MétaCan
Menu
Back to cohort
Record W4282836501 · doi:10.3389/frfst.2022.889360

Probing Prolamin-Anthocyanin Interactions for the Rational Design of Plant-Based Encapsulation Systems

2022· article· en· W4282836501 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

VenueFrontiers in Food Science and Technology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsProlaminChemistryQuenching (fluorescence)BiophysicsFluorescenceStorage proteinBiochemistryBiology

Abstract

fetched live from OpenAlex

Plant proteins are increasingly focused upon as alternatives to animal-derived macromolecules for the encapsulation of bioactives. The rational design of encapsulation carriers should be based on a solid understanding of the interactions between the proteins and bioactives. Encapsulation technology for food applications has focused predominantly on the protection and controlled release of hydrophobic bioactives. For hydrophilic molecules, although not less important from a nutritional and health perspective, significantly fewer encapsulation systems have been explored, designed and described. As hydrophilic molecules tend to partition into the aqueous food matrix, it is even more crucial to understand and to be able to modulate the interactions between the hydrophilic bioactive and the encapsulating matrix material in food relevant conditions. Therefore, examining the nature of the interactions between anthocyanins (ACNs), a hydrophilic bioactive, and prolamin plant proteins (gliadin, hordein, secalin, and avenin) is timely. These interactions were examined using steady-state and time-resolved luminescence spectroscopy techniques. The ACN-induced quenching of the prolamins intrinsic fluorescence emission did not follow a linear Stern-Volmer relationship, but rather displayed an upward curvature for all the prolamins tested. Hence, both static and dynamic quenching likely occurred in the prolamin-ACN systems. The quenching mechanism was further explored based on the changes in fluorescence lifetime as ACN concentration increased. As the independent lifetimes of the prolamin-ACN combinations did not decrease discernibly as a function of ACN concentration, static quenching is presumably the predominant quenching mechanism. The thermodynamic parameters revealed that the interactions between secalin- and avenin-ACN are mainly driven by the hydrophobic effect, while those between gliadin- and hordein-ACN are dominated by ionic interactions. Zeta-potential measurements support the dominant ionic interactions found for gliadin and hordein. The insights gained in this research will serve as a sound basis for further studies focusing on matrix selection with regard to creating performant encapsulation systems for ACNs.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.033
GPT teacher head0.226
Teacher spread0.193 · 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