Estimation of source spectra profiles and simultaneous determination of polycomponent in mixtures from ultraviolet spectra data using kernel independent component analysis and support vector regression

A method that use kernel independent component analysis (KICA) and support vector regression (SVR) was proposed for estimation of source ultraviolet (UV) spectra profiles and simultaneous determination of polycomponents in mixtures. In KICA-SVR procedure, the UV source spectra profiles were estimated using KICA, then the mixing matrix of the components were calculated using the estimated sources, and the calibration model was build using SVR based on the calculated mixing matrix. A simulated UV dataset of three-component mixtures was used to test the ability of KICA for estimating source spectra profiles from spectra data of mixtures. It was found that KICA has the potential power to estimate pure UV spectra profiles, and correlation coefficient of estimated sources correspond to the real adopted ones are better compared with that by FastICA and Infomax ICA. An UV dataset of polycomponent vitamin B was processed using the proposed KICA-SVR method. The results show that the estimated source spectra profiles are correlative with the real UV spectra of the components and chemically interpretable, and accurate results were obtained.     

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.     

Adaptive kernel independent component analysis and UV spectrometry applied to characterize the procedure for processing prepared rhubarb roots

By determination of the number of absorptive chemical components (ACCs) in mixtures using median absolute deviation (MAD) analysis and extraction of spectral profiles of ACCs using kernel independent component analysis (KICA), an adaptive KICA (AKICA) algorithm was proposed. The proposed AKICA algorithm was used to characterize the procedure for processing prepared rhubarb roots by resolution of the measured mixed raw UV spectra of the rhubarb samples that were collected at different steaming intervals. The results show that the spectral features of ACCs in the mixtures can be directly estimated without chemical and physical pre-separation and other prior information. The estimated three independent components (ICs) represent different chemical components in the mixtures, which are mainly polysaccharides (IC1), tannin (IC2), and anthraquinone glycosides (IC3). The variations of the relative concentrations of the ICs can account for the chemical and physical changes during the processing procedure: IC1 increases significantly before the first 5 h, and is nearly invariant after 6 h; IC2 has no significant changes or is slightly decreased during the processing procedure; IC3 decreases significantly before the first 5 h and decreases slightly after 6 h. The changes of IC1 can explain why the colour became black and darkened during the processing procedure, and the changes of IC3 can explain why the processing procedure can reduce the bitter and dry taste of the rhubarb roots. The endpoint of the processing procedure can be determined as 5-6 h, when the increasing or decreasing trends of the estimated ICs are insignificant. The AKICA-UV method provides an alternative approach for the characterization of the processing procedure of rhubarb roots preparation, and provides a novel way for determination of the endpoint of the traditional Chinese medicine (TCM) processing procedure by inspection of the change trends of the ICs.     

独立组分分析在红外光谱分析中的应用

针对红外光谱的黑色体系分析,提出了一种基于独立组分分析(ICA)的红外光谱定性分析方法.其主要优点在于可从混合光谱中分离出独立组分的光谱,且这种分离是盲源分离,混合物的组分预先是未知的.对ICA在正己醇、丙酮和正丁醇的混合中红外光谱分离中的应用进行了研究,验证了体系的独立性,并对ICA算法做了一定的改进,讨论了各种预处理方法对其分离结果的误差.     

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.     

Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis: application to foods and related products

The major challenge facing NMR spectroscopic mixture analysis is the overlapping of signals and the arising impossibility to easily recover the structures for identification of the individual components and to integrate separated signals for quantification. In this paper, various independent component analysis (ICA) algorithms [mutual information least dependent component analysis (MILCA); stochastic non‐negative ICA (SNICA); joint approximate diagonalization of eigenmatrices (JADE); and robust, accurate, direct ICA algorithm (RADICAL)] as well as deconvolution methods [simple‐to‐use‐interactive self‐modeling mixture analysis (SIMPLISMA) and multivariate curve resolution‐alternating least squares (MCR‐ALS)] are applied for simultaneous ¹H NMR spectroscopic determination of organic substances in complex mixtures. Among others, we studied constituents of the following matrices: honey, soft drinks, and liquids used in electronic cigarettes. Good quality spectral resolution of up to eight‐component mixtures was achieved (correlation coefficients between resolved and experimental spectra were not less than 0.90). In general, the relative errors in the recovered concentrations were below 12%. SIMPLISMA and MILCA algorithms were found to be preferable for NMR spectra deconvolution and showed similar performance. The proposed method was used for analysis of authentic samples. The resolved ICA concentrations match well with the results of reference gas chromatography–mass spectrometry as well as the MCR‐ALS algorithm used for comparison. ICA deconvolution considerably improves the application range of direct NMR spectroscopy for analysis of complex mixtures. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

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.     

