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.     

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.     

Estimation of component spectra by the principal components method

The principal components method enables component spectra from pigment mixtures to be estimated by evaluating the eigenvectors of the second moment matrix. The components are linear combinations of these eivenvectors, but cannot be identified unambiguously. With the conditions of non-negativity of spectral values and of concentrations, this ambiguity can be limited; components spectra for 2 and 3 components were calculated earlier. In the present work, maximal dissimilarity of component spectra is assumed as a further condition. An algorithm based on linear programming is described; it enables any number of components to be estimated from eigenvectors of the second moment matrix with better reliability than previously.     

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.     

Multi‐component analysis: blind extraction of pure components mass spectra using sparse component analysis

The paper presents sparse component analysis (SCA)‐based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures to be greater than or equal to the unknown number of pure components. Specifically, we have demonstrated experimentally the capability of the SCA to blindly extract five pure components mass spectra from two mixtures only. Two approaches to SCA are tested: the first one based on ?₁ norm minimization implemented through linear programming and the second one implemented through multilayer hierarchical alternating least square nonnegative matrix factorization with sparseness constraints imposed on pure components spectra. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm together with pure components concentration matrix. Proposed methodology can be implemented as a part of software packages used for the analysis of mass spectra and identification of chemical compounds. Copyright © 2009 John Wiley & Sons, Ltd.     

Empirical kernel map approach to nonlinear underdetermined blind separation of sparse nonnegative dependent sources: pure component extraction from nonlinear mixture mass spectra

Nonlinear underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed nonlinearly mixed signals into a greater number of original nonnegative and dependent component (source) signals. This hard problem is practically relevant for contemporary metabolic profiling of biological samples, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of nonlinear multicomponent mixtures. This paper presents a method for nonlinear underdetermined blind separation of nonnegative dependent sources that comply with a sparse probabilistic model, that is, sources are constrained to be sparse in support and amplitude. This model is validated on experimental pure component mass spectra. Under a sparse prior, a nonlinear problem is converted into an equivalent linear one comprised of original sources and their higher‐order, mostly second‐order, monomials. The influence of these monomials, which stand for error terms, is reduced by preprocessing a matrix of mixtures by means of robust principal component analysis and hard, soft and trimmed thresholding. Preprocessed data matrices are mapped in high‐dimensional reproducible kernel Hilbert space (RKHS) of functions by means of an empirical kernel map. Sparseness‐constrained nonnegative matrix factorizations in RKHS yield sets of separated components. They are assigned to pure components from the library using a maximal correlation criterion. The methodology is exemplified on demanding numerical and experimental examples related respectively to extraction of eight dependent components from three nonlinear mixtures and to extraction of 25 dependent analytes from nine nonlinear mixture mass spectra recorded in nonlinear chemical reaction of peptide synthesis. Copyright © 2014 John Wiley & Sons, Ltd.     

Influence of mixture design on multivariate prediction of PAHs in mixture spectra

This work describes a simulation study aimed at establishing the impact of mixture design on the prediction ability of PLS regression models. Data sets are formed by multiplying UV absorbance spectra of 12 PAHs by their concentration profiles. In these case studies, either all possible mixtures of 1-12 components are used or randomly chosen selections of the mixtures. The effects of the number of samples and the number of concentration levels in the mixture designs on the results of the calibration are assessed. Comparisons are made between models formed using orthogonal fractional factorial mixture designs and those based on random designs. The applicability limits of the orthogonal designs are analysed in terms of actual concentration ranges of individual components in the mixtures.     

UV-spectrophotometric determination of ketoprofen and paraben in a gel preparation by partial least-squares calibration

Partial least-squares calibration was used for the simultaneous UV spectrophotometric determination of the active principle (ketoprofen) and preservative (parabens) in a pharmaceutical preparation commercially available in gel form. Calibration mixtures were prepared by mixing pure solutions of the analytes. The analytes were extracted and the UV spectrum of the resulting dispersion was recorded. Suppression of the effect of undissolved gel components was accomplished by prior centrifugation, using first and second-derivative spectra, introducing artificial baseline changes and adding the turbidity effect on the sample spectrum to some of the spectra in the calibration matrix. Both the second-derivative and the addition of the turbidity effect allow the quantification of non-centrifuged samples, the last one seems to be a little more effective. The results thus obtained are compared with those provided by HPLC following centrifugation of the samples.     

