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.

尽管山药很重要（薯osspp。）块茎品质性状，更确切地说是质地属性，仍缺乏用于品种选择的高通量筛选方法。这项研究着手确定优质捣碎山药的轮廓，并基于使用近红外反射光谱法的预测模型提供筛选工具。216个研究样品中有74个被证明是可塑的，即适用于捣碎的山药。而干物质含量低（<25％），糖含量高（> 4％）和蛋白质含量高（> 6％），硬度低（<5 N），弹性高（> 0.5）和内聚力高（> 0.5）的样品分组的大多数是不可塑型的基因型，相反的说法是不正确的。这种理想的化学型的轮廓定义可以使育种者选择筛选阈值以支持他们的选择。而且，²  > 0.85（对于干物质，淀粉，蛋白质和糖含量），但不针对质地属性（R ²  <0.58）。相反，卷积神经网络分类模型能够对所有纹理参数（但硬度）（即，对于可模塑性，内聚性，弹性和硬度分别为80％，95％，100％和55％的精度）进行良好的定性预测。这项研究证明了近红外反射光谱作为表型捣打山药质量的高通量方法的有用性。总之，这些结果为山药的品质性状提供了一个有效的筛选工具箱。