Factor analysis of hyphenated chromatographic data

SigSel: A MATLAB package for the pre and post-treatment of high-resolution mass spectrometry signals using the ROIMCR methodology

The Regions of Interest Multivariate curve Resolution (ROIMCR) methodology has gained significance for analyzing mass spectrometry data. The new SigSel package improves the ROIMCR methodology by providing a filtering step to reduce computational costs and to identify chemical compounds giving low-intensity signals. SigSel allows the visualization and assessment of ROIMCR results and filters out components resolved as interferences and background noise. This improves the analysis of complex mixtures and facilitates the identification of chemical compounds for statistical or chemometrics analysis. SigSel has been tested using metabolomics samples of mussels exposed to the sulfamethoxazole antibiotic. It begins by analyzing the data according to their charge state, eliminating signals considered background noise, and reducing the size of the datasets. In the ROIMCR analysis, the resolution of 30 ROIMCR components was achieved. After evaluating these components, 24 were ultimately selected explaining 99.05% of the total data variance. From ROIMCR results, chemical annotation is performed using different methods: •Generating a list of signals and reanalyzing them in a data-dependent analysis.•Comparing the ROIMCR resolved mass spectra to those stored in online repositories.•Searching MS signals of chemical compounds in the ROIMCR resolved spectra profiles.

Determination and comparison of agarwood from different origins by comprehensive two-dimensional gas chromatography-quadrupole time-of-flight mass spectrometry

Agarwood, a species of resinous heartwood, is a precious medicinal plant and a type of rare natural spice, which is widely used in medicine, cosmetics, religious activities, and other fields. In this study, agarwood samples from eight different regions across four countries were analyzed by comprehensive two-dimensional gas chromatography-quadrupole time-of-flight mass spectrometry. A total of 232 species were identified (the match factors of these compounds were above 750). The main compounds of agarwood are oxygenated sesquiterpenes and chromones. The compositions of India1 and Malaysia2 were significantly different from those of other samples, which might be attributed to the different production processes of agarwood. For further investigation, factor analysis was conducted for six agarwood samples. The results showed that the data classification possessed a regional characteristic; according to the retention time and relative content, characteristic compositions were determined by factor scores. Finally, the differences of characteristic compositions were simply analyzed, and the reasons were speculated.

A novel systematic absence of cross peaks-based 2D-COS approach for bilinear data

A novel approach to use two-dimensional correlation spectroscopy (2D-COS) to analyze bilinear data is proposed. A phenomenon called Systematic Absence of Cross Peaks (SACPs) is observed in a 2D asynchronous spectrum. Two theorems relevant to SACPs have been derived. The SACP-based 2D-COS method has been successfully applied on analyzing bilinear data from mixed samples (including one model system and two real systems). Implicit isolated peaks can be identified and assigned to different components based on characteristic pattern of SACPs even if the time-related profiles of different components are severely overlapped. Based on the results of SACPs, spectra of pure components can be retrieved. Identification of SACPs can still be achieved in the presence of artifacts. Thus, neither noise nor baseline drift can produce significant influence on the results obtained from the approach described in this paper. We have used several well-established chemometric methods, including N-Findr, VCA, and MCR with various initial settings, on two systems that can be successfully solved using the 2D-COS method. The chemometric methods mentioned above cannot provide correct spectra of pure components because of severe problem of rotational ambiguity derived from severe overlapping of the time-related profiles. Only when the information from SACPs in 2D-COS is used as additional constraints in MCR calculation, correct spectra can be obtained. That is to say, the SACP-based 2D-COS method provides intrinsic information which is crucial in the analysis of chromatographic-spectroscopic and analogous data even if the time-related profiles of different components overlap severely.

A preliminary study on constructing a high-dimensional asynchronous spectrum to analyze bilinear data

A novel approach to constructing high-dimensional asynchronous spectra (nD-Asyn) is proposed. Three theorems relevant to 1D slices of nD-Asyn are revealed. nD-Asyn is used to analyze bilinear data from mixtures containing multiple components obtained via hyphenated techniques. The spectral contribution of different components can be removed in a stepwise manner by increasing the dimensions of asynchronous spectra. As a result, the spectra of different components can be faithfully recovered even if the time-related profiles of different components severely overlap. Moreover, correct results can still be obtained via the nD-Asyn even if a considerable level of noise and baseline drift are present. The nD-Asyn approach is compared with MCR-ALS using different constraints in analyzing the data for a simulated and also for a real system. The nD-Asyn produced correct spectrum of every component. Only when complete constraints obtained from nD-Asyn method is utilized in the MCR-ALS calculation, correct spectra of all the components can be obtained. Thus, nD-Asyn can be used alone or in conjunction with MCR-ALS to analyze bilinear data containing contributions of multiple components.

