通过不同的分类方法检测建筑和拆除废物中的石棉,这些方法适用于短波红外高光谱图像
G Bonifazi1, G Capobianco1, S Serranti1
1Department of Chemical Engineering Materials & Environment, Sapienza University of Rome, Rome, Italy.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|November 23, 2023
概括
这项研究使用短波红外高光谱成像 (SWIR-HSI) 和多变量分析来检测建筑和拆除废物 (C&DW) 中的石棉. 这项技术提供了一种快速可靠的质量控制方法,识别含有石棉的危险材料 (ACM).
科学领域:
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
- 分析化学 分析化学
背景情况:
- 建筑和拆除废物 (C&DW) 管理需要有效的质量控制来识别危险材料.
- 含有石棉的材料 (ACM) 对健康构成重大风险,需要精确的检测方法.
- 短波红外 (SWIR) 范围内的高光谱成像 (HSI) 为非破坏性材料分析提供了潜力.
研究的目的:
- 定义和评估使用SWIR-HSI的C&DW流量流的质量控制策略.
- 在ACM中检测和分类三种类型的石棉纤维 (石,石,石).
- 为了比较不同多变量分类方法的效率和稳定性,用于石棉检测.
主要方法:
- 在SWIR范围 (1000-2500nm) 获取超光谱图像.
- 使用主要组件分析 (PCA) 探索数据.
- 应用和对等级的部分最小平方-歧视性分析 (Hi-PLSDA),PCA k-最近邻居 (PCA-kNN),和错误纠正输出编码与支持向量机器 (ECOC-SVM) 的分类.
- 使用校准和验证数据集,预测图和性能参数 (灵敏度,特异性) 的验证.
- 使用微型X射线光 (微型XRF) 地图确认结果.
主要成果:
- 与多变量分析相结合的SWIR-HSI在分类石棉纤维方面表现出有效性.
- Hi-PLSDA,PCA-kNN和ECOC-SVM模型在检测ACM方面显示出不同程度的效率和稳定性.
- 预测地图和性能指标验证了分类准确性.
- 微XRF分析证实了基于HSI的分类结果.
结论:
- 与多变量建模相结合的SWIR-HSI技术是C&DW质量控制的一个有希望的方法.
- 这种综合方法使ACM存在的快速,可靠和强大的"离线"和"在线"评估成为可能.
- 该研究强调了开发废物流中危险物质的先进检测系统的潜力.
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