Understanding temporal and spatial changes of O3 or NO2 concentrations combining multivariate data analysis methods and air quality transport models

The application of the multivariate curve resolution method to the analysis of temporal and spatial data variability of hourly measured O₃ and NO₂ concentrations at nineteen air quality monitoring stations across Catalonia, Spain, during 2015 is shown. Data analyzed included ground-based experimental measurements and predicted concentrations by the CALIOPE air quality modelling system at three horizontal resolutions (Europe at 12 × 12 km², Iberian Peninsula at 4 × 4 km² and Catalonia at 1 × 1 km²). Results obtained in the analysis of these different data sets allowed a better understanding of O₃ and NO₂ concentration changes as a sum of a small number of different contributions related to daily sunlight radiation, seasonal dynamics, traffic emission patterns, and local station environments (urban, suburban and rural). The evaluation of O₃ and NO₂ concentrations predicted by the CALIOPE system revealed some differences among data sets at different spatial resolutions. NO₂ predictions, showed in general a better performance than O₃ predictions for the three model resolutions, specially at urban stations. Our results confirmed that the application of the trilinearity constraint during the multivariate curve resolution factor analysis decomposition of the analyzed data sets is a useful tool to facilitate the understanding of the resolved variability sources.

Proof-of-concept on the effect of human milk storage time: Lipid degradation and spectroscopic characterization using portable near-infrared spectrometer and chemometrics

Human milk (HM) modifications over time represent an important issue. This work proposed to evaluate the changes in HM during one-year storage through total lipids (TL) degradation and portable near-infrared (NIR) spectrometer combined with chemometrics. Colostrum, transition, and mature stages were obtained from donors and considered in the raw and pasteurized forms. Principal component analysis in TL content showed changes in the mature stages for both forms after 75 days. Multivariate curve resolution with alternating least squares in NIR spectral data reveals a decrease in protein and triacylglycerol contents while an increase in free fatty acids (palmitic acid) contents were observed through the storage after around 5-6 months. Therefore, more than 5-6 months of storage suggest possible biochemical changes in the HM nutritional composition. Moreover, the chemometrics investigation was crucial in extracting information, bringing coherent results, and helping to understand the chemical changes in human milk during storage.

Chemometric quality assessment of Paracetamol and Phenylephrine Hydrochloride with Paracetamol impurities; comparative UV-spectrophotometric implementation of four predictive models

Spectrophotometric data analysis using multivariate approaches has many useful applications. One of these applications is the analysis of active ingredients in presence of impurities. Four chemometric-assisted spectrophotometric methods, namely, principal component regression (PCR), partial least-squares (PLS), artificial neural networks (ANN) and multivariate curve resolution-alternating least squares (MCR-ALS) were proposed and validated. The developed chemometric methods were compared to resolve the severely overlapped spectrum of Paracetamol (PAR) and Phenylephrine HCl (PHE) along with PAR impurities namely, P-Aminophenol (PAP), P-Nitrophenol (PNP), Acetanilide (ACT) and P-Chloroacetanilide (CAC). The four multivariate calibration methods succeeded in simultaneous determination of PAR and PHE with further quantification of PAR impurities. So, the proposed methods could be used with no need of any separation step and successfully applied for pharmaceutical formulation analysis. Furthermore, statistical comparison between the results obtained by the proposed chemometric methods and the official ones showed no significant differences.

Methods for unsupervised contribution analysis of raw EEM data in water monitoring. Contaminant identification and quantification

Fluorescence EEM spectra provide the "fingerprint" of water contamination and is a very efficient way to access the quality of water bodies. These multivariate datasets correspond to complex mixtures and are very rich in information. Graphical approaches have been used for decades to characterize and quantify different contamination sources. It is very important to resolve mixed signals in raw EEM spectra in terms of signal sources and respective composition profiles - signal sources allow the identification of contamination type, while concentration profiles quantify the respective contribution inside the mixtures. In order to be able to use robust modeling algorithms, the first task is to accurately estimate the number of contributions that are present. We demonstrate the ability of Singular value Decomposition (SVD) in accessing this information content in raw EEM datasets. To decompose raw EEM information, several algorithms are tested: PARAFAC, MCR-ALS and ICA. In this work we suggest a systematic unsupervised algorithm to process raw EEM spectra of water samples.

