Formation of pores and bubbles and their impacts on the quality attributes of processed foods: A review
Bibliographic record
Abstract
Pores and bubbles significantly influence the physical attributes (like texture, density, and structural integrity), organoleptic properties, and shelf life of processed foods. Hence, the quality of foods and their acceptance by the consumers could be influenced by the properties and prevalence of pores and bubbles within the food structure. Considering the importance of pores, this review aimed to comprehensively discuss the factors and mechanisms involved in the generation of pores and bubbles during the processing of different food products. Moreover, the characteristics and effects of pores on the properties of chocolates, cheeses, cereal-based foods (like cake, puffed grains, and pasta), dried, and fried products were discussed. The impacts of bubbles on the quality of foam-based products, foam creamers, and beverages were also explored. This review concludes that intrinsic factors (like food compositions, initial moisture content, and porosity) and extrinsic factors (like applied technologies, processing, and storage conditions) affect various properties of the pores and bubbles including their number, size, orientation, and distribution. These factors collectively shape the overall structure and quality of processed food products such as density, texture (hardness, cohesiveness, chewiness), and water holding capacity. The desirability or undesirability of pores and their characteristics depends on the type of products; hence, some practical hints were provided to mitigate their adverse effects or to enhance their formation in foods. For example, pores could increase the nutrient digestion and reduce the shelf life of the products by enhancing the risk of fat oxidation and microbial growth. In conclusion, this study provides a valuable resource for food scientists and industry professionals by discussing the effects of pores on food preservation, heat, and mass transfer (including oxygen, moisture, flavors, and nutrients). Understanding the dynamic changes in porosity during processing will be effective in customization of final product quality with desired attributes, ensuring tailored outcomes for specific applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".