Simple-to-use Interactive Self-modeling Mixture Analysis Research Articles

Deciphering the biodegradation of petroleum hydrocarbons using FTIR spectroscopy: application to a contaminated site.

The chemical composition of groundwater in a petroleum-contaminated site is determined by the present functional groups and these play a vital role in a feasibility remediation technique. Based on the in situ investigation of a contaminated shallow groundwater in an oilfield, Fourier transform infrared (FTIR) spectroscopy associated with chemometric treatments, principal component analysis (PCA), and simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), were used to decipher the biodegradation process by analyzing the conversion of functional groups. Environmental factors that can influence microbial metabolism were also evaluated for a comprehensive explanation. FTIR spectroscopy and PCA results showed that the contamination in the study area can be divided into three parts based on FTIR spectra: (1) regular contamination plume distribution and biodegradation level to fresh oil, (2) moderate biodegradation area, and (3) intensive biodegradation area. FTIR spectra further revealed the present functional groups as aliphatic, aromatic, and polar family compounds. SIMPLISMA was used to discuss the degree of biodegradation along the flow path quantitatively and qualitatively and elucidated that the aliphatic and aromatic compounds were mainly metabolized into polar compounds with nitrogen, sulfur, and oxygen via microbes. During metabolism, microbial indices, such as the Shannon-Weaver, Simpson, and Pielou indices, indicated that microbial diversity did not greatly change; hence, hydrocarbons were constantly consumed to feed dominant microbes. Dissolved oxygen concentrations decreased from 4.58 ± 0.31 mg/L (in monitoring well Z1) to 3.21 ± 0.26 mg/L (in monitoring well Z16) and then became constant in the down-gradient area, demonstrating that aerobic biodegradation was the dominant process at the up-gradient plume. Results were in accordance with the oxidation index, which continuously increased from 0.028 ± 0.013 (in monitoring well Z1) to 0.669 ± 0.047 (in monitoring well Z10), showing that oxygen was consumed along the flow path. Similarly, concentration changes in Fe2+, Mn2+, and SO4 2- proved that the down-gradient area was in reduction condition.

A quick method based on SIMPLISMA-KPLS for simultaneously selecting outlier samples and informative samples for model standardization in near infrared spectroscopy

A novel method based on SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) and Kernel Partial Least Square (KPLS), named as SIMPLISMA-KPLS, is proposed in this paper for selection of outlier samples and informative samples simultaneously. It is a quick algorithm used to model standardization (or named as model transfer) in near infrared (NIR) spectroscopy. The NIR experiment data of the corn for analysis of the protein content is introduced to evaluate the proposed method. Piecewise direct standardization (PDS) is employed in model transfer. And the comparison of SIMPLISMA-PDS-KPLS and KS-PDS-KPLS is given in this research by discussion of the prediction accuracy of protein content and calculation speed of each algorithm. The conclusions include that SIMPLISMA-KPLS can be utilized as an alternative sample selection method for model transfer. Although it has similar accuracy to Kennard–Stone (KS), it is different from KS as it employs concentration information in selection program. This means that it ensures analyte information is involved in analysis, and the spectra (X) of the selected samples is interrelated with concentration (y). And it can be used for outlier sample elimination simultaneously by validation of calibration. According to the statistical data results of running time, it is clear that the sample selection process is more rapid when using KPLS. The quick algorithm of SIMPLISMA-KPLS is beneficial to improve the speed of online measurement using NIR spectroscopy.

In situ ATR-FTIR combined with SIMPLISMA algorithm to investigate the synthesis mechanism of 4-amino-3,5-dimethyl pyrazole

In situ attenuated total refletion-Fourier transform infrared spectroscopy(ATR-FTIR) was used to monitor and acquire spectral information on the synthesis of 4-amino-3,5-dimethyl pyrazole. Principal component analysis(PCA) was used to determine the number of principle components(PCs). The score vectors of the PCs were analysed using the simple-to-use interactive self-modelling mixture analysis(SIMPLISMA) algorithm to obtain spectral and concentration profiles for the reactants, intermediates and product. The vibrational frequencies of the intermediates were calculated via density functional theory(DFT) at the level of the B3LYP/6-311++G(d,p) basis set, and the geometrical configurations of the intermediates were simultaneously optimized. Finally, a reasonable synthesis mechanism for 4-amino-3,5-dimethyl pyrazole was determined based on the changes observed in the feature peaks. The results from the SIMPLISMA algorithm correlated well with the quantum chemistry calculations. This proved that the SIMPLISMA algorithm combined with ATR-FTIR can be used to determine the synthesis mechanism for 4-amino-3,5-dimethyl pyrazole and can even provide a new, useful method to explore dynamic synthesis reaction mechanisms.

