基于近红外光谱学和机器学习的科拉芳香梨的内部质量的非破坏性检测方法研究
Jikai Che1,2,3, Qing Liang1,2,3, Yifan Xia1,2,3
1College of Mechanical and Electronic Engineering, Tarim University, Alaer 843300, China.
Foods (Basel, Switzerland)
|November 9, 2024
概括
本研究引入了一种使用近红外光谱 (NIRS) 和机器学习的非破坏性方法,以准确评估Korla梨的可溶性固体含量 (SSC) 和度,从而提高质量控制.
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:
- 科拉香梨的质量控制和分类对于商业价值至关重要.
- 需要对溶性固体含量 (SSC) 和度进行快速,非破坏性的评估.
- 目前的方法可能耗时或破坏性,限制效率.
研究的目的:
- 开发一种快速,非破坏性的方法来检测Korla梨的SSC和坚固性.
- 将近红外光谱学 (NIRS) 与机器学习算法相结合.
- 优化光谱数据预处理和波长选择,以进行准确的预测.
主要方法:
- 从科拉梨收集近红外光谱 (900-1800nm).
- 应用了六种预处理技术 (SGD,SNV,MSC,SGS,VN,MMN).
- 无信息变量消除 (UVE) 和连续投影算法 (SPA) 用于波长提取.
- 部分最小平方回归 (PLSR) 模型用于SSC和坚固度预测.
主要成果:
- 所有预处理和波长提取方法都提高了模型的准确性.
- 最优的SSC预测模型是乘法散射校正-连续预测算法-部分最小平方回归 (MSC-SPA-PLSR),R=0.93.
- 最优的坚固度预测模型是多倍散射校正-无信息变量消除-部分最小平方回归 (MSC-UVE-PLSR) 的R=0.83.3.
结论:
- 开发的NIRS和机器学习方法为Korla梨提供了快速准确的非破坏性质量评估工具.
- 这种方法可以帮助生产者进行质量控制,帮助食品行业进行生产标准化.
- 加强质量评估可以提高科尔拉梨的市场竞争力.
相关概念视频
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