Starch granule size and shape characterization of yam (Dioscorea alata L.) flour using automated image analysis

Houngbo Mahugnon Ezekiel, Desfontaines Lucienne, Irep Jean-Luc, Dibi Konan Evrard Brice, Couchy Maritza, Otegbayo Bolanle Omolara, Cornet Denis. 2024. Starch granule size and shape characterization of yam (Dioscorea alata L.) flour using automated image analysis. Journal of the Science of Food and Agriculture, 104 (8), n.spéc. Tropical roots, tubers and bananas: New breeding tools and methods to meet consumer preferences : 4680-4688.

Résumé : BACKGROUND: Roots, tubers and bananas (RTB) play an essential role as staple foods, particularly in Africa. Consumer acceptance for RTB products relies strongly on the functional properties of, which may be affected by the size and shape of its granules. Classically, these are characterized either using manual measurements on microscopic photographs of starch colored with iodine, or using a laser light-scattering granulometer (LLSG). While the former is tedious and only allows the analysis of a small number of granules, the latter only provides limited information on the shape of the starch granule. RESULTS: In this study, an open-source solution was developed allowing the automated measurement of the characteristic parameters of the size and shape of yam starch granules by applying thresholding and object identification on microscopic photographs. A random forest (RF) model was used to predict the starch granule shape class. This analysis pipeline was successfully applied to a yam diversity panel of 47 genotypes, leading to the characterization of more than 205 000 starch granules. Comparison between the classical and automated method shows a very strong correlation (R2 = 0.99) and an absence of bias for granule size. The RF model predicted shape class with an accuracy of 83%. With heritability equal to 0.85, the median projected area of the granules varied from 381 to 1115 μm2 and their observed shapes were ellipsoidal, polyhedral, round and triangular. CONCLUSION: The results obtained in this study show that the proposed open-source pipeline offers an accurate, robust and discriminating solution for medium-throughput phenotyping of yam starch granule size distribution and shape classification.

Mots-clés Agrovoc : qualité des aliments, farine d'igname, analyse d'image, amidon, igname, Dioscorea alata

Mots-clés libres : Starch granule, Root tuber and banana crops, High-throughput phenotyping, Image analysis, Dioscorea alata L.

Classification Agris : Q04 - Composition des produits alimentaires
Q01 - Sciences et technologies alimentaires - Considérations générales
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 3 (2019-) - Systèmes alimentaires

Agences de financement hors UE : Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Bill and Melinda Gates Foundation

Projets sur financement : (FRA) Breeding RTB Products for End User Preferences

Auteurs et affiliations

• Houngbo Mahugnon Ezekiel, CIRAD-BIOS-UMR AGAP (FRA)
• Desfontaines Lucienne, INRAE (FRA)
• Irep Jean-Luc, INRAE (FRA)
• Dibi Konan Evrard Brice, CNRA (CIV)
• Couchy Maritza, INRAE (FRA)
• Otegbayo Bolanle Omolara, Bowen university (NGA)
• Cornet Denis, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0001-9297-2680 - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/605864/)

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