使用机器学习和Vis/NIR光谱仪在各种梅种中非破坏性检测多氧化酶活性
Meysam Latifi-Amoghin1, Yousef Abbaspour-Gilandeh1, Eduardo De La Cruz-Gámez2
1Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.
Foods (Basel, Switzerland)
|December 30, 2025
概括
这项研究开发了一种使用可见/近红外 (VIS/NIR) 光谱和决策树 (DT) 建模的非侵入性方法,以准确测量梅子中的多氧化酶 (PPO) 活性,保持水果质量.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 生物化学 生物化学
背景情况:
- 多氧化酶 (PPO) 导致收获水果的色和质量损失.
- 准确的PPO活动测量对于保持水果市场质量至关重要.
研究的目的:
- 开发一种非侵入性分析框架,用于估计梅子中PPO活动.
- 为了比较各种化学测量模型对PPO活动预测的有效性.
主要方法:
- 可见/近红外 (VIS/NIR) 光谱法用于数据采集.
- 使用化学建模,包括支持向量回归 (SVR),决策树 (DT) 和部分最小平方回归 (PLSR).
- 听特征选择确定了模型优化的关键波长.
主要成果:
- 非线性模型 (DT和SVR) 的表现优于线性PLSR,表明了复杂的光谱-PPO关系.
- 决策树 (DT) 模型展示了优越的概括性能.
- 使用减少波长的优化DT模型提高了预测准确性,降低了计算成本.
结论:
- VIS/NIR光谱学与优化的DT建模相结合,提供了一种可靠,快速和非破坏性的方法来量化梅的PPO活动.
- 这种方法有助于在现场现实地评估水果的质量,而不会造成身体损伤.
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