Extraction of Mass Spectra and Chromatographic Profiles from Overlapping GC/MS Signal with Background

Chromatographic parameters: Transport efficiency-A meaningful alternative to the plate number parameter

The plate number parameter officially defined in GC and LC as N = (tR/σ)2 (tR and σ are peak retention time and standard deviation, respectively, under no-programming conditions) can be substantially smaller than the actual number of plates. Therefore, N does not always represent what its name implies it does, and its interpretation is not clear. This is even more so for the extensions of N to multidimensional separations. N has other shortcomings. Metrics of separation performance of chromatographic analyses (peak resolution, Rs, peak capacity, nc, and others) are not proportional to N. A column transport efficiency, Q = tR/σ, is free of the shortcomings of N. Metrics Rs and nc are proportional to Q. Q also has a transparent interpretation - it is the number of σ-wide segments in the time-intervals of certain significance in static and programmable GC and LC analyses. Q also has meaningful extensions to multi-dimensional separations.

Assessing Methods for Evaluating the Number of Components in Non-Negative Matrix Factorization

Non-negative matrix factorization is a relatively new method of matrix decomposition which factors an m×n data matrix X into an m×k matrix W and a k×n matrix H, so that X≈W×H. Importantly, all values in X, W, and H are constrained to be non-negative. NMF can be used for dimensionality reduction, since the k columns of W can be considered components into which X has been decomposed. The question arises: how does one choose k? In this paper, we first assess methods for estimating k in the context of NMF in synthetic data. Second, we examine the effect of normalization on this estimate's accuracy in empirical data. In synthetic data with orthogonal underlying components, methods based on PCA and Brunet's Cophenetic Correlation Coefficient achieved the highest accuracy. When evaluated on a well-known real dataset, normalization had an unpredictable effect on the estimate. For any given normalization method, the methods for estimating k gave widely varying results. We conclude that when estimating k, it is best not to apply normalization. If underlying components are known to be orthogonal, then Velicer's MAP or Minka's Laplace-PCA method might be best. However, when orthogonality of the underlying components is unknown, none of the methods seemed preferable.

Deep-Learning-Assisted multivariate curve resolution

Gas chromatography-mass spectrometry (GC-MS) is one of the major platforms for analyzing volatile compounds in complex samples. However, automatic and accurate extraction of qualitative and quantitative information is still challenging when analyzing complex GC-MS data, especially for the components incompletely separated by chromatography. Deep-Learning-Assisted Multivariate Curve Resolution (DeepResolution) was proposed in this study. It essentially consists of convolutional neural networks (CNN) models to determine the number of components of each overlapped peak and the elution region of each compound. With the assistance of the predicted elution regions, the informative regions (such as selective region and zero-concentration region) of each compound can be located precisely. Then, full rank resolution (FRR), multivariate curve resolution-alternating least squares (MCR-ALS) or iterative target transformation factor analysis (ITTFA) can be chosen adaptively to resolve the overlapped components without manual intervention. The results showed that DeepResolution has superior compound identification capability and better quantitative performances when comparing with MS-DIAL, ADAP-GC and AMDIS. It was also found that baseline levels, interferents, component concentrations and peak tailing have little influences on resolution result. Besides, DeepResolution can be extended easily when encountering unknown component(s), due to the independence of each CNN model. All procedures of DeepResolution can be performed automatically, and adaptive selection of resolution methods ensures the balance between resolution power and consumed time. It is implemented in Python and available at https://github.com/XiaqiongFan/DeepResolution.

Multi-spectrometer calibration transfer based on independent component analysis

Calibration transfer is indispensable for practical applications of near infrared (NIR) spectroscopy due to the need for precise and consistent measurements across different spectrometers. In this work, a method for multi-spectrometer calibration transfer is described based on independent component analysis (ICA). A spectral matrix is first obtained by aligning the spectra measured on different spectrometers. Then, by using independent component analysis, the aligned spectral matrix is decomposed into the mixing matrix and the independent components of different spectrometers. These differing measurements between spectrometers can then be standardized by correcting the coefficients within the independent components. Two NIR datasets of corn and edible oil samples measured with three and four spectrometers, respectively, were used to test the reliability of this method. The results of both datasets reveal that spectra measurements across different spectrometers can be transferred simultaneously and that the partial least squares (PLS) models built with the measurements on one spectrometer can predict that the spectra can be transferred correctly on another.

