Work

Understanding wheat dough behavior through prediction of quality profiles with explainable Machine Learning

Year: 2025

Type: article

Abstract: While technological measurements help anticipate dough behavior during breadmaking, expert sensory evaluations remain the main quality indicator. Due to their subjective nature, predicting these test outcomes is challenging, but recent advances have simplified this by identifying a limited number of consistent data patterns known as Quality profiles (QPs). This study explores the prediction of wheat QPs using Machine Learning based on standard technological measurements. The goal is both practical (to assist in decision-making) and analytical (to identify the most relevant indicators and their optimal value ranges). A dataset of 287 wheats samples was analyzed, using 14 commonly used parameters (eg. protein content, damaged starch, hardness, falling number, gluten index, farinograph® and alveograph® parameters). Among tested models, the Support Vector Machine model (SVM) delivered the best results. To interpret model outputs, the SHapley Additive exPlanation (SHAP) approach was used, revealing each variable’s influence. The Alveograph Elasticity Index (Ie) parameter was the most informative for predicting extreme QPs. However, for Ie values between 40–60%, results were less clear, suggesting the need for additional indicators and further research.

Source: Journal of Cereal Science

DOI: 10.1016/j.jcs.2025.104292

Authors: Melanie Munch, Kamal Kansou, Flavien Alonzo, Luc Saulnier, Cedric Baudrit

Institutions: Université de Bordeaux, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Institut de Mécanique et d'Ingénierie de Bordeaux, Science et Technologie du Lait et de l'Oeuf, Biopolymères Interactions Assemblages

Language: English

Topic: Food composition and properties

Subfield: Nutrition and Dietetics

Field: Nursing

Domain: Health Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,930