Curve Resolution-Alternating Least Research Articles

Environmentally safe chemometric evaluation and data analysis for the kinetic investigation of mirabegron stability

The physicochemical characteristics of different pharmaceuticals may change with transportation, storage, and usage which directly affect their efficacy, quality, and other features. It is crucial to investigate the kinetic action of pharmaceutical compounds to produce predictive data that aid in the computation of their shelf lives. Several conventional analytical methods introduce unwanted dangerous risks, like employing unsafe solvents, that harm both the analyst and the environment. This created the motive for analysts to apply green approaches that consider using safe chemicals and producing as little waste as possible. In this study, powerful green assessment methods were investigated to study the kinetic process of Mirabegron (MIR) in different hydrolytic conditions giving its main degradation product. First, the optimal solvent was identified for the given kinetic procedures with the aid of Principal Component Analysis. Then two advanced chemometric methods were applied to estimate the kinetic parameters of the MIR degradation process namely: Multivariate Curve Resolution-Alternating Least Squares and Artificial Neural Networks. By data manipulation, both algorithms could provide an explanation of the MIR kinetic process in different hydrolytic conditions. Moreover, modified National Environmental Methods Index (NEMI), eco-scale, Hexagon algorithm, Complementary Green Analytical Procedure Index (Complex GAPI), and analytical greenness metric for sample preparation (AGREEprep) were adopted to ensure the greenness of the proposed study. Eventually, the presented investigation could successfully present an eco-friendly chemometric approach for monitoring the different kinetic processes of pharmaceutical compounds with minimum effort and solvent consumption and maximum time saving and analyst safety compared to other traditional methods.

Quantitative and qualitative analysis of cell culture media powders for mammalian cells by Raman microscopy

Cell culture media are essential for large-scale recombinant protein production using mammalian cell cultures. The composition and quality of media significantly impact cell growth and product formation. Analyzing media poses challenges due to complex compositions and undisclosed exact compositions. Traditional methods like NMR and chromatography offer sensitivity but require time-consuming sample preparation and lack spatial information. Raman chemical mapping characterizes solids, but its use in cell culture media analysis is limited so far. We present a chemometric evaluation for Raman maps to qualify and quantify media components, evaluate powder homogeneity, and perform lot-to-lot comparisons. Three lots of a marketed cell culture media powder were measured with Raman mapping technique. Chemometrics techniques have outlined a strategy to extract information from complex data. First, a spectral library has been structured. In addition to the 23 spectra for presumed ingredients, we obtained another 9 pure components with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Then the Spectral Angle Mapper-Orthogonal Projection (SAM-OP) algorithm revealed whether references actually occur in the mapped media powders. Finally, a quantification was provided by Classical Least Squares (CLS) modelling. Quantities of 18 significant amino acids mostly correlated with the reference method. The proposed method can be generally applied even for such complicated samples. Leveraging Raman mapping and innovative chemometric methods enhance recombinant protein production by improving the understanding of the spatial distribution and composition of cell culture media in mammalian cell cultivations.

Novel analytical method based on chemometric models applied to UV–Vis spectrophotometric data for simultaneous determination of Etoricoxib and Paracetamol in presence of Paracetamol impurities

The multivariate models that are used for spectral data analysis have many beneficial applications, and one of the important applications is the analysis of drugs and their impurities. Three Chemometrically-assisted spectrophotometric models have been proposed and validated. The proposed models are Partial Least Squares (PLS), Artificial Neural Networks (ANN), and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). The advanced chemometric models were applied to resolve the significantly overlapping spectra of Etoricoxib (ETO) and Paracetamol (PCM), along with impurities of PCM namely; P-aminophenol (PAP) and P-hydroxy acetophenone (PHA). The proposed models succeeded in simultaneously analyzing the mixture of ETO and PCM along with the impurities of PCM. So, the proposed techniques can be used without requiring a separation step in the analysis of pharmaceutical formulation. Moreover, no significant differences were found when the results of the suggested and published chemometric models were compared statistically with the reported HPLC method.

