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基于反向传播神经网络的激光诱导荧光光谱塑料分类识别方法研究

Classification and Identification of Plastic with Laser-Induced Fluorescence Spectroscopy Based on Back Propagation Neural Network Model

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摘要

塑料具有成本低、 质量好, 可塑性强等优点被广泛用于生产生活等领域, 但废弃塑料处置不当容易引发二次污染。 回收再利用有望成为解决废弃塑料污染问题的关键手段, 其前提是对废料的准确分选。 传统分选手段耗费时间, 效率低下, 难以实现废弃塑料的快速、 经济、 有效分类。 激光诱导荧光技术是一种快速灵敏的光谱检测技术。 具有操作简便, 检测效率高, 样品使用量小等优点常被应用于水体、 土壤中油类, 多环芳烃等有机污染物的快速识别与定量分析。 利用激光诱导荧光技术可以快速采集不同塑料的荧光光谱, 结合相应的模式识别算法, 可实现塑料材质的快速准确识别。 实验采集了8种塑料(ABS, HDPE, PA66, PLA, PP, PET, PS, PVC)共358组激光诱导荧光光谱, 依据特征峰信息构建358×10的光谱矩阵。 利用主成份分析法削减原光谱矩阵中的线性相关量, 提高数据精度。 结果显示前3个主成分的累计方差贡献值达98.085%, 足以表征原光谱矩阵的主要信息。 将降维的主成分PC1, PC2, PC3作为输入进行光谱分类, 其中同种塑料光谱聚合度高, 元素构成不同的塑料如PA66, PLA, HDPE和PVC的光谱分离度较好, 而元素构成相同的塑料如PET和PLA的光谱分离度较差。 PCA算法并不能准确的对未知塑料进行识别。 BP-神经网络具有收敛速度快, 预测精度高等特点被广泛用于模式识别和分类研究。 将经PCA算法得到的简化特征矩阵作为BP-神经网络算法的输入集, 其中随机抽取256组数据作为BP-神经网络算法模型的训练集, 剩余的102组数据作为模型检测集。 BP神经网络的隐藏层设定值为1, 激活函数选择双极性Sigmoid函数, 输出层为8种塑料样品。 识别结果显示, 102组数据中只有一组HDPE光谱数据被错识为PS, 其余101组数据全部正确识别。 8种塑料荧光光谱的综合识别准确率达到99%。 研究结果表明激光诱导荧光技术结合BP-神经网络算法可实现不同材质塑料的快速准确识别。 为实现废弃塑料的自动化智能分选, 降低回收成本, 减少废弃塑料危害提供新的参考。

Abstract

With the advantages of low cost, good quality, strong plasticity, plastics are widely used in industrial production and daily life. However, waste plastics are prone to environmental pollution and secondary hazards without being handled properly. Recycling is expected to be a silver bullet to solve the problem of waste plastics, with the premise of accurate classification. Traditional sorting methods of waste plastics are time consuming, inefficient, and difficult to classify rapidly and effectively. Laser-induced fluorescence technique is usually used for rapid identification and quantitative analysis of organic pollutants such as oil and polycyclic aromatic hydrocarbons in water and soil with simple operation, high detection efficiency and little sample usage. It can be used to quickly collect the fluorescence spectra of different plastics, combined with the corresponding pattern recognition algorithm, the rapid and accurate identification of plastic materials can be realized. In this study, 358 sets of fluorescence spectra from eight kinds of plastics (ABS, HDPE, PA66, PLA, PP, PET, PS, PVC) were collected. A spectral matrix of 358×10 was constructed based on the characteristic peak of the spectra. and then it was processed by the method of principal component analysis, after that the linear correlation in the original spectral matrix was reduced and the accuracy of the data was improved. The results show that the cumulative variance contribution of the first three principal components was 98.085%, which was enough to characterize the main information of the original spectral matrix. Spectral classification was performed using the principal components PC1, PC2, and PC3 as inputs. Among them, the spectral polymerization degree of the same kind of plastic was high, and plastics composed with different elements such as PA66, PLA, HDPE, and PVC have better spectral resolution, while plastics containing the same elements such as PET and PLA have poor spectral resolution. The PCA algorithm is not accurate enough to identify unknown plastics. BP-Neural network was widely used in pattern recognition and classification research. The simplified feature matrix obtained by the PCA algorithm was used as the input set of the BP-neural network algorithm. Among them, 256 sets of data were randomly selected as the training set of the BP-neural network model, and the remaining 102 sets of data were used as detection sets. The value of the hidden layer of the BP neural network was set to 1, while the bipolar Sigmoid function was selected as activation function. Eight plastics were set as the output layer. The results showed that only one set of HDPE spectra in the 102 sets of spectra was misidentified as PS, and the remaining 101 sets of data were all correctly identified. The total recognition accuracy of the fluorescence spectra of eight plastics was 99%. So the laser-induced fluorescence technology combined with BP-neural network algorithm can be used to quickly and accurately identify different plastics. This study provided a new reference for automated intelligent sorting of waste plastics, reducing recycling costs and lowering the risk of waste plastics.

