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卷积神经网络允许使用近红外光谱学预测 (Dioscorea alata L.) 面粉中的粉含量

Mahugnon Ezékiel Houngbo^1,2, Lucienne Desfontaines³, Jean-Louis Diman⁴

¹CIRAD, UMR AGAP Institut, Montpellier, France.

Journal of the science of food and agriculture

|July 3, 2023

概括

卷积神经网络 (CNN) 通过近红外光谱学 (NIRS) 准确预测氨含量. 这种深度学习方法克服了传统方法的局限性,使得高通量表型化能够改善质量.

科学领域:

农业科学农业科学
食品科学食品科学食品科学
频谱学是一种光谱学.

背景情况:

洋 (Dioscorea alata L.) 是一种主食,但却缺乏结核质量的表型.
近红外光谱 (NIRS) 是用于茎化学成分分析的可靠工具.
此前,NIRS未能预测粉糖含量,这是一个关键的质量特征.

研究的目的:

开发和验证基于NIRS的方法,用于预测山麦粉粉含量.
为了比较部分最小平方 (PLS) 和卷积神经网络 (CNN) 对于粉素预测的性能.
建立NIRS作为一种高吞吐量型化方法来确定质量.

主要方法:

使用NIRS分析了186个山麦面粉样本.
两种校准方法PLS和CNN被开发和验证.
在一个独立的数据集上使用R2,RMSE和RPD来评估模型性能.

主要成果:

与PLS模型 (R2=0.72,RMSE=1.33,RPD=2.13) 相比,CNN模型获得了更高的性能 (R2=0.89,RMSE=0.81,RPD=3.49).
该PLS模型不符合NIRS预测的质量标准 (RPD<3,R2<0.8).
CNN模型被证明是预测氨糖含量的可靠和有效方法.

结论:

使用NIRS,CNN模型显示了高精度和高效率的预测米洛斯含量.
使用NIRS的深度学习为质量评估提供了一种可行的高通量表型化方法.
使用NIRS准确预测粉素可以帮助繁殖计划,改善产品的质量和接受度.

关键词:

含有粉糖的含量消费者接受度消费者接受度卷积神经网络是一种卷积神经网络.高吞吐量表型化高吞吐量表型化接近红外光谱的近红外光谱学.优质的 tuber 的质量

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