The spectral resolution of unknown mixture based on THz spectroscopy with self-modeling technique

This study aims at resolving the spectral profiles of pure components from the mixture THz data. A method based on simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), one of self-modeling curve resolution (SMCR) techniques, was put forward to deal with two-way THz spectral data of unknown mixtures for spectral resolution of single species. SMCR requires pure variables which have intensity contributions from only one component for extracting the content information. However, for the spectra of many materials in THz region (about 0.1–3THz), pure variables are usually unavailable for the overlap of absorption peaks or the presence of baseline. To settle this problem, inverted second derivative was employed as an intermediate step to optimize the determination of pure variable. To test the effect, the proposed method was performed on simulated and experimental THz spectra of multicomponent systems. It is demonstrated that SIMPLISMA combined with inverted second derivative spectra is a promising tool to deconvolve multicomponent systems contributing to the identification of ingredients in unknown mixture in THz region.

Sliding window multi-curve resolution: Application to gas chromatography–ion mobility spectrometry

Blind source separation (BSS) techniques aim to extract a set of source signals from a measured mixture in an unsupervised manner. In the chemical instrumentation domain source signals typically refer to time-varying analyte concentrations, while the measured mixture is the set of observed spectra. Several techniques exist to perform BSS on ion mobility spectrometry, being simple-to-use interactive self-modelling mixture analysis (SIMPLISMA) and multivariate curve resolution (MCR) the most commonly used. The addition of a multi-capillary gas chromatography column using the ion mobility spectrometer as detector has been proposed in the past to increase chemical resolution. Short chromatography times lead to high levels of co-elution, and ion mobility spectra are key to resolve them. For the first time, BSS techniques are used to deconvolve samples of the gas chromatography–ion mobility spectrometry tandem. We propose a method to extract spectra and concentration profiles based on the application of MCR in a sliding window. Our results provide clear concentration profiles and pure spectra, resolving peaks that were not detected by the conventional use of MCR. The proposed technique could also be applied to other hyphenated instruments with similar strong co-elutions.

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 (1)H 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.

Mutual information concept for evaluation of separation quality in hyphenated chromatographic measurements

A new method for the evaluation of separation quality in hyphenated chromatographic measurements based on the information-theoretic concept of mutual information (MI) is developed. The MI values for the purest spectra selected using the simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and orthogonal projection approach (OPA) methods are calculated according to the differential information entropy (Shannon entropy). To calculate the MI values for more than two variables, the Kozachenko-Leonenko (KL) estimation of the Shannon entropy is used. The MI values of the purest spectra can reliably reflect the degree of peak overlap in the chromatographic direction. Herein, the developed method is employed on different simulated and real GC-MS and HPLC-DAD datasets (i.e., chromatographic segments and chromatographic fingerprints) to evaluate the potential of this new method. Inspection of the results showed that minimization of MI values is a good criterion for comprehensive evaluation of separation quality in hyphenated chromatographic measurements and to reach to the best chromatographic separation. Additionally, the performance of this method is compared with the previously developed overlap index (OVI) criterion and classical univariate criteria, such as ΣRs and ΠRs, which showed an improvement in all cases. As demonstrated by simulated and real chromatographic data, the MI index gives not only a comprehensive criterion for evaluation of separation quality, but also provides reliable information for the purity assessment of compounds of interest. Furthermore, the MI index can be used as a reliable criterion for multivariate optimization of hyphenated chromatographic measurements.

Combined Use of Post-Ion Mobility/Collision-Induced Dissociation and Chemometrics for b Fragment Ion Analysis

Although structural isomers may yield indistinguishable ion mobility (IM) arrival times and similar fragment ions in tandem mass spectrometry (MS), it is demonstrated that post-IM/collision-induced dissociation MS (post-IM/CID MS) combined with chemometrics can enable independent study of the IM-overlapped isomers. The new approach allowed us to investigate the propensity of selected b type fragment ions from AlaAlaAlaHisAlaAlaAla-NH2 (AAA(His)AAA) heptapeptide to form different isomers. Principle component analysis (PCA) of the unresolved post-IM/CID profiles indicated the presence of two different isomer types for b4(+), b5(+), and b6(+) and a single isomer type for b7(+) fragments of AAA(His)AAA. We employed a simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) to calculate the total IM profiles and CID mass spectra of b fragment isomers. The deconvoluted CID mass spectra showed discernible fragmentation patterns for the two isomers of b4(+), b5(+), and b6(+) fragments. Under our experimental conditions, calculated percentages of the "cyclic" isomers (at the 95% confidence level for n = 3) for b4(+), b5(+), and b6(+) were 61 (± 5)%, 36 (± 5)%, and 48 (± 2)%, respectively. Results from the SIMPLISMA deconvolution of b5(+) species resembled the CID MS patterns of fully resolved IM profiles for the two b5(+) isomers. The "cyclic" isomers for each of the two-component b fragment ions were less susceptible to ion fragmentation than their "linear" counterparts.

