基于高光谱成像和数据融合的烟草混合比例的定量分析
Yifan Jiang1, Qinlin Xiao1,2, Xudong Huang3
1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
Frontiers in plant science
|February 2, 2026
概括
本研究引入了一种使用可见近红外 (Vis-NIR) 和近红外 (NIR) 光谱数据的新方法,用于准确的烟草成分分析. 多光谱融合显著提高了烟草丝含量的预测准确度.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
背景情况:
- 准确检测烟草混合比例对于烟草行业的质量控制至关重要.
- 现有的光谱分析方法可能缺乏足够的准确性来进行复杂的烟草成分量化.
研究的目的:
- 开发和验证使用多光谱融合的烟草成分的新型定量分析方法.
- 通过优化光谱数据集成和预处理,提高烟草成分检测的准确性和效率.
主要方法:
- 可见近红外 (Vis-NIR) 和近红外 (NIR) 光谱数据的整合.
- 在异常检测中应用最小协差决定因素 (MCD).
- 使用部分最小平方回归 (PLSR) 构建定量模型.
主要成果:
- 多光谱融合显著超过单频谱分析.
- 预处理策略有效降低了噪音,并改善了特征提取.
- 聚合光谱模型在烟草丝含量方面取得了很高的预测精度 (R2 = 0.8873).
结论:
- 拟议的多谱融合和预处理战略为快速检测烟草成分提供了最佳和有效的方法.
- 这种方法提供了可靠的技术解决方案,在烟草行业推进了光谱检测技术.
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