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 ¹H 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. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm⁻².     

A new approach to near-infrared spectral data analysis using independent component analysis

This paper presents a new approach to near-infrared spectral (NIR) data analysis that is based on independent component analysis (ICA). The main advantage of the new method is that it is able to separate the spectra of the constituent components from the spectra of their mixtures. The separation is a blind operation, since the constituent components of mixtures can be unknown. The ICA based method is therefore particularly useful in identifying the unknown components in a mixture as well as in estimating their concentrations. The approach is introduced by reference to case studies and compared to other techniques for NIR analysis including principal component regression (PCR), multiple linear regression (MLR), and partial least squares (PLS) as well as Fourier and wavelet transforms.     

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.     

独立组分分析在红外光谱分析中的应用

针对红外光谱的黑色体系分析,提出了一种基于独立组分分析(ICA)的红外光谱定性分析方法.其主要优点在于可从混合光谱中分离出独立组分的光谱,且这种分离是盲源分离,混合物的组分预先是未知的.对ICA在正己醇、丙酮和正丁醇的混合中红外光谱分离中的应用进行了研究,验证了体系的独立性,并对ICA算法做了一定的改进,讨论了各种预处理方法对其分离结果的误差.     

Stepped collisional energy MSAll: an analytical approach for optimal MS/MS acquisition of complex mixture with diverse physicochemical properties

The analysis of complex mixtures is becoming increasingly important in various fields, such as nutrition, medicinal plants and metabolomics. The components contained in such complex mixtures are always characterized with diverse physiochemical properties that pose a major challenge during the optimization of various parameters using liquid chromatography‐mass spectrometer (LC‐MS). The parameter ‘CE energy’ that is normally set at a fixed value with a moderate range of CE spread during data‐dependent acquisition (DDA) analysis, a prevalent approach for untargeted identification, often fails to generate sufficient MS/MS fragment ions for untargeted identification of components from complex mixtures. Here we developed a simple and generally applicable acquisition method named stepped MS^All (sMS^All) in this study, aiming to obtain optimal MS/MS spectra for identification of chemically diverse compounds from complex mixtures. sMS^All collects serial MS^All scans acquired at low CE to gradually ramped‐up high CE values in a cycle that conventional DDA scans cannot afford. The resultant MS/MS spectra of each compound were compared and evaluated among serial MS^All scans, and the optimal spectra were used for identification. An untargeted data analysis strategy was then employed to analyze these optimal MS/MS spectra by searching common diagnostic ions and connecting the diagnostic ion families into a network via bridging components. This sMS^All‐based route enables identification of 71 natural products from a herbal preparation, whereas only 53 out of 71 compounds were identified using the classical DDA approach. Therefore, the sMS^All‐based approach is expected to find its wide applications for characterization of vastly diverse compounds with no priori knowledge from various complex mixtures. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

独立成分分析结合在线红外技术研究3,4-双(4'-氨基呋咱基-3')氧化呋咱的合成反应机理

利用在线红外技术监测3,4-双(4'-氨基呋咱基-3')氧化呋咱(DATF)的合成过程,并结合核独立成分分析算法对反应过程中获得的实时红外光谱数据进行解析,得到了反应物、中间体及产物各组分纯物质的红外光谱图.采用密度泛函理论B3LYP法,在6-31+G(d,p)基组水平上求得中间体的红外振动光谱,验证了所分离红外光谱图的正确性,从而推导出合理的合成反应机理.结果表明,核独立成分分析算法能合理地解析红外光谱在线数据,并有效捕捉合成反应的中间体,对合成反应机理的研究具有重要的指导意义.     

Analysis of terpenes and turpentines using gas chromatography with vacuum ultraviolet detection

The separation and identification of natural mixtures of terpenes is challenging and laborious. A gas chromatographic method based on vacuum ultraviolet spectroscopic detection, which is characterized by full‐scan absorption in the range of 125–240 nm, was developed and applied to analyze terpenes. In this study, the vacuum ultraviolet absorption spectra of 41 different standard terpenes were investigated and compared. The spectra were found to be highly featured and easily differentiated. Several commercial turpentine samples were analyzed and the vacuum ultraviolet detector demonstrated good specificity for qualitative identification of constituent terpenes. A total of 31 terpenes were detected in the four turpentine samples. α‐Pinene was the predominant terpene ranging from 744.2 ± 9.7 to 917 ± 21 mg/mL. The other major constituents in the turpentines included β‐pinene, δ‐3‐carene, camphene, and p‐isopropyltoluene. Deconvolution of co‐eluting signals of terpenes was achieved utilizing the data analysis software. The technique has been demonstrated to be a powerful tool for reliable and accurate qualitative and quantitative analysis of terpenes from complex natural mixtures.     

