摘要
随着"绿色轮胎"概念的深入,轮胎橡胶的有效成分直接关系到橡胶合格与否,而轮胎橡胶对于检测部门而言是一个"黑色"分析系统,如何利用现有手段进行橡胶成分的准确检测至关重要。太赫兹时域光谱(THz-TDS)技术已经成功应用于物质检测分析当中,但是在许多情况下,从橡胶这个复杂样品中观察到的太赫兹光谱数据代表了几个相互关联的组分或特征成分共同作用的综合结果,原始数据中包含的实际信息可能会重叠,进而影响橡胶混合物中各组分的分析。为了解决太赫兹光谱重叠的问题,利用太赫兹光谱矩阵连续平滑和浓度矩阵稀疏的特点,将具有平滑特性的2范数和具有稀疏特性的1/2范数引入到非负矩阵分解方法,提出基于光谱特征约束非负矩阵分解(constraint non-negative matrix factorization, CNMF)的太赫兹混叠光谱分离方法。首先通过THz-TDS获取丁腈橡胶(NBR)与硫化促进剂2-巯基苯并噻唑(MBT)二组分混合物,以及NBR、 MBT和一硫化四甲基秋兰姆(TMTM)三组分混合物的时域光谱;然后对时域光谱进行傅里叶变换得到其频域光谱,进而获取混合物所对应的吸光度混叠光谱矩阵;光谱分离之前对获得的光谱矩阵进行主成分分析,初步判定混合物组分数,最后利用非负矩阵分解算法(non-negative matrix factorization, NMF)、基于纯变量初始化的非负矩阵分解(purity non-negative matrix factorization, PNMF)和CNMF三种方法对混叠光谱进行光谱解析。对比发现,相较于NMF和PNMF方法, CNMF算法的分离效果更佳,特征吸收峰对应结果更准确,针对不同组分混合物分离结果的相关系数均高于89%,光谱角小于0.5,具有较高的纯净物光谱还原度。将带有约束的非负矩阵分解算法引入到太赫兹混叠光谱的分离上,较好的提取出复杂混合物中单一成分的特征信息,为后续的太赫兹多组分混合物的定性分析和定量计算奠定了较好的基础,在绿色轮胎及橡胶的质量检测领域具有一定的研究前景。
With the deepening of the concept of“green tires”,the effective composition of tire rubberis directly related to the qualification of rubber.But tire rubber is a“black”analysis system for the inspection department,and it’sexceedingly crucialto accurately detect rubber components by the existing methods.Terahertz time-domain spectroscopy(THz-TDS)technology has been successfully applied to material detection and analysis,but the terahertz spectral data observed from a complex sample of rubber represents the comprehensive results of several interrelated components or interaction of characteristic components in many cases,where as the actual information contained in the raw data may overlap,which will conversely affect the analysis of the components in the rubber mixture.In order to solve the problem of terahertz spectral overlap,the characteristics of continuous smoothing of terahertz spectral matrix and sparse concentration matrix are combined this paper,then the 2 norms with smoothing characteristics and the 1/2 norm with sparsity characteristics into the non-negative matrix factorization method is introduced,which are applied to the separation of terahertz aliased spectra,so as eparation method of terahertz aliasing spectral based on spectral feature Constrained Non-negative Matrix Factorization(CNMF)is proposed.Firstly,nitrile-butadiene rubber combined with vulcanization accelerator 2-Mercaptobenzothizzole(MBT)to form a binary mixture in diverse proportions,and it combined with vulcanization accelerators MBT and tetramethy1 thiuram monosulfide(TMTM)to form a ternary mixture in different proportions.Then the terahertz time domain spectrum of all samples ismeasured by terahertz spectroscopy system,which the measured data is subjected too btain a corresponding absorbance spectrum.Further,principal component analysis is performed on the obtained spectral matrix to initially determine the number of components of the mixture.Finally,the Non-negative Matrix Factorization(NMF),Non-negative Matrix Factorization based on pure variables initialization(PNMF)and CNMF methods are used to the decomposition of the mixture data matrix and spectral analysis of the aliased spectrum.The results show that the separation effect of the CNMF algorithm is better than that of NMF and PNMF method,and the corresponding results of the characteristic absorption peak are accurate.In addition,the correlation coefficients of separation results for different component mixtures are higher than 89%,and the spectral angles are less than 0.5 with a higher reduction degree of purity spectrum.Therefore,the constrained non-negative matrix factorization algorithm is introduced into the separation of terahertz aliasing spectra,which is preferable to extract the characteristic information of single components in complex mixtures and provides a better foundation for the qualitative analysis and quantitative calculation of subsequent terahertz multi-component mixtures as well as the considerable research prospects in the field of quality testing of green tires and rubber.
作者
殷贤华
刘昱
奉慕霖
李安
莫玮
YIN Xian-hua;LIU Yu;FENG Mu-lin;LI An;MO Wei(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China;Guangxi Key Laboratory of Automatic Detection Technology and Instrument,Guilin 541004,China;National Rubber and Rubber Products Quality Supervision and Inspection Center(Guangxi),Guilin 541004,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2020年第12期3736-3742,共7页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(61841502)
广西自然科学基金项目(2018GXNSFAA281341)
广西自动检测技术与仪器重点实验室基金项目(YQ19102)
桂林电子科技大学研究生创新项目(YJCXS201560)资助。