Class comparison enabled mass spectrum purification for comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry

Tile-based Fisher ratio (F-ratio) analysis is emerging as a versatile data analysis tool for supervised discovery-based experimentation using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). None the less, analyte identification can often be marred by poor 2D resolution and low analyte abundance relative to overlapping compounds. Linear algebra-based chemometric methods, in particular multivariate curve resolution alternating least squares (MCR-ALS), parallel factor analysis (PARAFAC) and PARAFAC2, are often applied in an effort to address this situation. However, these chemometric methods can fail to produce an accurate spectrum when the analyte is at low 2D resolution and/or in low relative abundance. To address this challenge, we introduce class comparison enabled mass spectrum purification (CCE-MSP), a method that utilizes the underlying requirement for signal consistency of the background interference compounds between the two classes in the F-ratio analysis to purify the mass spectrum of the analyte hits. CCE-MSP is validated using a dataset obtained for a neat JP-8 jet fuel spiked with 14 sulfur containing compounds at two levels (15 ppm and 30 ppm), using the p-value and lack-of-fit (LOF) for each analyte hit as consistency metrics. A purified mass spectrum was produced for each spiked analyte hit and their mass spectrum match value (MV) was compared to the MV obtained by MCR-ALS, PARAFAC, and PARAFAC2. The resulting MV for CCE-MSP were found to be as good or better than these chemometric methods, eg., for 2-butyl-5-ethylthiophene with an analyte-to-interference relative signal abundance of 1:87 and a 2D resolution of 0.2, CCE-MSP produced a MV of 831, compared to 476 for MCR-ALS, 403 for PARAFAC, and 336 for PARAFAC2. CCE-MSP is also extended to obtain the purified spectrum for more than one analyte, eg., two analyte hits in overlapping hit locations. The spectra produced by CCE-MSP can also be utilized as estimates to facilitate quantitative signal decomposition using MCR-ALS.

A new strategy for the computer-assisted development of reversed-phase liquid chromatography separation methods of unknown sample mixtures

A new strategy for the computer-assisted methods development in the reversed-phase liquid chromatographic separations of unknown sample mixtures has been developed using the latent spectral information in chromatogram raw data files of appropriately designed experiments, rather than resorting to elemental information functions (e.g., the number of peaks in chromatograms or similar criteria). The strategy developed allows unification of the approach for samples of both known and unknown composition and, thus, provide a general strategy for computer-aided tools in the chromatography laboratory. The operation principle of this strategy departs from extracting the spectra of components in the mixture chromatograms by resorting to multivariate curve resolution-alternating least squares (MCR-ALS). This technique allows the estimation of the true spectra for the individual components except when they have identical spectra or are fully overlapped. Thus, a convenient experimental design will try to perform separations of the sample mixture having at least partial resolution of components in some runs. This will allow estimating the spectra of components and, then, assign these components to the peaks in each run chromatogram. In this way, a retention model can be built for each component so computerized optimization process can be developed to provide the chromatographer with the best possible separation programs. Following this approach, strategies for sample mixtures of known and unknown composition are only different in the need of an initial spectrum discovery process for unknown mixtures and therefore a real general approach for the computer-assisted LC methods development is now available for the first time.

