基于多元曲率分辨算法的混合塑料光谱识别研究

Spectroscopic Classification of Mixed Plastics Based on Multiple Curvature Resolution

摘要: 塑料污染在世界范围内普遍存在。污染物被生物体摄入后通过食物链对人体健康造成损害。对塑料的检测和识别对生态风险评估具有重要意义。自然环境下的混合塑料的分类回收存在极大的不便，为了能使这些混合塑料被更精准更快速地识别回收，本文使用傅里叶红外光谱数据的多元曲率分辨算法分析来混合塑料的识别和可视化，该算法原理简单，适用性强。对四种常见的塑料类型聚乙烯(PE)、聚苯乙烯(PS)，聚丙烯(PP)和聚对苯二甲酸乙二醇酯(PET)以二元混合物的形式进行了成分的识别。基于MCR的傅里叶红外光谱技术还成功地应用于混合塑料成分分析。结果表明，该方法对于混合塑料识别的精确度都在0.95以上，并且识别速度快，无需进行样品的特殊预处理。

Abstract: Plastic pollution is a widespread and increasingly significant source of contamination globally. When ingested by organisms, these pollutants can cause harm to human health through the food chain. Therefore, the detection and identification of plastics are crucial for ecological risk assessment. Classifying and recycling mixed plastics in natural environments present significant challenges. To address these issues and enable more precise and rapid identification and recycling of mixed plastics, this study employs a multiple curvature resolution (MCR) algorithm based on Fourier transform infrared (FTIR) spectroscopy data. This algorithm is straightforward in principle and highly adaptable. In this study, we focused on four common types of plastics: polyethylene (PE), polystyrene (PS), polypropylene (PP), and polyethylene terephthalate (PET). We conducted component identification on binary mixtures of these plastics. The MCR-based FTIR spectroscopy technique was successfully applied to mixed plastic component analysis. The results demonstrate that this method achieves an accuracy of over 0.95 in identifying mixed plastics, with rapid recognition speeds and no need for special sample pretreatment.