Multivariate curve resolution-alternating least squares (MCR-ALS) applied to spectroscopic data from monitoring chemical reactions processes

Exploring cellular metabolic kinetics through spectroscopic analysis of extracellular environments

Studying the kinetics of metabolic pathways, such as glycolysis and glutaminolysis, is valuable due to their fundamental links to various diseases, including cancer. This study explores the potential of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy for analysing low concentrations of metabolites in extracellular media. It also evaluates the use of the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method to data mine the kinetic evolution of the spectroscopic signatures of the glycolysis metabolic pathway and to explore the impact of the presence of glutamine on it. By extracting samples at specific time intervals and drying them on the ATR crystal, ATR-FTIR could effectively measure individual metabolites of glucose, glutamine and lactate at low concentrations, providing clear spectra with strong correlations between peak absorbance and metabolite concentrations. In data mining, MCR-ALS successfully resolved two components, glucose and lactate, from time-series data of cellular glucose metabolism (glycolysis), showing approximately 28 % glucose consumption and 1 mM lactate production at a constant rate of 0.0016 min-1. However, when glutamine was introduced as a third component, the overlap of the peaks of glutamine and lactate limited the method's ability to deconvolute the data, highlighting constraints of MCR-ALS in complex mixtures.

Discriminating Analysis of Metal Ions via Multivariate Curve Resolution-Alternating Least Squares Applied to Silver Nanoparticle Sensor

Heavy metals are life-threatening pollutions because of their great toxicity, long-term persistence in nature and their bioaccumulation in living organisms. In this work, we performed multivariate curve resolution-alternating least squares analysis of UV-Vis raw spectra received by a colorimetric sensor constructed on mercaptoundecanoic acid functionalized silver nanoparticles (AgNPs@11MUA) to detect Cd2+, Cu2+, Mn2+, Ni2+, and Zn2+ in water. This combined approach allowed the rapid identification and quantification of multiple heavy metals and showed adequate sensitivity and selectivity, thus representing a promising analytical and computational method for both laboratory and field applications such as environmental safety and public health monitoring.

PLSR-colorimetric simultaneous determination of L-Tyrosine and L-Tryptophan in different pharmaceutical and biological samples using one-pot synthesized leaf shape Ag@Ag2O core-shell nanocomposites modified by β-CD

In the present study, a simple, innovative, and economically beneficial method has been proposed for the synthesis of Ag@Ag2O core-shell nanocomposites using Acanthophora muscoides algae extract. The host-guest recognition of targets was performed by modification of the Ag@Ag2O surface using β-CD. The Ag@Ag2O- β-CD NCs were used as a colorimetric sensor to determine L-Tryptophan and L-Tyrosine using a partial least square (PLS) approach. A crystalline hybrid structure of Ag core and an Ag2O shell was confirmed by XRD, FTIR, TEM and AFM research. Also, DLS analysis and surface zeta potential spectra illustrated the aggregated nature of nanocomposites in the presence of analytes. The literature review shows that the colorimetric simultaneous determination of L-Tryptophan (L-Try) and L-Tyrosine (L-Tyr) has not been reported. The Ag@Ag2O- β-CD sensor exhibited outstanding sensing capability in a broad linear range of 2.0 -200 μM for both amino acids and low detection limit of 0.32 and 0.51 μM, for L-Try and L-Tyr, respectively. The good sensitivity and excellent selectivity regarding possible interfering species, originated from the synergistic effect of host-guest recognition in combination with colorimetric sensing. Additionally, determination of analytes in various pharmaceutical, supplement and urine samples, approved the practical applicability of the constructed sensor. The computed results confirmed that colorimetric sensing in conjunction with a PLS technique was appropriate for the precise and accurate simultaneous determination of target amino acids in complex mixtures with RMSEP less than 2.5% and recovery in the range of 103-108% with R.S.D. values less than 3%.

Rapid-charging aluminium-sulfur batteries operated at 85 °C with a quaternary molten salt electrolyte

Molten salt aluminum-sulfur batteries are based exclusively on resourcefully sustainable materials, and are promising for large-scale energy storage owed to their high-rate capability and moderate energy density; but the operating temperature is still high, prohibiting their applications. Here we report a rapid-charging aluminium-sulfur battery operated at a sub-water-boiling temperature of 85 °C with a tamed quaternary molten salt electrolyte. The quaternary alkali chloroaluminate melt - possessing abundant electrochemically active high-order Al-Cl clusters and yet exhibiting a low melting point - facilitates fast Al3+ desolvation. A nitrogen-functionalized porous carbon further mediates the sulfur reaction, enabling the battery with rapid-charging capability and excellent cycling stability with 85.4% capacity retention over 1400 cycles at a charging rate of 1 C. Importantly, we demonstrate that the asymmetric sulfur reaction mechanism that involves formation of polysulfide intermediates, as revealed by operando X-ray absorption spectroscopy, accounts for the high reaction kinetics at such temperature wherein the thermal management can be greatly simplified by using water as the heating media.

Extraction of pure component spectra from ex situ illumination UV/Vis and NMR spectroscopy

Obtaining understanding of a photochemical reaction relies on the observation, identification and quantification of the compounds involved. The photochemical properties of the individual components are of particular importance, and their determination, however, is not always trivial. This is also true for the quantitative measure on the ability to absorb light, the extinction coefficient εi if more than one species i is present and two or more species absorb light of the same wavelength. In this work, it is demonstrated how pure component spectra can be obtained with a simple combination of successive and repeated ex situ illumination, UV/Vis and NMR spectroscopy. From the complementary information accessible, the wavelength-dependent extinction coefficients of all species can be calculated yielding the pure component spectra. A comparison with published data shows excellent agreement and thus proves that this approach is highly reliable.

