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使用近红外光谱学对大米种子的内部裂纹进行歧视

Liusan Wang¹, Weisheng Wang², Ziliang Huang³

¹Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|June 4, 2024

概括

近红外光谱学有效地识别了米种子 (Oryza sativa L.) 的内部裂,而没有破坏. 部分最小平方区分分析 (PLS-DA) 实现了最高的准确性,证明了质量评估的可行方法.

科学领域:

农业科学农业科学
分析化学分析化学
频谱学是一种光谱学.

背景情况:

米种子 (Oryza sativa L.) 的内部裂显著影响质量.
准确和非破坏性方法对于评估米种子质量至关重要.

研究的目的:

开发一种非破坏性方法,用于区分大米种子的内部裂.
为了评估近红外光谱学 (NIRS) 与化学测量相结合的有效性.

主要方法:

采用近红外光谱 (NIRS) 来获取光谱数据.
主要组件分析 (PCA) 用于光谱分析.
应用了监督的分类技术,包括部分最小平方-歧视性分析 (PLS-DA),支持向量机 (SVM),K-最近邻居 (KNN) 和随机森林 (RF).
测试了各种预处理技术 (SNV,MSC,SG).

主要成果:

使用原始光谱数据的PLS-DA产生了最好的性能 (Sn=0.8824,Sp=0.9429,Acc=0.913).
SVM的性能优于RF和KNN,但低于PLS-DA.
预处理技术通常会提高模型性能,除了PLS-DA.
粉糖含量被确定为裂歧视的一个关键变量.

结论:

接近红外光谱与化学测量相结合,提供了一种可行且非破坏性的方法,用于检测米种的内部裂.
对于这种应用,PLS-DA是一种非常有效的分类方法.
该研究强调了NIRS在自动化种子质量控制中的潜力.

关键词:

化学测量化学测量化学测量内部裂可能是内部裂.接近红外光谱学近红外光谱学没有破坏性的歧视.米种子米种子

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