基于拉曼光谱技术鉴别ABS废旧塑胶原料的方法研究

塑胶微粒原料已渗透到人类衣食住行的方方面面，并广泛应用于能源、工业、农业、交通乃至航空航天和海洋开发等各重要领域不可或缺的材料。在利益的诱惑下，废旧塑胶的走私现象屡禁不止。我国作为塑胶原料进口大国，现有检测方法耗时长，难以实现现场检测，因此，开发一种用于现场的废旧塑胶微粒判别方法，对快速通关和海关缉私有重要意义。拉曼光谱技术具有快速、无损、样品用量小、无需前处理且适应性强等优点，已在现场快速鉴别领域得到广泛应用。在研究塑胶废旧机理的基础上，将拉曼光谱技术结合化学判别方法，应用于废旧塑胶原料识别。选取两类成分相似的实际通关塑胶原料样品，包含标准品及废旧品各160份，并对样品的拉曼光谱信息进行了采集。对比分析了两种塑胶原料的原始拉曼光谱，并对样品的拉曼光谱特征峰进行了归属分析。选取1 603 cm-1作为归一化参照峰位，进一步探究废旧塑胶的成分变化，对比统计了废旧塑胶原料及标准塑胶原料的相对峰强变化，结果表明废旧塑胶原料发生了化学老化。基于主成分分析法（PCA）对原始拉曼光谱及预处理拉曼光谱进行降维处理，结果表面预处理拉曼光谱的前2主成分空间分离度较好，通过对原始拉曼光谱数据进行背景扣除及平滑预处理，可减少荧光背景及噪声对鉴别的影响，提高鉴别的准确度。将样品一半划分为校正集用于模型建立，另一半划分为预测集用于模型验证，基于偏最小二乘判别分析（PLS-DA），建废旧塑胶原料鉴别模型，该模型对建模训练集鉴别正确率为100％，模型验证集鉴别正确率为99.06％。研究表明，基于拉曼光谱技术，结合测试数据预处理及偏最小二乘判别分析方法，可以有效地实现塑胶原料的现场、快速、准确鉴别，为开发现场检测装备及方法提供理论参考。

Study on the Method of Identifying Waste Plastic Materials Based on Raman Spectroscopy

As an indispensable material widely used in various important fields such as information,energy,industry,agriculture,transportation,and even aerospace and marine development,plastic particle raw materials have penetrated into all aspects of human food,clothing,housing and transportation.China is a large importer country of plastic raw materials.The existing test methods often cost too much time and barely achieve on-site testing.Therefore,the development of a discriminating model for waste plastic particles used in the field is of great significance for fast clearance and anti-smuggling in customs.Raman spectroscopy has the advantages of fast,non-destructive,small sample consumption,non-pre-treatment and strong adaptability,and has been widely used in rapid on site identification.Firstly,this research establishes a Raman spectroscopy reproducibility test method.On the basis of ensuring the real and effective Raman spectroscopy data,Raman spectroscopy combined with chemical discrimination method is applied to the identification of waste plastic materials.Two kinds of actual customs clearance plastic materials with similar composition were selected,each including 40 standard and wasted products.The Raman spectrum information of the samples was collected by NCS Smart 200 Raman spectrometer.A total of 640 samples of data of plastic raw materials were collected.The original Raman spectra of the two kinds of plastic materials were compared and analyzed.To further explore the composition changes of waste plastics,1001 cm^-1 was selected as the normalized reference peak position.The relative peak intensity changes of waste plastic raw materials and standard plastic raw materials were compared.The changes of relative peak intensity indicated that the waste plastic raw materials had chemical aging causes a change in its molecular structure and composition.Based on the principal component analysis(PCA),the original Raman spectroscopy and pre-processed Raman spectroscopy are subjected to dimensionality reduction.The first two principal component spaces of the original Raman spectroscopy have intertwined,which is difficult to completely separate.The spatial separation of the first two principal components of the pre-treatment Raman spectrum conducts well.Therefore,by performing background subtraction and smoothing pre-processing on the original Raman spectral data,the influence of the fluorescence background and noise on the discrimination can be reduced,and the accuracy of the discrimination can be improved.Half of the sample is divided into a calibration set for model building,half is divided into prediction sets for model verification,and partial least square discriminant analysis(PLS-DA)is used to build a waste plastic raw material identification model.The correctness rate is 100% for the modeling training set and 99.06% for the model verification set.The research shows that based on Raman spectroscopy technology,combined with test data pre-processing and partial least squares discriminant analysis method,it can effectively achieve the on-site,fast and accurate identification of plastic raw materials,and provide theoretical reference for the development of on-site testing equipment and methods.