Performance assessment of linear iterative optimization technology (IOT) for Raman chemical mapping of pharmaceutical tablets

Raman chemical mapping is an inherently slow analysis tool. Accurate and robust multivariate analysis algorithms, which require least amount of time and effort in method development are desirable. Calibration-free regression and resolution approaches such as classical least squares (CLS) and multivariate curve resolution using alternating least squares (MCR-ALS), respectively, help in reducing the resources required for method development. However, conventional CLS does not consider appropriate constraints, which may result in negative and/or greater than 100 % Raman concentration scores, while MCR-ALS may not always be as accurate as regression-based algorithms. Linear iterative optimization technology (IOT) is another calibration-free algorithm, which with appropriate constraints has previously shown promise in online and offline pharmaceutical mixture composition determination. This paper aims to evaluate the performance of the linear IOT algorithm for Raman chemical mapping of the active pharmaceutical ingredient (API), diluent, and lubricant in pharmaceutical tablets. Two pre-processing strategies were applied to the raw Raman mapping spectra. The results were compared with CLS (current reference method) and MCR-ALS. Special emphasis was given to mapping at low Raman exposure times to enable feasible total acquisition times (< 5 h). The quality of IOT/CLS/MCR-ALS estimated Raman concentration predictions were assessed by calculating a correlation factor between the spectrum corresponding to the maximum predicted concentration (or resolved spectra) of a component for IOT/CLS (or MCR-ALS) and the pure powder component spectrum. The Raman chemical maps were visualized, and the average Raman concentrations scores were compared. The results demonstrated the utility of IOT in Raman chemical mapping of pharmaceutical tablets. The diluent (lactose) and API (semi-fine APAP) used in this study were reliably estimated by IOT at relatively short Raman exposure times. On the other hand, as expected, the lubricant (magnesium stearate) could not be detected in any of the cases investigated here, irrespective of the algorithm used. Overall, for the API and diluent used in this formulation as well as the chemical mapping conditions, linear IOT seemed to better estimate the pure spectrum intensities and the average Raman scores (closer to CLS) in comparison to MCR-ALS. Moreover, application of appropriate constraints in linear IOT avoided the presence of negative and/or greater than 100 % Raman concentration scores, as observed in CLS-based Raman chemical maps.

Optimization of a saccharin molecularly imprinted solid-phase extraction procedure and evaluation by MIR hyperspectral imaging for analysis of diet tea by HPLC

Saccharin was determined based on a new molecularly imprinted solid-phase extraction (MISPE) procedure. The polymer was synthesized with a hybrid monomer of metacrylic acid and 3-amino propril tetraethoxysilane and saccharin as template. After the synthesis, the saccharin removal from the MIP was verified by the UV analysis of the solutions used in the template removal procedure, as well as by the direct MIP analysis using FTIR hyperspectral image and chemometrics. The residual saccharin concentrations observed in the image analysis revealed a narrow concentration distribution consistent with a homogenous material. The MISPE was performed with homemade cartridges containing 200 mg of the MIP. The results obtained with standards and diet tea samples confirmed high affinity, adsorption capacity and selectivity of the MIP. The MISPE cartridge exhibited recoveries of 100 ± 3% in six extraction cycles. The diet tea analysis showed a significant reduction of the interferences, which can considerable simplifies the HPLC-UV analysis.

A Novel Muscle Synergy Extraction Method Used for Motor Function Evaluation of Stroke Patients: A Pilot Study

In this paper, we present a novel muscle synergy extraction method based on multivariate curve resolution–alternating least squares (MCR-ALS) to overcome the limitation of the nonnegative matrix factorization (NMF) method for extracting non-sparse muscle synergy, and we study its potential application for evaluating motor function of stroke survivors. Nonnegative matrix factorization (NMF) is the most widely used method for muscle synergy extraction. However, NMF is susceptible to components’ sparseness and usually provides inferior reliability, which significantly limits the promotion of muscle synergy. In this study, MCR-ALS was employed to extract muscle synergy from electromyography (EMG) data. Its performance was compared with two other matrix factorization algorithms, NMF and self-modeling mixture analysis (SMMA). Simulated data sets were utilized to explore the influences of the sparseness and noise on the extracted synergies. As a result, the synergies estimated by MCR-ALS were the most similar to true synergies as compared with SMMA and NMF. MCR-ALS was used to analyze the muscle synergy characteristics of upper limb movements performed by healthy (n = 11) and stroke (n = 5) subjects. The repeatability and intra-subject consistency were used to evaluate the performance of MCR-ALS. As a result, MCR-ALS provided much higher repeatability and intra-subject consistency as compared with NMF, which were important for the reliability of the motor function evaluation. The stroke subjects had lower intra-subject consistency and seemingly had more synergies as compared with the healthy subjects. Thus, MCR-ALS is a promising muscle synergy analysis method for motor function evaluation of stroke patients.

