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Cocaine use has been reported for more than 2500 years, but along this period of time its damage to society in general became evident. Nowadays, cocaine is classified as an illicit drug and studies which give support to drug enforcement institutions, particularly in the area of police intelligence, are very relevant. Often, trafficked cocaine is not traded in its pure form, but mixed with adulterants and diluents. In the analysis of seized samples, the reference method is based on gas chromatography, however the interest in the use of vibrational spectroscopy has increased. This work aims at developing a method for determination of the concentrations of cocaine, adulterants and diluents in cocaine samples employing Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) associated with Multivariate Curve Resolution with Alternating Least-Squares (MCR-ALS). A total of 6 adulterants, 3 diluents, 2 forms of cocaine (base and hydrochloride) were determined and the method developed based on 55 synthetic mixtures prepared according to a mixture design and 20 seized samples. For validation purposes 708 seized samples and 9 synthetic mixtures were analyzed. The results proved to be satisfactory, showing that the proposed method has a great potential in the classification of the chemical form of cocaine, identification of adulterants, diluents and cocaine in seized samples, as well as providing an estimate for their concentration. [ABSTRACT FROM AUTHOR]

In the forensic examination of automotive paint, each layer of paint is analyzed individually by infrared (IR) spectroscopy. Laboratories in North America typically hand section each layer and present each separated layer to the spectrometer for analysis, which is time consuming. In addition, sampling too close to the boundary between adjacent layers can pose a problem as it produces an IR spectrum that is a mixture of the two layers. Not having a “pure” spectrum of each layer will prevent a meaningful comparison between each paint layer or in the situation of searching an automotive database will prevent the forensic paint examiner from developing an accurate hit list of potential suspects. These two problems can be addressed by collecting concatenated IR data from all paint layers in a single analysis by scanning across the cross sectioned layers of the paint sample using a FTIR imaging microscope. Decatenation of the IR data is achieved by multivariate curve resolution using a Varimax extended rotation to select the starting point (i.e., initial estimates of the reconstructed IR spectra of each layer) for the alternating least squares algorithm to obtain a pure IR spectrum of each automotive paint layer. Comparing the reconstructed IR spectrum of each layer against the IR spectral library of the PDQ database demonstrated that it is possible to identify the correct model of the vehicle from these reconstructed spectra. This imaging approach to IR analysis of automotive paint, not only saves time and eliminates the need to analyze each layer separately, but also ensures that the final spectrum of each layer is “pure” and not a mixture. [ABSTRACT FROM AUTHOR]

In recent years, in the context of the application of Multivariate Curve Resolution (MCR) to hyperspectral image analysis, attention has been more and more put onto the possibility of exploiting not only the spectral but also the spatial information for constraining the algorithmic solution. Examples involve the introduction of different spatial constraints during the iterative Alternating Least Squares (ALS) calculation of the MCR solution or the post-processing of the score images using conventional image processing techniques. In this framework, this work proposes an approach for constraining concentration distribution maps within MCR-ALS analysis of hyperspectral images, based on the use of spatio-spectral fuzzy clustering in order to obtain smoother, more contrasted, and better interpretable chemical images. We show the relevance of the proposed approach and investigate the effect of the application of a spectral-spatial fuzzy clustering constraint on samples of different nature. [ABSTRACT FROM AUTHOR]

The influence of six common substrates on the Raman imaging of micron-sized inorganic aerosol particles was examined. Laboratory-generated single-component particles of calcite (CaCO 3 ) and mixed particles of calcite (CaCO 3 ), nitratine (NaNO 3 ), hematite (Fe 2 O 3 ) and anglesite (PbSO 4 ) were deposited by cascade impaction on Ag, In, Si, SiO 2 , microscope slide and TEM-grid substrates. The spectral contribution of substrates to Raman images of the deposited particles was evaluated by Multivariate Curve Resolution. The shape and intensity of the substrate spectra affect the effectiveness capability of the spectral deconvolution. The substrates were characterized and compared with respect to their effect on the reconstruction of Raman images of aerosol particles. The TEM-grid substrate yielded spatially stable sample measurements with a homogeneous spectral contribution, satisfactory Raman map reconstruction and the potential for application in other techniques (e.g., SEM-EDX). [ABSTRACT FROM AUTHOR]

