Self-modeling curve resolution analysis of on-line vibrational spectra of polymerisation and transesterification

This article discusses the potential of self-modeling curve resolution analysis (SMCR) for the evolution of on-line vibrational spectral data of polymerisation and transesterification. After the general introduction of the SMCR approach, representative SMCR techniques like orthogonal projection analysis (OPA) and simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) are briefly outlined. As examples the SMCR analysis of the Raman spectra of the block copolymerisation of styrene and 1,3-butadiene and that of the near-infrared (NIR) spectra of the melt-extrusion transesterification of ethylene-vinylacetate copolymer will be illustrated. In the last part of this review paper, a new powerful SMCR method that we have recently proposed is demonstrated.     

Self-modeling curve resolution (SMCR) analysis of near-infrared (NIR) imaging data of pharmaceutical tablets

The idea of quality by design (QbD) has been proposed in pharmaceutical field. QbD is a systematic approach to control the product performance based on the scientific understanding of the product quality and its manufacturing process. In the present study, near-infrared (NIR) imaging is utilized as a tool to achieve this concept. A practical use of a chemometrics technique called self-modeling curve resolution (SMCR) is demonstrated with NIR imaging analysis of pharmaceutical tablets containing two ingredients, a soluble active ingredient, pentoxifylline (PTX), and an insoluble excipient, palmitic acid. Concentration profiles obtained by SMCR reveal that the homogenous distribution of chemical ingredients strongly depends on the grinding time and that its process plays a central role in quantitative control, say sustained-release of PTX. In addition, pure component spectra by SMCR indicate a sequential change of specific NIR peak intensities following the increase of the grinding time. The spectra change shows a molecular structure change related to its crystallinity during grinding process. Accordingly, this study clearly demonstrates that NIR imaging combined with SMCR can be a powerful tool to reveal chemical or physical mechanism induced by the manufacturing process of pharmaceutical products and that it may be a solid solution for QbD of pharmaceutical products.     

Chemometric resolution of ATR-IR spectra data for polycondensation reaction of bis(hydroxyethylterephthalate) with a combination of self-modeling curve resolution (SMCR) and local rank analysis

Self-modeling curve resolution (SMCR) methods, simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and alternating least squares (ALS) were used to calculate pure concentration profiles and pure spectra for the two-way spectral data collected during the on-line polycondensation reaction of bis(hydroxyethylterephthalate) with an ATR-FT-IR spectrometer. In order to improve the resolution results, SIMPLISMA was combined with local rank analysis method, fixed size moving window evolving factor analysis (FSMWEFA) to search for selective regions of various components and then look for the purest wavenumber variables in the selective regions. Such combination allows more accurate determination of the number of chemical components in the reaction system and the calculations of more accurate concentration profiles and spectra.     

Self-modeling curve resolution techniques applied to comparative analysis of volatile components of Iranian saffron from different regions

Volatile components of saffron from different regions of Iran were extracted by ultrasonic-assisted solvent extraction (USE) and were analyzed by gas chromatography-mass spectrometry (GC-MS). Self-modeling curve resolution (SMCR) was proposed for resolving the co-eluted GC-MS peak clusters into pure chromatograms and mass spectra. Multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least square (MCR-ALS) were successfully used for this purpose. The accuracy of the qualitative and quantitative results was improved considerably using SMCR techniques. Comparison of the results of saffron from different regions of Iran showed that their volatile components are different from chemical components and relative percentages points of view. Safranal is the main component of all samples. In addition, 4-hydroxy-2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde (HTCC), 2(5H)-furanone, 2,4,4-trimethyl-3-carboxaldehyde-5-hydroxy-2,5-cyclohexadien-1-one and 2(3H)-furanone, dihydro-4-hydroxy were common in all samples with high percentages. The results proved that combining of SMCR techniques with USE-GC-MS produces a powerful tool for the analysis of the complex samples.     

Robust curve fitting method for optical spectra by least median squares (LMedS) estimator with particle swarm optimization (PSO).

A curve fitting technique for optical spectra based on a robust estimator, least median squares (LMedS), is introduced in this study. For the effective calculation of LMedS, particle swarm optimization (PSO) is also introduced. Unlike a standard curve fitting method using least squares (LS) estimator, the method based on LMedS estimator is less influenced by outliers in experimental data. Two kinds of data sets, simulated data with outliers and temperature-dependent near-infrared (NIR) spectra of oleic acid (OA) are applied for the demonstration of the proposed method. The results clearly reveal that, compared with the LS estimator, the proposed method can effectively reduce undesirable effects of low SN ratio and can yield more accurate fitting results.     

Characterization and determination of stability constants of copper(II)-l-histidine complexation system by using multivariate curve resolution method of visible spectra and two hard modeling methods in aqueous solutions

Multivariate curve resolution, alternating least-squares is applied to spectra data obtained in the study of Cu(II) complexation by l-histidine. The combination of several chemometric techniques based on factor analysis (FA), singular value decomposition (SVD), evolving factor analysis (EFA), and multivariate curve resolution with constrained alternating least-squares (ALS) is used to determine the number of species and their distribution diagram. This multivariate analysis data treatment simultaneously reveals the species Cu, CuL, CuLH, CuL₂, CuL₂H, and CuLOH, through the calculated concentration profiles and allows the assignment of numerically obtained pure individual spectra. Formation constants of these species were calculated by hard-modeling methods applied potentiometric and spectrophotometric measurements.     