Deep UV Resonance Raman Spectroscopy for Characterizing Amyloid Aggregation

Deep UV resonance Raman spectroscopy is a powerful technique for probing the structure and formation mechanism of protein fibrils, which are traditionally difficult to study with other techniques owing to their low solubility and noncrystalline arrangement. Utilizing a tunable deep UV Raman system allows for selective enhancement of different chromophores in protein fibrils, which provides detailed information on different aspects of the fibrils' structure and formation. Additional information can be extracted with the use of advanced data treatment such as chemometrics and 2D correlation spectroscopy. In this chapter we give an overview of several techniques for utilizing deep UV resonance Raman spectroscopy to study the structure and mechanism of formation of protein fibrils. Clever use of hydrogen-deuterium exchange can elucidate the structure of the fibril core. Selective enhancement of aromatic amino acid side chains provides information about the local environment and protein tertiary structure. The mechanism of protein fibril formation can be investigated with kinetic experiments and advanced chemometrics.

Quantitative analysis of 17 amino acids in tobacco leaves using an amino acid analyzer and chemometric resolution

A method was developed for quantifying 17 amino acids in tobacco leaves by using an A300 amino acid analyzer and chemometric resolution. In the method, amino acids were eluted by the buffer solution on an ion-exchange column. After reacting with ninhydrin, the derivatives of amino acids were detected by ultraviolet detection. Most amino acids are separated by the elution program. However, five peaks of the derivatives are still overlapping. A non-negative immune algorithm was employed to extract the profiles of the derivatives from the overlapping signals, and then peak areas were adopted for quantitative analysis of the amino acids. The method was validated by the determination of amino acids in tobacco leaves. The relative standard deviations (n = 5) are all less than 2.54% and the recoveries of the spiked samples are in a range of 94.62-108.21%. The feasibility of the method was proved by analyzing the 17 amino acids in 30 tobacco leaf samples.

Discrimination of tomatoes bred by spaceflight mutagenesis using visible/near infrared spectroscopy and chemometrics

Visible/near infrared spectroscopy (Vis/NIR) based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate different tomatoes bred by spaceflight mutagenesis from their leafs or fruits (green or mature). The tomato breeds were mutant M1, M2 and their parent. Partial least squares (PLS) analysis and least squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavebands including the visible region (400-700 nm) and the near infrared region (700-1000 nm). The best PLS models were achieved in the visible region for the leaf and green fruit samples and in the near infrared region for the mature fruit samples. Furthermore, different latent variables (4-8 LVs for leafs, 5-9 LVs for green fruits, and 4-9 LVs for mature fruits) were used as inputs of LS-SVM to develop the LV-LS-SVM models with the grid search technique and radial basis function (RBF) kernel. The optimal LV-LS-SVM models were achieved with six LVs for the leaf samples, seven LVs for green fruits, and six LVs for mature fruits, respectively, and they outperformed the PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs of 550-560 nm, 562-574 nm, 670-680 nm and 705-71 5 nm for the leaf samples; 548-556 nm, 559-564 nm, 678-685 nm and 962-974 nm for the green fruit samples; and 712-718 nm, 720-729 nm, 968-978 nm and 820-830 nm for the mature fruit samples. All of them had better performance than PLS and LV-LS-SVM, with the parameters of correlation coefficient (rp), root mean square error of prediction (RMSEP) and bias of 0.9792, 0.2632 and 0.0901 based on leaf discrimination, 0.9837, 0.2783 and 0.1758 based on green fruit discrimination, 0.9804, 0.2215 and -0.0035 based on mature fruit discrimination, respectively. The overall results indicated that ICA was an effective way for the selection of SWs, and the Vis/NIR combined with LS-SVM models had the capability to predict the different breeds (mutant M1, mutant M2 and their parent) of tomatoes from leafs and fruits.

