AI 中文总结
本研究提出一种微塑料FT-IR光谱成像的多级预处理与建模框架，通过多尺度校正及聚类中心光谱匹配，提升了微塑料识别的鲁棒性、效率与可解释性，对四种塑料实现完美分类且处理时间大幅缩短。
AI 中文摘要
光谱成像可提供微塑料的化学特异性和空间分辨分析，但大量数据、采集伪影、光谱变异性以及因光谱相似导致的聚合物误识别阻碍了其常规应用。本研究针对微塑料的FT-IR光谱成像，提出一种多级预处理与建模框架，整合图像级、分块级和光谱级校正及可扩展识别策略：采用潜在变量选择处理采集条件变化导致的图像级变异，基于背景的分块校正减少照明相关伪影；光谱预处理结合基线校正、平滑、导数计算、归一化和波长选择，仅保留颗粒光谱以提升计算效率；可扩展识别采用颗粒光谱聚类，对聚类中心而非单个像素进行光谱库匹配。在12种评估的匹配策略中，符号不变导数余弦相似度方法对聚苯乙烯（PS）、聚对苯二甲酸乙二醇酯（PET）、聚乙烯（PE）和聚丙烯（PP）实现了完美分类精度。基于聚类的工作流生成的空间相干颗粒图优于直接软件匹配，且大幅缩短处理时间。该框架针对基于监督分类的微塑料识别进行评估，结果表明，多级校正结合聚类中心光谱匹配可提升基于光谱成像的微塑料识别的鲁棒性、效率和可解释性。
英文摘要
Spectral imaging provides chemically specific and spatially resolved analysis of microplastics, but its routine application is hindered by large data volumes, acquisition artefacts, spectral variability, and misidentification of polymers due to alike spectra. This study proposes a multi-level preprocessing and modelling framework for FT-IR spectral imaging of microplastics that integrates image-level, tile-level, and spectral-level corrections with scalable identification strategies. Image-level variation associated with changing acquisition conditions was done with latent variable selection, while a background-based tile correction reduced illumination-related artefacts. Spectral preprocessing combined baseline correction, smoothing, derivative calculation, normalization, and wavelength selection, and only particle spectra were retained for further analysis to improve computational efficiency. For scalable identification, clustering was applied to particle spectra and spectral library matching was performed on cluster centroids instead of individual pixels. Among twelve evaluated matching strategies, a sign-invariant derivative-based cosine similarity method achieved perfect classification accuracy for polystyrene (PS), polyethylene terephthalate (PET), polyethylene (PE), and polypropylene (PP). The clustering-based workflow also produced more spatially coherent particle maps than direct software-based matching while substantially reducing processing time. The framework was evaluated for supervised classification-based MP indentification. These results show that multi-level correction combined with cluster-centroid spectral matching improves the robustness, efficiency, and interpretability of spectral-imaging-based microplastic identification.
