一种新的目标转换因子分析算法用于混合物红外光谱中纯组分光谱的解析

本文针对运用目标转换因子分析从混合物红外光谱中解析出纯组分光谱的有关限制,进行了有效的改进。把抽象正交特征光谱与以单一向量为初始向量的目标转换因子分析相结合,提出了一种新的算法,结果令人满意。     

Fourier transform Raman spectral measurements of powdered quaternary mixtures of organic compounds: Exceptional pure component spectral reconstruction using band-target entropy minimization (BTEM)

Fourier transform Raman spectra of eight mixtures of four organic solids, namely dicyandiamide, melamine, acetamide and urea were measured. Matrices formed from these spectra were first subjected to singular value decomposition to obtain the right singular vectors. The right singular vectors were then subjected to blind source separation using band-target entropy minimization (BTEM), thus no a priori information (i.e. involving the nature of the components present, their spectra, nor their concentrations) was included in the analysis. The recovered pure component spectra are of exceptionally high quality and are consistent with pure reference spectra. Various empirical and statistical tests, such as the Euclidean norm and target transform factor analysis, were employed to assess the quality of the recovered spectra. The present results indicate the applicability of combined Raman and BTEM analysis for solid mixtures.     

Application of cause-and-effect diagrams to the interpretation of UV-Vis spectroscopic data

The application of cause-and-effect diagrams to the evaluation of thermodynamic data from UV-Vis absorption spectroscopic analysis is demonstrated. The contributions of measurement uncertainty identified from a cause-and-effect diagram are implemented into a Monte Carlo procedure based on the threshold bootstrap computer-assisted target factor analysis (TB CAT). This algorithm aims at an improvement of data comparability and accounts for non-normality, spectral, residual and parameter correlation as well as random noise in target factor analysis. The ISO Type-B measurement uncertainties are included into the process by normally distributed random numbers with specified mean values and dispersions. The TB CAT procedure is illustrated by a flow diagram and a case study of Nd(III) complexation by picolinic acid N-oxide (pic NO) in aqueous solution. Using 12 experimental spectra as input data, the single component spectra and the formation constant 1g betaML of the Nd(pic NO)2+ species are obtained together with the respective probability density distributions. The role of the cause-and-effects approach on the further development of chemical thermodynamics is discussed.     

Characterization and determination of fatty acids in fish oil using gas chromatography-mass spectrometry coupled with chemometric resolution techniques

Characterization and determination of a complex mixture of fatty acid methyl esters was performed for commercial fish oil using two-dimensional GC-MS data coupled with resolution techniques. Various principle component analysis methods such as significant factor analysis and fixed size moving window evolving factor analysis were used for the number of factors, zero concentration and selective regions. Then, the convoluted chromatograms were resolved into pure chromatograms and mass spectra using heuristic evolving latent projections (HELP) method. Fatty acids of C16:1omega7, C18:4omega3, C18:1omega11, C18:1omega9, C18:0, C20:2omega6, C20:1omega9, C22:1omega11, C22:1omega9 and C24:1omega9 were resolved an fied by using similarity searches between deconvoluted mass spectra and MS database, in different parts of total ion current chromatogram. Window target testing factor analysis is also applied for confirming the presence or absence of target analytes. The results of the present work show that combination of hyphenated chromatographic methods and resolution techniques provide a complementary method for accurate analysis of real multi-component systems such as fish oil.     

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。     

Enhanced target factor analysis

Target testing or target factor analysis, TFA, is a well-established soft analysis method. TFA answers the question whether an independent target test vector measured at the same wavelengths as the collection of spectra in a data matrix can be excluded as the spectrum of one of the components in the system under investigation. Essentially, TFA cannot positively prove that a particular test spectrum is the true spectrum of one of the components, it can, only reject a spectrum. However, TFA will not reject, or in other words TFA will accept, many spectra which cannot be component spectra. Enhanced Target Factor Analysis, ETFA addresses the above problem. Compared with traditional TFA, ETFA results in a significantly narrower range of positive results, i.e. the chance of a false positive test result is dramatically reduced. ETFA is based on feasibility testing as described in Refs. [16–19]. The method has been tested and validated with computer generated and real data sets.     

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。     

Quantitative resolution of spectroscopic systems using computer-assisted target factor analysis (CAT)

Factor analysis (FA) is widely applicable in analytical chemistry. For suitable data structures, the generality of the approach is a strong point of FA. Using target testing procedures, a wealth of specific informations on the components in a chemical system can be extracted without stating a specific model. Target testing, however, is time-consuming and often heuristic. In the following, application of a computer-assisted target factor analysis (CAT) algorithm is described. CAT estimates a rotation matrix that transforms abstract factors into physically meaningful informations by using general constraints, i.e. non-negativity of absorptions and/or concentrations. Thus, the rotation step in factor analysis is automated and allows application of target factor analysis to situations where none or only very limited information on the real factors and their respective contributions is available. Thus, a target testing procedure of guessing physically meaningful factors and iteratively adapting these factors is performed automatically. Nevertheless, CAT is not a black box procedure. The relative importance of different optimization constraints is balanced interactively. CAT can be applied to all situations where factor analysis can be used. CAT is demonstrated using UV-Vis absorption spectra of a Nd(III) polyoxometalate cryptate system and the system U(VI)-CO2-H2O as examples.     