Study of the heat stability of sunflower oil enriched in natural antioxidants by different analytical techniques and front-face fluorescence spectroscopy combined with Independent Components Analysis

The aim of this study was to find objective analytical methods to study the degradation of edible oils during heating and thus to suggest solutions to improve their stability. The efficiency of Nigella seed extract as natural antioxidant was compared with butylated hydroxytoluene (BHT) during accelerated oxidation of edible vegetable oils at 120 and 140 °C. The modifications during heating were monitored by 3D-front-face fluorescence spectroscopy along with Independent Components Analysis (ICA), (1)H NMR spectroscopy and classical physico-chemical methods such as anisidine value and viscosity. The results of the study clearly indicate that the natural seed extract at a level of 800 ppm exhibited antioxidant effects similar to those of the synthetic antioxidant BHT at a level of 200 ppm and thus contributes to an increase in the oxidative stability of the oil.

Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS)

Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem.

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 l(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.

Extraction of chemical information from complex analytical signals by a non-negative independent component analysis

Extraction of chemical information of the components from a complex analytical signal has been a great challenge in chemometrical studies for complex sample analysis. Independent component analysis (ICA) has been widely applied in complex signal separation, including the multicomponent overlapping signals in analytical chemistry. Difficulties, however, still exist in the application of ICA in chemical signal processing because chemical signals of different components are generally correlated and non-negative, instead of independence as hypothesized in ICA. In this study, a non-negative ICA method is proposed by means of a post rotation of the independent components (ICs) and applied to the extraction of the chemical information of the components from the signals of complex samples. Raman spectra of pharmaceutical tablets and gas chromatography-mass spectrometry (GC-MS) data of cigarette smoke are qualitatively analyzed. The results show that the Raman spectrum of the active substance in the pharmaceutical tablets and the mass spectra of the components in the overlapping GC-MS signal can be effectively and accurately extracted by using the proposed method.

Characterization of radix rehmanniae processing procedure using FT-IR spectroscopy through nonnegative independent component analysis

A method is proposed for monitoring the radix rehmanniae proparate processing procedure and determining the endpoint of the process using attenuated total reflectance (ATR) FT-IR through nonnegative independent component analysis (ICA). In the proposed method, ATR FT-IR spectra of the samples were firstly measured at different steaming periods. Then, nonnegative ICA was used for direct estimation of the feature spectra of the pure components in the mixture without pre-separation and other prior information. The estimated independent components (ICs) and their variation of the relative concentrations were used to characterize the processing procedure and determine the endpoint. The results show that the estimated three ICs are consistent with that of the chemical components in the mixtures, i.e. catalpol/rehmaionoside, glucose, and other compounds that nearly keep invariant during the processing procedure. The endpoint determined by the IR-ICA method is 15 h, which was located in the range obtained by expert sensory analysis, whereas the endpoint determined by the traditional sensory analysis is 14-17 h and even 14-20 h, which showed the significant deviation of the endpoints determined by different operators.

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.

Dose detection of radiated rice by infrared spectroscopy and chemometrics

Infrared spectroscopy based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate the nine different radiation doses (0, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 Gy) of rice. Samples ( n = 16 each dose) were selected randomly for the calibration set, and the remaining 36 samples ( n = 4 each dose) were selected for the prediction set. Partial least-squares (PLS) analysis and least-squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavelength bands including near-infrared (NIR) regions and mid-infrared (MIR) regions. The best PLS models were achieved in the MIR (400-4000 cm (-1)) region. Furthermore, different latent variables (5-9 LVs) were used as inputs of LS-SVM to develop the LV-LS-SVM models with a grid search technique and radial basis function (RBF) kernel. The optimal models were achieved with six LVs, and they outperformed PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs (756, 895, 1140, and 2980 cm (-1)) selected by ICA and had better performance than PLS and LV-LS-SVM with the parameters of correlation coefficient ( r), root-mean-square error of prediction, and bias of 0.996, 80.260, and 5.172 x 10 (-4), respectively. The overall results indicted that the ICA was an effective way for the selection of SWs, and infrared spectroscopy combined with LS-SVM models had the capability to predict the different radiation doses of rice.

