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Record W3013055706 · doi:10.7251/jepm1902073u

An exploration of the effects of low-pressure plasma discharge on the physico-chemical properties of chia (Salvia hispanica L.) flour

2019· article· en· W3013055706 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 ENGINEERING & PROCESSING MANAGEMENT · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides Composition and Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsChemistryLightnessFood scienceLinseed oil

Abstract

fetched live from OpenAlex

<p>This work explores the preliminary feasibility of employing low-pressure cold plasma technology for the modification of the properties of chia flour. Chia flour was exposed to low-pressure plasma in the air for 5 min, 10 min, and 15 min, at two different power levels (40 W and 60 W). The oils extracted from untreated and treated chia flour were exhaustively characterized for fatty acid composition, nutritional value, and rancidity indices using thermal calorimetric methods (DSC/TGA). The results indicated a significant change in the color of flour with an increase in lightness. Infrared and ultraviolet spectroscopy indicated changes in the tocopherol groups of the oil extracted from plasma-treated chia flour. However, the oil extracted from plasma-treated chia flour revealed a loss of conjugated dienes and the formation of trans-fatty acids as seen in conventional hydrogenation of edible oils. DSC and TGA results revealed better oxidative stability of low-pressure plasma-treated oils than in control, which was linked to a relative increase of MUFA in the former.</p>

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 score0.014
Threshold uncertainty score0.098

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.192
Teacher spread0.182 · 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