Multivariate curve Resolution-Alternating least squares coupled with Raman microspectroscopy: new insights into the kinetic response of primary oral squamous carcinoma cells to cisplatin

Raman MicroSpectroscopy (RMS) is a powerful label-free tool to probe the effects of drugs at a cellular/subcellular level. It is important, however, to be able to extract relevant biochemical and kinetic spectroscopic signatures of the specific cellular responses. In the present study, a combination of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis (PCA) is used to analyse the RMS data for the example of exposure of primary Oral Squamous Carcinoma Cells (OSCC) to the chemotherapeutic agent cisplatin. Dosing regimens were established by cytotoxicity assays, and the effects of the drug on cellular spectral profiles were monitored from 16 to 72 hours post-exposure using an apoptosis assay, to establish the relative populations of viable (V), early (EA) and late apoptotic/dead (LA/D) cells after the drug treatment. Based on a kinetic model of the progression from V > EA > D, MCR-ALS regression analysis of the RMS responses was able to extract spectral profiles associated with each stage of the cellular responses, enabling a quantitative comparison of the response rates for the respective drug treatments. Moreover, PCA was used to compare the spectral profiles of the viable cells exposed to the drug. Spectral differences were highlighted in the early stages (16 hours exposure), indicative of the initial cellular response to the drug treatment, and also in the late stages (48-72 hours exposure), representing the cell death pathway. The study demonstrates that RMS coupled with multivariate analysis can be used to quantitatively monitor the progression of cellular responses to different drugs, towards future applications for label-free, in vitro, pre-clinical screening.

Holistic Approach to Process Design and Scale-Up for Itaconic Acid Production from Crude Substrates

Bio-based bulk chemicals such as carboxylic acids continue to struggle to compete with their fossil counterparts on an economic basis. One possibility to improve the economic feasibility is the use of crude substrates in biorefineries. However, impurities in these substrates pose challenges in fermentation and purification, requiring interdisciplinary research. This work demonstrates a holistic approach to biorefinery process development, using itaconic acid production on thick juice based on sugar beets with Ustilago sp. as an example. A conceptual process design with data from artificially prepared solutions and literature data from fermentation on glucose guides the simultaneous development of the upstream and downstream processes up to a 100 L scale. Techno-economic analysis reveals substrate consumption as the main constituent of production costs and therefore, the product yield is the driver of process economics. Aligning pH-adjusting agents in the fermentation and the downstream process is a central lever for product recovery. Experiments show that fermentation can be transferred from glucose to thick juice by changing the feeding profile. In downstream processing, an additional decolorization step is necessary to remove impurities accompanying the crude substrate. Moreover, we observe an increased use of pH-adjusting agents compared to process simulations.

Multivariate Curve Resolution Alternating Least Squares Analysis of In Vivo Skin Raman Spectra

In recent years, Raman spectroscopy has been used to study biological tissues. However, the analysis of experimental Raman spectra is still challenging, since the Raman spectra of most biological tissue components overlap significantly and it is difficult to separate individual components. New methods of analysis are needed that would allow for the decomposition of Raman spectra into components and the evaluation of their contribution. The aim of our work is to study the possibilities of the multivariate curve resolution alternating least squares (MCR-ALS) method for the analysis of skin tissues in vivo. We investigated the Raman spectra of human skin recorded using a portable conventional Raman spectroscopy setup. The MCR-ALS analysis was performed for the Raman spectra of normal skin, keratosis, basal cell carcinoma, malignant melanoma, and pigmented nevus. We obtained spectral profiles corresponding to the contribution of the optical system and skin components: melanin, proteins, lipids, water, etc. The obtained results show that the multivariate curve resolution alternating least squares analysis can provide new information on the biochemical profiles of skin tissues. Such information may be used in medical diagnostics to analyze Raman spectra with a low signal-to-noise ratio, as well as in various fields of science and industry for preprocessing Raman spectra to remove parasitic components.

Reaction Analysis and Process Optimization with Online Infrared Data Based on Kinetic Modeling and Partial Least Squares Quantitation

In-situ Fourier transform infrared (FT-IR) spectroscopy has been recognized as an important technology for online monitoring of chemical reactions. However, analysis of the real-time IR data for identification and quantification of uncertain reactants or intermediates is often ambiguous and difficult. Here, we propose an analysis algorithm based on reaction kinetic modeling and the chemometric method of partial least squares (PLS) to comprehensively and quantitatively study reaction processes. Concentration profiles and apparent kinetic parameters can be simultaneously calculated from the spectral data, without the demand of complicated analysis on characteristic absorbance peaks or tedious sampling efforts for multivariate modeling. Paal-Knorr reactions and glyoxylic acid synthesis reactions were selected as typical reactions to validate the algorithm. A lack of fit of the Paal-Knorr reaction spectra was less than 2.5% at various conditions, and the absolute errors between the predicted values and HPLC measurement of glyoxylic acid synthesis were less than 6% during the reaction process. Moreover, the reaction kinetic models extracted from FT-IR data were used to simulate reaction processes and optimize the conditions in order to maximize product yields, which proved that this analysis method could be used for process optimization.

