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作为SO-PLS回归的选择工具的PROSAC:多块数据融合的战略.

Jose A Diaz-Olivares1, Ryad Bendoula2, Wouter Saeys3

  • 1KU Leuven, Department of Biosystems, Division of Animal and Human Health Engineering, Campus Geel, Kleinhoefstraat 4, 2440, Geel, Belgium.

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概括

我们开发了PROSAC-SO-PLS,用于高效的多块化学测量建模. 这种方法优化了数据预处理和区块选择,大大减少了近红外分析中的预测错误.

关键词:
化学测量 化学测量 化学测量数据融合数据融合多个区块的多个区块.在NIR中,NIR是NIR.预处理 预处理频谱学是一种光谱学.

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科学领域:

  • 化学测量 化学测量 化学测量
  • 数据科学数据科学数据科学
  • 频谱学是一种光谱学.

背景情况:

  • 像SO-PLS这样的多块融合化学模型整合了光谱数据,以改善样品质量预测.
  • 预处理对于降低噪声至关重要,但选择方法和块是复杂的,因为数据来源很多.
  • 有效地处理预处理,区块选择和订单对于SO-PLS模型性能至关重要.

研究的目的:

  • 为解决针对性SO-PLS应用程序数据块的高效预处理,选择和排序的挑战.
  • 引入一种新的方法,使这些复杂的步骤自动化和优化.
  • 提高化学测量模型在处理大型多源光谱数据集的准确性和效率.

主要方法:

  • 介绍了PROSAC-SO-PLS方法,使用以响应为导向的顺序交替校准 (PROSAC) 的预处理组件.
  • 实施一个逐步前进的选择策略,在Gram-Schmidt过程的帮助下,对数据块进行优先排序.
  • 区块是根据其在最小化预测错误方面的有效性来选择的,以减少预测余数来表示.

主要成果:

  • 对于SO-PLS,PROSAC-SO-PLS成功地确定了最佳的预处理数据块及其顺序顺序.
  • 在三种近红外 (NIR) 数据集上的实证验证表明,它们在单块PLS和仅使用PROSAC的方法上始终优越.
  • 预测错误显著减少,RMSEP在分析的八个响应变量中的七个下降了5-25%.

结论:

  • 在NIR数据建模中,PROSAC-SO-PLS为集成预处理提供了一种多功能和高效的方法.
  • 它通过减轻有关预处理序列和块顺序的担忧来简化SO-PLS的应用.
  • 这种方法简化了数据预处理和模型构建,提高了化学分析的准确性和效率.