基于超光谱反射成像技术结合机器学习的烟草碎片中检测异物质的研究
Dongfu Xie1,2,3, Zongwen Yu4, Jianhong Lai5,6
1School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, China. 3278@ecjtu.edu.cn.
Scientific reports
|December 18, 2025
概括
这项研究引入了超光谱成像 (HSI) 和机器学习方法来检测烟草中的异物. 该方法准确识别塑料和纸质污染物,提高产品安全和质量控制.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 计算机科学 计算机科学
背景情况:
- 烟草碎片中的塑料和纸张等外来材料存在质量和安全风险.
- 传统的检测方法由于污染物与烟草的视觉相似性而扎.
- 自动检测对于保持烟草加工中的产品完整性至关重要.
研究的目的:
- 开发一种有效的方法来检测烟草碎片中的塑料和纸张异物.
- 克服传统基于颜色和尺寸的检测系统的局限性.
- 为食品和制药行业提供可靠的实时质量控制解决方案.
主要方法:
- 将高光谱成像 (HSI) 与机器学习算法相结合.
- 图像细分和无色化以提取像素级光谱数据.
- 使用主要组件分析 (PCA) 来减少维度.
- 使用支持矢量机 (SVM),线性差异分析 (LDA) 和随机森林 (RF) 开发分类模型.
主要成果:
- 随机森林 (RF) 模型在PCA维度减少后实现了100%的准确性.
- 使用全光谱波段进行训练的线性差异分析 (LDA) 模型也实现了100%的准确性,显示出卓越的稳定性.
- 开发的方法可靠地检测即使部分隐藏的外来物质.
- 分类结果被映射回原始图像以进行视觉检测.
结论:
- 超光谱成像与机器学习相结合,为烟草中异物检测提供了一个强大的解决方案.
- 在质量控制应用中,LDA模型表现出卓越的概括能力.
- 这种方法显著提高了烟草产品的可靠性和安全性,并且可以应用于其他行业.
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