Label-Free Plasmon-Enhanced Spectroscopic HER2 Detection for Dynamic Therapeutic Surveillance of Breast Cancer

The expression of human epidermal growth factor receptor-2 (HER2) has important implications for pathogenesis, progression, and therapeutic efficacy of breast cancer. The detection of its variation during the treatment is crucial for therapeutic decision-making but remains a grand challenge, especially at the cellular level. Here, we develop a machine learning-driven surface-enhanced Raman spectroscopy (SERS)-integrated strategy for label-free detection of cellular HER2. Specifically, our method allows the extraction of cell-rich spectral signatures utilized for identification and classification of cancer cells with distinct HER2 expression with a high accuracy of 99.6%. By combining label-free SERS detection and machine learning-driven chemometric analysis, we are able to perform longitudinal monitoring of therapeutic efficacy at the cellular level during the treatment of HER2+ breast cancer, which aids in the subsequent decision-making and management. This work provides a promising technique capable of performing dynamic label-free spectroscopic detection for therapeutic surveillance of diseases.

Flexible Implementation of the Trilinearity Constraint in Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) of Chromatographic and Other Type of Data

Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) can analyze three-way data under the assumption of a trilinear model using the trilinearity constraint. However, the rigid application of this constraint can produce unrealistic solutions in practice due to the inadequacy of the analyzed data to the characteristics and requirements of the trilinear model. Different methods for the relaxation of the trilinear model data requirements have been proposed, like in the PARAFAC2 and in the direct non-trilinear decomposition (DNTD) methods. In this work, the trilinearity constraint of MCR-ALS is adapted to different data scenarios where the profiles of all or some of the components of the system are shifted (not equally synchronized) or even change their shape among different slices in one of their data modes. This adaptation is especially useful in gas and liquid chromatography (GC and LC) and in Flow Injection Analysis (FIA) with multivariate spectroscopic detection. In a first data example, a synthetic LC-DAD dataset is built to investigate the possibilities of the proposed method to handle systematic changes (shifts) in the retention times of the elution profiles and the results are compared with those obtained using alternative methods like ATLD, PARAFAC, PARAFAC2 and DNTD. In a second data example, multiple wine samples were simultaneously analyzed by GC-MS where elution profiles presented large deviations (shifts) in their peak retention times, although they still preserve the same peak shape. Different modelling scenarios are tested and the results are also compared. Finally, in the third example, sample mixtures of acid compounds were analyzed by FIA under a pH gradient and monitored by UV spectroscopy and also examined by different chemometric methods using a different number of components. In this case, however, the departure of the trilinear model comes from the acid base speciation of the system depending on the pH more than from the shifting of the FIA diffusion profiles.

Resolving fluorescence signatures of a photoconvertible fluorophore by fluorescence spectroscopy and MCR-ALS-based combinatorial approach

Photoconvertible fluorophores are important for a myriad of applications in chemistry and biology. Here, we spectrally resolve and quantify individual photophysical information of a dual-emitting photoconvertible fluorophore by fluorescence spectroscopy and multivariate curve resolution-alternate least square techniques. We found that the reactant fluorophore, which shows a weak locally excited (LE) emission and a dominant intramolecular charge transfer (ICT) emission, also exhibits an intermolecular charge transfer emission. The ICT emission bands of both the reactant and product fluorophores are originated from their respective LE states. The reactant fluorophore is a mixture of its different ground state conformers. Higher yields of photoconversion of the yellow-emitting reactant fluorophore are achieved via a visible light photoreaction, leading to formation of pure white light at an intermediate photoreaction time. These findings together help us to glean new photophysical and photochemical insights into the photoreaction of a dual-emitting photoconvertible fluorophore.

Mechanism and kinetic study of Paal-Knorr reaction based on in-situ MIR monitoring

An in-depth understanding of reaction processes is beneficial to the development and quality control of chemical products. In this work, the mechanism and kinetics of the Paal-Knorr reaction for pyrrole derivatives are thoroughly studied using in-situ Fourier transform infrared (FTIR) spectroscopy. The hemiacetal amine intermediate, reactants, and products were identified and quantified by the treatment of real-time infrared spectra via chemometrics method and two-dimensional correlation spectroscopy (2DCOS) technique. Based on the IR quantitative models, influences of operating conditions on reaction processes were investigated, and the reaction kinetic model was built with kinetic parameters of two rate-limiting reaction steps calculated. This approach of analysis on the in-situ FTIR data demonstrated the ability to extract useful information on reaction components, especially the intermediate spectrum, from the confounding real-time IR data. The in-situ FTIR monitoring combined with the IR analysis methods is proved as a powerful tool for revealing the reaction mechanism and kinetics.

Chemometric Tools to Point Out Benchmarks and Chromophores in Pigments through Spectroscopic Data Analyses

Spectral preprocessing data and chemometric tools are analytical methods widely applied in several scientific contexts i.e., in archaeometric applications. A systematic classification of natural powdered pigments of organic and inorganic nature through Principal Component Analysis with a multi-instruments spectroscopic study is presented here. The methodology allows the access to elementary and molecular unique benchmarks to guide and speed up the identification of an unknown pigment and its recipe. This study is conducted on a set of 48 powdered pigments and tested on a real-case sample from the wall painting in S. Maria Delle Palate di Tusa (Messina, Italy). Four spectroscopic techniques (X-ray Fluorescence, Raman, Attenuated Total Reflectance and Total Reflectance Infrared Spectroscopies) and six different spectrometers are tested to evaluate the impact of different setups. The novelty of the work is to use a systematic approach on this initial dataset using the entire spectroscopic energy range without any windows selection to solve problems linked with the manipulation of large analytes/materials to find an indistinct property of one or more spectral bands opening new frontiers in the dataset spectroscopic analyses.

