以单质硫为固定相的薄层色谱-红外光谱技术

以中红外光透明的单质硫作为薄层色谱的新型固定相.减小固定相颗粒大小有助于分离,因此通过优化实验条件制备了粒径约为500 nm的硫颗粒,并用其制备了普通的薄层板和窄带薄层板以开展原位薄层色谱-红外光谱联用分析.以罗丹明B和龙胆紫以及罗丹明B和溴酚蓝为研究对象,实验结果表明,以单质硫作为新型固定相的薄层板具备一定的分离混合组分的能力,且不影响分离组分的红外检测,可以实现薄层色谱-红外光谱联用分析.     

ICA方法与NIR技术用于药片中活性成分含量的测定

用独立分量分析(ICA)方法提取药片近红外光谱数据矩阵的独立成分和相应的混合矩阵, 再用BP神经网络对混合矩阵和药片中活性成分的浓度矩阵进行建模, 提出了新的药片活性成分含量测定的基于独立分量分析-神经网络回归(ICA-NNR)的近红外光谱分析方法. 通过分析独立分量数和网络中间隐层的神经元数对模型性能的影响, 分别建立三类药片定量分析的最优模型. 该方法用于实测的三类药片中活性成分含量的测定, 测试样品集的化学检测值与近红外预测值的相关系数分别达到0.962, 0.980及0.979. 结果表明, 基于ICA-NNR的近红外光谱分析方法对制药业的药片进行定量分析是可行的.     

独立成分分析结合在线红外技术研究3,4-双(4'-氨基呋咱基-3')氧化呋咱的合成反应机理

利用在线红外技术监测3,4-双(4'-氨基呋咱基-3')氧化呋咱(DATF)的合成过程,并结合核独立成分分析算法对反应过程中获得的实时红外光谱数据进行解析,得到了反应物、中间体及产物各组分纯物质的红外光谱图.采用密度泛函理论B3LYP法,在6-31+G(d,p)基组水平上求得中间体的红外振动光谱,验证了所分离红外光谱图的正确性,从而推导出合理的合成反应机理.结果表明,核独立成分分析算法能合理地解析红外光谱在线数据,并有效捕捉合成反应的中间体,对合成反应机理的研究具有重要的指导意义.     

计算机差谱法测定桔子粉中混合色素含量

将红外光谱分析中“独立峰”差谱法应用于可见光区混合物谱峰的分离和测定。数据用微机处理。结果表明:此方法对于吸光度具有加合性的混合体系中单一组分含量的测定,准确度良好。对桔子粉中柠檬黄、胭脂红两种色素联合测定,其回收率分别为93.1％～99.3％和99.1％～112.6％。相对标准偏差分别为1.25％和0.00％。     

完全未知混合体系中纯组份的红外光谱解析

提出一种新的目标转化因子分析，其初始向量的构造基于实际最大差异光谱，无需分离即可从红外混合体系中解析出纯组份光谱。该算法简单易懂，不必迭代，运算量小，应用于模拟及实际体系，结果令人满意。     

连续小波变换-独立成分回归算法及其在多组分分析中的应用

采用连续小波变换（CWT）对光谱数据进行处理，用独立成分分析（ICA）进行特征提取，再用回归分析方法对被测组分进行测定，建立了连续小波变换一独立成分回归（CWT-ICR）方法。方法用于肉样品中水分、脂肪和蛋白质多组分的同时测定，所得结果与化学法测得结果相符。     

高光谱图像技术检测黄瓜叶片的叶绿素叶面分布

以黄瓜叶片为材料,利用高光谱图像技术结合独立分量法(ICA),研究了叶绿素浓度叶面分布的快速、无损检测方法.用高光谱相机采集了80片黄瓜叶子的高光谱图像(408～1117 nm),利用ICA方法提取了高光谱图像的8个独立分量信号,通过逐步线性回归(SMLR)优选出第1、第2和第5个ICA信号,并在此基础上建立了叶绿素浓...     