Metabolomic analysis of the effects of cadmium and copper treatment in Oryza sativa L. using untargeted liquid chromatography coupled to high resolution mass spectrometry and all-ion fragmentation

While the knowledge of plant metabolomes has increased in the last few years, their response to the presence of toxicants is still poorly understood. Here, we analyse the metabolomic changes in Japanese rice (Oryza sativa var. Japonica) upon exposure to heavy metals (Cd(ii) and Cu(ii)) in concentrations from 10 to 1000 μM. After harvesting, rice metabolites were extracted from aerial parts of the plants and analysed by HPLC (HILIC TSK gel amide-80 column) coupled to a mass spectrometer quadrupole-Orbitrap (Q-Exactive). Full scan and all ion fragmentation (AIF) mass spectrometry modes were used during the analysis. The proposed untargeted metabolomics data analysis strategy is based on the application of the multivariate curve resolution alternating least squares (MCR-ALS) method for feature detection, allowing the simultaneous resolution of pure chromatographic profiles and mass spectra of all metabolites present in the analysed rice extracts. All-ion fragmentation data were used to confirm the identification of MCR-ALS resolved metabolites. A total of 112 metabolites were detected, and 97 of them were subsequently identified and confirmed. Pathway analysis of the observed metabolic changes suggested an underlying similarity of the responses of the plant to Cd(ii) and Cu(ii), although the former treatment appeared to be the more severe of the two. In both cases, secondary metabolism and amino acid-, purine-, carbon- and glycerolipid-metabolism pathways were affected, in a pattern consistent with reduction in plant growth and/or photosynthetic capacity and with induction of defence mechanisms to reduce cell damage.

A non-targeted metabolomic approach to identify food markers to support discrimination between organic and conventional tomato crops

In the last decade, the consumption trend of organic food has increased dramatically worldwide. However, the lack of reliable chemical markers to discriminate between organic and conventional products makes this market susceptible to food fraud in products labeled as "organic". Metabolomic fingerprinting approach has been demonstrated as the best option for a full characterization of metabolome occurring in plants, since their pattern may reflect the impact of both endogenous and exogenous factors. In the present study, advanced technologies based on high performance liquid chromatography-high-resolution accurate mass spectrometry (HPLC-HRAMS) has been used for marker search in organic and conventional tomatoes grown in greenhouse under controlled agronomic conditions. The screening of unknown compounds comprised the retrospective analysis of all tomato samples throughout the studied period and data processing using databases (mzCloud, ChemSpider and PubChem). In addition, stable nitrogen isotope analysis (δ¹⁵N) was assessed as a possible indicator to support discrimination between both production systems using crop/fertilizer correlations. Pesticide residue analyses were also applied as a well-established way to evaluate the organic production. Finally, the evaluation by combined chemometric analysis of high-resolution accurate mass spectrometry (HRAMS) and δ¹⁵N data provided a robust classification model in accordance with the agricultural practices. Principal component analysis (PCA) showed a sample clustering according to farming systems and significant differences in the sample profile was observed for six bioactive components (L-tyrosyl-L-isoleucyl-L-threonyl-L-threonine, trilobatin, phloridzin, tomatine, phloretin and echinenone).

Enhanced target factor analysis

Target testing or target factor analysis, TFA, is a well-established soft analysis method. TFA answers the question whether an independent target test vector measured at the same wavelengths as the collection of spectra in a data matrix can be excluded as the spectrum of one of the components in the system under investigation. Essentially, TFA cannot positively prove that a particular test spectrum is the true spectrum of one of the components, it can, only reject a spectrum. However, TFA will not reject, or in other words TFA will accept, many spectra which cannot be component spectra. Enhanced Target Factor Analysis, ETFA addresses the above problem. Compared with traditional TFA, ETFA results in a significantly narrower range of positive results, i.e. the chance of a false positive test result is dramatically reduced. ETFA is based on feasibility testing as described in Refs. [16-19]. The method has been tested and validated with computer generated and real data sets.

Evaluation of changes induced in rice metabolome by Cd and Cu exposure using LC-MS with XCMS and MCR-ALS data analysis strategies

The comprehensive analysis of untargeted metabolomics data acquired using LC-MS is still a major challenge. Different data analysis tools have been developed in recent years such as XCMS (various forms (X) of chromatography mass spectrometry) and multivariate curve resolution alternating least squares (MCR-ALS)-based strategies. In this work, metabolites extracted from rice tissues cultivated in an environmental test chamber were subjected to untargeted full-scan LC-MS analysis, and the obtained data sets were analyzed using XCMS and MCR-ALS. These approaches were compared in the investigation of the effects of copper and cadmium exposure on rice tissue (roots and aerial parts) samples. Both methods give, as a result of their application, the whole set of resolved elution and spectra profiles of the extracted metabolites in control and metal-treated samples, as well as the values of their corresponding chromatographic peak areas. The effects caused by the two considered metals on rice samples were assessed by further chemometric analysis and statistical evaluation of these peak area values. Results showed that there was a statistically significant interaction between the considered factors (type of metal of treatment and tissue). Also, the discrimination of the samples according to both factors was possible. A tentative identification of the most discriminant metabolites (biomarkers) was assessed. It is finally concluded that both XCMS- and MCR-ALS-based strategies provided similar results in all the considered cases despite the completely different approaches used by these two methods in the chromatographic peak resolution and detection strategies. Finally, advantages and disadvantages of using these two methods are discussed. Graphical Abstract Summary of the workflow for untargeted metabolomics using the compared approaches.