Processing of NMR,UV, and IR spectrometric data prior to chemometric simulation by independent component and principal component analysis

We consider methods for the mathematical preprocessing of signals in the spectrometric analysis of multicomponent mixtures using chemometric algorithms aimed at adjusting the baseline, experimental noise, and random shift of spectral bands. Practical examples of using simple mathematical operations (scaling, centering, derivatization) are given. The effectiveness of algorithms is illustrated by a wide range of spectroscopic signals (electronic absorption, IR, and NMR spectra) combined with chemometric methods of principal component analysis and independent component analysis.     

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。     

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.     

Chemometrics-assisted spectrophotometric method for simultaneous determination of vitamins in complex mixtures

An advanced independent component analysis algorithm (MILCA) is applied for simultaneous chemometric determination of fat- and water-soluble vitamins in complex mixtures. The analysis is based on the decomposition of spectra of multicomponent mixtures in the UV region. The key features of the proposed method are simplicity, accuracy, and reliability. Comparisons between the new algorithm and other established methods (MCR-ALS, SIMPLISMA, other ICA techniques) were made. Our results indicate that in most cases, MILCA is comparable or even outperforms other chemometrics methods taken for comparisons. The influence of different factors (abundance of components, noise, step of spectral scan, and scan speed) on decomposition performance has been investigated. The optimal conditions for spectroscopic registration have been identified. The proposed method was used for analysis of model mixtures and real objects (multivitamin drugs, food additives, and energy drinks). The resolved concentrations match well with the declared amounts and the results of reference methods.     

Fast determination of the ripeness stage of strawberries using infrared spectroscopy combined with principal component analysis.

The utility of infrared (IR) spectroscopy for the determination of strawberry ripeness has been successfully demonstrated. Transmission IR spectra were collected using dried liquid extracts from strawberry flesh. The overall IR feature provided fairly noticeable differences, and the ripeness stage was clearly identified using principal component analysis (PCA). Although all of the extracted components contributed to the resulting spectral features for discrimination, the variation of carbohydrate and amide residues played a major role for providing the selective spectral feature. Additionally, NMR spectra were also collected to quantify the concentrations of three small sugars (alpha-glucose, beta-glucose and sucrose) as well as to evaluate the NMR spectral features at each ripeness step. The concentrations of three sugars increased from early to late growth stages. Both IR and NMR spectroscopies were valuable to elucidate the metabolic signatures for the determining of ripeness stage; however, IR spectroscopy could be more advantageous when fast and high throughput analysis is essential.     

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.     

Methods of the decomposition of spectra of various origin in the analysis of complex mixtures

Different algorithms of the decomposition of spectral curves are compared by the precision of identification and quantitative analysis of complex mixtures. The available conventional methods of self-modeling curve resolution (SIMPLISMA, MCR-ALS) and algorithms implementing the independent component analysis (MILCA, SNICA) are used. The results are illustrated by a series of examples of different spectral signals (UV, IR, Raman, fluorescence).     

From theory of spectra to standardless analysis of molecular objects

The authors discuss the methodology of quantitative analysis of pure substances and mixtures by optical spectra (IR, Raman, UV, etc.) without using samples of standard composition (standardless molecular spectral analysis). An algorithm of quantitative mixture analysis using the reduction of ideal spectra to real ones and computational algorithms of the determination of component concentrations in a mixture taking into account error distribution is proposed. The applicability of the method is estimated using computational experiments parsed and conclusions about the specific features of its work are drawn.     

Two-dimensional cross-spectral correlation analysis and its application to time-resolved Fourier transform emission spectra of transient radicals

A spectral analysis method, based on the generalized two-dimensional (2D) vibrational spectra correlation analysis, is developed for deciphering the correlation among the spectral peaks of two different spectra. This 2D cross-spectral correlation (2DCSC) analysis is aimed at revealing the vibrational features associated with a common species in two spectra, each obtained from a system containing multiple species with at least one common species. The cross-spectral correlation is based on the premise that the spectral features of the same species should have the same time and frequency responses toward similar perturbations. The effectiveness of the cross-spectral correlation analysis is first illustrated with model systems, with spectral peaks decaying linearly or exponentially with time, before being applied to analyzing time-resolved emission spectra obtained, by a Fourier transform IR spectrometer, for samples consisting of the vibrationally excited transient cyanooxomethyl radical (OCCN). 2DCSC among the three different sets of time-resolved spectra collected following the photodissociation of three different precursor molecules of OCCN, respectively, allows the identification of the CN and CO stretching modes of this radical.     

Analysis of IR spectra of carbon clusters trapped in noble gas matrices using algorithms based on digital filtering techniques

IR spectra of carbon clusters C(n) (n>/=3) trapped in noble gas matrices (Ar and Kr) at temperatures of 13, 30 and 35 K are analyzed using algorithms based on digital filtering techniques and non-linear least-squares fitting. The spectral features at different temperatures and in different matrices are discussed. The majority of the resolved lines are assigned to C(n) cluster species based on data obtained via quantum chemical computations and from tunable laser IR spectrometry of C(n) species in the gas phase. A complete analysis of the IR spectra is available upon request.     

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     

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。