Liquid Structure of a Water-in-Salt Electrolyte with a Remarkably Asymmetric Anion

Water-in-salt systems, i.e., super-concentrated aqueous electrolytes, such as lithium bis(trifluoromethanesulfonyl)imide (21 mol/kgwater), have been recently discovered to exhibit unexpectedly large electrochemical windows and high lithium transference numbers, thus paving the way to safe and sustainable charge storage devices. The peculiar transport features in these electrolytes are influenced by their intrinsically nanoseparated morphology, stemming from the anion hydrophobic nature and manifesting as nanosegregation between anions and water domains. The underlying mechanism behind this structure-dynamics correlation is, however, still a matter of strong debate. Here, we enhance the apolar nature of the anions, exploring the properties of the aqueous electrolytes of lithium salts with a strongly asymmetric anion, namely, (trifluoromethylsulfonyl)(nonafluorobutylsulfonyl) imide. Using a synergy of experimental and computational tools, we detect a remarkable level of structural heterogeneity at a mesoscopic level between anion-rich and water-rich domains. Such a ubiquitous sponge-like, bicontinuous morphology develops across the whole concentration range, evolving from large fluorinated globules at high dilution to a percolating fluorous matrix intercalated by water nanowires at super-concentrated regimes. Even at extremely concentrated conditions, a large population of fully hydrated lithium ions, with no anion coordination, is detected. One can then derive that the concomitant coexistence of (i) a mesoscopically segregated structure and (ii) fully hydrated lithium clusters disentangled from anion coordination enables the peculiar lithium diffusion features that characterize water-in-salt systems.

Inline Raman Spectroscopy and Indirect Hard Modeling for Concentration Monitoring of Dissociated Acid Species

We propose an approach for monitoring the concentration of dissociated carboxylic acid species in dilute aqueous solution. The dissociated acid species are quantified employing inline Raman spectroscopy in combination with indirect hard modeling (IHM) and multivariate curve resolution (MCR). We introduce two different titration-based hard model (HM) calibration procedures for a single mono- or polyprotic acid in water with well-known (method A) or unknown (method B) acid dissociation constants pK_a. In both methods, spectra of only one acid species in water are prepared for each acid species. These spectra are used for the construction of HMs. For method A, the HMs are calibrated with calculated ideal dissociation equilibria. For method B, we estimate pK_a values by fitting ideal acid dissociation equilibria to acid peak areas that are obtained from a spectral HM. The HM in turn is constructed on the basis of MCR data. Thus, method B on the basis of IHM is independent of a priori known pK_a values, but instead provides them as part of the calibration procedure. As a detailed example, we analyze itaconic acid in aqueous solution. For all acid species and water, we obtain low HM errors of < 2.87 × 10^-4mol mol^-1 in the cases of both methods A and B. With only four calibration samples, IHM yields more accurate results than partial least squares regression. Furthermore, we apply our approach to formic, acetic, and citric acid in water, thereby verifying its generalizability as a process analytical technology for quantitative monitoring of processes containing carboxylic acids.

Modeling and optimization of simultaneous degradation of rhodamine B and acid red 14 binary solution by homogeneous Fenton reaction: a chemometrics approach

This study aimed to propose a mathematical method to investigate and optimize the simultaneous elimination process of multiple organic pollutants using the Fenton process. Hence, the treatment of rhodamine B (RB) and acid red 14 (AR14) dyes in their binary solution was studied. Multivariate curve resolution alternating least square (MCR-ALS), a novel chemometric method, was applied along with correlation constraints to resolute the UV-Vis spectrophotometric data, enabling quantification of investigated dyes despite a high spectral overlapping. Response surface methodology was adopted to assess the model and optimize individual and interactive effects of three independent factors (Fe²⁺, H₂O₂ and initial pH) on the simultaneous elimination of RB and AR14. The values of the regression coefficient for RB and AR14 were determined as 98.48 and 98.67 percent, respectively, revealing the reliability of the obtained polynomial models to predict decolorization efficiencies. Desirability function was employed to optimize the independent variables to attain the highest possible degradation performance for both dyes in their binary solution. At the optimum point of operation ([Fe²⁺] = 143.88 mg/L, [H₂O₂] = 126.89 mg/L and pH = 3.71), degradation efficiencies of RB and AR14 were found as 81.58% and 80.22%, respectively, which were nearly identical to the experimental results.

A data-driven approach to generate pseudo-reaction sequences for the thermal conversion of Athabasca bitumen

We use self-modeling multivariate curve resolution to identify pseudo-components and chemical transformations in thermal conversion of Athabasca bitumen.

