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Record W4406781283 · doi:10.1002/aocs.12934

Thermostable phospholipase C: A key to efficient and sustainable enzymatic oil degumming processes

2025· article· en· W4406781283 on OpenAlexaff
Diego S. Val, Luisina Di Nardo, Fiorela Marchisio, Franco Emanuel Lacava, Andrés Aguirre, Salvador Peirú, María Eugenia Castelli, Martín Espariz, Luciano A. Abriata, Hugo G. Menzella, Rodolfo M. Rasia

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsBioinformatics Solutions (Canada)
FundersConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsKey (lock)ChemistryEnzymePhospholipasePulp and paper industryBiotechnologyBiochemical engineeringBiochemistryBiologyEngineeringEcology

Abstract

fetched live from OpenAlex

Abstract Phospholipase C (PLC) enzymes play a pivotal role in the degumming process of edible oils, which is essential for producing high‐quality refined oils. The enzymatic degumming process, compared with conventional chemical methods, offers significant advantages including improved oil yield, reduced environmental impact, and lower operational costs. However, the industrial application of PLC enzymes is often hindered by their limited stability under harsh processing conditions. This has driven extensive research efforts toward engineering thermostable PLC variants that can withstand the high temperatures and harsh environments typical of oil refining processes. In this review, we explore the latest advancements in the application, design and optimization of thermostable PLC enzymes. We discuss the fundamental economic and ecological interest behind the goal of obtaining thermostable enzymes, and two approaches to the problem, namely the search for enzymes in thermophilic organisms and the design of new sequences with improved stability and activity parameters.

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.

How this classification was reachedexpand

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)0.001
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 score0.014
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.003
GPT teacher head0.234
Teacher spread0.231 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Oil Chemists SocietySame topicEnzyme Catalysis and ImmobilizationFrench-language works237,207