Geographical origin authentication of southern Brazilian red wines by means of EEM-pH four-way data modelling coupled with one class classification approach

EEM data recorded at different pH values was exploited by MCR-ALS in order to determine qualitative information about Brazilian red wines. In addition, the geographical traceability of wines produced in the Serra Gaúcha (Rio Grande do Sul) was carried out by DD-SIMCA considering 53 samples from the target class and 20 from other producing regions. The fluorescence signal corresponds to 9 EEMs recorded at different pH (3-11), generating four-way data. By MCR-ALS decomposition, eight factors were retrieved and related to typical chemical compounds found in red wine. In addition, the EEM pH data was used to build a one-class classification model, considering that MCR scores and all samples of the target class were properly recognised as belonging to the target class, with maximal sensitivity equal to 1. Samples of the non-target class were also adequately rejected by the model, and the specificity was found to be 0.97.

PARAFAC and MCR-ALS approaches to the pKa determination of benzoic acid and its derivatives

In general, the identification of biological activities of a molecule requires the observation of its physicochemical characteristics with its molecular interactions in an organism. The acid-base ionization constant (or pKa) is one of the key parameters that shows the physicochemical behaviors of molecules used in pharmaceuticals, foods, cosmetics etc. Therefore, the development of new methods (or approaches) is necessary to get simple, rapid, inexpensive and reliable determination of the acidity constants of active and inactive ingredients used in commercial products. In this paper, new UV spectroscopic methods were developed for the first time, by applying parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least squares (MCR-ALS) to the pH-UV spectral data arrays for determining the pKa values of benzoic acid and its five derivatives (4-fluorobenzoic acid, thiosalicylic acid, anthranilic acid, phthalic acid, 4-aminobenzoic acid). The pH profiles obtained by the PARAFAC and MCR-ALS decomposition of the pH-UV data arrays were used for the quantitative estimation of the acid-base ionization constants for the investigated compounds without classical titration procedure. We concluded that the proposed PARAFAC and MCR-ALS provided us an opportunity for simple and rapid pKa determination of relevant compounds, which have functional importance in pharmaceutical and food industries.

Three-way calibration using PARAFAC and MCR-ALS with previous synchronization of second-order chromatographic data through a new functional alignment of pure vectors for the quantification in the presence of retention time shifts in peak position and shape

In the present contribution is shown the application of the recently developed functional alignment of pure vectors (FAPV) as a proper algorithm to align second-order chromatographic data with severe retention time shifts in peak position and shape. FAPV decomposed a three-way chromatographic data array in their three modes (sample, spectral and elution time vectors), using a basis function to pre-process the non-linear mode (elution time) and then it aligns the functionalized pure vectors and reshapes the transformed vectors into matrices, restoring the trilinearity of second-order chromatographic data. The well-aligned three-way chromatographic data array is then successfully decomposed by advanced chemometric models such as parallel factor analysis (PARAFAC) and multivariate curve resolution - alternating least-squares (MCR-ALS) with the trilinearity constraint. The performance of this innovative analytical strategy based on PARAFAC and MCR-ALS with previous synchronization of data through FAPV algorithm is properly evaluated using real second-order chromatographic data with multiple artifacts, i.e., shifts in peak position and shape for the simultaneous quantification of amoxicillin and potassium clavulanate in commercial medicinal drugs. The present contribution compares some analytical results achieved by: (1) the usual MCR-ALS as a bilinear model applied in augmented data matrix without previous synchronization and with interval correlation optimized shifting (ICOSHIFT) and FAPV and (2) trilinear models using PARAFAC with ICOSHIFT and FAPV and trilinearity constraint in MCR-ALS with FAPV. Available results suggest that these strongly shifted and warped elution time profiles cause for the loss of trilinearity, which can be adequately restored by FAPV algorithm. PARAFAC performed a successful trilinear decomposition of three-way chromatographic data array with law values of relative prediction error (REP) in the order of 1.34-1.42% in both analytes.