Resolution of multicomponent overlapped peaks A comparison of several curve resolution methods

Several curve resolution methods were compared in their performance of identification and quantification of overlapping peaks. These resolution methods are heuristic evolving latent projections (HELP), sub-window factor analysis (SFA), and orthogonal projection resolution (OPR). In this paper, HELP, SFA and OPR with the rank map produced by evolving factor analysis (EFA) or fixed size moving window-evolving factor analysis (FSMW-EFA) were applied to the simulated and experimental data. The resolved results were compared qualitatively and quantitatively. In addition, the further comparison was carried out with the results obtained from multivariate curve resolution-alternate least square (MCR-ALS) by using the initial estimates provided by EFA.     

Identification of an artifact peak co-eluting with formaldehyde-2,4-dinitrophenylhydrazone derivative by GC-MS and chemometrics

A chemometric approach was utilized to identify an artifact peak co-eluting with formaldehyde-2,4-dinitophenylhydrazone. Application of the methods of principal component analysis to the mass spectra of the formaldehyde-hydrazone peaks collected at 200-280 °C has revealed the presence of two factors contributing to these spectra. Moreover, the use of self-modeling curve resolution (SMCR) techniques has enabled the reconstruction of the spectra of the pure components. One of the identified components that contribute to the spectra of the mixture was formaldehyde-2,4-dintrophenylhydrazone, whereas the other component was 1-amino-2,4-dinitrobenzene, a degradation product of 2,4-dintrophenylhydrazine.     

Self-modeling curve resolution method applied for the evaluation of dissolution testing data: A case study of meloxicam-mannitol binary systems

This paper introduces some chemometric methods, i.e., self-modeling curve resolution (SMCR), multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis (PARAFAC and PARAFAC2), which are used to evaluate in vitro dissolution testing data detected by a UV-vis spectrophotometer on meloxicam-mannitol binary systems. These systems were chosen because of their relative simplicity to apply as part of the validation process illustrating the effectiveness of the developed and applied chemometric method. The paper illustrates the failure of PARAFAC methods used before for pharmaceutical data evaluations as well, and we suggest application of the feasible band form given by SMCR as a more general procedure.Steps to improve the dissolution behavior of drugs have become among the most interesting aspects of pharmaceutical technology, and our results show that a larger particle size of meloxicam is advantageous for dissolution. Instead of the use of only one characteristic wavelength, appropriate chemometric methods can furnish more information from dissolution testing data, i.e., the individual dissolution rate profiles and the individual spectra for all the components can be obtained without resorting to any separation techniques such as HPLC.     

Investigation of the interaction of carbofuran and DNA by voltammetry and fluorescence with the aid of chemometrics

<正>The interactions of carbofuran and DNA were studied using voltammetry and fluorescence spectroscopy.The formation of carbofuran-DNA makes the current peak of DNA decreased by voltammetry method.The binding number(n) and constant(K_a) for complex carbofuran-DNA were calculated to be 1.06±0.04 and 0.11±0.03mol~(-1) L,respectively by fluorescence measurement.Chemometrics approach,such as singular value decomposition(SVD) was used to evaluate the number of spectral species in the drug-DNA binding process.And the pure spectra and concentration profiles in the kinetic system were clearly deduced by multivariate curve resolution alternating least squares(MCR-ALS) with the initial estimates by evolving factor analysis(EFA).     

Characterization of subcritical water oxidation with in situ monitoring and self-modeling curve resolution

In this paper, a subcritical water oxidation (SBWO) process was monitored using self-modeling curve resolution (SMCR) of in situ UV-Vis measurements to estimate time-dependant composition profiles of reactants, intermediates and products. A small laboratory scale reactor with UV-Vis fiber-optic probes and a flow cell was used to demonstrate the usefulness of SMCR for monitoring the destruction of model compounds phenol, benzoic acid, and aniline in a dilute aqueous solutions. Hydrogen peroxide was used as the oxidizing reagent at moderate temperature (150-250 °C) and pressure (60-90 atm) in a single phase. By use of in situ monitoring, reaction times were easily determined and conditions for efficient oxidations were easily diagnosed without the need for time consuming off-line reference measurements. For selected runs, the destruction of the model compound was confirmed by gas chromatography and chemical oxygen demand (COD) measurements. Suspected intermediate oxidation products were easily detected by the use of UV-Vis spectrometry and self-modeling curve resolution, but could not be detected by gas chromatography.     

