预测米 (Oryza sativa L.) 中的蛋白质含量,结合近红外光谱和深度学习算法
Ha-Eun Yang1, Nam-Wook Kim1, Hong-Gu Lee1
1Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon, Republic of Korea.
Frontiers in plant science
|August 15, 2024
概括
一项新的研究开发了一种快速,非破坏性的方法来测量米蛋白含量,使用近红外光谱和深度学习. 这项技术为米的传统化学分析提供了商业可行的替代方案.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 米蛋白含量对于其营养和物理性质至关重要.
- 传统的蛋白化学分析是劳动密集型,耗时且昂贵的.
- 需要一种快速,非破坏性的方法来评估在收获和储存期间的米蛋白质.
研究的目的:
- 开发一种非破坏性技术,用于预测米的蛋白质含量.
- 为了比较不同机器学习模型对蛋白质预测的性能.
- 评估开发技术的商业可行性.
主要方法:
- 采用近红外光谱 (NIR) 来从1800个米和1200个米样本中收集光谱数据.
- 开发了深度神经网络 (DNN),部分最小平方回归和支持向量回归模型.
- 应用了光谱数据预处理技术,包括第一顺序导数,以优化模型性能.
主要成果:
- 深度神经网络 (DNN) 模型与其他模型相比,表现优越.
- 不米的最佳DNN模型实现了0.972的确定系数 (Rp2) 和0.048%的RMSEP.
- 米的最佳DNN模型实现了0.987的确定系数 (Rp2) 和0.033%的RMSEP.
结论:
- 接近红外光谱与深度学习相结合,提供了一种准确而有效的方法,用于预测大米中的非破坏性蛋白质含量.
- 开发的模型显示了对米和米实时蛋白质评估的商业可行性.
- 这项技术可以简化大米行业的质量控制流程.
关键词:
深度神经网络 (DNN) 是一个深度神经网络.接近红外光谱学 (NIRS)帕迪米饭 (paddy rice) 是一种大米.部分最小平方回归 (PLSR)蛋白质预测 预测 蛋白质预测支持向量的回归 (SVR)更多相关视频
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