Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy.

Journal: Journal of the science of food and agriculture
PMID: 37400424

Abstract

BACKGROUND: Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product.

Authors

  • Mahugnon Ezékiel Houngbo
    CIRAD, UMR AGAP Institut, Montpellier, France.
  • Lucienne Desfontaines
    INRAE, UR 1321 ASTRO Agrosystèmes tropicaux, Centre de recherche Antilles-Guyane, Petit-Bourg, France.
  • Jean-Louis Diman
    INRAE, UE 0805 PEYI, Centre de recherche Antilles-Guyane, Petit-Bourg, France.
  • Gemma Arnau
    CIRAD, UMR AGAP Institut, Montpellier, France.
  • Christian Mestres
    CIRAD, UMR Qualisud, Montpellier, France.
  • Fabrice Davrieux
    CIRAD, UMR Qualisud, Univ Montpellier, Institut Agro, Avignon Université, Université de La Réunion, Montpellier, France.
  • Lauriane Rouan
    CIRAD, UMR AGAP Institut, Montpellier, France.
  • Grégory Beurier
    CIRAD, UMR AGAP Institut, Montpellier, France.
  • Carine Marie-Magdeleine
    INRAE, UR 0143 URZ Unité de Recherches Zootechniques, Centre de recherche Antilles-Guyane, Petit-Bourg, France.
  • Karima Meghar
    CIRAD, UMR Qualisud, Montpellier, France.
  • Emmanuel Oladeji Alamu
    Food and Nutrition Sciences Laboratory, International Institute of Tropical Agriculture, Oyo, Nigeria.
  • Bolanle O Otegbayo
    DFST, Bowen University, Iwo, Nigeria.
  • Denis Cornet
    CIRAD, UMR AGAP Institut, Montpellier, France.

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