Application of an Augmentation Method to MCR-ALS Analysis for XAFS and Raman Data Matrices in the Structural Change of Isopolymolybdates

We measured X-ray absorption fine structure (XAFS) and Raman spectra of isopolymolybdates(VI) in highly concentrated HNO3 solution (0.15 - 4.0 M), which change their geometries depending on the acid concentration, and performed the simultaneous resolution of the XAFS and Raman data using a multivariate curve resolution by alternating least-squares (MCR-ALS) analysis. In iterative ALS optimization, initial data matrices were prepared by two different methods. For low sensitivity of the XAFS spectra to the geometrical change of the isopolymolybdates, the MCR-ALS result of single XAFS data matrix shows a large dependence on the preparation method of the initial data matrices. This problem is improved by the simultaneous resolution of the XAFS and Raman data: the MCR-ALS result of an augmented matrix of these data has little dependence on the initial data matrices. This indicates that the augmentation method effectively improves the rotation ambiguities in the MCR-ALS analysis of the XAFS data.

Photostability study of multicomponent drug formulations via MCR-ALS: The case of the hydrochlorothiazide-amiloride mixture

The kinetics and photodegradation mechanism of the pharmaceutical mixture of hydrochlorothiazide (HCT) and amiloride (AML) has been studied in depth using a chemometric approach. Water solutions of HCT and AML, separately or in binary mixtures, were irradiated with forced light at different pH values (3, 7, 9 and 12). Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) modelling has been applied to the experimental data recorded by UV spectrophotometry and HPLC-UV/MS. 78 data sets were collected and their chemometric processing has allowed the simultaneous determination of the behaviour of the two drugs in the mixture when exposed to light and the dependence of their photodegradation kinetics on pH. MCR-ALS has been applied using three different implementations. Soft-MCR-ALS and hybrid Hard/Soft-MCR-ALS have been used to resolve the experimental data and to get the equilibrium and kinetic parameters of the investigated chemical processes. A third implementation of the MCR-ALS method has been used in the analysis of the incomplete data sets obtained when UV spectrophotometric and HPLC-UV/MS data were simultaneously analysed, using a row- and column-wise incomplete augmented data matrix arrangement. In these matrices, information from HPLC-UV detector was used as a bridge between the data recorded by UV spectrophotometry (acid-base and kinetic reactions monitoring) and the data obtained by HPLC-MS.

μXRF Mapping as a Powerful Technique for Investigating Metal Objects from the Archaeological Site of Ferento (Central Italy)

This research concerns the application of micro X-ray fluorescence (µXRF) mapping to the investigation of a group of selected metal objects from the archaeological site of Ferento, a Roman and then medieval town in Central Italy. Specifically, attention was focused on two test pits, named IV and V, in which metal objects were found, mainly pertaining to the medieval period and never investigated before the present work from a compositional point of view. The potentiality of µXRF mapping was tested through a Bruker Tornado M4 equipped with an Rh tube, operating at 50 kV, 500 μA, and spot 25 μm obtained with polycapillary optics. Principal component analysis (PCA) and multivariate curve resolution (MCR) were used for processing the X-ray fluorescence spectra. The results showed that the investigated items are characterized by different compositions in terms of chemical elements. Three little wheels are made of lead, while the fibulae are made of copper-based alloys with varying amounts of tin, zinc, and lead. Only one ring is iron-based, and the other objects, namely a spatula and an applique, are also made of copper-based alloys, but with different relative amounts of the main elements. In two objects, traces of gold were found, suggesting the precious character of these pieces. MCR analysis was demonstrated to be particularly useful to confirm the presence of trace elements, such as gold, as it could differentiate the signals related to minor elements from those due to major chemical elements.

Direct Mechanistic Evidence for a Nonheme Complex Reaction through a Multivariate XAS Analysis

In this work, we propose a method to directly determine the mechanism of the reaction between the nonheme complex Fe^II(tris(2-pyridylmethyl)amine) ([Fe^II(TPA)(CH₃CN)₂]²⁺) and peracetic acid (AcOOH) in CH₃CN, working at room temperature. A multivariate analysis is applied to the time-resolved coupled energy-dispersive X-ray absorption spectroscopy (EDXAS) reaction data, from which a set of spectral and concentration profiles for the reaction key species is derived. These “pure” extracted EDXAS spectra are then quantitatively characterized by full multiple scattering (MS) calculations. As a result, structural information for the elusive reaction intermediates [Fe^III(TPA)(κ²-OOAc)]²⁺ and [Fe^IV(TPA)(O)(X)]^+/2+ is obtained, and it is suggested that X = AcO^– in opposition to X = CH₃CN. The employed strategy is promising both for the spectroscopic characterization of reaction intermediates that are labile or silent to the conventional spectroscopic techniques, as well as for the mechanistic understanding of complex redox reactions involving organic substrates.

A combined multivariate and theoretical XAS analysis was proven to be a powerful method to obtain direct evidence for the mechanism of the reaction between the nonheme complex Fe^II(tris(2-pyridymethyl)amine) ([Fe^II(TPA)(CH₃CN)₂]²⁺) and peroxyacetic acid. This approach allowed to determine the time evolution of the concentration profiles for all reaction intermediates and to quantitatively characterize their structures, suggesting the sixth coordinating ligand of the nonheme oxo complex [Fe^IV(TPA)(O)(X)]^+/2+ is X = AcO⁻ in opposition to X = CH₃CN.