Tracing the identity of Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” cheese using NMR spectroscopy and multivariate data analysis

BackgroundNuclear magnetic resonance (NMR) spectroscopy is one of the well-established tools for food metabolomic analysis, as it proved to be very effective in authenticity and quality control of dairy products, as well as to follow product evolution during processing and storage. The analytical assessment of the EU mountain denomination label, specifically for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, has received limited attention. Although it was established in 2012 the EU mountain denomination label has not been much studied from an analytical point of view. Nonetheless, tracing a specific profile for the mountain products is essential to support the value chain of this specialty. ResultsThe aim of the study was to produce an identity profile for Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” (Mountain-CQ) cheese, and to differentiate it from Parmigiano Reggiano PDO samples (conventional-PDO) using 1H NMR spectroscopy coupled with multivariate data analysis. Three different approaches were applied and compared. First, the spectra-as-such were analysed after proper preprocessing. For the other two approaches, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used for signals resolution and features extraction, either individually on manually-defined spectral intervals or by reapplying MCR-ALS on the whole spectra with selectivity constraints using the reconstructed “pure profiles” as initial estimates and targets. All approaches provided comparable information regarding the samples’ distribution, as in all three cases the separation between the two product categories conventional-PDO and Mountain-CQ could be highlighted. Moreover, a novel MATLAB toolbox for features extraction via MCR-ALS was developed and used in synergy with the Chenomx library, allowing for a putative identification of the selected features. SignificanceA first identity profile for Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” obtained by interpreting the metabolites signals in NMR spectroscopy was obtained. Our workflow and toolbox for generating the features dataset allows a more straightforward interpretation of the results, to overcome the limitations due to dimensionality and to peaks overlapping, but also to include the signals assignment and matching since the early stages of the data processing and analysis.

Diagnosis and correction methods for spectral interference in the framework of LIBS imaging

Laser-Induced Breakdown Spectroscopy (LIBS) has become a powerful imaging technique for elemental characterization in analytical chemistry due to its advantages over other techniques. Major, minor, and trace elements are detected with high measurement dynamic, a low limit of detection and a high acquisition rate, allowing for the quick analysis of large sample surfaces. Today, chemometric tools are commonly used to ensure the most comprehensive and unbiased exploration of such spectroscopic data. However, the integration of the signal from a wavelength assumed to be specific to the element of interest remains the basic tool for generating a chemical distribution map from a hyperspectral dataset. This classical approach is based on a strong assumption, the specificity of the chemical information on the spectral domain being considered. Any spectral interference inevitably result in the generation of a biased distribution image. In this publication, we demonstrate how Principal Component Analysis (PCA) can diagnose the potential presence of a spectral interference and how Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) can ultimately correct it if necessary using a LIBS imaging dataset obtained from the analysis of a complex rock sample. The proposed approach combines the simplicity and effectiveness of the integration method with the diagnostic and correction capabilities of chemometric tools, providing a comprehensive solution for spectral interference in LIBS imaging.

The classification performance of multivariate curve resolution-discriminant analysis: A comparative study

Pattern recognition models establish the relationship between the selected variables and objects' belonging to different classes. Variations in measured variables, different object patterns, and relationship models explain the need to develop different algorithms. Partial least squares-discriminant analysis (PLS-DA) is based on the projection of object responses in the space of latent vectors of the PLS calibration model. Recently, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method has been introduced for discriminant analysis by our group. The possibility of incorporation of real physical and chemical information in the MCR decomposition of data leads to solutions that directly have meaning and simplifies the interpretation of results. Considering the new application of MCR in supervised pattern recognition, there is still a need to evaluate and compare the efficiency of MCR-DA with tested methods such as PLS-DA.Detection and discrimination of extra virgin olive oil from four other classes of pure edible oils and also their mixture with extra virgin olive oil, as adulteration was chosen just as a typical system to evaluate and compare the efficiency of MCR-DA and PLS-DA methods. Raman spectroscopy was used forresponse measurement of oil samples. Different classifications were made between extra virgin olive oil and diverse combinations of four other edible oils, and similar sets were used for the comparative evaluation of the two discrimination methods. Various criteria evaluated the performance of the two methods and in most cases the new MCR-DA method was comparable to PLS-DA. However, MCR-DA can assign pure Raman spectra resulting from decomposition to different classes, which can provide direct information about the components of the studied system.

