一个基于机器学习的预测模型,用于在尤肯黄金土豆中总的糖类积累
Saipriya Ramalingam1, Diksha Singla1, Mainak Pal Chowdhury1
1Advanced Post-Harvest Technology Centre, Lethbridge Polytechnic, Lethbridge, AB T1K 1L6, Canada.
Foods (Basel, Switzerland)
|October 16, 2025
概括
超光谱成像可以检测土豆中的总甘油 (TGA),这是与绿化相关的质量问题. 这种非破坏性方法显示出保证整个供应链的土豆质量的前景.
科学领域:
- 农业科学 农业科学
- 食品质量控制 食品质量控制
- 频谱学是一种光谱学.
背景情况:
- 马是加拿大主要的作物,其质量对于加工食品至关重要.
- 马的绿化导致有毒的总糖类化合物 (TGA) 的积累.
- 监测TGA对于食品安全和土豆产品质量至关重要.
研究的目的:
- 开发一种非破坏性的方法来预测土豆中的TGA水平.
- 评估短波红外 (SWIR) 超光谱成像对于TGA检测的实用性.
- 为实际质量控制应用优化光谱数据分析.
主要方法:
- 在受控的光照条件下,尤肯黄金土豆被人工绿化.
- 短波红外 (SWIR) 超光谱成像 (900-2500 nm) 用于光谱数据采集.
- 部分最小平方回归 (PLSR) 模型是使用光谱数据和高性能液态染色学 (HPLC) 构建的,用于TGA量化.
主要成果:
- 使用超光谱成像数据开发了预测模型.
- 使用波长选择技术 (CARS,BE) 来提高模型的效率.
- 最好的模型实现了0.72的交叉验证确定系数 (R2cv) 和51.50 ppm的RMSEcv.
结论:
- SWIR高光谱成像是一种可行的工具,用于在土豆中进行非破坏性的TGA估计.
- 这项技术可以通过检测绿化诱导的TGA来帮助保持土豆的质量和安全.
- 对光谱分析的进一步改进可以在土豆行业带来实际应用.
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