Real-Time Quantitative In-Cell NMR: Ligand Binding and Protein Oxidation Monitored in Human Cells Using Multivariate Curve Resolution

In-cell NMR can investigate protein conformational changes at atomic resolution, such as those changes induced by drug binding or chemical modifications, directly in living human cells, and therefore has great potential in the context of drug development as it can provide an early assessment of drug potency. NMR bioreactors can greatly improve the cell sample stability over time and, more importantly, allow for recording in-cell NMR data in real time to monitor the evolution of intracellular processes, thus providing unique insights into the kinetics of drug-target interactions. However, current implementations are limited by low cell viability at >24 h times, the reduced sensitivity compared to “static” experiments and the lack of protocols for automated and quantitative analysis of large amounts of data. Here, we report an improved bioreactor design which maintains human cells alive and metabolically active for up to 72 h, and a semiautomated workflow for quantitative analysis of real-time in-cell NMR data relying on Multivariate Curve Resolution. We apply this setup to monitor protein–ligand interactions and protein oxidation in real time. High-quality concentration profiles can be obtained from noisy 1D and 2D NMR data with high temporal resolution, allowing further analysis by fitting with kinetic models. This unique approach can therefore be applied to investigate complex kinetic behaviors of macromolecules in a cellular setting, and could be extended in principle to any real-time NMR application in live cells.

Simultaneous Quantification of Multiple Polycyclic Aromatic Hydrocarbons in Aqueous Media using Micelle Assisted White Light Excitation Fluorescence

Qualitative and quantitative display of multiple fluorescent analytes is made simple and reliable in this micelle assisted methodology. The adopted method involves micelle assisted evincing of ppb level of PAHs in water; measurement of total fluorescence (white light excitation fluorescence, WLEF) and data deciphering using multivariate analysis. This protocol yields sensitive and accurate quantification of the cancerous pollutants (PAHs) in aqueous media with Limit of Quantification of the order 1-10 μg/L and accuracy of >98%. The use of WLEF enables the simultaneous acquisition of fluorescence signatures of all the PAHs. It has the additional advantage of being portable, layman-friendly and cost-effective. The optimized amount of surfactants for the simultaneous extraction of PAHs from real samples was estimated as 27.8 mg (19.3 mM) of SDS and 9.1 mg (5 mM) of CTAB. Also, the analytical fidelity of the quantification such as percentage recovery (98 ± 2%), linear dynamic range (2-250 μg/L), RMSEP (<0.5), etc. explains the veracity of methodology.

Multivariate Analysis of Coupled Operando EPR/XANES/EXAFS/UV-Vis/ATR-IR Spectroscopy: A New Dimension for Mechanistic Studies of Catalytic Gas-Liquid Phase Reactions

Operando EPR, XANES/EXAFS, UV‐Vis and ATR‐IR spectroscopic methods have been coupled for the first time in the same experimental setup for investigation of unclear mechanistic aspects of selective aerobic oxidation of benzyl alcohol by a Cu/TEMPO catalytic system (TEMPO=2,2,6,6‐tetramethylpiperidinyloxyl). By multivariate curve resolution with alternating least‐squares fitting (MCR‐ALS) of simultaneously recorded XAS and UV‐Vis data sets, it was found that an initially formed (bpy)(NMI)Cu^I‐ complex (bpy=2,2′‐bipyridine, NMI=N‐methylimidazole ) is converted to two different Cu^II species, a mononuclear (bpy)(NMI)(CH₃CN)Cu^II‐OOH species detectable by EPR and ESI‐MS, and an EPR‐silent dinuclear (CH₃CN)(bpy)(NMI)Cu^II(μ‐OH)₂⋅Cu^II (bpy)(NMI) complex. The latter is cleaved in the further course of reaction into (bpy)(NMI)(HOO)Cu^II‐TEMPO monomers that are also EPR‐silent due to dipolar interaction with bound TEMPO. Both Cu monomers and the Cu dimer are catalytically active in the initial phase of the reaction, yet the dimer is definitely not a major active species nor a resting state since it is irreversibly cleaved in the course of the reaction while catalytic activity is maintained. Gradual formation of non‐reducible Cu^II leads to slight deactivation at extended reaction times.

OCTAVVS: A Graphical Toolbox for High-Throughput Preprocessing and Analysis of Vibrational Spectroscopy Imaging Data

Modern vibrational spectroscopy techniques enable the rapid collection of thousands of spectra in a single hyperspectral image, allowing researchers to study spatially heterogeneous samples at micrometer resolution. A number of algorithms have been developed to correct for effects such as atmospheric absorption, light scattering by cellular structures and varying baseline levels. After preprocessing, spectra are commonly decomposed and clustered to reveal informative patterns and subtle spectral changes. Several of these steps are slow, labor-intensive and require programming skills to make use of published algorithms and code. We here present a free and platform-independent graphical toolbox that allows rapid preprocessing of large sets of spectroscopic images, including atmospheric correction and a new algorithm for resonant Mie scattering with improved speed. The software also includes modules for decomposition into constituent spectra using the popular Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) algorithm, augmented by region-of-interest selection, as well as clustering and cluster annotation.