Study of the photobleaching phenomenon to optimize acquisition of 3D and 4D fluorescence images. A special scenario for trilinear and quadrilinear models

Emission (3D) and excitation-emission (4D) fluorescence images allow covering wide excitation and emission spectral ranges and, hence, provide very complete information for a good characterization and location of fluorophores in samples. However, when the acquisition time of the image is too long, degradation of the fluorescence signal of compounds and sample photodamage can occur due to photobleaching. This phenomenon is due to the long exposure time of the sample to the light source and can hinder the detection and the proper characterization of the fluorophores in samples.The main purpose of this research is providing a methodology to obtain and interpret the information of fluorescence images for the characterization of samples without suffering the consequences of photobleaching. Such a goal implies a first thorough knowledge of the photobleaching phenomenon to adapt the fluorescence imaging measurement for an optimal characterization of the fluorophores present in samples.The proposed approach relies first on a study of time-series of 3D or 4D fluorescence images to characterize spatially and spectroscopically the fluorophores present in the samples and their photobleaching behaviour. Since photobleaching is fluorophore-dependent, the unmixing algorithm Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is applied to the set of fluorescence images acquired as a function of time to understand the specific behaviour of every fluorophore. The characteristics of the photobleaching phenomenon and the nature of the fluorescence measurement offer a challenging scenario to look for adapted implementations of trilinear and quadrilinear models within the MCR framework. From the results obtained, appropriate instrumental settings are adopted for an image acquisition that allows the correct spatial and spectroscopic characterization of fluorophores in samples.To test the potential of this methodology, the characterization of thin cross-sections of the Oryza sativa (commonly called rice) root have been studied due to the co-occurrence of several natural fluorophores in vegetal tissues.

Univariate versus multivariate spectrophotometric data analysis of triamterene and xipamide; a quantitative and qualitative greenly profiled comparative study

Triamterene (TRI) and xipamide (XIP) mixture is used as a binary medication of antihypertension which is considered as a major cause of premature death worldwide. The purpose of this research is the quantitative and qualitative analysis of this binary mixture by green univariate and multivariate spectrophotometric methods. Univariate methods were zero order absorption spectra method (D0) and Fourier self-deconvolution (FSD), as TRI was directly determined by D0 at 367.0 nm in the range (2.00–10.00 µg/mL), where XIP show no interference. While XIP was determined by FSD at 261.0 nm in the range (2.00–8.00 µg/mL), where TRI show zero crossing. Multivariate methods were Partial Least Squares, Principal Component Regression, Artificial Neural Networks, and Multivariate Curve Resolution-Alternating Least Squares. A training set of 25 mixtures with different quantities of the tested components was used to construct and evaluate them, 3 latent variables were displayed using an experimental design. A set of 18 synthetic mixtures with concentrations ranging from (3.00–7.00 µg/mL) for TRI and (2.00–6.00 µg/mL) for XIP, were used to construct the calibration models. A collection of seven synthetic mixtures with various quantities was applied to build the validation models. All the proposed approaches quantitative analyses were evaluated using recoveries as a percentage, root mean square error of prediction, and standard error of prediction. Strong multivariate statistical tools were presented by these models, and they were used to analyze the combined dosage form available on the Egyptian market. The proposed techniques were evaluated in accordance with ICH recommendations, where they are capable of overcoming challenges including spectral overlaps and collinearity. When the suggested approaches and the published one were statistically compared, there was no discernible difference between them. The green analytical method index and eco-scale tools were applied for assessment of the established models greenness. The suggested techniques can be used in product testing laboratories for standard pharmaceutical analysis of the substances being studied.

Exploring dynamic changes of fungal cellular components during nanoemulsion treatment by multivariate microRaman imaging

Recently, essential oils (EO) have gained a lot of interest for use as antifungal agent in food and agricultural industry and extensive research is ongoing to understand their mode of action. However, the exact mechanism is not yet elucidated. Here, we integrated spectral unmixing and Raman microspectroscopy imaging to unveil the antifungal mechanism of green tea EO based nanoemulsion (NE) against Magnaporthe oryzae. The dramatic change in protein, lipid, adenine, and guanine bands indicate that NE has a significant impact on the protein, lipid and metabolic processes of purine. The results also demonstrated that the NE treatment caused damage to fungal hyphae by inducing a physical injury leading to cell wall damage and loss of integrity. Our study shows that MCR−ALS (Multivariate Curve Resolution-Alternating Least Squares) and N-FINDR (N-finder algorithm) Raman imaging could serve as a suitable complementary package to the traditional methods, for revealing the antifungal mechanism of action of EO/NE.

