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Record W2802843940 · doi:10.1080/07373937.2018.1454942

Effects of drying methods on quality attributes of peach (<i>Prunus persica</i>) leather

2018· article· en· W2802843940 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

VenueDrying Technology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsMcGill University
Fundersnot available
KeywordsFlavorAromaOdorMicrostructureWater contentFood scienceMoisturePrunusChemistryElectronic tongueMathematicsTasteMaterials scienceHorticultureComposite materialBiology

Abstract

fetched live from OpenAlex

In this article, the effect of four drying techniques namely hot air drying (AD), infrared drying (IRD), hot air-assisted radio frequency drying (RFD), and microwave-assisted hot air drying (MWD) on quality attributes of dried peach (Prunus persica) leather (PL) was investigated. Drying tests were conducted at 70°C, air velocity of 1.0 m/s and at fixed power level of 4 W/g for RFD, IRD, and MWD. Moisture distribution, texture, rehydration ratio, color, and microstructure of PL were investigated. The results showed that the samples dried by MWD had the shortest drying time (180 min) followed by IRD (210 min), RFD (210 min) and AD (300 min). Study on microstructure and flavor analysis reveals that IRD gave the best results. Sensory tests using electronic tongue and electronic nose that evaluate the odor and taste profiles of dried PL indicates that IRD produced the best quality among the four drying techniques.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.118
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.043
GPT teacher head0.330
Teacher spread0.287 · 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