Buildup of Triplet-State Population in Operating TQ1:PC71BM Devices Does Not Limit Their Performance

Triplet generation in organic solar cells has been considered a major loss channel. Determining the density of the triplet-state population in an operating device is challenging. Here, we employ transient absorption (TA) spectroscopy on the quinoxaline-thiophene copolymer TQ1 blended with PC₇₁BM, quantify the transient charge and triplet-state densities, and parametrize their generation and recombination dynamics. The charge recombination parameters reproduce the experimentally measured current-voltage characteristics in charge carrier drift-diffusion simulations, and they yield the steady-state charge densities. We demonstrate that triplets are formed by both geminate and nongeminate recombination of charge carriers and decay primarily by triplet-triplet annihilation. Using the charge densities in the rate equations describing triplet-state dynamics, we find that triplet-state densities in devices are in the range of charge carrier densities. Despite this substantial triplet-state buildup, TQ1:PC₇₁BM devices exhibit only moderate geminate recombination and significantly reduced nongeminate charge recombination, with reduction factors between 10^-4 and 10^-3 compared to Langevin recombination.

Analysis of Carcinogenic Polycyclic Aromatic Hydrocarbons in the Complex Background of the Petroleum Fluorescence Using Multivariate Curve Resolution Alternating Least Square and Total Synchronous Fluorescence Spectroscopic Technique

Multivariate curve resolution alternating least square (MCR-ALS) analysis allows the simultaneous retrieval of pure concentration and spectral profiles for each of the analysed chemical components from the composite spectrum even in the presence of unknown interferences. Total synchronous fluorescence spectroscopy (TSFS), a multidimensional fluorescence technique that describes the variation of synchronous fluorescence profile acquired as a function of increasing offset, has become a useful analytical technique. Suitably arranged TSFS data set can be easily processed using MCR-ALS and thereby a simple and sensitive analytical tool could be developed. The present work successfully used the combination of the MCR-ALS and TSFS to analyse the three carcinogenic and mutagenic polycyclic aromatic hydrocarbons (PAHs) namely Benzo[a]Pyrene, Chrysene and Pyrene in the presence of complex fluorescence background originated from petroleum product. MCR-ALS assisted TSFS can be used for the routine analyses of these carcinogenic PAHs to ensure the quality of water and other samples belonging to different part of the ecosystem.

Multivariate Curve Resolution for 2D Solid-State NMR spectra

We present a processing method, based on the multivariate curve resolution approach (MCR), to denoise 2D solid-state NMR spectra, yielding a substantial S/N ratio increase while preserving the lineshapes and relative signal intensities. These spectral features are particularly important in the quantification of silicon species, where sensitivity is limited by the low natural abundance of the ²⁹Si nuclei and by the dilution of the intrinsic protons of silica, but can be of interest also when dealing with other intermediate-to-low receptivity nuclei. This method also offers the possibility of coprocessing multiple 2D spectra that have the signals at the same frequencies but with different intensities (e.g.: as a result of a variation in the mixing time). The processing can be carried out on the time-domain data, thus preserving the possibility of applying further processing to the data. As a demonstration, we have applied Cadzow denoising on the MCR-processed FIDs, achieving a further increase in the S/N ratio and more effective denoising also on the transients at longer indirect evolution times. We have applied the combined denoising on a set of experimental data from a lysozyme-silica composite.

Simultaneous Determination of Drugs Affecting Central Nervous System (CNS) in Bulk and Pharmaceutical Formulations Using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS)

The quality of medications is important to maintain the overall health care of patients. This study aims to develop and validate a spectrophotometric method using multivariate curve resolution-alternating least squares (MCR-ALS) with correlation constraint for simultaneous resolution and quantification of selected drugs affecting the central nervous system (imipramine, carbamazepine, chlorpromazine, haloperidol, and phenytoin) in different pharmaceutical dosage forms. Figures of merit such as root-mean-square error of prediction, bias, standard error of prediction, and relative error of prediction for the developed method were calculated. High values of correlation coefficients ranged between 0.9993 and 0.9998 reflected high predictive ability of the developed method. The results are linear in the concentration range of 0.3-5 μg/mL for carbamazepine, 0.3-15 μg/mL for chlorpromazine, 0.5-10 μg/mL for haloperidol, 0.5-10 μg/mL for imipramine, and 3-20 μg/mL for phenytoin. The optimized method was successfully applied for the analysis of the studied drugs in their pharmaceutical products without any separation step. The optimized method was also compared with a reported HPLC method using Student's t test and F ratio at 95% confidence level, and the results showed no significant difference regarding accuracy and precision. The proposed chemometric method is fast, reliable, and cost-effective and can be used as an eco-friendly alternative to chromatographic techniques for the analysis of the studied drugs in commercial pharmaceutical products.

Formation of Indomethacin-Saccharin Cocrystals during Wet Granulation: Role of Polymeric Excipients

Formulation of a cocrystal into a solid pharmaceutical dosage form entails numerous processing steps during which there is risk of dissociation. In an effort to reduce the number of unit operations, we have attempted the in situ formation of an indomethacin-saccharin (INDSAC) cocrystal during high-shear wet granulation (HSWG). HSWG of IND (poorly water-soluble drug) and SAC (coformer), with polymers (granulating agents), was carried out using ethanol as the granulation liquid and yielded INDSAC cocrystal granules. Therefore, cocrystal formation and granulation were simultaneously accomplished. Our objectives were to (i) evaluate the influence of polymers on cocrystal formation kinetics during wet granulation and (ii) mechanistically understand the role of polymers in facilitating the cocrystal formation. Polyvinylpyrrolidone (PVP), hydroxypropyl cellulose (HPC), and polyethylene oxide (PEO) were chosen to investigate the influence of soluble polymers. The cocrystal formation kinetics was influenced by the polymer (PVP < HPC < PEO) and its concentration. The interaction of the polymer with cocrystal components inhibited the cocrystal formation. Complete cocrystal formation was observed in the presence of PEO, a polymer which does not interact with IND and SAC.