近红外漫反射光谱法对固态样品的无损分析

本文应用目标因子分析研究了固态样品的近红外漫反射光谱，用目标因子分析成功地确定了固态样品的物种数、物种种类和各物种的含量。     

Spectroelectrochemical study of the oxidative doping of polydialkylphenyleneethynylene using iterative target transformation factor analysis

Iterative target transformation factor analysis (ITTFA) was used to determine the spectra of the individual species generated during the oxidative p-doping of films of poly(para-phenyleneethynylene) (PPE). UV-visible spectra of PPE films on transparent electrodes were obtained in-situ during an anodic sweep. ITTFA identified 4 species present during the oxidation, which we assign as neutral polymer, polaron species, bipolaron species, and a species formed by further bipolaron reaction. The region of electrochemical stability for each of these species was identified and their potential-dependent profiles were obtained. This work is the first deconvolution of conjugated polymer spectroelectrochemistry.     

Quantitative resolution of spectroscopic systems using computer-assisted target factor analysis (CAT)

Factor analysis (FA) is widely applicable in analytical chemistry. For suitable data structures, the generality of the approach is a strong point of FA. Using target testing procedures, a wealth of specific informations on the components in a chemical system can be extracted without stating a specific model. Target testing, however, is time-consuming and often heuristic. In the following, application of a computer-assisted target factor analysis (CAT) algorithm is described. CAT estimates a rotation matrix that transforms abstract factors into physically meaningful informations by using general constraints, i.e. non-negativity of absorptions and/or concentrations. Thus, the rotation step in factor analysis is automated and allows application of target factor analysis to situations where none or only very limited information on the real factors and their respective contributions is available. Thus, a target testing procedure of guessing physically meaningful factors and iteratively adapting these factors is performed automatically. Nevertheless, CAT is not a black box procedure. The relative importance of different optimization constraints is balanced interactively. CAT can be applied to all situations where factor analysis can be used. CAT is demonstrated using UV-Vis absorption spectra of a Nd(III) polyoxometalate cryptate system and the system U(VI)-CO₂-H₂O as examples.     

Study on multimers and their structures in molecular association mixture

Self-association system of(R)-1,3-butanediol in dilute carbon tetrachloride(CCl4)solution is studied as a model of molecular association mixture.Analysis methods including FSMWEFA(fixed-size moving window evolving factor analysis)combined with PCA(principal component analysis),SIMPLISMA (simple-to-use interactive self-modeling mixture analysis),and ITTFA(iterative target transformation factor analysis)are adopted to resolve infrared spectra of(R)-1,3-butanediol solution.Association number and equilibrium constant are computed.(R)-1,3-butanediol in dilute inert solution is determined as a monomer-trimer equilibrium system.Theoretical investigation of trimer structures is carried out with DFT(density functional theory),and structural factors are analyzed.     

完全未知混合体系的纯物种光谱辨析

基于因子分析的迭代目标转换算法,本文提出一种从完全未知混合物体系中获取纯物种光谱的解析方法。该法简单明了,不增加任何困难即可拓广至含n组份的混合体系,对光谱峰形状亦无任何附加限制要求。先采用计算机模拟技术对该法进行了校验,继用于紫外可见与荧光光谱的实际混合体系的解析,均获满意结果。     

Investigation of the acid-base properties of mononitro-calix[4]arene with chemometric methods

The acid-base properties of mononitro-calix[4]arene was studied with chemometric methods by measurement of its UV absorbance under different pH. The chemometric method-iterative target transformation factor (ITTFA) was employed to resolve the acid-base fraction curves. Combining with other chemometric methods-principal component analysis (PCA) and evolving factor analysis (EFA), the proton dissociation behavior of the derivative was investigated in detail. The pK(a) values of the derivative were determined and the fraction curves and pure absorbing spectra of each absorbing component were obtained.     

Resolving overlapping GC–MS signals with a multistep screening chemometric approach for the fast determination of pesticides

For the rapid analysis of multicomponent mixtures using GC–MS, a chemometric multistep screening approach was proposed to extract the signals of the components from the overlapping signals measured with a very fast temperature program. At first, independent component analysis was used to find all the possible mass spectra from the overlapping signal in the moving windows along the retention time, and iterative target transformation factor analysis was employed to validate the existence of the extracted spectra from each window. Then, identical signals in the validated spectra were excluded using match ratio as a criterion. Finally, the chromatographic profiles for each spectrum were calculated using non‐negative immune algorithm, and the spectra with a reasonable profile were taken as the identified components. A mixture of 53 pesticides was analyzed with a very fast temperature program of 7 min. A total of 48 pesticides and 16 interferences were identified from the overlapping GC–MS signal.     

