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

Adou Emmanuel Ehounou 1, 2 Denis Cornet 3 Lucienne Desfontaines 4 Carine Marie-Magdeleine 5 Erick Maledon 3 Elie Nudol 3 Gregory Beurier 3 Lauriane Rouan 3 Pierre Brat 3, 6 Mathieu Lechaudel 6 Camille Nous 7 Assanvo Simon Pierre N’guetta 1, 2 Amani Michel Kouakou 2 Gemma Arnau 3
6 Qualisud Réunion - Qualisud - Pôle de La Réunion
Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement, IRD - Institut de Recherche pour le Développement, AU - Avignon Université, UR - Université de La Réunion, UM - Université de Montpellier, Montpellier SupAgro - Institut national d’études supérieures agronomiques de Montpellier
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 (R 2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R 2 < 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.
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Contributor : Carine Marie-Magdeleine <>
Submitted on : Friday, April 30, 2021 - 4:02:40 PM
Last modification on : Friday, July 30, 2021 - 4:33:29 PM


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Adou Emmanuel Ehounou, Denis Cornet, Lucienne Desfontaines, Carine Marie-Magdeleine, Erick Maledon, et al.. Predicting quality, texture and chemical content of yam ( Dioscorea alata L.) tubers using near infrared spectroscopy. Journal of Near Infrared Spectroscopy, NIR Publications, 2021, 29 (3), pp.128-139. ⟨10.1177/09670335211007575⟩. ⟨hal-03213799⟩



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