Weighted two-band target entropy minimization for the reconstruction of pure component mass spectra: Simulation studies and the application to real systems

A method is proposed, on the basis of a recently developed algorithm--Band Target Entropy Minimization (BTEM)--to reconstruct mass spectra of pure components from mixture spectra. This method is particular useful in dealing with spectral data with discrete features (like mass spectra). Compared to the original BTEM, which has been applied to differentiable spectroscopies such as Fourier-transfer infrared spectroscopy (FTIR), ultraviolet (UV), Raman, and nuclear magnetic resonance (NMR), the latest modifications were obtained through: (1) Reformulating the objective function using the peak heights instead of their derivatives; (2) weighting the abstract vector VT to reduce the effect of noise; (3) using a two-peak targeting strategy (tBTEM) to deal with strongly overlapping peaks; and (4) using exhaustive search to locate all the component spectra. A set of 50 multi-component mass spectra was generated from ten reference experimental pure component spectra. Many of the compounds chosen have common MS fragments and therefore, many of the pure component spectra have considerable intensity in same data channels. In addition, a set of MS spectra from a real system with four components was used to examine the newly developed algorithm. Successful reconstruction of the ten component spectra of the simulated system and the four component spectra of the real system was rapidly achieved using the new tBTEM algorithm. The advantages of the new algorithm and its implication for rapid system identification of unknown mixtures are readily apparent.     

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.     

UF6 enrichment measurements using TDLS techniques

The objective of this work was investigation of possibility of tunable diode laser spectroscopy (TDLS) technique application for gaseous uranium hexafluoride (UF6) isotope measurement. Spectra of uranium hexafluoride gas mixture were investigated using two different Fourier Transform Spectrometers Vector 22 and Bruker 66v. Observed spectral features were identified and model spectra of different gas mixture components were developed. Optimal spectral range for measurements was determined near maximum of UF6 combination band nu1+nu3. Laboratory prototype of multi-channel instrument under consideration based on tunable diode lasers was built and algorithms were developed to measure gaseous UF6 isotopic ratios. Diode laser used operated at the wavelengths near lambda=7.68 microm. It was placed in a liquid nitrogen cooled cryostat. Three instrument channels were used for laser frequency calibration and spectra recording. Instrument was tested in measurements of real UF6 gas mixtures. Measurement accuracy was analyzed and error sources were identified. The root-mean-square random error in the 235U isotopic content is characterized by a spread of about 0.27% for quick measurements (at times less than 1 min) and 1% for periods of more than an hour. It was estimated that the measurement accuracy could be improved by at least an order of magnitude by minimizing the error sources.     

用HAM/3法研究胞嘧啶及其甲基衍生物的光电子能谱与紫外光谱

本文用半经验SCF-MO-HAM/3方法计算了胞嘧啶和它的某些甲基衍生物的电离能、激发能和振子强度.指认了这些分子的紫外光电子能谱和紫外电子光谱.讨论了在紫外光电子能谱指认上与CNDO/S的不同之处.分析了胞嘧啶在磷酸三甲酯中可能存在的主要异构体形式.     

Determination of endpoint of procedure for radix rehmanniae steamed based on ultraviolet spectrophotometry combination with continuous wavelet transform and kernel independent component analysis

A method for determination of the endpoint of the procedure for radix rehmanniae steamed was proposed based on UV spectrophotometry combination with continuous wavelet transform and kernel independent component analysis (UV-CWT-KICA). In the proposed method, the raw UV spectra of the rehmanniae samples during steamed procedure were measured. The raw UV spectral data were firstly pretreated by CWT for elimination of the noise signal and enrichment of the spectral resolution, then the independent components (ICs) were estimated from the mixed CWT coefficient matrix. The results show that the ICs are chemical significance with their relative concentrations gradually decreasing or increasing during the first steamed period, and the endpoint of the steamed procedure can be determined by inspection of the relative concentration profiles, at which the ICs should be approached maximum or minimum. Furthermore, the estimated ICs of rehmanniae samples from different areas or with different grades are similar, and the relative concentration of the similar ICs in different groups are increasing or decreasing before the first 14 h, and nearly steady or some decreasing after 16 h. Based on the variations of the relative concentration profiles of the ICs, the endpoint of the steamed procedure can be determined as 15 h, while that determined by sensory analysis is 14-20 h. The proposed UV-CWT-KICA method can avoid the higher deviations of the endpoints that were determined by sensory analysis. It provides an alternative approach for determination of the endpoint of the procedure for processing traditional Chinese medicine (TCM).     

Calibration transfer of near‐infrared spectra for extraction of informative components from spectra with canonical correlation analysis

A new calibration transfer method that applies canonical correlation analysis (CCA) to transfer the informative components extracted from a spectral dataset is proposed to reduce the interference of noise, background and non‐predicted properties. This method employs the partial least squares method to extract the informative components related to the predicted properties from the raw spectra and then corrects the informative components based on CCA. The performance of this algorithm was tested using three pairs of spectra batches: two pairs of corn spectra and one pair of tri‐component solvent spectra. The results showed that this method can significantly reduce prediction errors compared with CCA and piecewise direct standardization. Copyright © 2014 John Wiley & Sons, Ltd.     