Investigation of the binding properties between levamlodipine and HSA based on MCR-ALS and computer modeling

Levamlodipine (LEE) is a drug commonly used for antihypertensive treatment in clinical therapy. The overlapping fluorescence spectra of LEE and human serum albumin (HSA) cause some trouble in analysis of interactions between them by using the classic fluorescence method. Here, the multivariate curve resolution-alternating least squares (MCR-ALS) approach was used to overcome this disadvantage. Meanwhile, the binding properties of LEE-HSA complex were then explored through computer modeling. The MCR-ALS results suggested that LEE-HSA complex was present in the mixture solution of LEE and HSA. This conclusion was then confirmed by the Stern-Volmer equation and time-resolved fluorescence experiment. The binding constant (K_a) was 2.139 × 10⁴ L·mol^-1 at 298 K. LEE was located close to the Trp-214 residue of HSA, with van der Waals forces and hydrogen bonding as main driving forces for this interaction. LEE can alter the conformation of HSA, in which the content of α-helix reduced from 57.2% to 52.3%. The Pi-Alkyl interactions contributed to maintaining the stability of the LEE-HSA complex. The results of molecular dynamics simulations showed that LEE-HSA complex was formed within 5 ns, and the particle size (R_g) of HSA was altered by the binding reaction. This study would promote better understanding of the transportation and distribution mechanisms of LEE in the human body.

Identification of Molecular Basis for Objective Discrimination of Breast Cancer Cells (MCF-7) from Normal Human Mammary Epithelial Cells by Raman Microspectroscopy and Multivariate Curve Resolution Analysis

Raman spectroscopy (RS), a non-invasive and label-free method, has been suggested to improve accuracy of cytological and even histopathological diagnosis. To our knowledge, this novel technique tends to be employed without concrete knowledge of molecular changes in cells. Therefore, identification of Raman spectral markers for objective diagnosis is necessary for universal adoption of RS. As a model study, we investigated human mammary epithelial cells (HMEpC) and breast cancer cells (MCF-7) by RS and employed various multivariate analyses (MA) including principal components analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) to estimate diagnostic accuracy. Furthermore, to elucidate the underlying molecular changes in cancer cells, we utilized multivariate curve resolution analysis–alternating least squares (MCR-ALS) with non-negative constraints to extract physically meaningful spectra from complex cellular data. Unsupervised PCA and supervised MA, such as LDA and SVM, classified HMEpC and MCF-7 fairly well with high accuracy but without revealing molecular basis. Employing MCR-ALS analysis we identified five pure biomolecular spectra comprising DNA, proteins and three independent unsaturated lipid components. Relative abundance of lipid 1 seems to be strictly regulated between the two groups of cells and could be the basis for excellent discrimination by chemometrics-assisted RS. It was unambiguously assigned to linoleate rich glyceride and therefore serves as a Raman spectral marker for reliable diagnosis. This study successfully identified Raman spectral markers and demonstrated the potential of RS to become an excellent cytodiagnostic tool that can both accurately and objectively discriminates breast cancer from normal cells.