This study evaluates the use of Raman spectroscopy with a multivariate curve resolution-alternating least squares (MCR-ALS) analysis to monitor the adulteration and purity of coconut oil. Sunflower, soybean, canola, sesame, corn, castor bean, peanut, palm kernel, babassu, mineral, and Vaseline oils have been used as adulterants in this work. Control charts were developed to evaluate the purity of an oil sample using the scores from the MCR-ALS analysis of a data set containing pure and adulterated oils. These control charts were able to detect the adulteration of coconut oil in a range of 2-30% with all the oils tested. Additionally, quantification models were developed using MCR-ALS with correlation constraints for coconut oil adulterated with sunflower, canola, Vaseline, babassu, and palm kernel oils. The models presented satisfactory results, which had absolute errors below 5%, for samples adulterated with sunflower, canola, and Vaseline oils. The babassu and palm kernel adulterants could also be quantified with a superior margin of error. The results indicated that using Raman spectroscopy with MCR is a clean and non-destructive method for assessing coconut oil purity that can be used without removing a sample from its bottle. [ABSTRACT FROM AUTHOR]

Multivariate curve resolution (MCR) was applied to a hetero-spectrally combined dataset consisting of mid-infrared (MIR) and near-infrared (NIR) spectra collected during the isothermal curing reaction of an epoxy resin. An epoxy monomer, bisphenol A diglycidyl ether (BADGE), and a hardening agent, 4,4′-diaminodiphenyl methane (DDM), were used for the reaction. The fundamental modes of the N H and O H stretches were highly overlapped in the MIR region, while their first overtones could be independently identified in the NIR region. The concentration profiles obtained by MCR using the hetero-spectral combination showed good agreement with the results of calculations based on the Beer-Lambert law and the mass balance. The band assignments and absorption sites estimated by the analysis also showed good agreement with the results using two-dimensional (2D) hetero-correlation spectroscopy. [ABSTRACT FROM AUTHOR]

Iterative optimization technology (IOT), a method that predicts the component composition from only the infrared (IR) spectra of the pure components and mixtures by using Beer's law, has been proposed to reduce the number of calibration samples for process analytical technology in the pharmaceutical industry. However, IOT cannot be applied to mixtures that have wavelength regions where Beer's law does not hold, such as liquid mixtures. The objective of this study is to apply IOT to liquid mixtures to realize a calibration-minimum method. We propose a novel calibration-minimum method that formulates spectral changes by polynomials of the mole fractions considering reasonable boundary conditions for online monitoring. The prediction ability of the proposed method was verified by three case studies: two binary mixtures and one ternary mixture. The model selection strategy, conditions for calibration, and estimation of missing pure component spectra are also discussed. This research represents a step towards advanced calibration-minimum methods. [ABSTRACT FROM AUTHOR]

Multivariate curve resolution-alternating least-squares (MCR-ALS) is the model of choice when dealing with some non-trilinear arrays, specifically when the data are of chromatographic origin. To drive the iterative procedure to chemically interpretable solutions, the use of constraints becomes essential. In this work, both simulated and experimental data have been analyzed by MCR-ALS, applying chemically reasonable constraints, and investigating the relationship between selectivity, analytical sensitivity (γ) and root mean square error of prediction (RMSEP). As the selectivity in the instrumental modes decreases, the estimated values for γ did not fully represent the predictive model capabilities, judged from the obtained RMSEP values. Since the available sensitivity expressions have been developed by error propagation theory in unconstrained systems, there is a need of developing new expressions or analytical indicators. They should not only consider the specific profiles retrieved by MCR-ALS, but also the constraints under which the latter ones have been obtained. [ABSTRACT FROM AUTHOR]