ADAP-GC 4.0: Application of Clustering-Assisted Multivariate Curve Resolution to Spectral Deconvolution of Gas Chromatography-Mass Spectrometry Metabolomics Data

We report a multivariate curve resolution (MCR)-based spectral deconvolution workflow for untargeted gas chromatography-mass spectrometry metabolomics. As an essential step in preprocessing such data, spectral deconvolution computationally separates ions that are in the same mass spectrum but belong to coeluting compounds that are not resolved completely by chromatography. As a result of this computational separation, spectral deconvolution produces pure fragmentation mass spectra. Traditionally, spectral deconvolution has been achieved by using a model peak approach. We describe the fundamental differences between the model peak-based and the MCR-based spectral deconvolution and report ADAP-GC 4.0 that employs the latter approach while overcoming the associated computational complexity. ADAP-GC 4.0 has been evaluated using GC-TOF data sets from a 27-standards mixture at different dilutions and urine with the mixture spiked in, and GC Orbitrap data sets from mixtures of different standards. It produced the average matching scores 960, 959, and 926 respectively. Moreover, its performance has been compared against MS-DIAL, eRah, and ADAP-GC 3.2, and ADAP-GC 4.0 demonstrated a higher number of matched compounds and up to 6% increase of the average matching score.

Kinetics Study of Cocrystal Formation Between Indomethacin and Saccharin Using High-Shear Granulation With In Situ Raman Spectroscopy

Pharmaceutical manufacturing processes are necessary to make solid dosage form even in cocrystal formation. In an effort to reduce the number of unit operations, high-shear wet granulation with cocrystallization system was proposed. In the present study, indomethacin-saccharin was chosen as a model compound, and the cocrystal formation kinetics was investigated during the consistent process. The role of each initial indomethacin crystal state (γ-form, α-form, or amorphous) for the kinetics was explored using in situ Raman spectroscopy with multivariate curve resolution by alternating least-squares analysis as a chemometrics. Obtained granules were characterized by X-ray diffraction and tablet dissolution testing. The Raman peaks assigned to indomethacin-saccharin cocrystal were increased with granulation when ethanol was used as a binding solvent. In addition, the reaction kinetics of run samples which had different indomethacin forms was distinguished by best fitting using Avrami-Erofeev or Ginstling-Brounshtein model. The kinetic variance depended on the initial thermodynamic state of indomethacin because they had a different crystallization mechanism for the cocrystal. The scalable and feasible granulation method is required in the pharmaceutical industry.

Quantitative analysis and resolution of pharmaceuticals in the environment using multivariate curve resolution-alternating least squares (MCR-ALS)

The study presents the application of multivariate curve resolution alternating least squares (MCR-ALS) with a correlation constraint for simultaneous resolution and quantification of ketoprofen, naproxen, paracetamol and caffeine as target analytes and triclosan as an interfering component in different water samples using UV-Vis spectrophotometric data. A multivariate regression model using the partial least squares regression (PLSR) algorithm was developed and calculated. The MCR-ALS results were compared with the PLSR obtained results. Both models were validated on external sample sets and were applied to the analysis of real water samples. Both models showed comparable and satisfactory results with the relative error of prediction of real water samples in the range of 1.70-9.75 % and 1.64-9.43 % for MCR-ALS and PLSR, resp. The obtained results show the potential of MCR-ALS with correlation constraint to be applied for the determination of different pharmaceuticals in complex environmental matrices.

Ranking the solubility of ammonia in ionic liquids using near infrared spectroscopy and multivariate curve resolution

We rank the expected solubilities of ammonia in three hydroxyl ionic liquids - [HOEMIm][BF₄], [HOEMIm][NTf₂] and [Ch][NTf₂] - in the temperature range 20-105 °C by analyzing the cations and anions available for interaction with ammonia. As this availability depends on ion-pair formation in ionic liquids, in this paper it is evaluated using the concentration and spectral profiles recovered in the analysis of their near infrared spectra by the multivariate resolution curve - alternating least squares method. The results indicate that the main effect of temperature on ion pairs is to decrease the number of structural configurations with cooperative hydrogen bonds between cation and cation, although in a lesser extent the number of cation-anion interactions increases. Regardless of the type of ionic liquid cation, the cation-anion interactions are higher in the tetrafluorborate ionic liquid than in the imide ionic liquid, hydroxyl imidazolium or choline. Assuming that the solubility of ammonia is limited by the concentration profile values representative of the cation-cation interactions, we deduce that at temperatures higher than 80 °C, ammonia solubility increases in the following order [HOEMIm][BF4] < [HOEMIm][NTf2] < [Ch][NTf2]. At lower temperatures, this order varies with the ammonia concentration in the NH3/ILs mixtures considered. We deduce that if the ammonia concentration is relatively low, the ammonia solubility will be governed by the evolution of cation-anion interaction in the ionic liquids and the solubility order is the same as at higher temperatures. However, when the ammonia concentration is higher, the ammonia solubility in the [Ch][NTf2] ionic liquid is lower than in the hydroxyl-ionic liquids. This conclusion is supported by the experimental vapor-liquid equilibria (VLE) data of ammonia-/ILs mixtures with ammonia mass fractions between 0.2 and 0.8.