聚醚氨酯的红外酰胺Ⅰ.谱带分析

通过对聚醚氨酯红外光谱酰胺-Ⅰ谱带的二级微商及傅里埃自解卷积处理,发现了位于1715cm~(-1)的新组分并可归属于无序的相间界面。应用差减光谱及曲线拟合对热处理试样的光谱谱带进行分析表明聚醚氨酯的加热/冷却过程是与可逆的无序/有序化过程及微相混合/分离过程有关的。新发现的1715cm(-1)组分强度的变化与无序/有序化过程是一致的。     

目标识别因子分析解析完全未知混合体系的研究

本文以目标识别因子分析为基础, 将因子分析与聚类分析相结合, 给出了从完全未知混合体系中提取纯物种光谱的新方法。所得纯物种光谱经光谱检索作定性判别, 然后利用外标法对混合物中的各组份进行标定。该方法用于计算机模拟体系及三组份实际混合体系的红外光谱解析, 结果令人满意。     

人工神经网络在多组分红外光谱分析中的应用

本文介绍人工神经网络及其在多组分红外分析中的应用。对于邻二氯苯、间二氯苯、对二甲苯和环己烷四组分红外分析,结果表明,当组分间相互作用或非线性性仪器响应因素引起的非线性响应存在时,人工神经网络能提供优良的光谱校准作用,因而为不经分离直接测定的多组分体系红外分析提供了一种新的途径。     

基于峭度的重叠峰解析新方法

峭度是表征曲线陡峭程度的物理量。本实验提出了基于峭度的组分分析(component analysis based on kurtosis,CABK)方法来解析重叠峰,从中分离出纯组分信息。这种新方法的优点在于半盲源分离。即只要判断出重叠峰所含组分的数目,就可从混合谱中分离出各组分的纯谱信息。将它用来解析模拟两组分重叠峰体系和酒样的GC-MS混合谱,得到了令人满意的结果。     

A new approach to near-infrared spectral data analysis using independent component analysis

This paper presents a new approach to near-infrared spectral (NIR) data analysis that is based on independent component analysis (ICA). The main advantage of the new method is that it is able to separate the spectra of the constituent components from the spectra of their mixtures. The separation is a blind operation, since the constituent components of mixtures can be unknown. The ICA based method is therefore particularly useful in identifying the unknown components in a mixture as well as in estimating their concentrations. The approach is introduced by reference to case studies and compared to other techniques for NIR analysis including principal component regression (PCR), multiple linear regression (MLR), and partial least squares (PLS) as well as Fourier and wavelet transforms.     

Identifying constituents in commercial gasoline using Fourier transform-infrared spectroscopy and independent component analysis

A new method is proposed that enables the identification of five refinery fractions present in commercial gasoline mixtures using infrared spectroscopic analysis. The data analysis and interpretation was carried out based on independent component analysis (ICA) and spectral similarity techniques. The FT-IR spectra of the gasoline constituents were determined using the ICA method, exclusively based on the spectra of their mixtures as a blind separation procedure, i.e. assuming unknown the spectra of the constituents. The identity of the constituents was subsequently determined using similarity measures commonly employed in spectra library searches against the spectra of the constituent components. The high correlation scores that were obtained in the identification of the constituents indicates that the developed method can be employed as a rapid and effective tool in quality control, fingerprinting or forensic applications, where gasoline constituents are suspected.     

Estimation of source infrared spectra profiles of acetylspiramycin active components from troches using kernel independent component analysis

Kernel independent component analysis (KICA), a kind of independent component analysis (ICA) algorithms based on kernel, was preliminarily investigated for blind source separation (BSS) of source spectra profiles from troches. The robustness of different ICA algorithms (KICA, FastICA and Infomax) was first checked by using them in the retrieval of source infrared (IR), ultraviolet (UV) and mass spectra (MS) from synthetic mixtures. It was found that KICA is the most robust method for retrieval of source spectra profiles. KICA algorithm is subsequently adopted in the analysis of diffuse reflection IR of acetylspiramycin (ASPM) troches. It is observed that KICA is able to isolate the theoretically predicted spectral features corresponding to the ASPM active components, excipients and other minor components as different independent (spectral) component. A troche can be authenticated and semi-quantified using the estimated ICs. KICA is an useful method for estimation of source spectral features of molecules with different geometry and stoichiometry, while features belonging to very similar molecules remain grouped.     

A new regression method based on independent component analysis

Based on independent component analysis (ICA), a new regression method, independent component regression (ICR), was developed to build the model of NIR spectra and the routine components of plant samples. It is found that ICR and principal component regression (PCR) are completely equivalent when they are applied in quantitative prediction. However, independent components (ICs) can give more chemical explanation than principal components (PCs) because independence is a high-order statistic that is a much stronger condition than orthogonality. Three ICs are obtained by ICA from the NIR spectra of plant samples; it is found that they are strongly correlated to the NIR spectra of water, hydrocarbons and organonitrogen compounds, respectively. Therefore, ICA may be a promising tool to retrieve both quantitative and qualitative information from complex chemical data sets.     

