Influence of Refining and Conching Systems on Rheological and Sensory Properties of Chocolate
Bibliographic record
Abstract
There are different refining systems on the market and knowing the differences between these systems can help choose equipment according to the needs of each chocolate manufacturer. This work aimed to evaluate different technologies for the chocolate refining stage and evaluate the impacts on the physical, chemical and sensory characteristics of the chocolates produced. We evaluated the following refining systems: System 1: Double refining; System 2: Refining in ball mill; System 3: Refining in ball mill and BLC conching; System 4: Refining and conching in refining conche; System 5: Refining and conching in stone mill – melanger; System 6: Simple refining. All of these refining systems were evaluated for pH, total titratable acidity, moisture, maximum particle size, particle size distribution, rheology, and sensory characteristics. The refining and conching system resulted in considerable changes in the evaluation of features such as total titratable acidity, moisture, maximum particle size, particle size distribution, and rheological properties. In the sensory evaluation, attributes such as aroma, hardness, melting, and color did not show significant differences. On the other hand, we observed significant differences in attributes such as overall impression, flavor, grittiness, and acidity. It was possible to conclude that the combination of refining systems with homogenizing conche can be favorable for obtaining chocolates due to greater fluidity and better results in sensory evaluation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".