Lunar meteorites provide a more random sampling of the surface of the Moon than do the returned lunar samples, and they provide valuable information to help estimate the chemical composition of the lunar crust, the lunar mantle, and the bulk Moon. As of July 2014, ∼96 lunar meteorites had been documented and ten of these are unbrecciated mare basalts. Using Raman imaging with multivariate curve resolution–alternating least squares (MCR-ALS), we investigated portions of polished thin sections of paired, unbrecciated, mare-basalt lunar meteorites that had been collected from the LaPaz Icefield (LAP) of Antarctica—LAP 02205 and LAP 04841. Polarized light microscopy displays that both meteorites are heterogeneous and consist of polydispersed sized and shaped particles of varying chemical composition. For two distinct probed areas within each meteorite, the individual chemical species and associated chemical maps were elucidated using MCR-ALS applied to Raman hyperspectral images. For LAP 02205, spatially and spectrally resolved clinopyroxene, ilmenite, substrate-adhesive epoxy, and diamond polish were observed within the probed areas. Similarly, for LAP 04841, spatially resolved chemical images with corresponding resolved Raman spectra of clinopyroxene, troilite, a high-temperature polymorph of anorthite, substrate-adhesive epoxy, and diamond polish were generated. In both LAP 02205 and LAP 04841, substrate-adhesive epoxy and diamond polish were more readily observed within fractures/veinlet features. Spectrally diverse clinopyroxenes were resolved in LAP 04841. Factors that allow these resolved clinopyroxenes to be differentiated include crystal orientation, spatially distinct chemical zoning of pyroxene crystals, and/or chemical and molecular composition. The minerals identified using this analytical methodology—clinopyroxene, anorthite, ilmenite, and troilite—are consistent with the results of previous studies of the two meteorites using electron microprobe analysis. To our knowledge, this is the first report of MCR-ALS with Raman imaging used for the investigation of both lunar and other types of meteorites. We have demonstrated the use of multivariate analysis methods, namely MCR-ALS, with Raman imaging to investigate heterogeneous lunar meteorites. Our analytical methodology can be used to elucidate the chemical, molecular, and structural characteristics of phases in a host of complex, heterogeneous geological, geochemical, and extraterrestrial materials. [ABSTRACT FROM AUTHOR]

In this study, we proposed a rapid and sensitive method for quantification and spatial distribution of salicylic acid in film tablets using FT-Raman spectroscopy with multivariate curve resolution (MCR). For this purpose, the constituents of film tablets were identified by using FT-Raman spectroscopy, and then eight different concentrations of salicylic acid tablets were visualized by Raman mapping. MCR was applied to mapping data to expose the active pharmaceutical ingredients in the presence of other excipients by monitoring distribution maps and combination of FT-Raman mapping with MCR enabled the determination of lower salicylic acid concentrations. In addition, the distribution of major excipient, lactose,was examined in the tablet form. A calibration curve was obtained by plotting the intensity of the Raman signal at 1635 cm-1 versus the concentration of salicylic acid and the correlation was found to be linear within the range of 0.5%-3.9% with a correlation coefficient of 0.99. The limit of detection for the technique was determined 0.35%. The ability of the technique to quantify salicylic acid in tablet test samples was also investigated. [ABSTRACT FROM AUTHOR]

Determining quantitative molecular composition of atmospheric particles is required for assessing their environmental and health impacts. The presented algorithm was designed to analyse numerous Raman spectra of metal-rich atmospheric particles. Multivariate curve resolution-alternating least squares procedure (MCR-ALS) has been applied to resolve complex data from Raman microanalysis by means of a computer-assisted analytical procedure called Single Particle Analysis (SPA). The SPA – contrary to Raman mapping – provides data in which each single particle is assigned to a single spectrum, in the group with a statistically significant size. During the procedure, the relative contributions of individual compounds in the recorded Raman spectra have been specified. Grouping and relationship determination of the collected data have been performed by hierarchical cluster analysis (HCA) and principal component analysis (PCA). A new methodology is proposed to quantitatively determine the molecular composition and chemical mixing of single airborne particles based on the data from the automated Raman microspectrometry measurements. [ABSTRACT FROM AUTHOR]

