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

Autor: Houngbo ME; CIRAD, UMR AGAP Institut, F-34398 Montpellier, France.; UMR AGAP Institut, Université de Montpellier, CIRAD, INRAE, Institut Agronomie, F-34398 Montpellier, France., Desfontaines L; INRAE, UR 1321 ASTRO Agrosystèmes tropicaux. Centre de recherche Antilles-Guyane, Petit-Bourg, France., Irep JL; INRAE, UE 0805 PEYI, Centre de recherche Antilles-Guyane, Petit-Bourg, France., Dibi KEB; CNRA, Station de Recherche sur les Cultures Vivrières, Bouaké, Côte d'Ivoire., Couchy M; INRAE, UR 1321 ASTRO Agrosystèmes tropicaux. Centre de recherche Antilles-Guyane, Petit-Bourg, France., Otegbayo BO; DFST, Bowen University, Iwo, Nigeria., Cornet D; CIRAD, UMR AGAP Institut, F-34398 Montpellier, France.; UMR AGAP Institut, Université de Montpellier, CIRAD, INRAE, Institut Agronomie, F-34398 Montpellier, France.
Jazyk: angličtina
Zdroj: Journal of the science of food and agriculture [J Sci Food Agric] 2024 Jun; Vol. 104 (8), pp. 4680-4688. Date of Electronic Publication: 2023 Aug 02.
DOI: 10.1002/jsfa.12861
Abstrakt: 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 (R 2  = 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 μm 2 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. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
(© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)
Databáze: MEDLINE
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