Estimation of rotation ambiguity in multivariate curve resolution with charged particle swarm optimization (cPSO‐MCR)

Rotation ambiguity (RA) in multivariate curve resolution (MCR) is an undesirable case, when the physicochemical constraints are not sufficiently strong to provide a unique resolution of the data matrix of the mixtures into spectra and concentration profiles of individual chemical components. RA is often met in MCR of overlapped chromatographic peaks, kinetic and equilibrium data, and fluorescence two‐dimensional spectra. In case of RA, a single candidate solution has little practical value. So, the whole set of feasible solutions should be characterized somehow. It is a quite intricate task in a general case. In the present paper, a method was proposed to estimate RA with charged particle swarm optimization (cPSO), a population‐based algorithm. The criteria for updating the particles were modified, so that the swarm converged to the steady state, which spanned the set of feasible solutions. The performance of cPSO‐MCR was demonstrated on test functions, simulated datasets, and real‐world data. Good accordance of the cPSO‐MCR results with the analytical solutions (Borgen plots) was observed. cPSO‐MCR was also shown to be capable of estimating the strength of the constraints and of revealing RA in noisy data. As compared with analytical methods, cPSO‐MCR is simpler to implement, expands to more than three chemical compounds, is immune to noise, and can be easily adapted to virtually all types of constraints and objective functions (constraint based or residue based). cPSO‐MCR also provides natural visual information about the level of RA in spectra and concentration profiles, similar to the methods of two extreme solutions (e.g., MCR‐BANDS). Copyright © 2014 John Wiley & Sons, Ltd.     

Multivariate curve resolution of mixed bacterial DNA sequence spectra: identification and quantification of bacteria in undefined mixture samples

A comprehensive understanding of factors that influence microbial competition and cooperation, their diversity and processes will be greatly beneficial in many research areas. Current tools for microflora determinations are far from suitable for high‐throughput monitoring of development in complex microbial communities. Here, we describe the application of a calibration free method, multivariate curve resolution with alternating least squares (MCR‐ALS), for identification and quantification of different microbes in mixture samples. The idea is to utilize MCR‐ALS to enable close monitoring of ecology in a variety of microbial communities. The data from two designed experiments consisting of DNA sequence spectra measured on mixtures were analysed with MCR‐ALS using no prior information on the data except for appropriate constraints, such as non‐negativity and closure. The results were compared both to the known true concentrations as well as to the results obtained from the well‐established multivariate calibration method partial least squares (PLS) regression. MCR‐ALS performed as well as PLS regression, successfully extracting all pure bacterial spectra and quantitative information on these, with 97.81% and 97.91% explained variance for the first and the second data set, respectively. Copyright © 2008 John Wiley & Sons, Ltd.     

Metabolic profiling for the identification of Huntington biomarkers by on‐line solid‐phase extraction capillary electrophoresis mass spectrometry combined with advanced data analysis tools

In this work, an untargeted metabolomic approach based on sensitive analysis by on‐line solid‐phase extraction capillary electrophoresis mass spectrometry (SPE‐CE‐MS) in combination with multivariate data analysis is proposed as an efficient method for the identification of biomarkers of Huntington's disease (HD) progression in plasma. For this purpose, plasma samples from wild‐type (wt) and HD (R6/1) mice of different ages (8, 12, and 30 weeks), were analyzed by C₁₈‐SPE‐CE‐MS in order to obtain the characteristic electrophoretic profiles of low molecular mass compounds. Then, multivariate curve resolution alternating least squares (MCR‐ALS) was applied to the multiple full scan MS datasets. This strategy permitted the resolution of a large number of metabolites being characterized by their electrophoretic peaks and their corresponding mass spectra. A total number of 29 compounds were relevant to discriminate between wt and HD plasma samples, as well as to follow‐up the HD progression. The intracellular signaling was found to be the most affected metabolic pathway in HD mice after 12 weeks of birth, when mice already showed motor coordination deficiencies and cognitive decline. This fact agreed with the atrophy and dysfunction of specific neurons, loss of several types of receptors, and changed expression of neurotransmitters.     

Automated QSPR modeling and data curation of physicochemical properties using KNIME platform: Prediction of partition coefficients

In the last two decades, the volumes of chemical and biological data are constantly increasing. The problem of converting data sets into knowledge is both expensive and time-consuming, as a result a workflow technology with platforms such as KNIME, was built up to facilitate searching through multiple heterogeneous data sources and filtering for specific criteria then extracting hidden information from these large data. Before any QSAR modeling, a manual data curation is extremely recommended. However, this can be done, for small datasets, but for the extensive data accumulated recently in public databases a manual process of big data will be hardly feasible. In this work, we suggest using KNIME as an automated solution for workflow in data curation, development, and validation of predictive QSAR models from a huge dataset.In this study, we used 250250 structures from NCI database, only 3520 compounds could successfully pass through our workflow safely with their corresponding experimental log P, this property was investigated as a case study, to improve some existing log P calculation algorithms.     