High-quality wine vinegars have been registered in Spain under protected designation of origin (PDO): “ Vinagre de Jerez ”, “ Vinagre de Condado de Huelva ” and “ Vinagre de Montilla - Moriles ”. The raw material, production and aging processes determine their quality and their aromatic composition. Vinegar volatile profile is usually analyzed by gas chromatography–mass spectrometry (GC–MS), being necessary a previous extraction step. Thus, three different sampling methods (Headspace solid phase microextraction “HS-SPME”, Headspace stir bar sorptive extraction “HSSE” and Dynamic headspace extraction “DHS”) were studied for the analysis of the volatile composition of Spanish PDO wine vinegars. Multivariate curve resolution (MCR) was used to solve chromatographic problems, improving the results obtained. Principal component analysis (PCA) showed that not all the sampling methods were equally suitable for the characterization and differentiation between PDOs and categories, being HSSE the technique that made able the best vinegar characterization. [ABSTRACT FROM AUTHOR]

Digital images are commonly used to monitor processes that are based on colour changes due to their simplicity and easy capture. Colour information in these images can be analysed objectively and accurately using colour histograms. One such process is olive ripening, which is characterized by changes in chemical composition, sensory properties and can be followed by changes in physical appearance, mainly colour. The reference method to quantify the ripeness of olives is the Maturity Index (MI), which is determined by trained experts assigning individual olives into a colour scale through visual inspection. Instead, this study proposes a methodology based on Chemometrics Assisted Colour Histogram-based Analytical Systems (CACHAS) to automatically assess the MI of olives based on R, G, and B colour histograms derived from digital images. The methodology was shown to be easily transferable for routine analysis and capable of controlling the ripening of olives. The study also confirms the high potential of digital images to understand the ripening process of olives (and potentially other fruits) and to predict the MI with satisfactory accuracy, providing an objective and reproducible alternative to visual inspection of trained experts. [Display omitted] • Digital images objectively analyse color changes for olive ripening. • CACHAS offers reproducible MI assessment, replacing visual inspection. • R, G, and B histograms determine MI of olives in CACHAS. • A methodology potentially applicable to other fruits is proposed. • Study confirms digital images' capability for accurate fruit ripeness prediction. [ABSTRACT FROM AUTHOR]

The interaction between near infrared (NIR) light and the sample is a key factor to take into consideration when developing applications using NIR spectroscopy. The aim of this study was to assess the penetration depth of the NIR light through both intact watermelons and the red flesh of the cut fruit, using an interactance NIR system. The results showed a maximum penetration of between 9 and 11 mm in the assays carried out on intact watermelons and 11 mm in the watermelon flesh. The prediction performance of the different NIR system configurations used were compared by quantifying the SSC in intact watermelons in a non-destructive way. The results obtained for the assay in which the illuminated regions were separated by 28 mm (Distance 4) reported the best results for determination of SSC (%) obtaining an R2 cv = 0.73 and RMSECV = 0.39 % and an R2 p = 0.81 and RMSEP = 0.30 % when the set of data for that distance was split into calibration and internal validation sets. • The penetration depth of NIR light into different watermelons tissues was studied. • The penetration depth of NIR light was assessed using MCR-ALS. • The optimum optical measurement geometry of the NIR system was identified. • Penetration depth through the rind of the watermelon was about 9–11 mm. • NIR interactance spectroscopy was used to measure the SSC in intact watermelons. [ABSTRACT FROM AUTHOR]

