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Implementation of machine learning models to predict functionality of pea flour from its composition

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DOITrouver le DOI : https://doi.org/10.1002/cche.70072
AuteurRechercher : 1; Rechercher : 2; Rechercher : 2; Rechercher : 2Identifiant ORCID : https://orcid.org/0000-0003-3628-9179; Rechercher : 1Identifiant ORCID : https://orcid.org/0000-0002-9420-2791; Rechercher : 2Identifiant ORCID : https://orcid.org/0000-0003-3059-0606; Rechercher : 1Identifiant ORCID : https://orcid.org/0000-0002-3162-5252; Rechercher : 2Identifiant ORCID : https://orcid.org/0000-0002-9040-5639
Affiliation
  1. Conseil national de recherches Canada. Développement des cultures et des ressources aquatiques
  2. University of Saskatchewan
Bailleur de fondsRechercher : Pulse Science Cluster; Rechercher : Natural Sciences and Engineering Research Council of Canada
FormatTexte, Article
SujetGaussian process regression; multi‐layer perceptron; neural network; solubility; support vector regression; XGBoost
Résumé
Background and Objectives: The goal of this research was to examine the relationship between the composition and functionality of pea flour using the following machine learning algorithms: linear regression, partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression, gradient‐boosted decision trees, and a standard feed‐forward neural network. Findings: In general, linear models outperformed non‐linear models. PLSR provided best fits for prediction of emulsion stability, oil holding capacity, foam stability and foam capacity; but was less effective for solubility and water holding capacity, which were best described by the GPR model. Variable Importance in Projection scores, calculated for each PLSR model, showed that protein and acid detergent fiber were both highly influential in predicting foaming capacity (1.52 and 1.55), foaming stability (1.30 and 1.54), oil holding capacity (1.64 and 1.50), and water holding capacity (1.56 and 1.53). Protein was also highly important in predicting solubility (1.80), alongside starch (1.60) whereas lipid was highly predictive (2.02) for emulsion stability. Conclusion: Application of machine learning models was successful in relating compositional features of pea flour to functionality. Significance: and Novelty Using machine learning to predict the functional behavior of pea will aid both breeders and product developers in ingredient selection.
Date de publication
Maison d’éditionWiley
Déclaration de droit d’auteur
  • © 2026 His Majesty the King in Right of Canada and The Author(s)
Licence
Dans
  • Cereal Chemistry, 70072 : 1–15.
Langueanglais
Publications évaluées par des pairsOui
Exporter la noticeExporter en format RIS
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Identificateur de l’enregistrement169bd387-2e60-4470-a499-af205e4cf3e2
Enregistrement créé2026-05-07
Enregistrement modifié2026-05-22

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