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.     

Imaging mass spectrometry data reduction: Automated feature identification and extraction

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues.     

Variable ranking based on the estimated degree of separation for two distributions of data by the length of the receiver operating characteristic curve

Variable responses are fundamental for all experiments, and they can consist of information-rich, redundant, and low signal intensities. A dataset can consist of a collection of variable responses over multiple classes or groups. Usually some of the variables are removed in a dataset that contain very little information. Sometimes all the variables are used in the data analysis phase. It is common practice to discriminate between two distributions of data; however, there is no formal algorithm to arrive at a degree of separation (DS) between two distributions of data. The DS is defined herein as the average of the sum of the areas from the probability density functions (PDFs) of A and B that contain a ≥ percentage of A and/or B. Thus, DS90 is the average of the sum of the PDF areas of A and B that contain ≥90% of A and/or B. To arrive at a DS value, two synthesized PDFs or very large experimental datasets are required. Experimentally it is common practice to generate relatively small datasets. Therefore, the challenge was to find a statistical parameter that can be used on small datasets to estimate and highly correlate with the DS90 parameter. Established statistical methods include the overlap area of the two data distribution profiles, Welch’s t-test, Kolmogorov–Smirnov (K–S) test, Mann–Whitney–Wilcoxon test, and the area under the receiver operating characteristics (ROC) curve (AUC). The area between the ROC curve and diagonal (ACD) and the length of the ROC curve (LROC) are introduced. The established, ACD, and LROC methods were correlated to the DS90 when applied on many pairs of synthesized PDFs. The LROC method provided the best linear correlation with, and estimation of, the DS90. The estimated DS90 from the LROC (DS90–LROC) is applied to a database, as an example, of three Italian wines consisting of thirteen variable responses for variable ranking consideration. An important highlight of the DS90–LROC method is utilizing the LROC curve methodology to test all variables one-at-a-time with all pairs of classes in a dataset.     

Monitoring the melt-extrusion transesterification of ethylene-vinylacetate copolymer by self-modeling curve resolution analysis of on-line near-infrared spectra

The transesterification of molten ethylene-vinylacetate (EVA) copolymers by octanol with sodium methoxide as catalyst in an extruder has been monitored by on-line near infrared (NIR) spectroscopy. A total of 60 NIR spectra were acquired for 37 min with the last spectrum recorded 31 min after the addition of octanol and catalyst was stopped. The experimental spectra show strong baseline fluctuations which are corrected for by multiplicative scatter correction (MSC). The chemometric methods of the orthogonal projection approach (OPA) and multivariate curve resolution (MCR) were used to resolve the spectra and to derive concentration profiles of the species. The detailed analysis reveals the absence of completely pure variables which leads to small errors in the calculation of pure spectra. The initial estimation of a concentration that is necessary as an input parameter for MCR also presents a non-trivial task. We obtained results that were not ideal but applicable for practical concentration control. They enable a fast monitoring of the process in real-time and resolve the spectra of the EVA copolymer and the ethylene-vinyl alcohol (EVAL) copolymer to be very close to the reference spectra. The chemometric methods used and the decomposed spectra are discussed in detail.     

Multivariate curve resolution-particle swarm optimization: A high-throughput approach to exploit pure information from multi-component hyphenated chromatographic signals

Multivariate curve resolution-particle swarm optimization (MCR-PSO) algorithm is proposed to exploit pure chromatographic and spectroscopic information from multi-component hyphenated chromatographic signals. This new MCR method is based on rotation of mathematically unique PCA solutions into the chemically meaningful MCR solutions. To obtain a proper rotation matrix, an objective function based on non-fulfillment of constraints is defined and is optimized using particle swarm optimization (PSO) algorithm. Initial values of rotation matrix are calculated using local rank analysis and heuristic evolving latent projection (HELP) method. The ability of MCR-PSO in resolving the chromatographic data is evaluated using simulated gas chromatography–mass spectrometry (GC–MS) and high-performance liquid chromatography–diode array detection (HPLC–DAD) data. To present a comprehensive study, different number of components and various levels of noise under proper constraints of non-negativity, unimodality and spectral normalization are considered. Calculation of the extent of rotational ambiguity in MCR solutions for different chromatographic systems using MCR-BANDS method showed that MCR-PSO solutions are always in the range of feasible solutions like true solutions. In addition, the performance of MCR-PSO is compared with other popular MCR methods of multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least squares (MCR-ALS). The results showed that MCR-PSO solutions are rather similar or better (in some cases) than other MCR methods in terms of statistical parameters. Finally MCR-PSO is successfully applied in the resolution of real GC–MS data. It should be pointed out that in addition to multivariate resolution of hyphenated chromatographic signals, MCR-PSO algorithm can be straightforwardly applied to other types of separation, spectroscopic and electrochemical data.     