独立分量分析预处理法提高苹果糖度模型预测精度研究

为了提高苹果近红外光谱糖度预测模型精度,利用独立分量分析方法(ICA)对苹果近红外光谱进行了预处理,并且建立了糖度的偏最小二乘(PLS)预测模型。结果表明,独立分量分析不但能分离出噪声信号,而且所分离出来的光谱信号也比原始光谱信号光滑。在预处理后的最佳PLS糖度模型校正时的相关系数rc和标准偏差SEC分别为0.9549和0.3361,用于预测时的相关系数rp和标准偏差SEP分别为0.9071和0.4355。与普通的平均处理法的PLS模型相比,其精度有所提高,且模型更加简洁。     

Standardless multicomponent qNMR analysis of compounds with overlapped resonances based on the combination of ICA and PULCON

A fast and reliable nuclear magnetic resonance (NMR) method for quantitative analysis of targeted compounds with overlapped signals in complex mixtures has been established. The method is based on the combination of chemometric treatment for spectra deconvolution and the PULCON principle (pulse length based concentration determination) for quantification. Independent component analysis (ICA) (mutual information least dependent component analysis (MILCA) algorithm) was applied for spectra deconvolution in up to six component mixtures with known composition. The resolved matrices (independent components, ICs and ICA scores) were used for identification of analytes, calculating their relative concentrations and absolute integral intensity of selected resonances. The absolute analyte concentrations in multicomponent mixtures and authentic samples were then calculated using the PULCON principle. Instead of conventional application of absolute integral intensity in case of undisturbed signals, the multiplication of resolved IC absolute integral and its relative concentration in the mixture for each component was used. Correction factors that are required for quantification and are unique for each analyte were also estimated. The proposed method was applied for analysis of up to five components in lemon and orange juice samples with recoveries between 90% and 111%. The total duration of analysis is approximately 45 min including measurements, spectra decomposition and quantification. The results demonstrated that the proposed method is a promising tool for rapid simultaneous quantification of up to six components in case of spectral overlap and the absence of reference materials. Copyright © 2015 John Wiley & Sons, Ltd.     

Independent component analysis and its applications in signal processing for analytical chemistry

Independent component analysis (ICA) is a statistical method the goal of which is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible. In an ICA procedure, the estimated independent components (ICs) are identical to or highly correlated to the spectral profiles of the chemical components in mixtures under certain circumstances, so the latent variables obtained are chemically interpretable and useful for qualitative analysis of mixtures without prior information about the sources or reference materials, and the calculated demixing matrix is useful for simultaneous determination of polycomponents in mixtures. We review commonly used ICA algorithms and recent ICA applications in signal processing for qualitative and quantitative analysis. Furthermore, we also review the preprocessing method for ICA applications and the robustness of different ICA algorithms, and we give the empirical criterion for selection of ICA algorithms in signal processing for analytical chemistry.     

Characterization of radix rehmanniae processing procedure using FT-IR spectroscopy through nonnegative independent component analysis

A method is proposed for monitoring the radix rehmanniae proparate processing procedure and determining the endpoint of the process using attenuated total reflectance (ATR) FT-IR through nonnegative independent component analysis (ICA). In the proposed method, ATR FT-IR spectra of the samples were firstly measured at different steaming periods. Then, nonnegative ICA was used for direct estimation of the feature spectra of the pure components in the mixture without pre-separation and other prior information. The estimated independent components (ICs) and their variation of the relative concentrations were used to characterize the processing procedure and determine the endpoint. The results show that the estimated three ICs are consistent with that of the chemical components in the mixtures, i.e. catalpol/rehmaionoside, glucose, and other compounds that nearly keep invariant during the processing procedure. The endpoint determined by the IR-ICA method is 15 h, which was located in the range obtained by expert sensory analysis, whereas the endpoint determined by the traditional sensory analysis is 14 ∼ 17 h and even 14 ∼ 20 h, which showed the significant deviation of the endpoints determined by different operators. Figure Characterisation of radix rehmanniae processing procedure using FT-IR spectroscopy through nonnegative independent component analysis