Multivariate curve resolution-assisted determination of pseudoephedrine and methamphetamine by HPLC-DAD in water samples

In the present study, a simple strategy based on solid-phase extraction (SPE) with a cation exchange sorbent (Finisterre SCX) followed by fast high-performance liquid chromatography (HPLC) with diode array detection coupled with chemometrics tools has been proposed for the determination of methamphetamine and pseudoephedrine in ground water and river water. At first, the HPLC and SPE conditions were optimized and the analytical performance of the method was determined. In the case of ground water, determination of analytes was successfully performed through univariate calibration curves. For river water sample, multivariate curve resolution and alternating least squares was implemented and the second-order advantage was achieved in samples containing uncalibrated interferences and uncorrected background signals. The calibration curves showed good linearity (r(2) > 0.994).The limits of detection for pseudoephedrine and methamphetamine were 0.06 and 0.08 μg/L and the average recovery values were 104.7 and 102.3% in river water, respectively.

Combined SAXS/UV-vis/Raman as a diagnostic and structure resolving tool in materials and life sciences applications

In order to diagnose and fully correlate structural, chemical, and functional features of macromolecules and particles in solution, we propose the integration of spectroscopy and scattering on the same measuring volume and at the same time in a dedicated sample environment with multiple probes. Combined SAXS/UV-vis and SAXS/Raman information are employed to study the radiation damage effect in proteins in solution and the scattering from single wall carbon nanotubes (SWNTs) in SDS dispersion, respectively. In the first case, a clear correlation is observed between the time dependence of the radius of gyration (Rg) of the protein determined by SAXS and the turbidity of the protein solution extracted from simultaneous UV-vis measurements. In the second case, the ratio of bundled/isolated carbon nanotubes is obtained unambiguously through proper modeling of the scattering data and cross-validated with the Raman information. The uses of convex constraint analysis (CCA) and two-dimensional correlation analyses (2DCOS and 2DHCOS) are introduced to fully explore the combination of data sets from different techniques and to extract unique insights from the sample.

Chemometrics approach for determination and optimization of simultaneous photooxidative decolourization of a mixture of three textile dyes

In this paper simultaneous photooxidation of three textile dyes (CI Basic Red 46 (BR46), Malachite Green (MG) and CI Basic Blue 3 (BB3)) by UV/H2O2 process is reported. A rapid analytical methodology based on recording UV-Vis spectra during the photooxidative decolourization process and the data treatment using multivariate curve resolution with alternating least squares (MCR-ALS) was developed. The three textile dyes were quantified simultaneously despite the overlap of their spectra. Also, the central composite design was applied for optimization of the photooxidative decolourization of the solution containing three dyes using the UV/H2O2 process. The investigated factors (variables) were the reaction time, initial concentration of the three dyes and initial concentration of H2O2. The optimum initial concentration of the three dyes, H2O2 initial concentration and reaction time were found to be 4 mg/L, 48 mg/L and 30 min, respectively. The photodegradation products of three dyes were identified by the GC-MS technique.

Characterisation of the degradation of salvianolic acid B using an on-line spectroscopic analysis system and multivariate curve resolution

INTRODUCTION

Chinese herbal medicines contain complex ingredients that are unstable during the manufacturing process. Current off-line methods are usually time-consuming and inaccurate.

OBJECTIVE

The aim of this study was to develop a continuous-flow-based on-line spectroscopic system for monitoring the degradation of unstable compounds in Chinese herbal medicines.

METHODOLOGY

An on-line spectroscopic analysis system in the evaluation of the degradation of unstable compounds was implemented, using salvianolic acid B from Danshen (Radix Salviae Miltiorrhizae) as a model compound. Resolution of the mixture spectra obtained during the reaction was performed with multivariate curve resolution (MCR), and the pure spectra and kinetic profiles of reactant species were reconstructed.

RESULTS

The degradation process of salvianolic acid B is probably a consecutive two-step reaction of the form A → B → C, where A is salvianolic acid B, and B and C are the intermediate and final products, respectively. The intermediate and final products were suggested to be a mixture of lithospermic acid and danshensu [3-(3, 4-dihydroxyphenyl) lactic acid], and a mixture of danshensu and salvianolic acid A, respectively.

CONCLUSION

The findings suggested that the combined use of on-line spectroscopic analysis and MCR might be a cheap, readily available, sensitive tool to monitor the transformation of unstable components of Chinese herbal medicines.