Investigation of bread staling by handheld NIR spectroscopy in tandem with 2D-COS and MCR-ALS analysis

The present investigations were carried out using the technique of handheld near-infrared (NIR) spectroscopy for time-dependent diffuse-reflection measurements of the cut face of a fresh baguette. The critical structural factors in the staling process of bread are the crystallization of amylopectin in starch and the decrease of water content by evaporation and diffusion from core to crust. To monitor the synchronicity and sequence of these changes, Two-Dimensional Correlation Spectroscopy (2D-COS) was applied. Due to the significant variation of spectral changes at different stages of the aging process, the spectra were split into two sets of 0 – 6/6 – 48 hr. 2D-COS analysis allowed to differentiate the detailed sequence of the following structural events for these time intervals: crystallization of amylopectin/evaporation of weakly and strongly hydrogen-bonded water/reorganization of starch OH-functionalities. Furthermore, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used to investigate the changes in the spectra profile as a function of aging time.

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.

Modeling changes in chocolate during production and storage by ATR-FT-IR spectroscopy and MCR-ALS hybrid soft and hard modeling

Industrially tempered dark chocolate processed under different conditions was analyzed using Attenuated Total Reflection-Fourier Transform-Infrared spectroscopy (ATR-FT-IR). Spectra were collected during the cooling process right after tempering using two distinct machines and machine operational modes and over short and long storage (1 day and two months, respectively). To interpret the spectra collected, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis was used. MCR-ALS incorporated a dedicated implementation of hard modeling constraints for the elucidation of the spectral and concentration profiles of chocolate contributions during the monitored process with the aim of reducing ambiguity in the data interpretation. Thus, the spectral signature of the amorphous form of chocolate was constrained to follow a Gaussian shape, in line with previously reported works and with measurements observed at the beginning of the cooling process. Additionally, hard modeling constraints were applied to the concentration profiles linked to the cooling process by implementing the Avrami kinetic model, often postulated to describe thermally induced fat crystal transformations. The application of the hard modeling constraints eased the generation of interpretable solutions and resulted in concentration profiles and spectral signatures that were defining much better the crystal state of the chocolate samples. A two-component system explained the studied procedures, with a contribution S1 representing the highly crystalline state of the chocolate fats and S2 representative of the amorphous/less stable state. In general, we were able to track the transition from a less ordered to a highly ordered crystal state of the fats during the cooling stage of chocolate. After short storage, the chocolates had contributions of both S1 and S2 profiles. After long storage, the S1 crystalline form was the most dominant interpreted as the reorganization of the triglyceride acyl chains towards a thermodynamically favorable state. The latter was observed for all analyzed chocolates, implying that, regardless the tempering process or temper regime tested, the fats in chocolates reach the same high crystalline state after maturing.

Analysis of longitudinal metabolomic data using multivariate curve resolution-alternating least squares and pathway analysis

Extraction of meaningful biological information from longitudinal metabolomic studies is a major challenge and typically involves multivariate analysis and dimensional reduction methods for data visualization such as Principal Component Analysis or Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Besides, a variety of computational tools have been developed to identify changes in metabolic pathways including functional analysis and pathway analysis. In this work, the joint analysis of results from MCR-ALS and metabolic pathway analysis is proposed to facilitate the interpretation of dynamic changes in longitudinal metabolomic data. The strategy is based on the use of MCR-ALS to remove unstructured random variation in the raw data, thus facilitating the interpretation of dynamic changes observed by metabolic pathway analysis over time. A simulated data set representing dynamic longitudinal changes in the intensities of a subset of metabolites from three metabolic pathways was initially used to test the applicability of MCR-ALS to support pathway analysis for detecting pathway perturbations. Then, the strategy is applied to real data acquired for the analysis of changes during CD8+ T cell activation. Results obtained show that MCR-ALS facilitates the interpretation of longitudinal metabolomic profiles in multivariate data sets by identifying metabolic pathways associated with each detected dynamic component.