Coupling of soft‐modeling methods with multivariate pattern recognition technique for the identification of nitroaniline isomers

A new approach that takes advantage of both soft‐modeling and pattern recognition methods is proposed to analyze kinetic data monitored spectrometrically to classify structurally similar nitroaniline isomers. The colorimetric condensation reaction of 1,2‐naphthoquinone‐4‐sulfonate (NQS) with amines has been used to classify 2‐, 3‐, and 4‐nitroaniline on the basis of their different kinetic properties. These nitroanilines react differentially with NQS at pH 7 to produce a colored product. Soft‐modeling approaches such as multivariate curve resolution–alternating least squares (MCR–ALS) and simple‐to‐use interactive self‐modeling mixture analysis (SIMPLISMA) followed by multiplicative signal correction have been used as preprocessing procedures to obtain patterns diagnostic for the analytes. Rank analysis was initially performed using determination of rank by augmentation to detect any species present during the course of the reaction in the system under investigation. The spectra and kinetic profiles of all absorbing species present in the raw measurements were obtained from SIMPLISMA and MCR–ALS resolution. The kinetic profiles were then used as input of the pattern recognition algorithm. The response patterns were systematically classified using linear discriminant analysis with high classification accuracy. Either all or one of the resolved profiles related to the components involved in the reaction can be used for identification of nitroaniline isomers. Copyright © 2013 John Wiley & Sons, Ltd.

Chemometric resolution techniques combined with GC-MS to enhance determination of the volatile chemical constituents of bay leaves

The 71 constituents of essential oil (EO) of bay leaves were identified with gas chromatography-mass spectrometry (GC-MS) by applying a similarity search between the obtained mass spectra and database. This number was increased to 131 constituents with the help of different chemometric resolution techniques. Multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure chromatograms and mass spectra for the constituents present in each overlapping/embedded peaks cluster as well as their relative concentrations. Other chemometric methods such as simple to use interactive self-modeling mixture analysis (SIMPLISMA), morphological score (MS) and Fixed Size Moving Window Evolving Factor Analysis (FSMWEFA) were used to determine the number of constituents, pure variables, zero concentration and selective regions. The most important constituents of the Iranian bay leaves are linalool (12.31%), 1,8-cineole (11.38%), α-terpineol acetate (7.71%), eugenol methyl ether (6.87%) and α-phellandrene (4.30%). Despite previous studies, chemometric resolution techniques were implemented as a necessary step for the comprehensive analysis of the volatile constituents of bay leaves.

Characterization of volatile components in Calligonum comosum by coupling gas chromatography-mass spectrometry and mean field approach independent component analysis

The volatile components of Calligonum comosum were extracted using a simultaneous distillation extraction (SDE) method and were analyzed by gas chromatography-mass spectrometry (GC-MS). The mass spectra obtained from their components were surveyed by searching in NIST and Wiley libraries, but some of the components did not show a satisfactory matching percentage. Morphological score (MS) has used for chemical rank determination, and simple-to use interactive self-modeling mixture analysis (SIMPLISMA) was used as an initial estimate in the iterative process. In the next step, the overlapping peaks were resolved into pure chromatograms and mass spectra using independent component analysis (ICA) methods. Among different independent component analysis algorithms, mean field (MF) applying non-negativity constraints on concentration and spectra profiles is used in this research. MF-ICA can be used as an alternative method for a rapid and accurate analysis of real, multi-component, challenging systems such as essential oils. The resolved mass spectra were satisfactorily identified using mass spectral searches in NIST and Wiley libraries. The consequences of this study show that joining of hyphenated chromatographic methods and resolution techniques provide a satisfactory method for precise analysis of essential oils along with expanding the number of constituents.

