根据可见和近红外高光谱成像系统对的准确成熟阶段分类
Hongjuan Chang1,2, Qinghua Meng1,2, Zhefeng Wu1,2
1Nanning Normal University, School of Physics and Electronics, Nanning 530001, China.
Journal of AOAC International
|February 19, 2025
概括
精确的成熟度分类对于质量控制至关重要. 超光谱成像与机器学习相结合,特别是随机森林,提供了一种非破坏性的方法来精确识别成熟度,改善水果分类.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 是经济上重要的热带水果.
- 评估的成熟度对于收获,营销和加工至关重要.
- 确保的最佳质量需要准确的成熟度确定.
研究的目的:
- 为了确定成熟阶段的最佳分类模型.
- 在成熟期间分析光谱信息和可溶性固体含量 (SSC).
- 使用高光谱成像 (HSI) 进行非破坏性成熟度评估.
主要方法:
- 使用可见和近红外高光谱成像 (VIS-NIR-HSI) 从400-1000nm.
- 应用了四种预处理方法 (SNV,MSC,规范化,SG光滑).
- 使用了具有机器学习分类器 (SVM,ELM,KNN,RF) 的特征提取算法 (SPA,BOSS).
主要成果:
- 使用SNV预处理的随机森林 (RF) 实现了94.44%的准确性.
- 总共由BOSS选择的33个波长与RF相结合,产生了97.22%的测试准确度.
- 该研究确定了成熟度分类的最佳波长.
结论:
- 近红外高光谱成像 (NIR-HSI) 是一种可行的非破坏性工具,用于确定的成熟度.
- 这种方法可以提高的分类和分级,解决质量问题.
- 基于关键波长的低成本多谱系统的开发是可行的工业应用.
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