Isomer and congener identification of chlorinated pyrenes by narrow-bore liquid chromatography—Fourier transform infrared spectrometry

Liquid chromatography (LC) was coupled semi on-line to Fourier transform infrared spectrometry (FT-IR) using a spray-jet assembly interface to eliminate the LC eluent prior to IR detection. The usefulness of the LC-FT-IR system in the identification of closely related compounds in complex mixtures is demonstrated by the analysis of a chlorinated pyrene sample which contains a number of chloropyrene isomers and congeners. Characteristic FT-IR transmission spectra of all constituents could be recorded. Since most of these compounds have not been analysed by IR before, spectral assignment was mainly based on the empirical Hansen—Berg rules for substituted pyrenes. The identification limits for the chloropyrenes typically were 5–10 ng injected.     

Simultaneous analysis of butene isomer mixtures using process mass spectrometry

The feasibility of simultaneous analysis of mixtures containing two to four butene isomers and up to six total components using process mass spectrometry is assessed. As for typical (nonisomeric) applications of process mass spectrometry, simultaneous analysis is based on the assumption that the electron ionization mass spectra of mixtures are linear combinations of the spectra of the individual constituents. Limits of detection for binary isomer mixtures are on the order of 0.1% to 10%, limited by the ability to distinguish small differences between similar spectra. As spectral and mixture complexity increase, both accuracy and precision decrease. Not surprisingly the similarity of the spectra of stereoisomers cis- and trans-2-butene is greater than that of the other (nonstereoisomeric) isomer pairs, and mixtures containing both cis- and trans-2-butene are the most difficult to quantitate. However, even for mixtures of all four butenes, accuracy (root-mean-square ERROR = 2.43%), precision (average coefficient of VARIATION = 6.72%), and linearity (correlation coefficient of a plot of measured versus actual concentration r² = 0.985 ± 0.002) are reasonably good.     

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. Figure Characterisation of radix rehmanniae processing procedure using FT-IR spectroscopy through nonnegative independent component analysis     

Evaluation of a spectral searching algorithm for the comparison of Raman band positions

Raman spectroscopic identification of unknown materials involves often the comparison of the spectrum of the unknown spectrum with previously recorded reference spectra or data from literature. However, when spectra with many Raman bands or spectra of mixtures are involved, searching can be quite complex. Different chemometrical approaches have been proposed, but these have some drawbacks. Therefore, in this paper a novel approach is proposed, which is based on a multivariate comparison of Raman band positions. Different similarity measures can be used and are evaluated with spectra of test samples that were recorded on different spectrometers, using different laser wavelengths. Moreover, this study evaluates the performances of this algorithm for identifying different compounds in mixtures, by using an iterative approach.     

气质联用结合保留指数对比在五种木兰科芳香精油成分鉴定中的应用

采用气相色谱-质谱联用技术结合Kovats保留指数（KI）比较法，对南岭自然保护区内野生的毛桃木莲、乳源木莲、乐昌含笑、金叶含笑和深山含笑等5种木兰科芳香植物精油进行了化学成分对比鉴定。结果表明，这种方法与单纯质潜匹配度的方法相比，大大提高了组分化学物质鉴定的准确性。除深山含笑外，另外4种木兰科芳香精油所含的大部分成分均为倍半萜类，，毛桃木莲和乳源木莲精油在基本组分构成上具有高度一致性，且在主要成分上具有相似性，这表明它们可能具有非常近的植物亲缘关系。乐昌含笑和金叶含笑精油均以倍半萜类为主体成分，深山含笑精油则以单萜烯类为主体成分，这3种含笑属芳香精油在组分构成上没有明显的一致性。     

Characterization of essential oil components of Iranian geranium oil using gas chromatography-mass spectrometry combined with chemometric resolution techniques

The essential oil components of geranium oil cultivated in center of Iran were identified and determined using gas chromatography-mass spectrometry data combined with the chemometric resolution techniques. A total of 61 components accounting for 91.51% were identified using similarity searches between the mass spectra and MS database. This number was extended to 85 components using chemometric techniques. Various chemometric methods such as morphological scores, simplified Borgen method (SBM) and fixed size moving window evolving factor analysis (FSMWEFA) were used for determining the number of components, pure variables, zero concentration and selective regions. Then the overlapping peak clusters were resolved into pure chromatograms and pure mass spectra using heuristic evolving latent projections (HELP) method. A characteristic feature of the Iranian geranium oil is the absence of 10-epi-gamma-eudesmol in its constituents compared with the oil from northern and southern parts of India. The results of this work show that combination of hyphenated chromatographic methods and resolution techniques provide a complementary method for accurate analysis of essential oils.     