Analysis of the volatile chemical constituents in Mindium laevigatum by gas chromatography — Mass spectrometry and correlative chemometric resolution methods

The essential oil of Mindium laevigatum was extracted by applying simultaneous distillation extraction (SDE) method and studied with gas chromatography–mass spectrometry (GC–MS). The whole of 61 constituents was identified by applying similarity search between the obtained mass spectra and database. This number was expanded to 91 using multivariate curve resolution — alternation least square (MCR–ALS) method. Different chemometric methods such as simple-to use interactive self-modeling mixture analysis (SIMPLISMA), morphological score (MS) and Fixed Size Moving Window Evolving Factor Analysis (FSMWEFA) were used for determining the number of constituents, pure variables, zero concentration and selective regions. Self‐modeling curve resolution (SMCR) was suggested for resolving the co-eluted GC–MS peak into pure chromatograms and mass spectra. MCR–ALS prosperously applied for this aim. Pure variables from SIMPLISMA were applied as initial mixing matrix in iterative process. The resolved mass spectra were properly identified applying mass spectral search in NIST and Wiley library. The outcomes of this work proved that the combination of hyphenated chromatographic methods and resolution techniques provides a complementary method for the exact analysis of essential oils.

Investigation of gamma radiation effect on the anion exchange resin Amberlite IRA-400 in hydroxide form by Fourier transformed infrared and 13C nuclear magnetic resonance spectroscopies

Radiation-induced decomposition of the anion exchange resin Amberlite IRA-400 in hydroxide form by gamma radiolysis has been studied under different irradiation doses and irradiation atmospheres. In this work, we focused on the degradation of the solid part of the resin by Fourier transformed infrared (FTIR) and 13C nuclear magnetic resonance (NMR) spectroscopies associated with chemometric treatments. FTIR and 13C NMR techniques showed that only CH2N+(CH3)3 groups were detached from the resin whereas the polystyrene divinylbenzene backbone remains intact. The quaternary ammonium groups were replaced by amine or carbonyl groups according to the irradiation atmosphere (with or without water or oxygen). Principal components analysis (PCA) was used to classify the degraded resins according to their irradiation conditions by separating the effect of the dose or the environment. The PCA loadings have shown spectral regions which discriminate the irradiated resins whereas SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) allows to identify families of component characterizing the chemical structure of resins and estimate their relative contributions according to the irradiation atmospheres.

Background Correction and Multivariate Curve Resolution of Online Liquid Chromatography with Infrared Spectrometric Detection

The use of multivariate curve resolution-alternating least-squares (MCR-ALS) in liquid chromatography-infrared detection (LC-IR) is troublesome due to the intense background absorption changes during gradient elution. Its use has been facilitated by previous removal of a significant part of the solvent background IR contributions due to common mobile phase systems employed during reversed phase gradient applications. Two straightforward background correction approaches based on simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and principal component analysis (PCA) are proposed and evaluated on reversed phase gradient LC-IR data sets obtained during the analysis of carbohydrate and nitrophenol mixtures. After subtraction of the calculated background signal, MCR-ALS provided improved signal-to-noise ratios, removed remaining mobile phase and background signal contributions, and resolved overlapping chromatographic peaks. The present approach tends to enable easy-to-use background correction to facilitate the use of MCR-ALS in online LC-IR, even in challenging situations when gradient conditions are employed and only poor chromatographic resolution is achieved. It, therefore, shows great potential to facilitate the full exploitation of the advantages of simultaneous quantification and identification of a vast amount of analytes employing online IR detection, making new exciting applications more accessible.

Kinetics Study on the Sol−Gel Reactions in Supercritical CO2 by Using In situ ATR-FTIR Spectrometry

Direct sol−gel processing in supercritical CO2 (scCO2) has been of significant interest for synthesizing oxide nanomaterials, and the reactions of metal alkoxides with organic acids have been extensively used for preparing metal complex crystals; however, a kinetics analysis of these reactions has not been reported. In this research, in situ ATR-FTIR spectra were collected during the sol−gel synthesis of Si, Ti, and Zr oxides in scCO2. Because of overlapping of the IR peaks, it was difficult to directly correlate the optical absorbance with the analyte concentrations. To solve this problem, we used a chemometrics method, simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), to calculate the concentration profiles. The precursor conversions and the intermediate concentration profiles under different synthesis conditions were obtained. The different kinetics profiles of Ti and Zr complexes in the sol−gel reactions were related to the activity of the metal complexes, and the competition of th...