The trilinear constraint adapted to solve data with strong patterns of outlying observations or missing values

The possibility to perform trilinear decompositions of data sets has the clear advantage of providing unique solutions. Excitation-emission fluorescence matrices (EEM) are the best known paradigm of chemical measurements providing a trilinear structure associated with the configuration of excitation, emission and sample modes. Chemometric tools, such as Parallel Factor Analysis (PARAFAC) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) with trilinear constraint, assist in solving the mixture analysis problem by exploiting the trilinear behavior of the EEM measurements. However, the spectroscopic nature of EEM measurements makes that no emission signal can be recorded below the current excitation wavelength, generating a strong and systematic pattern of outlier (zero observations) in EEM data that challenges the classical analysis by MCR-ALS or PARAFAC. Several approaches have been proposed to deal with this problem, such as the identification of outlying values below the excitation wavelength and, thus, the use of data imputation in PARAFAC, but they show severe limitations when systematic outlying data patterns occur. In this paper, we propose a new implementation of the trilinear constraint in MCR-ALS algorithm to cope with EEM measurements where a strongly patterned of outlying data is present. This approach preserves the trilinear property and does not require any data imputation step to replace the outlying observations. Its performance is tested on simulated data, controlled pharmaceutical mixtures and hyperspectral images of a plant tissue (HSI). It should be noted that the approach proposed is applicable to EEM data, where a systematic pattern of outlying observations exist, but can be generalized to the treatment of any trilinear data set with a strong pattern of missing values.

Effects of sulfamethoxazole exposure on mussels (Mytilus galloprovincialis) metabolome using retrospective non-target high-resolution mass spectrometry and chemometric tools

In this work, the Regions of Interest-Multivariate Curve Resolution (ROIMCR) method is proposed for the analysis of non-target metabolomics data. Samples from marine mussels (Mytilus galloprovincialis) exposed to a sublethal concentration (10 μg/L) of sulfamethoxazole (SMX) during 4 days in different seasonal conditions (summer and winter) were analyzed by High-Performance Liquid Chromatography – High-Resolution Mass Spectrometry (HPLC-HRMS) to study the effect of their exposure to SMX and the different seasonal conditions. The Regions of Interest (ROI) procedure has been applied for data filtering, compression, preprocessing and storage steps. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is then applied to the previously MS ROI preprocessed data sets to resolve the elution profiles and spectral fingerprints of the chemical constituents of the analyzed samples. The peak areas of the elution profiles of the chemical constituents resolved by the combined ROIMCR procedure were analyzed by Principal Component Analysis (PCA) and samples were clustered according to their experimental seasonal and SMX exposure. The effects of the two investigated factors and of their interaction on the concentrations of the metabolites were statistically assessed by ANOVA simultaneous component analysis (ASCA). Both types of analyses, PCA clustering and ASCA, confirmed that the seasonal conditions (summer versus winter) produced larger effects than those produced by the exposure to SMX and by the interaction of these two factors. The concentration changes of 16 identified endogenous metabolites were validated individually using a Wilcoxon statistical test, which confirmed the presence of significant disturbances in the levels of some of these metabolites (free fatty acids, amino acids and nucleic acids), and indicated the possible alteration of six different biological pathways, affected by the investigated seasonal and SMX exposure factors.

Combining Pharmacokinetics and Vibrational Spectroscopy: MCR-ALS Hard-and-Soft Modelling of Drug Uptake In Vitro Using Tailored Kinetic Constraints.

Raman microspectroscopy is a label-free technique which is very suited for the investigation of pharmacokinetics of cellular uptake, mechanisms of interaction, and efficacies of drugs in vitro. However, the complexity of the spectra makes the identification of spectral patterns associated with the drug and subsequent cellular responses difficult. Indeed, multivariate methods that relate spectral features to the inoculation time do not normally take into account the kinetics involved, and important theoretical information which could assist in the elucidation of the relevant spectral signatures is excluded. Here, we propose the integration of kinetic equations in the modelling of drug uptake and subsequent cellular responses using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and tailored kinetic constraints, based on a system of ordinary differential equations. Advantages of and challenges to the methodology were evaluated using simulated Raman spectral data sets and real Raman spectra acquired from A549 and Calu-1 human lung cells inoculated with doxorubicin, in vitro. The results suggest a dependency of the outcome on the system of equations used, and the importance of the temporal resolution of the data set to enable the use of complex equations. Nevertheless, the use of tailored kinetic constraints during MCR-ALS allowed a more comprehensive modelling of the system, enabling the elucidation of not only the time-dependent concentration profiles and spectral features of the drug binding and cellular responses, but also an accurate computation of the kinetic constants.