Antibodies purification from human plasma using fractionation, chromatography and gel electrophoresis assisted by multivariate analysis of complimentary absorption and fluorescence spectra

Employing simple precipitation (fractionation) using Cohn method and weak anion exchange chromatography with DEAE resin, antibodies such as Immunoglobulin G are purified from human plasma. Fractions are eluted from column in four different regions depending on washing NaCl concentrations. Absorbance and excitation-emission fluorescence spectral data are measured for separated chromatographic fractions and analyzed using Multivariate Curve Resolution- Alternating Least Squares (MCR-ALS) and Parallel Factor Analysis (PARAFAC) techniques. Resolved concentration and spectral profiles provided information about existing components in each fraction. Protein and non-protein components are distinguished considering their resolved pure spectra and information from the two applied spectroscopic techniques is complementary. A number of components displayed both fluorescence and absorbance signals. When concentration of component (protein or non-protein) in sample is low and no significant absorbance signal is observed, sensitive fluorescence is useful to recognize the component and for non-fluorescent components absorbance spectra are utilized. Electrophoresis is utilized for separation of proteins in each fraction and showed that one distinguished protein from fluorescence and/or absorbance data can be a group of proteins with similar pure spectra and retention volume. Results showed presence of two protein in the first region (IgM and IgA), a group of proteins in second region (IgM, α-globulin, and IgG), a pure protein in third region (IgG), and a group of β-globulin proteins in fifth region. It is well and clearly shown that multivariate analysis of different data sets with complementary information is necessary for better interpretation of such technically simple and biochemically complicated systems.

Multivariate probing of antitumor metal-based complexes damage on living cells through Raman imaging

Intracellular modifications caused by two metal-based antitumor compounds were assessed by confocal Raman imaging assisted by multivariate curve resolution method, a very powerful deconvolution tool that can be used to extract the characteristic spectral profile of the individual or "purest" components from an image dataset. The use of this Raman methodology has the advantage of being non-invasive and totally label-free. Four main different intracellular processes were observed under the Raman imaging and multivariate approach combination, and even, significant differences could be identified between the treatments with both metallodrugs. Leakage of the nucleus and nucleolus content into the cytoplasm, along with releasing of cytochrome c were observed for the treatment with the Cu-based complex. At the same time, changes of hydrogen-bonding network were also evidenced, indicating an apoptotic cellular death process, consistent with complementary Total Reflection X-Ray fluorescence (TXRF) and fluorescence experiments attesting mitochondria and DNA as main targets after uptake of the complex by cells. For treatment with the Zn-based complex, changes associated with cytochrome c were not detected, neither a rapid leakage of nucleus content upon 24 h treatment. The hydrogen-bonding network also followed a quite different pattern, suggesting that with this metallodrug, the cellular death follows a different mechanism.

Exploring the potential of combining chemometric approaches to model non-linear multi-way data with quantitative purposes - A case study

Second-order based calibration methods have been widely investigated capitalizing on the inherent benefits of the data structure and the decomposition models, demonstrating that second-order advantage is a property that conspires to a high likelihood success in the resolution of systems of varying complexity. This work aims to demonstrate the applicability of a combined chemometric strategy to solve non-linear multivariate calibration systems in the presence of non-multilinear multi-way data. The determination of histamine by differential pulse voltammetry at different pH is presented as case study. The experimental system has the outstanding difficulty arisen from the large displacement along the potential axis by the pH, which was successfully overcome by implementation of the presented combined strategy. For data modeling, MCR-ALS, U-PLS/RBL and U-PCA/RBL-RBF were used. MCR-ALS allowed unraveling the non-linear behavior between the signal and the concentration, and extracting the underlying profiles of the constituent. Quantitative analysis was performed through the three models, and a comparative evaluation of the predictive performance was done. The best results were achieved with U-PCA/RBL-RBF (mean recovery = 101%) whereas, MCR-ALS yield the lowest mean recovery for all samples (70%).

HPLC-DAD fingerprints combined with chemometric techniques for the authentication of plucking seasons of Laoshan green tea

Laoshan green teas plucked in summer and autumn were measured by high performance liquid chromatography-diode array detector (HPLC-DAD). After baseline correction, the fingerprints data were resolved by multivariate curve resolution-alternating least squares (MCR-ALS) and a total of 57 components were acquired. Relative concentrations of these components were afterwards applied to distinguish plucking seasons using principal component analysis (PCA), support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA). For both SVM and PLS-DA models, the total recognition rates of training set, cross-validation and testing set were 100%, 91.3% and 100%, respectively. Besides, three variable selection methods were employed to determine characteristic components for the authentication of summer and autumn teas. Results showed that PLS-DA model based on three characteristic components selected by VIP possesses identical predictive ability as the original model. This study demonstrated that our proposed strategy is competent for the authentication of plucking seasons of Laoshan green tea.

