A Practical Framework for Textural Categorization as a Guide to Food Bar Formulation
Bibliographic record
Abstract
Model food bars were produced with different cooking temperatures and compression distance in order to determine if changing these processing parameters could produce meaningful differences in texture. The food bars were characterized by instrumental texture analysis using a cutting test and three-point bending test, and texture parameters were extracted from the resulting force curves. In order to determine if the changes in texture were meaningful, a selected set of texture-parameters were used to propose a novel approach to the classification of the texture of different food bars. The novel approach is to coarse-grain each texture parameter into a discrete number of bins, such as "high" and "low" value bins, based on some algorithm for defining the number of bins and the cutoff value between bins; thereby reducing the texture characterization from essentially infinite variability, to one with a fixed number of texture categories (e.g., 32 or 243, depending of the number of bins and parameters selected). Using a set of 30 commercial food bars, seven classification algorithms were evaluated for their utility in showing similarities and differences between the texture of the various food bars. When applied to the model food bars, the results showed that both the cooking temperature and the compression distance were able to alter the texture, though the former had a stronger effect. We conclude that this novel approach to texture classification is an easy-to-implement framework that can provide valuable insights for market research and product formulation of food bars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".