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Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

Ehounou Adou Emmanuel, Cornet Denis, Desfontaines Lucienne, Marie-Magdeleine Carine, Maledon Erick, Nudol Elie, Beurier Grégory, Rouan Lauriane, Brat Pierre, Lechaudel Mathieu, Nous Camille, N'Guetta Assanvo Simon-Pierre, Kouakou Amani Michel, Arnau Gemma. 2021. Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 29 (3) : pp. 128-139.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
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Abstract : Despite the importance of yam (Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

Mots-clés Agrovoc : Dioscorea alata, Spectroscopie infrarouge, Qualité des aliments, Propriété physicochimique, Texture, Qualité, Tubercule, Essai de variété, Choix des variétés

Mots-clés géographiques Agrovoc : Guadeloupe

Mots-clés libres : Yam (Dioscorea alata L.), Quality, Texture, Near infrared spectrometry, Convolutional neural network

Classification Agris : Q01 - Food science and technology - General aspects
Q04 - Food composition

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

Agence(s) de financement européenne(s) : European Commission, European Regional Development Fund

Auteurs et affiliations

  • Ehounou Adou Emmanuel, CIRAD-BIOS-UMR AGAP (GLP)
  • Cornet Denis, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0001-9297-2680 - auteur correspondant
  • Desfontaines Lucienne, INRAE (FRA)
  • Marie-Magdeleine Carine, INRAE (FRA)
  • Maledon Erick, CIRAD-BIOS-UMR AGAP (GLP)
  • Nudol Elie, CIRAD-BIOS-UMR AGAP (GLP)
  • Beurier Grégory, CIRAD-BIOS-UMR AGAP (FRA)
  • Rouan Lauriane, CIRAD-BIOS-UMR AGAP (FRA)
  • Brat Pierre, CIRAD-PERSYST-UMR Qualisud (FRA) ORCID: 0000-0003-0429-9575
  • Lechaudel Mathieu, CIRAD-PERSYST-UMR Qualisud (GLP) ORCID: 0000-0002-1108-8357
  • Nous Camille, Cogitamus Laboratory (FRA)
  • N'Guetta Assanvo Simon-Pierre, UFHB (CIV)
  • Kouakou Amani Michel, CNRA (CIV)
  • Arnau Gemma, CIRAD-BIOS-UMR AGAP (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/598113/)

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