Quinoa (Chenopodium quinoa Willd.) grain is gaining great popularity worldwide because it is a rich source of nutrients, bioactive compounds, complete essential amino acids, and high-quality proteins. The demand for quinoa-based products is on the rise, which makes them prone to adulteration with less expensive cereals. In this study, we described a rapid and simple procedure for fingerprinting of quinoa grain protein extracts based on the combination of liquid chromatography with ultraviolet absorption diode array detection (LC-UV-DAD) and chemometrics. First, we developed a novel LC-UV-DAD method to obtain distinctive multiwavelength chromatographic profiles of protein extracts from various commercial quinoa grains, which encompass different quinoa varieties sold as black, red, white (from Peru), and royal (white from Bolivia). Then, the components of the LC-UV-DAD fingerprints were deconvoluted by multivariate curve resolution alternating least squares (MCR-ALS), and principal component analysis (PCA) followed by partial least squares discriminant analysis (PLS-DA) were applied to efficiently discriminate the commercial quinoa grains according to their differential composition. The chemometrics-assisted LC-UV-DAD fingerprinting methodology demonstrated its potential to rapidly and reliably discriminate quinoa grains according to the differential composition of their protein extracts and it may be applied in food quality and food fraud control. • Chemometrics-assisted LC-UV-DAD is proposed for classification of quinoa grains. • Protein extracts of 4 commercial quinoa grains are fingerprinted by LC-UV-DAD. • Fingerprints are deconvoluted by MCR-ALS to obtain representative components. • The differential protein extract composition allows classification by PLS-DA. [ABSTRACT FROM AUTHOR]

Protein glycation with sugars such as glucose start by the initial reaction between the carbonyl group of sugars and the amino group of proteins such as albumin and hemoglobin. This leads to the generation of Schiff base and following that the amadori and advanced glycation end products (AGEs). AGEs products can store in tissues and blood of diabetic patients. So, they play key roles to generate free radicals, which cause the development of diabetes complications. Inhibition of protein glycation plays an important role in controlling diabetes, so the identification of anti-glycation compounds is now receiving considerable interest. In this study, the protection effects of thymoquinone and its mixture with p-cymene on the glycation of bovine serum albumin (BSA) have been investigated by differential pulse voltammetry (DPV) and UV–vis spectrophotometry (UV–vis) techniques. Also, the multivariate curve resolution–alternating least squares (MCR–ALS) method was used since the results of the experimental measurements have a strongly overlapping signals. Molecular docking was applied to determine the site specific binding of inhibitor with residues of BSA. This study demonstrates that thymoquinone and its mixture with p-cymene have the potent ability to use as a dietary adjuvant against glycation. [ABSTRACT FROM AUTHOR]

The present study describes a fast high performance liquid chromatography-diode array detection analytical methodology for quantification of tacrolimus, everolimus and cyclosporine A in whole blood samples, with minimum sample preparation steps. A short isocratic chromatographic elution was coupled with second-order calibration using multivariate curve resolution to stablish a smart and green methodology. Due to presence of matrix effect, a sample-added calibration strategy was used for quantification purposes. The serious issues related to background drift, chromatographic shifts and co-elution of non-calibrated blood components, were resolved by a proper background correction and multivariate curve resolution-alternating least squares (MCR/ALS) methods The main features of this study were based on the fact that the acquired data matrices were handled intelligently and all features of the concerned target analytes were taken into account. Satisfactory resolution and quantification results in the presence of matrix interferences were achieved and the second-order advantage was fully exploited. The average recoveries in therapeutic concentration ranges were 102 ± 10%, 99 ± 11% and 104 ± 12% for TAC, EVR and CsA, with average relative prediction errors of less than 7%. Considering the advantages of the present strategy, such as increased selectivity, sensitivity and sufficiency of lower limit of quantification through multivariate advantage, simplicity of sample treatment steps, a fast elution pattern and also a low-cost instrumentation compared with LC–MS/MS, the proposed method has the significant merits as an alternative for simultaneous therapeutic monitoring of immunosuppressants. [ABSTRACT FROM AUTHOR]