Study of reaction processes by in-line near-infrared spectroscopy in combination with multivariate curve resolution: Esterification of myristic acid with isopropanol

The acid-catalysed esterification of myristic acid with isopropanol was studied by using near-infrared spectroscopy (NIR) in combination with soft-modeling curve resolution (MCR) methodology with a view to establishing the effect of experimental variables on the reaction kinetics. The reaction was conducted at temperatures above the boiling point of the alcohol, with continuous addition of an isopropanol/water mixture to the reactor. Spectral and concentration profiles were determined by applying soft-modeling curve resolution methodology to a column-wise augmented data matrix containing the spectra for the pure components. MCR profiles were compared with reference values and found to depart from then by less than 3% as %RSE for concentrations and to exhibit correlation above 0.999 for spectra.The reaction kinetics as estimated from the concentration profiles was found to be pseudo-first-order. Also, the pseudo-first-order rate constant was found to depend on the flow-rate of the isopropanol/water mixture and its water content; although the constant decreased with increase in the proportion of water, a content of ca. 15% could be used without important retarding effects on the kinetics. The proposed NIR-MCR method allows the rate constant and the influence of the initial water content to be determined with a view to minimizing consumption of the raw materials and optimizing the experimental conditions.     

A Multivariate Curve Resolution Approach to the Study of the Degradation Kinetics of Valsartan under Photolytic and Acid Conditions

The analysis of UV‐spectrophotometric data with second‐order chemometrics techniques, including multivariate curve resolution with alternating least‐squares (MCR‐ALS) and hybrid hard‐ and soft MCR (HS‐MCR), was examined as an alternative tool for studying the kinetics of drug degradation under stress conditions, employing valsartan (VAL) as a model drug. Despite small structural and spectroscopic differences between VAL and its degradation products, MCR‐ALS and HS‐MCR were able to detect the generation of two photoneutral degradation products (DP‐1 and DP‐2) and a single acid hydrolysis product (DP‐3), providing good approximations to their pure spectra and concentration profiles, from which estimations of the kinetic profiles and rate constants were obtained. Kinetic models based on first‐order reactions explained the degradation processes. MCR‐ALS and HS‐MCR analyses yielded similar rate constants; however, the latter was capable of more properly fitting the experimental data to a kinetic model in the case of drug photolysis. The results were confirmed by comparison with data obtained by HPLC analysis of the degraded samples.     

Extended data analysis strategies for high resolution imaging MS: New methods to deal with extremely large image hyperspectral datasets

The large size of the hyperspectral datasets that are produced with modern mass spectrometric imaging techniques makes it difficult to analyze the results. Unsupervised statistical techniques are needed to extract relevant information from these datasets and reduce the data into a surveyable overview. Multivariate statistics are commonly used for this purpose. Computational power and computer memory limit the resolution at which the datasets can be analyzed with these techniques. We introduce the use of a data format capable of efficiently storing sparse datasets for multivariate analysis. This format is more memory-efficient and therefore it increases the possible resolution together with a decrease of computation time. Three multivariate techniques are compared for both sparse-type data and non-sparse data acquired in two different imaging ToF-SIMS experiments and one LDI-ToF imaging experiment. There is no significant qualitative difference in the use of different data formats for the same multivariate algorithms. All evaluated multivariate techniques could be applied on both SIMS and the LDI imaging datasets. Principal component analysis is shown to be the fastest choice; however a small increase of computation time using a VARIMAX optimization increases the decomposition quality significantly. PARAFAC analysis is shown to be very effective in separating different chemical components but the calculations take a significant amount of time, limiting its use as a routine technique. An effective visualization of the results of the multivariate analysis is as important for the analyst as the computational issues. For this reason, a new technique for visualization is presented, combining both spectral loadings and spatial scores into one three-dimensional view on the complete datacube.     

