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Boosting Food System Sustainability through Intelligent Packaging: Application of Biodegradable Freshness Indicators

2023· article· en· W4313592939 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Food Science & Technology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsMcGill University
FundersMcGill University
KeywordsFood wasteFood packagingSustainabilityActive packagingBusinessFood systemsEnvironmental scienceEnvironmental economicsRisk analysis (engineering)Waste managementEngineeringFood securityAgriculture

Abstract

fetched live from OpenAlex

World hunger is getting worse, while one-third of foods produced around the globe is wasted and never consumed. It is vital in reducing food waste to promote the sustainability of agri-food systems. Intelligent packaging embedded with freshness indicators can monitor food freshness in a real-time manner and be deployed for cutting food waste produced due to predetermined expiration date and informing consumers of food safety. Biodegradable halochromic films have been increasingly utilized as freshness indicators because of their low environmental impact. In this review, recent advances in biodegradable halochromic indicators for intelligent packaging are reported. The pH-responsive behaviors of natural pigments, the development of biodegradable solid supports for freshness indicators, the colorimetric response of freshness indicators to food products and simulated models, and future challenges in this field are discussed. Particularly, novel technologies coupled with halochromic indictors are highlighted, including sensor arrays, nanocomposites, smartphone-assisted detection, and ink-free printing.

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)0.002
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 score0.446
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.011
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.079
GPT teacher head0.294
Teacher spread0.215 · 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