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根据NIR光谱学,使用模型转移和转移学习对基维果多年来可溶性固体含量和度的预测模型进行校准

Jianing Luo¹, Jiabao Li¹, Qingji Tian¹

¹College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|April 9, 2025

概括

校正两阶段TrAdaBoost.R2 (Cor-TTB) 算法有效地解决了使用近红外光谱学预测基维果质量的生物变异性. 这种方法改善了可溶性固体含量 (SSC) 和固度 (FI) 的模型,即使新数据有限.

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:

近红外 (NIR) 光谱对于非破坏性水果质量评估至关重要.
果实中的生物变异性,如,可以损害光谱预测模型的准确性.
由于不同收获年份水果质感的变化,现有的模型往往会失败.

研究的目的:

研究和比较四种模型校准算法 (MC,SBC,TCA,Cor-TTB) 来解决果中的生物变异性.
通过使用光谱数据,提高可溶性固体含量 (SSC) 和固度 (FI) 的建模性能.
评估基于相关性 (WSC) 的新浪波长选择算法的有效性,并与模型校准相结合.

主要方法:

利用不同年份的"徐翔"桃样本来模拟生物变异性.
应用了四种不同的模型校准算法:模型校准 (MC),斜率偏差校准 (SBC),转移组件分析 (TCA) 和两阶段校正TrAdaBoost.R2 (Cor-TTB).
开发并测试了基于相关性 (WSC) 的波长选择算法,与校准方法相结合.

主要成果:

科尔-TTB表现出卓越的性能,成功校准了20%的SSC新样本 (RPD_p = 2.31) 和10%的FI (RPD_p = 2.35).
模型校准算法显示稳定性,最大RPD_p标准偏差在十次重复中为0.15.
当与校准方法相结合时,WSC算法提高了模型校准准确度,将RPD_p提高了5.65%至13.41%.

结论:

在测试的算法中,Cor-TTB表现出最好的模型准确性和适应性,用于小样本大小.
科尔-TTB算法显示,在基维果质量预测中减轻生物变异的巨大潜力.
将Cor-TTB与WSC算法的结合提供了一种可靠的方法,用于准确和稳定的非破坏性水果质量评估.

关键词:

坚定的坚定性坚定的坚定性果 (Kiwi) 是一种果.模型校准模型的校准.近红外光谱学近红外光谱学含有可溶性固体的含量

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