Kernel principal component analysis residual diagnosis (KPCARD): An automated method for cosmic ray artifact removal in Raman spectra

A new, fully automated, rapid method, referred to as kernel principal component analysis residual diagnosis (KPCARD), is proposed for removing cosmic ray artifacts (CRAs) in Raman spectra, and in particular for large Raman imaging datasets. KPCARD identifies CRAs via a statistical analysis of the residuals obtained at each wavenumber in the spectra. The method utilizes the stochastic nature of CRAs; therefore, the most significant components in principal component analysis (PCA) of large numbers of Raman spectra should not contain any CRAs. The process worked by first implementing kernel PCA (kPCA) on all the Raman mapping data and second accurately estimating the inter- and intra-spectrum noise to generate two threshold values. CRA identification was then achieved by using the threshold values to evaluate the residuals for each spectrum and assess if a CRA was present.     

Pesticide residue analysis in foodstuffs applying capillary gas chromatography with mass spectrometric detection. State-of-the-art use of modified DFG-multimethod S19 and automated data evaluation

This paper focuses on recent developments in the author's laboratory and reports on the "ultimate" analysis scheme which has evolved over the last 20 years in our laboratory. This demonstrates the feasibility of screening analyses for pesticide residue identification, mainly by full scan GC-MS, down to the 0.01 ppm concentration level in plant foodstuffs. It is based on a miniaturized DFG S19 extraction applying acetone for extraction followed by liquid-liquid extraction with ethyl acetate-cyclohexane followed by gel permeation chromatography. The final chromatographic determination is carried out with a battery of three parallel operating gas chromatographic systems using effluent splitting to electron-capture and nitrogen-phosphorus detection, one with a SE-54 the other with a OV-17 capillary column and the third one with a SE-54 capillary column and mass selective detection for identification and quantitation. The method is established for monitoring more than 400 pesticides amenable to gas chromatography. These pesticide residues are identified in screening analyses by means of the dedicated mass spectral library PEST.L containing reference mass spectra and retention times of more than 400 active ingredients and also metabolites applying the macro program AuPest (Automated residue analysis on Pesticides) for automated evaluation which runs with Windows based HP ChemStation software. The two gas chromatographic systems with effluent splitting to electron-capture and nitrogen-phosphorus detection are used to check the results obtained with the automated GC-MS screening and also to detect those few pesticides which exhibit better response to electron-capture and nitrogen-phosphorus detection than to mass spectrometry in full scan.     

Toward Understanding the Formation Mechanism and OER Catalytic Mechanism of Hydroxides by In Situ and Operando Techniques

Developing efficient and affordable electrocatalysts for the sluggish oxygen evolution reaction (OER) remains a significant barrier that needs to be overcome for the practical applications of hydrogen production via water electrolysis, transforming CO₂ to value-added chemicals, and metal-air batteries. Recently, hydroxides have shown promise as electrocatalysts for OER. In situ or operando techniques are particularly indispensable for monitoring the key intermediates together with understanding the reaction process, which is extremely important for revealing the formation/OER catalytic mechanism of hydroxides and preparing cost-effective electrocatalysts for OER. However, there is a lack of comprehensive discussion on the current status and challenges of studying these mechanisms using in situ or operando techniques, which hinders our ability to identify and address the obstacles present in this field. This review offers an overview of in situ or operando techniques, outlining their capabilities, advantages, and disadvantages. Recent findings related to the formation mechanism and OER catalytic mechanism of hydroxides revealed by in situ or operando techniques are also discussed in detail. Additionally, some current challenges in this field are concluded and appropriate solution strategies are provided.     

Low absorption vitreous carbon reactors for operando XAS: a case study on Cu/Zeolites for selective catalytic reduction of NO(x) by NH3

We describe the use of vitreous carbon as an improved reactor material for an operando X-ray absorption spectroscopy (XAS) plug-flow reactor. These tubes significantly broaden the operating range for operando experiments. Using selective catalytic reduction (SCR) of NO(x) by NH(3) on Cu/Zeolites (SSZ-13, SAPO-34 and ZSM-5) as an example reaction, we illustrate the high-quality XAS data achievable with these reactors. The operando experiments showed that in Standard SCR conditions of 300 ppm NO, 300 ppm NH(3), 5% O(2), 5% H(2)O, 5% CO(2) and balance He at 200 °C, the Cu was a mixture of Cu(I) and Cu(II) oxidation states. XANES and EXAFS fitting found the percent of Cu(I) to be 15%, 45% and 65% for SSZ-13, SAPO-34 and ZSM-5, respectively. For Standard SCR, the catalytic rates per mole of Cu for Cu/SSZ-13 and Cu/SAPO-34 were about one third of the rate per mole of Cu on Cu/ZSM-5. Based on the apparent lack of correlation of rate with the presence of Cu(I), we propose that the reaction occurs via a redox cycle of Cu(I) and Cu(II). Cu(I) was not found in in situ SCR experiments on Cu/Zeolites under the same conditions, demonstrating a possible pitfall of in situ measurements. A Cu/SiO(2) catalyst, reduced in H(2) at 300 °C, was also used to demonstrate the reactor's operando capabilities using a bending magnet beamline. Analysis of the EXAFS data showed the Cu/SiO(2) catalyst to be in a partially reduced Cu metal-Cu(I) state. In addition to improvements in data quality, the reactors are superior in temperature, stability, strength and ease of use compared to previously proposed borosilicate glass, polyimide tubing, beryllium and capillary reactors. The solid carbon tubes are non-porous, machinable, can be operated at high pressure (tested at 25 bar), are inert, have high material purity and high X-ray transmittance.     

