted in more prominent fat‐related attributes such as slipperiness and fattiness compared with simultaneous aggregation of casein and soy proteins. Practical Applications Recently, there has been a growing interest in mixed protein gels; however, little information exists about their sensory properties. Such gels have the potential to be a novel category of healthy high‐protein products exhibiting consumer‐acceptable sensory properties. However, more work is needed to improve understanding of how to generate such products and to understand the processes that impact their sensory properties. The aim of the present study was to examine the sensory texture changes induced when the organization of components is modified within a mixed soymilk–dairy milk gel without changing ingredients or their concentrations. The present study also contributes to the understanding of texture perception as it demonstrates a clear link between texture perception and structure modification in a protein gel. Mixed protein systems present an attractive opportunity for the study of texture–structure relationships as they allow the development of a range of structures without modifying the system's composition.
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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".