基于超光谱数据的LIBSVM质量评估模型,用于储存期间果的损坏
Zhihao Wang1, Yong Yin1, Huichun Yu1
1College of Food and Bioengineering, Henan University of Science and Technology, Luoyang 471003, China. yinyong@haust.edu.cn.
概括
这项研究开发了一个超光谱成像破坏基准和一个LIBSVM模型来评估果在储存期间的质量. 该模型的准确性超过99%,证明了其在长期质量监测方面的有效性.
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:
- 在储存期间评估果的质量对于减少食物浪费至关重要.
- 传统的方法往往是破坏性的和耗时的.
- 像超光谱成像这样的非破坏性技术为实时质量评估提供了潜力.
研究的目的:
- 通过使用超光谱数据,为果建立腐败基准.
- 开发一个强大的质量评估模型储存期间的果.
- 为了验证模型的准确性和适用性.
主要方法:
- 超光谱成像用于收集光谱和图像数据.
- 使用颜色,纹理和波形数据包能量指标创建了一个损坏基准.
- 连续投影算法 (SPA) 确定了20个特征波长.
- 马哈拉诺比斯距离 (MD) 验证了损坏基准.
- 使用预处理的光谱数据训练和测试LIBSVM模型.
主要成果:
- 在LIBSVM模型实现了高精度: 99.94% (培训) 和 99.66% (测试).
- 验证实验显示100% (培训) 和99.83% (测试) 的准确性.
- 腐败基准与果质量指标有效相关.
- 该模型证明了长期存储监控的稳定性和适用性.
结论:
- 拟议的腐烂基准和LIBSVM模型对于在储存期间评估果质量是有效的.
- 超光谱成像为质量控制提供了一种可靠的非破坏性方法.
- 这些发现支持在食品工业中使用这种技术进行质量保证.
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