Eliminate indeterminacies of independent component analysis for chemometrics

An improved method has been proposed to eliminate the indeterminacies of independent component analysis (ICA) for chemometrics. Following the arrangement of principal components analysis (PCA), the ICA mixing matrix is selected as signal content indexes, and ICA output are sorted and directed. After many times reputations, independent components (ICs) are paired according to the maximum correlation coefficient, and then the mean values of each IC substitutes the original ICs. This indicates that the ICA indeterminacies are eliminated. A simulation example is tested to validate this improvement. Finally, a set of experimental LC–MS data is processed without any prior knowledge or specific limitation and the results show that the improved ICA can directly separate the mixed signals in chemometrics, and it is simpler and more reasonable than the simple to use interactive self-modeling mixture analysis (SIMPLISMA).

Discriminant Analysis of Fused Positive and Negative Ion Mobility Spectra Using Multivariate Self-Modeling Mixture Analysis and Neural Networks

A new method coupling multivariate self-modeling mixture analysis and pattern recognition has been developed to identify toxic industrial chemicals using fused positive and negative ion mobility spectra (dual scan spectra). A Smiths lightweight chemical detector (LCD), which can measure positive and negative ion mobility spectra simultaneously, was used to acquire the data. Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) was used to separate the analytical peaks in the ion mobility spectra from the background reactant ion peaks (RIP). The SIMPLSIMA analytical components of the positive and negative ion peaks were combined together in a butterfly representation (i.e., negative spectra are reported with negative drift times and reflected with respect to the ordinate and juxtaposed with the positive ion mobility spectra). Temperature constrained cascade-correlation neural network (TCCCN) models were built to classify the toxic industrial chemicals. Seven common toxic industrial chemicals were used in this project to evaluate the performance of the algorithm. Ten bootstrapped Latin partitions demonstrated that the classification of neural networks using the SIMPLISMA components was statistically better than neural network models trained with fused ion mobility spectra (IMS).

Quantitative iterative target transformation factor analysis

AbstractTraditionally, improvement of the constrained alternating least squares (ALS) solution has been executed by the addition of a priori information in the initial estimates and or constraints. However, there are cases where this information simply does not exist or is impossible to acquire under the process conditions. Therefore, new strategies are required to produce starting estimates close to the actual solution without the need of a priori information. Quantitative iterative target transformation factor analysis (QITTFA) is developed as a solution to this problem. The QITTFA approach combines the strengths of both iterative target transformation factor analysis (ITTFA) and simple‐to‐use interactive self‐modelling mixture analysis (SIMPLISMA) to (1) produce a solution space spanned by the independent factors such that the variance contribution due to noise is reduced, (2) to iteratively refine the solutions space prior to ALS and (3) to select the most pure variables from the refined solution space using the purity criterion. It has been observed that the QITTFA approach markedly improves the conventional SIMPLISMA and second derivative SIMPLISMA performance in the presence and absence of selectivity. In addition, components of differing spectral characteristics (narrow or broad spectral features) can be resolved, without a priori knowledge of the shapes of the pure components. This has been demonstrated with a simulated high performance liquid chromatography‐diode array detection (HPLC‐DAD) dataset, a laboratory‐based UV–Vis calibration dataset and a gaseous near infrared (NIR) dataset from an industrial process. Copyright © 2007 John Wiley & Sons, Ltd.

Self-association of ( R)-1,3-butanediol in an inert dilute solution studied by infrared spectroscopy in combination with density functional theory and chemometrics

Self-association of ( R)-1,3-butanediol in a dilute carbon tetrachloride (CCl 4) solution was studied by infrared (IR) spectroscopy in combination with density functional theory (DFT), Sugeta’s nonlinear least square (NLS) method and chemometrics methods including fixed-size moving window evolving factor analysis (FSMWEFA), principal component analysis (PCA), simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), and iterative target transformation factor analysis (ITTFA). The association size of ( R)-1,3-butanediol in the CCl 4 solution estimated from PCA, SIMPLISMA and ITTFA are 3.2730, 3.2797 and 3.2950, respectively, suggesting that the major multimer species of ( R)-1,3-butanediol in the solution are trimers. A theoretical investigation of trimer structures of ( R)-1,3-butanediol was carried out with DFT. It was found from the DFT calculation that cyclic trimers are more stable than linear ones. Structure of hydrogen-bonded OH ring and position of side chains are the two main factors that determine the stability of trimers. The intra-molecular hydrogen bonded OH…OH structure in the ( R)-1,3-butanediol monomer brings additional influence on the trimers’ structures.

Study on multimers and their structures in molecular association mixture

Self-association system of ( R )-1,3-butanediol in dilute carbon tetrachloride (CCl 4 ) 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.