Rapid analysis of phthalic acid esters in environmental water using fast elution gas chromatography with mass spectrometry and adaptive library spectra

A method for the fast determination of the components in a complex sample by using gas chromatography with mass spectrometry was developed and used for the quantitative analysis of phthalic acid esters in environmental water. In the method, the adaptively corrected mass spectra were used to compensate for the differences between the library spectra and the measured ones in the experiment. The correction was obtained by the iterative transformation of the library spectra using iterative target transformation factor analysis, and the resolution was performed by non‐negative immune algorithm using the corrected spectra. Rapid analysis of 16 phthalic acid esters in water samples was achieved using fast elution gas chromatography with mass spectrometry measurements. The results show that the mass spectra and chromatographic profiles of the phthalic acid esters can be obtained from the overlapping signal of 13 min elution, and accurate quantitative analysis can be obtained. The recoveries of the phthalic acid esters obtained by standard addition are between 90.3 and 107.4%, and the relative standard deviations obtained in repeated measurements are less than 9%.     

Resolving co-eluting chromatographic patterns by means of dissimilarity analysis in iterative target transformation factor analysis

The initialization of concentration vector for iterative target transformation factor analysis (ITTFA) and identification of pure or key variables are the important issue in MCR. In this study, dissimilarity analysis and evolving factor analysis (EFA) are combined to find the selective or key variables and subsequently obtain initial estimates of the concentration vectors for resolution of gas chromatography/mass spectrometry (GC/MS) data by ITTFA. For systems containing components with highly similar mass spectra, a new constraint setting the elements out of elution window to 0 is used to improve convergence rate and accuracy of results. Tested by standard mixture of two wax esters and real GC/MS data of gasoline 97#, dissimilarity based ITTFA could obtain accurate results (average Dot product of concentration vectors, average deviation of peak area ratio and average similarity of mass spectra are 0.9929, 0.0228 and 981.0, respectively).     

Multicomponent analysis in luminescence spectroscopy

Multicomponent analysis of absorption spectra (infrared or electronic spectra) by factor analysis is a suitable method to use. The algorithm of factor analysis is now available for luminescence spectroscopy. The first possibility, as in absorption spectroscopy, is to determine pure components emission spectra for a unique set of spectra of mixtures in overdetermined conditions. On the other hand, it is also possible to extract pure components emission and excitation spectra of the only data corresponding to the 3D fluorescent spectrum of a single mixture. The results are evaluated in respect to noise level and concentrations ratio on both simulated and real spectra.     

Multicomponent analysis in luminescence spectroscopy

Multicomponent analysis of absorption spectra (infrared or electronic spectra) by factor analysis is a suitable method to use. The algorithm of factor analysis is now available for luminescence spectroscopy. The first possibility, as in absorption spectroscopy, is to determine pure components emission spectra for a unique set of spectra of mixtures in overdetermined conditions. On the other hand, it is also possible to extract pure components emission and excitation spectra of the only data corresponding to the 3D fluorescent spectrum of a single mixture. The results are evaluated in respect to noise level and concentrations ratio on both simulated and real spectra.     

Detection of bio-constituents in complex biological tissue using Raman microscopy. Application to human nail clippings

Raman spectra of human nail clippings from various sources were collected and then deconvoluted to obtain the pure component spectra of the underlying constituents present. This blind-deconvolution was performed using a self-modeling curve resolution technique, namely band-target entropy minimization (BTEM). The aim was to simplify the complexity of the Raman spectra and hence to identify the underlying biological molecules in more detail. BTEM analysis could recover 13 pure component Raman spectral estimates from the collected 438 spectra measured from 113 nail samples. Six recovered pure component spectral estimates correspond to proteins or polypeptides that contain various amino acids such as phenylalanine, tyrosine, tryptophan, and cysteine. Two are associated with the secondary structures of proteins, and five are associated with two carotenoid species, lipid, ferulic acid, and calcium phosphate. Subsequently, the relative concentrations of these bio-constituents were calculated from the measured mixture spectra and the pure component BTEM estimates. These profiles indicated that the concentrations of some bio-constituents are correlated while others are not. A further analysis using target transformation factor analysis (TTFA) revealed the possible presence of curcumin in the human nails. Since the present approach and analysis is rather general, it might be extended to many other biological tissues in a rather straightforward and similar manner, thus revealing more detailed underlying biochemical information such as biomarkers that may be useful for diagnostic purposes.