Digital Image Analysis to Evaluate Sensory Attributes of Protein-Enriched Whole-Wheat Bread
Bibliographic record
Abstract
New food products or reformulated food products require intensive sensory assessment using a group of panelists before launching in the market. Sometimes, the sensory results obtained by the panelists are inconclusive due to their subjective scores. An indirect and accurate method to evaluate the sensory attributes using images is highly beneficial to conduct preliminary screening during product development stages. Therefore, the objective of this study was to determine the potential of red-green-blue (RGB) color images to evaluate the sensory qualities of whole wheat bread reformulated with pea and soy protein isolates as model food. In this study, reformulated whole wheat (WW) bread was used as model food to determine the potential of digital color images in assessing the selected sensory attributes. Seven types of WW bread was evaluated by ten untrained panelists. Four features (edge detection, pore numbers, pore area and Hu-moment similarity) were extracted from the images of the bread slices and compared with measured sensory scores. In general, the polynomial regression models yielded higher R2 values than linear regression models. The R2 values in polynomial regression models ranged 0.82-0.97, 0.60-0.92, 0.55-0.96, 0.77-0.99, 0.67-0.97, and 0.50-0.87 for chewiness, graininess, moistness, taste, desired aroma and overall acceptability, respectively. Hu-moment similarity provided the highest R2 values for the sensory attributes in polynomial regression models. In conclusion, although image-based sensory assessment may not substitute the current human sensory, it can provide valuable information to supplement the decision making process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".