Extraction of multiple pure component H and C NMR spectra from two mixtures: Novel solution obtained by sparse component analysis-based blind decomposition

Sparse component analysis (SCA) is demonstrated for blind extraction of three pure component spectra from only two measured mixed spectra in ¹³C and ¹H nuclear magnetic resonance (NMR) spectroscopy. This appears to be the first time to report such results and that is the first novelty of the paper. Presented concept is general and directly applicable to experimental scenarios that possibly would require use of more than two mixtures. However, it is important to emphasize that number of required mixtures is always less than number of components present in these mixtures. The second novelty is formulation of blind NMR spectra decomposition exploiting sparseness of the pure components in the wavelet basis defined by either Morlet or Mexican hat wavelet. This enabled accurate estimation of the concentration matrix and number of pure components by means of data clustering algorithm and pure components spectra by means of linear programming with constraints from both ¹H and ¹³C NMR experimental data. The third novelty is capability of proposed method to estimate number of pure components in demanding underdetermined blind source separation (uBSS) scenario. This is in contrast to majority of the BSS algorithms that assume this information to be known in advance. Presented results are important for the NMR spectroscopy-associated data analysis in pharmaceutical industry, medicine diagnostics and natural products research.     

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.     

Quantification of target components in complex mixtures using alternative moving window factor analysis and two-step iterative constraint method

Alternative moving window factor analysis (AMWFA) has shown the powerfulness for comprehensive comparison and individual identification of chemical components among different but related mixture systems. However, quantification of these components can only be attained after extraction of all spectra of pure components in samples with least square technique. In this study, a novel two-step iterative constraint method (TICM) is developed for independent quantification of the interested target analytes. The pure chromatographic profiles of the components can be mined out from mixtures with high complexity using a two-step iterative operation and stepwise purification of the targets from interferers. Some effective constraints of chromatographic profiles, such as non-negative and single-peaked properties, as well as zero-concentration outside of elution windows of components, are employed to further improve the efficiency of the method. One of the strong advantages of TICM is simplification of complex mixtures to several sub-systems for processing easily with the help of AMWFA, as well as bi-linear property of data sets obtained from coupled chromatographic instruments. It meets the urgent requirements and challenges of qualitative and quantitative analysis of complicated systems with multi-component in the investigation of herbal medicines (HMs), metabonomics and systems biology. From the results of simulated LC–DAD data, GC–MS data of volatile chemical components in three kinds of ginseng with different growth conditions, and four different medicinal parts of the same herb, good performance of the proposed method is achieved.     

Infrared spectra and tautomerism of isocytosine; an ab initio and matrix isolation study

Prototropic tautomerism of isocytosine has been investigated using both theoretical ab initio and experimental matrix isolation IR methods. The coexistence of the amino-hydroxy and amino-oxo N(3)H forms, with a clear predominance of the hydroxy form, was observed. The tautomerization constant [oxo]/[hydroxy] obtained from experimental and calculated IR intensities was 0.11 at the micro-oven temperature of 400 K. The ab initio prediction of the relative energies of the tautomers is in reasonable agreement with the experimental estimation. The change of the tautomeric form oxo→hydroxy upon UV irradiation was used to separate the IR spectra of both tautomers. A theoretically assisted interpretation of the IR spectra of both observed tautomers is proposed.     

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.     

Towards obtaining more information from gas chromatography–mass spectrometric data of essential oils: An overview of mean field independent component analysis

Mean field independent component analysis (MF-ICA) along with other chemometric techniques was proposed for obtaining more information from multi-component gas chromatographic–mass spectrometric (GC–MS) signals of essential oils (mandarin and lemon as examples). Using these techniques, some fundamental problems during the GC–MS analysis of essential oils such as varying baseline, presence of different types of noise and co-elution have been solved. The parameters affecting MF-ICA algorithm were screened using a 2⁵ factorial design. The optimum conditions for MF-ICA algorithm were followed by deconvolution of complex GC–MS peak clusters. The number of independent components (ICs) (chemical constituents) in each peak cluster was estimated using morphological score method. Eigenvalue profiles of evolving factor analysis (EFA) and pure variables from orthogonal projection approach (OPA) were used as initial mixing matrix (chromatograms) in iterative process. The resolved mass spectra were satisfactorily identified using NIST mass spectral search system. Finally, the results of optimized MF-ICA were compared with those obtained using multivariate curve resolution-alternating least square (MCR-ALS), multivariate curve resolution-objective function minimization (MCR-FMIN) and heuristic evolving latent projection (HELP) methods. It is demonstrated that MF-ICA can be used as an alternative method for a quick and accurate analysis of real multi-component problematic systems such as essential oils.