Microplastic research is an emerging field. Consistent accurate identification of microplastic polymer composition is vital for understanding the effect of microplastic pollution in the environment. Fourier transform infrared (FT-IR) spectroscopy is becoming commonplace for identifying microplastics. Conventional spectral identification is based on library searching, a process that utilizes a search algorithm against digital databases containing single spectra of pristine reference plastics. Several conditions on environmental microplastic particles such as weathering, additives, and residues cause spectral alterations relative to pristine reference library spectra. Thus, library searching is vulnerable to misidentification of microplastic samples. While a classification process (classifier) based on a collection of spectra can alleviate misidentification problems, optimization of each classifier (tuning parameter) is required. Additionally, erratic results relative to the particular optimized tuning parameter can occur when microplastic samples originate from new environmental or biological conditions than those defining the class. Presented in this study is a process that utilizes spectroscopic measurements in a hybrid fusion algorithm that depending on the user preference, simultaneously combines high-level fusion with low- and mid-level fusion based on an ensemble of non-optimized classifiers to assign microplastic samples into specific plastic categories (classes). The approach is demonstrated with 17 classifiers using FT-IR for binary classification of polyethylene terephthalate (PET) and high-density polyethylene (HDPE) microplastic samples from environmental sources. Other microplastic types are evaluated for non-class PET and HDPE membership. Results show that high accuracy, sensitivity, and specificity are obtained thereby reducing the risk of misidentifying microplastics.

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EXPRESS：基于傅里叶变换红外光谱的混合融合分类过程在微塑料识别中的应用

微塑料研究是一个新兴领域。对微塑料聚合物成分的一致准确识别对于了解微塑料污染对环境的影响至关重要。傅里叶变换红外 (FT-IR) 光谱在识别微塑料方面变得司空见惯。传统的光谱识别基于库搜索，该过程利用针对包含原始参考塑料的单个光谱的数字数据库的搜索算法。环境微塑料颗粒的若干条件，例如风化、添加剂和残留物，会导致相对于原始参考库光谱的光谱变化。因此，图书馆搜索容易受到微塑料样品的错误识别。虽然基于光谱集合的分类过程（分类器）可以缓解误识别问题，但需要优化每个分类器（调整参数）。此外，当微塑料样品来自新的环境或生物条件而不是定义类别的环境或生物条件时，可能会出现相对于特定优化调整参数的不稳定结果。本研究中介绍的过程是在混合融合算法中利用光谱测量，该算法根据用户偏好，同时将高级融合与基于一组非优化分类器的低级和中级融合相结合，以分配微塑料样品进入特定的塑料类别（类）。使用 FT-IR 对来自环境来源的聚对苯二甲酸乙二醇酯 (PET) 和高密度聚乙烯 (HDPE) 微塑料样品进行二元分类的 17 个分类器展示了该方法。其他微塑料类型被评估为非类 PET 和 HDPE 成员资格。结果表明，获得了高精度、灵敏度和特异性，从而降低了错误识别微塑料的风险。