Using mean field approach independent component analysis to fatty acid characterization with overlapped GC–MS signals

In this paper, mean field independent component analysis (MF-ICA) was applied as a deconvolution method to separate complex gas chromatographic-mass spectrometric (GC-MS) signals obtained from fatty acid analysis of fish oil. The separation which is a blind operation was used as a complementary method in identification of the unknown components of a mixture and in quantification purposes, as well. In MF-ICA, the sources (mass spectra) are recovered from the mean of their posterior distributions and mixing matrix (chromatograms) and noise level are estimated through the maximum a posterior (MAP) solution. The number of independent components (ICs) in the overlapping signals can be estimated by the difference between the reconstructed and original GC-MS data. It was found that the chromatographic profiles and the mass spectra of the components in overlapping multicomponent GC-MS data can be accurately recovered with and without previously background correction. The resolved mass spectral sources satisfactory are identified using mass spectral search system. The recovered chromatographic area and the relative content of each analyte considering selected number of ICs are calculated and the results are compared with the ones obtained previously by using heuristic evolving latent projections (HELP) method.

Signal processing of GC–MS data of complex environmental samples: Characterization of homologous series

Identification and characterization of homologous series by GC-MS analysis provide very relevant information on organic compounds in complex mixtures. A chemometric approach, based on the study of the autocovariance function, EACVF(tot), is described as a suitable tool for extracting molecular-structural information from the GC signal, in particular for identifying the presence of homologous series and quantifying the number of their terms. A data pre-processing procedure is introduced to transform the time axis in order to display a strictly homogenous retention pattern: n-alkanes are used as external standard to stretch or shrink the original chromatogram in order to build up a linear GC retention scale. This addition can be regarded as a further step in the direction of a signal processing procedure for achieving a systematic characterization of complex mixture from experimental chromatograms. The EACVF(tot) was computed on the linearized chromatogram: if the sample presents terms of homologous series, the EACVF(tot) plot shows well-defined deterministic peaks at repeated constant interdistances. By comparison with standard references, the presence of such peaks is diagnostic for the presence of the ordered series, their position can be related to the chemical structure of the compounds, their height is the basis for estimating the number of terms in the series. The power of the procedure can be magnified by studying SIM chromatograms acquired at specific m/z values characteristic of the compounds of interest: the EACVF(tot) on these selective signals makes it possible to confirm the results obtained from an unknown mixture and check their reliability. The procedure was validated on standard mixtures of known composition and applied to an unknown gas oil sample. In particular, the paper focuses on the study of two specific classes of compounds: n-alkanes and oxygen-containing compounds, since their identification provides information useful for characterizing the chemical composition of many samples of different origin. The robustness of the method was tested in experimental chromatograms obtained under unfavorable conditions: chromatograms acquired in non-optimal temperature program conditions and chromatographic data affected by signal noise.

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

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

Use of gas chromatography–mass spectrometry combined with resolution methods to characterize the essential oil components of Iranian cumin and caraway

Gas chromatography-mass spectrometry combined with iterative and non-iterative resolution methods was used to characterize the essential oil components of Iranian cumin and caraway. Orthogonal projection resolution (OPR) as a non-iterative and distance-selection-multivariate curve resolution-alternative least squares (DS-MCR-ALS) as an iterative method were used as auxiliary means to the analysis in the case of overlapping peaks. A total of 19 and 39 components were identified by direct similarity searches for cumin and caraway oils, respectively. These numbers were extended to 49 and 98 components, respectively with the help of chemometric techniques. Major constituents in cumin are gamma-terpinene (15.82%), 2-methyl-3-phenyl-propanal (32.27%) and myrtenal (11.64%) and in caraway are gamma-terpinene (24.40%), 2-methyl-3-phenyl-propanal (13.20%) and 2, 4(10)-thujadien (14.02%). In spite of different cultivation conditions, there are 28 components which are common between the two seeds.