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机器学习和PLSDA算法的比较,用于使用直线NIR光谱对杜兰果粉进行分类.

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概括

机器学习算法,特别是神经网络,在使用近红外 (NIR) 光谱对Monthong榴肉类进行分类方面表现优异,而不是部分最小方程差异分析 (PLS-DA). 这一进步有助于对榴纸进行质量控制.

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一个月长的榴莲.在NIR光谱学中使用NIR光谱.部分最小平方区分分析 (PLS-DA)干物质含量 干物质含量机器学习是机器学习.多变量分类算法多变量分类算法神经网络的神经网络的神经网络可溶性固体含量可溶性固体含量

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科学领域:

  • 农业科学 农业科学
  • 分析化学 分析化学
  • 数据科学数据科学数据科学

背景情况:

  • 准确地分类榴肉质量对于生产和储存至关重要.
  • 近红外光谱 (NIR) 为分析水果果粉组成提供了一种非破坏性的方法.
  • 多变量分类算法用于解释复杂的光谱数据.

研究的目的:

  • 为了比较部分最小平方差分分析 (PLS-DA) 和机器学习 (ML) 算法的分类性能.
  • 根据使用NIR光谱的干物质含量 (DMC) 和可溶性固体含量 (SSC) 来分类Monthong榴纸.
  • 确定最佳的光谱预处理技术,以提高分类准确度.

主要方法:

  • 收集和分析了 415 个月龙榴莲肉质样本.
  • 每个样本获得的近红外线 (NIR) 光谱.
  • 使用五种技术进行预处理的光谱:MA+SNV,SG+SNV,SG+MN,SG+BC,SG+MSC.
  • 应用PLS-DA和机器学习算法,包括广泛的神经网络.

主要成果:

  • 采用标准正常变量 (SG+SNV) 的萨维茨基-戈莱光谱预处理为这两种算法产生了最佳结果.
  • 优化的大型神经网络实现了最高的分类准确率85.3%.
  • PLS-DA模型的整体分类准确率为81.4%,低于ML模型.

结论:

  • 机器学习算法,特别是神经网络,在与PLS-DA相比,使用NIR光谱学来分类杜肉质的潜力更高.
  • SG+SNV预处理方法有效提高了分类准确性.
  • 这些发现支持ML算法的应用在杜肉生产和储存的质量控制中.