Determining the fate of lead (Pb) and phosphorus (P) in alkaline Pb-polluted soils amended with P and acidified using multiple synchrotron-based techniques

The effect of acidification on lead (Pb) and phosphorus (P) speciation in alkaline Pb-polluted soils that are amended with P to stabilize Pb is still unclear. It was studied in three alkaline Pb-polluted soils containing specific amounts of Soil Organic Matter (SOM), using multiple synchrotron-based techniques, i.e. bulk X-ray Absorption Fine Structure (XAFS) spectroscopy at Pb LIII- and P K-edges, micro-X-ray Fluorescence (μ-XRF), and micro-X-ray Diffraction (μ-XRD). These techniques provided unambiguous evidences that the formation of pyromorphite, i.e. the desired Pb stabilized chemical form, was severely limited in the acidified soil samples amended with fish bones or phosphoric acid (H₃PO₄). Most Pb present in the H₃PO₄-amended soil samples did not convert to pyromorphite due to Pb and P leaching and PbSO_4(s) formation. In contrast, most Pb present in the fish bone-amended soil samples was unaffected by acidification and did not convert to pyromorphite as it was inaccessible to soil solution or retained by SOM, similarly to P. Additionally, Pb-SOM association increased with increasing SOM content. Results had important implications on the applicability of the P-based method to stabilize Pb within the first centimeters below surface of Pb-polluted alkaline soils, which potentially represent the most hazardous part of these soils.

Systematic investigation of the measurement error structure in a smartphone-based spectrophotometer

Smartphones are state-of-the-art devices with several interesting features which make them promising for analytical purposes. After modification to a spectrophotometer (smart spectrophotometer), they can be utilized for the quantitative or qualitative applications. Although smartphones have widely been applied for sensing∖biosensing purposes, the error structure/type of their outputs remained unexplored. Error structure information values the objects/channels in a given data set and variables have the same importance when the noise has identical independent distribution (i.i.d). Otherwise, error structure weights them for further data analysis. In this contribution, a smartphone-based spectrophotometer was constructed integrating simple optical elements-a tungsten lamp as source and a piece of digital versatile disc (DVD) as a reflecting diffraction grating to investigate the error sources of the smartphone-spectrophotometer. For this purpose, error covariance matrices (ECMs) were calculated using a series of replication capturing error information. Afterwards, PCA and MCR-ALS were employed for the decomposition of the ECMs and resolved profiles were translated to the error types. Finally, proportional error as a heteroscedastic noise was highlighted as the most important source of variation in the error structure of the smartphone-based spectrophotometer.

MCR-ALS analysis of 1H NMR spectra by segments to study the zebrafish exposure to acrylamide

Metabolomics is currently an important field within bioanalytical science and NMR has become a key technique for drawing the full metabolic picture. However, the analysis of ¹H NMR spectra of metabolomics samples is often very challenging, as resonances usually overlap in crowded regions, hindering the steps of metabolite profiling and resonance integration. In this context, a pre-processing method for the analysis of 1D ¹H NMR data from metabolomics samples is proposed, consisting of the blind resolution and integration of all resonances of the spectral dataset by multivariate curve resolution-alternating least squares (MCR-ALS). The resulting concentration estimates can then be examined with traditional chemometric methods such as principal component analysis (PCA), ANOVA-simultaneous component analysis (ASCA), and partial least squares-discriminant analysis (PLS-DA). Since MCR-ALS does not require the use of spectral templates, the concentration estimates for all resonances are obtained even before being assigned. Consequently, the metabolomics study can be performed without neglecting any relevant resonance. In this work, the proposed pipeline performance was validated with 1D ¹H NMR spectra from a metabolomics study of zebrafish upon acrylamide (ACR) exposure. Remarkably, this method represents a framework for the high-throughput analysis of NMR metabolomics data that opens the way for truly untargeted NMR metabolomics analyses. Graphical abstract.