Feasibility of Individual Carotenoid Quantification in Mixtures Using UV-Vis Spectrophotometry with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS)

A fast and low-cost analytical method to determine the concentrations of carotenoids (β-carotene, lutein, and lycopene) from mixed standard solutions or from fruit extracts (kiwi fruits, tomato paste, pink grapefruit juice, kiwi-pineapple smoothie, and apricot nectar) was tested. The methodology was based on UV-Vis spectrophotometry and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Results showed that relative concentrations of β-carotene, lutein, and lycopene in solvent were successfully determined by this technique with an error inferior to 6%. In real extracts, the procedure succeeded well in identifying the major carotenoid type of the fruit samples but also a more complex profile of a fruit mixture. The results also showed that accuracy of carotenoids determination by UV-Vis spectrophotometry-MCR-ALS in fruit extracts was conditioned by their spectral characteristics (III/II ratios and λmax), their relative proportion, and the extract purity.

Advanced chemometric methods as powerful tools for impurity profiling of drug substances and drug products: Application on bisoprolol and perindopril binary mixture

Impurity profiling has a rising importance nowadays due to the increased health problems associated with impurities and degradation products found in several drug substances and formulations. Three advanced, accurate and precise chemometric methods were developed as impurity profiling methods for a mixture of bisoprolol fumarate (BIS) and perindopril arginine (PER) with their degradation products which represent drug impurity or a precursor to such impurity. The methods applied were Partial Least Squares (PLS-1), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Artificial Neural Networks (ANN). Genetic Algorithm (GA) was used as a variable selection tool to select the most significant wavelengths for the three chemometric models. For proper analysis, a 5-factor 5-level experimental design was used to establish a calibration set of 25 mixtures containing different ratios of the drugs and their degradation products (impurities). The validity of the proposed methods was assessed using an independent validation set. The designed models were able to predict the concentrations of the drugs and the degradation products/impurities in the validation set and pharmaceutical formulation. The proposed methods presented a powerful alternative to traditional and expensive chromatographic methods as impurity profiling tools.

Linear unmixing protocol for hyperspectral image fusion analysis applied to a case study of vegetal tissues

Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and is particularly suitable to describe clearly the necessary steps required to perform unmixing in an image fusion context. Although this protocol is presented and applied to a study of vegetal tissues, it can be generally used in many other samples and combinations of imaging platforms.

Behaviour of Platinum-Tin during CO2-assisted propane dehydrogenation: Insights from quick X-ray absorption spectroscopy

CO2-assisted propane dehydrogenation has been studied on Pt-Sn/MgAl2O4 catalysts with support surface area of ~ 127 m2/g or ~ 5 m2/g, 3 wt% Pt and a Pt/Sn molar ratio of 3/1. In situ XAS was employed to track the dynamic changes occurring to the catalyst in presence of a reductive (H2) or oxidative (CO2) atmosphere. Reduction leads to the formation of a Pt-Sn alloy, the active compound for propane dehydrogenation. Oxidation by CO2 led to the loss of the Pt-Sn alloy due to firstly oxidation of Sn to SnO and subsequent oxidation of SnO to SnO2. The electronic and structural properties of the catalyst were determined by modelling of the EXAFS data. The Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method was used in conjunction with the XAS data to determine the amount of Sn present in the Pt-Sn alloy phase and the phase of the alloy itself: after a single step reduction 42% of all Sn goes into Pt3Sn alloy, participating in the reaction, with the remainder being SnOx. The percentage of Sn going into the Pt3Sn alloy increased after 10 H2/O2 redox cycles to 72%. A combination of in-situ XAS with CO2-PDH activity data covering CO2:C3H8 ratios from 0.25:1 to 1:1 allowed to show that CO2 helps to improve conversion of propane by means of the reverse water gas shift reaction, wherein the product H2 generated from PDH reacts with feed CO2 to shift the equilibrium towards products. The reaction performed better at lower ratios of CO2:C3H8. Increasing ratios of CO2:C3H8 induced faster deactivation of the catalyst by the oxidation of Sn to SnOx, leading to loss of Pt-Sn alloy. Suppression of carbon accumulation occurred by means of the reverse Boudouard reaction with the carbon formed during PDH. As possible reaction network for the entire CO2-PDH reaction, a combination of Langmuir-Hinshelwood (L-H) and the Mars-van Krevelen mechanism (MvK) was proposed. The MvK steps were the oxidation of Sn to SnO by CO2, which would then subsequently react with the H2 and C generated from PDH that takes place on Pt sites by the L-H mechanism, to go back to the Pt3Sn alloy.