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Record W4396776586 · doi:10.1016/j.foodres.2024.114494

Formation of pores and bubbles and their impacts on the quality attributes of processed foods: A review

2024· review· en· W4396776586 on OpenAlexafffund
Sara Aghajanzadeh, Afroza Sultana, Aman Mohammad Ziaiifar, Seddik Khalloufi

Bibliographic record

VenueFood Research International · 2024
Typereview
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversité LavalAgriculture and Agri-Food Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChewinessFood scienceTexture (cosmology)OrganolepticShelf lifeFood qualityPorosityMoistureMaterials scienceChemistryComposite materialComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.365
GPT teacher head0.503
Teacher spread0.138 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations51
Published2024
Admission routes2
Has abstractyes

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