CIRAD, UMR AGAP Institut, Montpellier, France.
UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
J Sci Food Agric. 2024 Jun;104(8):4915-4921. doi: 10.1002/jsfa.12825. Epub 2023 Jul 31.
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.
This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model).
According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
山药（Dioscorea alata L.）是热带地区许多人群的主食，在这些地区都有种植。缺乏对薯蓣块茎质量的表型鉴定方法，阻碍了从育种计划中采用新的基因型。最近，近红外光谱（NIRS）已被用作一种可靠的工具来描述山药块茎的化学成分。然而，它未能预测直链淀粉含量，尽管该特性强烈影响产品的质量。
本研究使用 NIRS 从 186 个山药粉样本中预测直链淀粉含量。建立了两种校准方法，并在独立数据集上进行了验证：偏最小二乘法（PLS）和卷积神经网络（CNN）。为了评估最终模型的性能，使用独立验证数据集上的预测值计算了决定系数（R）、均方根误差（RMSE）和偏差比（RPD）。测试的模型表现出不同的性能（即 PLS 模型的 R 为 0.72，RMSE 为 1.33，RPD 为 2.13，CNN 模型的 R 为 0.89，RMSE 为 0.81，RPD 为 3.49）。
根据食品科学中 NIRS 模型预测的质量标准，PLS 方法在预测山药粉中的直链淀粉含量方面不成功（RPD < 3 和 R < 0.8），但 CNN 模型被证明是一种可靠和高效的方法。通过应用深度学习方法，本研究证明了概念验证，即使用 NIRS 作为高通量表型鉴定方法，可以准确预测直链淀粉含量，这是山药质地和可接受性的关键驱动因素。 © 2023 作者。《食品科学杂志》由 John Wiley & Sons Ltd 代表化学工业协会出版。
