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使用VIS-NIR高光谱成像和深度学习进行非破坏性的高通量量化和小麦粒中营养素的可视化

Taotao Shi¹, Yuan Gao¹, Jingyan Song¹

¹National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China.

|August 18, 2024

概括

这项研究引入了一种快速,负担得起的方法,使用可见近红外高光谱成像来测量小麦的营养成分. 深度学习模型可视化营养分布,为食品和营养研究提供非破坏性分析.

科学领域:

农业科学农业科学
分析化学分析化学
计算机科学计算机科学

背景情况:

传统的方法量化作物谷物营养素是缓慢和破坏性的.
需要高吞吐量,低成本,非破坏性的营养分析方法.
准确的营养量化对于食品加工和营养研究至关重要.

研究的目的:

开发一种高通量,低成本的方法,使用可见近红外 (VIS-NIR) 超光谱成像来量化小麦谷物的营养.
为了准确预测营养含量,并可视化小麦粒中的营养分布.
为了利用深度学习来增强营养可视化.

主要方法:

使用VIS-NIR (400-1700nm) 超光谱成像用于小麦粒分析.
采用渐进线性回归 (SLR) 来定量营养素,并通过第一导数处理数据以提高准确性.
开发了一种改进的pix2pix条件生成网络,用于营养分布可视化.

主要成果:

逐步线性回归准确地预测了数百种营养素 (R2 > 0.6).
超光谱数据的第一个衍生处理增强了预测准确性.
确定了各种营养素的特征波长,主要在400-500nm和900-1000nm区域.
与原始模型相比,改进的pix2pix模型在可视化营养分布方面表现出更高的性能.

结论:

VIS-NIR高光谱成像提供了一种可行的高通量,非破坏性的小麦营养物质确定方法.
深度学习模型,特别是改进的pix2pix网络,显著增强了营养分布的可视化.
这种方法在食品加工,质量控制和营养研究中具有很大的应用潜力.

关键词:

具有特征的波长是波长的特征.深度学习是一种深度学习.超频谱的超频谱的不具有破坏性的非破坏性.一步一步的线性回归.视觉化的可视化小麦营养素是小麦的营养素.

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