A new approach to data reduction and evaluation in high-resolution time-of-flight mass spectrometry using a time-to-digital convertor data-recording system

A new effective and robust approach to the detection of incompletely resolved peaks, and evaluation of their parameters in high-resolution time-of-flight mass spectra for time-to-digital convertor (TDC) data acquisition mode, is described. The method is based on fast construction of a smoothed continuous curve that approximates the initial data (transformed to a constant relative width of time intervals for ion counting) with respect to precision of measurements. The first derivative of this curve is used for correction of skewness of the peak shape as far as possible. A contribution of the second derivative is subtracted from the smoothed curve for better resolution of partially resolved peaks. The comparison of local maxima of this resulting final curve with those for the initial smoothed curve allows reliable detection of the peaks and to test whether or not they are spoiled by overlapping. Ion counting performed by TDC gives an opportunity to estimate standard deviations of peak locations and their intensities. These values proved to be close to theoretically minimal standard deviations for these parameters for single fully resolved peaks. Thus, estimates of the main parameters of mass peaks by the described method are close to statistically efficient estimators for these parameters.     

多元曲线分辨-交替最小二乘方法在联用数据分辨中的应用

通过对模拟数据和高效毛细管电泳实验数据的分析,讨论了多元曲线分辨-交替最小二乘方法(MCR-ALS)在毛细管电泳-二极管阵列检测(CE-DAD)联用数据分辨中的应用.讨论了几种因素对MCR-ALS单个数据矩阵分辨结果的影响,包括待分析物光谱间的相似程度、浓度曲线的重叠程度以及由渐进因子分析(EFA)所得到的浓度初始值等.MCR-ALS还可用于多个数据矩阵的同时分析,即二阶MCR-ALS.结果表明,与一阶MCR-ALS相比,二阶MCR-ALS方法能够更好地解决各种分辨问题,得到合理和满意的分辨结果.     

Local resolution of two-way data from multicomponent equilibria

A self-modeling curve resolution (SMCR) method is proposed to calculate concentration and spectral profiles for the two-way spectral data from an equilibrium containing several chemical components. The proposed method has three main distinctive steps: (i) fixed size moving window evolving factor analysis (FSMWEFA) is used to identify the selective and zero concentration regions for a desired component, (ii) orthogonal projection resolution (OPR) is used to calculate its concentration profile and (iii) the component striping is done directly to resolve other components. The results of simulated and real polyprotic acid dissociation equilibria showed that the proposed combined method performs well even in situation when the successive stepwise equilibrium constants are close to each other. The applicability of method for resolving the triprotic acid system with rank deficiency due full spectral overlapping of two involved chemical species also is shown.     

Study on the Bleaching Mechanism of Indigo—carmine Catalyzed by Laccase Using EFA and MCR—ALS Methods

The bleaching of indigo-carmine catalyzed by laccase was monitored on-line by UV/Vis spectroscopy.The resulted data were analyzed by evolving factor analysis(EFA) and multivariate curve resolution(MCR),and the reaction mechanism was proposed.     

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).     

Multivariate curve resolution applied to Fourier transform infrared spectra of macromolecules: structural characterisation of the acid form and the salt form of humic acids in interaction with lead

The significance of evolving mixtures structural spectroscopic studies might appear limited when the experimental spectra do not present a sufficient quality for a precise interpretation. It is the case when the chemical behaviour of macromolecules is studied on the basis of infrared spectra. If the effective resolution is low, the spectral profiles appear similar despite the applied chemical conditions change. This makes impossible the interpretation of the raw spectra and mathematical treatments are required to separate the different contributions that overlap.To determine the behaviour of the reactive sites of humic acids in the binding with heavy metals, infrared spectra are recorded under various chemical conditions. The cation to be considered is Pb²⁺ and the two chemical variables to be studied are pH and initial lead concentration. Four series of FTIR spectra are recorded, but no visible difference can be directly assigned to the different chemical states of the macromolecules. Multivariate self-modelling curve resolution is thus proposed as a tool for resolving these complex and strong overlapping datasets. First, initial estimates are obtained from pure variable detection methods: it comes out that two spectra are enough to reconstruct the experimental matrices. In a further step, the application of the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm with additional constraints on each individual dataset, as well as on column-wise augmented matrices, allows to optimise the profiles and spectra that appear to be highly characterising the acid and the salt form of the molecule. Moreover, the concentrations profiles associated to these two limit spectral forms allow interpreting the analytical measurements made during the reactions between humic acids and H⁺ or Pb²⁺. Consequently, depending on the initial state of the humic acid, two distinct reactional mechanisms are proposed.     

Genetic algorithms (GA) applied to the orthogonal projection approach (OPA) for variable selection

Multivariate curve resolution (MCR) and especially the orthogonal projection approach (OPA) can be applied to spectroscopic data and were proved to be suitable for process monitoring. To improve the quality of the on-line monitoring of batch processes, it is interesting to get as many as possible spectra in a given period of time. Nevertheless, hardware limitations could lead to the fact that it is not possible to acquire more than a certain number of spectra in this given period of time. Wavelength selection could be a good way to limit this problem since it decreases size, and consequently the acquisition time, of each recorded spectrum. This paper details an industrial application of genetic algorithms (GA) coupled with a curve resolution method (OPA) for such purpose.