HPLC/APCI-FTICR-MS as a tool for identification of partial polar mutagenic compounds in effect-directed analysis

Identification of unknown compounds remains one of the biggest challenges for the assignment of adverse effects of sediment contamination and other complex environmental mixtures to responsible toxicants by effect-directed analysis (EDA). The identification depends on information gained from biotesting, chromatographic separation, and mass spectrometric detection. Thus, a methodology is provided for non-target identification of partial polar mutagenic polyaromatic compounds in sediment extracts by using polymeric reversed-phase HPLC column, high-resolution mass spectrometry and PubChem database. After visualization and processing the chromatogram constituents by using deconvolution software, the unambiguous elemental compositions generated were used as input in PubChem database to find a possible identity for the suspected species. The retrieved structures from the database search were refined by characterized chromatographic and mass spectrometric classifiers based on 55 model compounds comprising eight different classes representing mutagenic substructures. The applicability of the method was demonstrated by positive and tentative identification of constituents of mutagenic sediment fractions similar to selected model compounds.     

Chemometrics-assisted spectrophotometric method for simultaneous determination of vitamins in complex mixtures

An advanced independent component analysis algorithm (MILCA) is applied for simultaneous chemometric determination of fat- and water-soluble vitamins in complex mixtures. The analysis is based on the decomposition of spectra of multicomponent mixtures in the UV region. The key features of the proposed method are simplicity, accuracy, and reliability. Comparisons between the new algorithm and other established methods (MCR-ALS, SIMPLISMA, other ICA techniques) were made. Our results indicate that in most cases, MILCA is comparable or even outperforms other chemometrics methods taken for comparisons. The influence of different factors (abundance of components, noise, step of spectral scan, and scan speed) on decomposition performance has been investigated. The optimal conditions for spectroscopic registration have been identified. The proposed method was used for analysis of model mixtures and real objects (multivitamin drugs, food additives, and energy drinks). The resolved concentrations match well with the declared amounts and the results of reference methods.     

Screening Brazilian C gasoline quality: application of the SIMCA chemometric method to gas chromatographic data

A total of 2400 samples of commercial Brazilian C gasoline were collected over a 6-month period from different gas stations in the S?o Paulo state, Brazil, and analysed with respect to 12 physicochemical parameters according to regulation 309 of the Brazilian Government Petroleum, Natural Gas and Biofuels Agency (ANP). The percentages (v/v) of hydrocarbons (olefins, aromatics and saturated) were also determined. Hierarchical cluster analysis (HCA) was employed to select 150 representative samples that exhibited least similarity on the basis of their physicochemical parameters and hydrocarbon compositions. The chromatographic profiles of the selected samples were measured by gas chromatography with flame ionisation detection and analysed using soft independent modelling of class analogy (SIMCA) method in order to create a classification scheme to identify conform gasolines according to ANP 309 regulation. Following the optimisation of the SIMCA algorithm, it was possible to classify correctly 96% of the commercial gasoline samples present in the training set of 100. In order to check the quality of the model, an external group of 50 gasoline samples (the prediction set) were analysed and the developed SIMCA model classified 94% of these correctly. The developed chemometric method is recommended for screening commercial gasoline quality and detection of potential adulteration.     

Rapid separation and identification of phenolic and diterpenoid constituents from Radix Salvia miltiorrhizae by high-performance liquid chromatography diode-array detection, electrospray ionization time-of-flight mass spectrometry and electrospray ionization quadrupole ion trap mass spectrometry

High-performance liquid chromatography-diode array detection (HPLC-DAD), electrospray ionization time-of-flight mass spectrometry (HPLC-ESI-TOF-MS) and electrospray ionization quadrupole ion trap mass spectrometry (HPLC-ESI-MSn) were used for the isolation, identification and structural analysis of water-soluble phenolic and nonpolar diterpenoid constituents in Dan-shen (Radix Salvia miltiorrhizae) which was prepared by sonication in 70% methanol. Mass spectra were obtained by ESI-TOF-MS and electrospray ionization quadrupole ion trap mass spectrometry (ESI-QIT-MS). A formula database of known constituents in Dan-shen was established and most constituents were rapidly identified by HPLC-DAD/ESI-TOF-MS by matching their accurate molecular masses with the formulae of the compounds in the database. Compounds with the same molecular formula could not be differentiated by TOF-MS; however, QIT-MS could differentiate those compounds and elucidate their structures based on their characteristic fragmentation. HPLC-DAD, HPLC/ESI-TOF-MS and HPLC/ESI-MSn provided complementary information for the identification of the constituents in Dan-shen. Forty constituents were identified in 30 min based on their positive and negative ion ESI mass spectra and liquid chromatographic information. Thus the method described is useful for the rapid analysis of multiple constituents in Dan-shen.