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Record W4411220792 · doi:10.1111/jfpe.70163

Review of the Mechanisms, Pros and Cons of Some Drying Technologies Applicable to Agricultural Food Products' Processing

2025· article· en· W4411220792 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Food Process Engineering · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsMcGill University
Fundersnot available
KeywordsconsAgricultureAgricultural engineeringComputer scienceBiochemical engineeringBiotechnologyEngineeringBiologyEcology

Abstract

fetched live from OpenAlex

ABSTRACT Drying of agricultural food products is an essential unit operation that aims at preventing the alteration of the product's microstructure due to moisture and microbial activities, which invariably deters the shelf life of agricultural food products. This review presents the drying mechanisms of agricultural food products with a critical assessment of drying applications, from first‐generation sun drying and shade drying methodologies to contemporary approaches like freeze‐drying, infrared drying, microwave drying, spray drying, foam‐mat drying, electrohydrodynamic drying, and a combination of some drying technologies like hot air with infrared, vacuum, ultrasound‐assisted vacuum, and microwave drying methods. This review also identified the advantages, limitations, and future prospects of each drying application, with a keen interest in the drying time and quality of the agricultural food end products. This review presents guiding information that could aid in selecting a suitable drying method that will not only be relatively economical but will also yield end products with desirable qualities.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.211
Teacher spread0.201 · 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