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中图分类号:O433.4

DOI:10.3964/j.issn.1000-0593(2019)10-3136-06

基金项目:国家自然科学基金项目(61705238), 国家重点研发计划项目(2016YFD0800902-2), 安徽省教育厅高等学校自然科学重大研究项目(KJ2017ZD46), 安徽省科技重大专项(16030801117), 安徽光机所所长基金(AGHH201602 )资助

收稿日期:2018-09-07

修改稿日期:2019-01-29

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王 翔:中国科学院安徽光学精密机械研究所, 中国科学院环境光学与技术重点实验室, 安徽 合肥 230031中国科学技术大学, 安徽 合肥 230026
赵南京:中国科学院安徽光学精密机械研究所, 中国科学院环境光学与技术重点实验室, 安徽 合肥 230031
殷高方:中国科学院安徽光学精密机械研究所, 中国科学院环境光学与技术重点实验室, 安徽 合肥 230031
孟德硕:中国科学院安徽光学精密机械研究所, 中国科学院环境光学与技术重点实验室, 安徽 合肥 230031皖江新兴产业技术发展中心, 安徽 铜陵 244000
马明俊:中国科学院安徽光学精密机械研究所, 中国科学院环境光学与技术重点实验室, 安徽 合肥 230031皖江新兴产业技术发展中心, 安徽 铜陵 244000
俞志敏:合肥学院生物与环境工程系, 安徽 合肥 230601
石朝毅:合肥学院生物与环境工程系, 安徽 合肥 230601
覃志松:中国科学院安徽光学精密机械研究所, 中国科学院环境光学与技术重点实验室, 安徽 合肥 230031桂林电子科技大学计算机与信息安全学院, 广西 桂林 541004
刘建国:中国科学院安徽光学精密机械研究所, 中国科学院环境光学与技术重点实验室, 安徽 合肥 230031

联系人作者:王翔(amoeba0101@163.com)

备注:王 翔, 1988年生, 中国科学与技术大学博士研究生

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引用该论文

WANG Xiang,ZHAO Nan-jing,YIN Gao-fang,MENG De-shuo,MA Ming-jun,YU Zhi-min,SHI Chao-yi,QIN Zhi-song,LIU Jian-guo. Classification and Identification of Plastic with Laser-Induced Fluorescence Spectroscopy Based on Back Propagation Neural Network Model[J]. Spectroscopy and Spectral Analysis, 2019, 39(10): 3136-3141

王 翔,赵南京,殷高方,孟德硕,马明俊,俞志敏,石朝毅,覃志松,刘建国. 基于反向传播神经网络的激光诱导荧光光谱塑料分类识别方法研究[J]. 光谱学与光谱分析, 2019, 39(10): 3136-3141

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