Spectral transformations for deconvolution methods applied on gas chromatography-mass spectrometry data

In hyphenated chromatography, overlapping chromatographic peaks can be resolved into pure spectra and pure chromatographic profiles by several multivariate deconvolution techniques. In general, these methods require bilinearity, which implies that the spectrum of each analyte is constant. The slow scan speeds normally used in gas chromatography-mass spectrometry (GC-MS) will destroy bilinearity and introduce systematic noise in the data because the concentration in the detector changes during the scan. This effect, described as the scan effect, may hinder successful resolution by multivariate deconvolution. In selected ion monitoring (SIM) GC-MS, the scan effect may be removed by simple transformations of the mass spectra. The effects of different transformations are demonstrated both on pure chromatographic peaks and on difficult resolution problems where there are small differences between the spectra of the analytes.     

Extensible automated dispersive liquid–liquid microextraction

In this study, a convenient and extensible automated ionic liquid-based in situ dispersive liquid–liquid microextraction (automated IL-based in situ DLLME) was developed. 1-Octyl-3-methylimidazolium bis[(trifluoromethane)sulfonyl]imide ([C₈MIM]NTf₂) is formed through the reaction between [C₈MIM]Cl and lithium bis[(trifluoromethane)sulfonyl]imide (LiNTf₂) to extract the analytes. Using a fully automatic SPE workstation, special SPE columns packed with nonwoven polypropylene (NWPP) fiber, and a modified operation program, the procedures of the IL-based in situ DLLME, including the collection of a water sample, injection of an ion exchange solvent, phase separation of the emulsified solution, elution of the retained extraction phase, and collection of the eluent into vials, can be performed automatically. The developed approach, coupled with high-performance liquid chromatography–diode array detection (HPLC–DAD), was successfully applied to the detection and concentration determination of benzoylurea (BU) insecticides in water samples. Parameters affecting the extraction performance were investigated and optimized. Under the optimized conditions, the proposed method achieved extraction recoveries of 80% to 89% for water samples. The limits of detection (LODs) of the method were in the range of 0.16–0.45 ng mL⁻¹. The intra-column and inter-column relative standard deviations (RSDs) were <8.6%. Good linearity (r > 0.9986) was obtained over the calibration range from 2 to 500 ng mL⁻¹. The proposed method opens a new avenue for automated DLLME that not only greatly expands the range of viable extractants, especially functional ILs but also enhances its application for various detection methods. Furthermore, multiple samples can be processed simultaneously, which accelerates the sample preparation and allows the examination of a large number of samples.     

Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples

A hyperspectral image in the near infrared contains thousands of position-referenced spectra. After imaging reference materials of known composition it is possible to build Partial Least Squares (PLS) regression models for predicting unknown compositions from new images or spectra. In this paper a comparison is made between spectra from a hyperspectral image and spectra from two spectrometers: a scanning grating instrument with rotating sample holders and an FT-NIR instrument utilizing a fiber-optic probe. The raw spectra and the quality of the PLS calibration models and predictions are compared. Two sample datasets consist of a set of 13 designed artificial mixtures of pure constituents and a selection of 13 sampled cheeses. The prediction error from the hyperspectral image spectra is between that of the two spectrometers. For a typical food sample, the average bias [and replicate standard deviation] was -0.6% [0.5%] for protein and -0.2% [1.3%] for fat. Comparable values for the best spectrometer were -0.2% bias for protein and -0.5% for fat. Some of the advantages of working with hyperspectral images are highlighted: the simultaneous exploration of representations of both spectral and spatial data, and the analysis of concentration profiles and concentration maps all contribute to better characterization of organic and biological materials.