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.

Application of chemometric methods to the analysis of multimodal chemical images of biological tissues

Current histology techniques, such as tissue staining or histochemistry protocols, provide very limited chemical information about the tissues. Chemical imaging technologies such as infrared, Raman, and mass spectrometry imaging, are powerful analytical techniques with a huge potential in describing the chemical composition of sample surfaces. In this work, three images of the same tissue slice using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, infrared microspectroscopy, and an RGB picture from a conventional hematoxylin/eosin (H/E) staining are simultaneously analyzed. These fused images were analyzed by multivariate curve resolution-alternating least squares (MCR-ALS), which provided, for each component, its distribution within the tissue surface, its IR spectrum fingerprint, its characteristic mass values, and the contribution of the RGB channels of the H/E staining. Compared with the individual analysis of each of the images alone, the fusion of the three images showed the relationship between the different types of chemical/biological information and enabled a better interpretation of the tissue under study. In addition, the least-squares projection of the MCR-ALS resolved spectra of components at low spatial resolution onto the IR and RBG images at high spatial resolution, provided a better delimitation of the sample constituents on the image, giving a more precise description of their distribution on the investigated tissue. The application of this procedure can be of interest in different research areas in which a good description of the spatial distribution of the chemical constituents of the samples is needed, such as in biomedicine, food, or environmental research.

Bilinear and trilinear algorithms utilizing full and selected variables for resolution and quantitation of four components with overlapped spectral signals in bulk and syrup dosage form

Spectrophotometric-assisted chemometric techniques are beneficial for resolving spectral overlapping and are considered comparable to traditional chromatographic methods. In this work, different chemometric approaches were applied for simultaneous determination of Bromhexine HCl (BRHX), Guaifenesin (GUA) and Salbutamol sulphate (SALB) in the presence of Guaiacol (GUAIA), without any prior separation. Two-way and three-way techniques were applied. The resolving power of genetic algorithm (GA-PLS), trilinear partial least square (N-PLS) and multivariate curve resolution (MCR-ALS) were investigated. A set of 17 synthetic samples in the concentration range 10.0-30.0 μg/mL of BRHX, GUA and SALB and 6.0-10.0 μg/mL of GUAIA were used in the construction of the calibration models. Commercially available syrup dosage form was successfully analyzed by the developed methods without interference from formulation additives. The developed models were evaluated through calculation of root mean squared error of prediction (RMSEP), the obtained values were 0.263, 0.419 and 0.342 for BRHX, 0.254, 0.318 and 0.503 for GUA and 0.298, 0.268 and 0.302 for SALB using N-PLS, MCR-ALS and GA-PLS, respectively. The resolving power of the developed models was emphasized through comparison with a reported HPLC method, where no significant difference was found regarding both accuracy and precision.

MCR-ALS analysis of IR spectroscopy and XRD for the investigation of ibuprofen - nicotinamide cocrystal formation

To improve aqueous solubility, a poorly water-soluble active ingredient is classically combined with a conformer to form cocrystals. Hot melt extrusion is one preparation method for the formation of cocrystal solids. The aim of our study was to determine the optimal temperature conditions for the formation of ibuprofen and nicotinamide cocrystals using real-time infrared (IR) and X-ray diffraction (XRD) measurements. IR spectra and XRD patterns were subjected to multivariate curve resolution alternating least squares (MCR-ALS) analysis and decomposed into several components. Each component was descriptive of a specific step in the formation of the cocrystal. Cocrystal formation was followed by a separation phase between amorphous ibuprofen and crystalline nicotinamide. Our results suggest that, when using the hot melt exclusion method, careful consideration should be made towards optimizing processing temperatures in order to prevent amorphization and promote control over the process of cocrystal formation.