Understanding interaction mechanisms between polycyclic aromatic hydrocarbons (PAHs) and soil-washing agents can help in choosing efficient agents which are able to desorb and solubilize PAHs. This study investigated interaction mechanisms between pyrene and four washing agents including: two dissolved organic matters (DOM) F-DOM and CRC-DOM, and two commercial bio-based surfactants BBE-1000 and Supersolv using fluorescence spectroscopy combined with multivariate curve resolution alternating regression (MCR-AR). The efficiencies of these washing agents in removing PAHs from the soil were tested in a soil washing experiment. Pyrene showed π-π interactions with F-DOM and no interaction with CRC-DOM. This could be attributed to the more aromatic structures in F-DOM compared to CRC-DOM. The two DOMs were inefficient in soil washing which might be attributed to the relatively weak effect of π-π interactions in releasing PAHs from the soil. Interaction mechanisms between pyrene and the bio-based surfactants were elucidated with MCR-AR, which resolved three spectroscopically active species from pyrene emission spectra as a function of pyrene and bio-based surfactants concentrations. These species resembled pyrene emission in a polar and nonpolar microenvironment, respectively and of an excimer. Concentration profiles retrieved by the model for the three species showed that, below the critical micelle concentration (CMC), Supersolv created more nonpolar interactions with pyrene compared to BBE-1000. In soil washing, Supersolv showed the highest efficiency in extracting PAHs from the soil. This highlighted the importance of nonpolar interactions in desorbing PAHs from soils, which could then be solubilized in micelles. This study demonstrated the potential of fluorescence spectroscopy combined with the MCR-AR model for selecting efficient soil-washing agents. [ABSTRACT FROM AUTHOR]

The essential oil components of Iranian Anethumgraveolens L.(dill) were extracted using a Clevenger-type apparatus and microwave-assisted hydro distillation and analysed by gas chromatography-mass spectrometry. A multivariate curve resolution approach was used to overcome the problem of background, baseline offset, and overlapping/embedded peaks. Based on the results related to the GC-MS, 33 and 18 components for dill oil were extracted by hydro distillation and microwave-assisted hydro distillation, respectively. The numbers were extended to 64 and 55 components with concentrations higher than 0.01 g/100 g dry-solids using chemometric techniques. The results of this study showed that hydro distillation was suitable for extraction of β-phellandrene, O-cymen, 3, 6-Dimethyl-2, 3, 3a, 4, 5, 7a-hexahydrobenzofuran, Thymol, Germacrene D, Dill Apiole, 3-Octadecyne, and Phytol; and microwave distillation was suitable for the extraction of a-pinene and a-Phellandrene. [ABSTRACT FROM AUTHOR]

In 1985, Borgen and Kowalski [DOI:10.1016/S0003-2670(00)84361-5] introduced a geometric construction algorithm for the regions of feasible nonnegative factorizations of spectral datamatrices for three-component systems. The resulting Borgen plots represent the so-called area of feasible solutions (AFS). The AFS can be computed either for the spectral factor or for the factor of the concentration profiles. In the latter case, the construction algorithm is applied to the transposed spectral data matrix. The AFS is a low-dimensional representation of all possible nonnegative solutions, either of the possible spectra or of the possible concentration profiles. This work presents an improved algorithm for the simultaneous construction of the two dual Borgen plots for the spectra and for the concentration profiles. The new algorithm makes it possible to compute the two Borgen plots roughly at the costs of a single classical Borgen plot. The new algorithm comes without any loss of precision or spatial resolution. The new method is benchmarked against various program codes for the geometric-constructive and for the numerical optimization-based AFS computation. [ABSTRACT FROM AUTHOR]

Mammalian cells contain various macromolecules that can be investigated non-invasively with Raman spectroscopy. The particular mixture of major macromolecules present in a cell being probed are reflected in the measured Raman spectra. Determining macromolecular identities and estimating their concentrations from these mixture Raman spectra can distinguish cell types and otherwise enable biological research. However, the application of canonical multivariate methods, such as principal component analysis (PCA), to perform spectral unmixing yields mathematical solutions that can be difficult to interpret. Non-negative matrix factorization (NNMF) improves the interpretability of unmixed macromolecular components, but can be difficult to apply because ambiguities produced by overlapping Raman bands permit multiple solutions. Furthermore, theoretically sound methods can be difficult to implement in practice. Here we examined the effects of a number of empirical approaches on the quality of NNMF results. These approaches were evaluated on simulated mammalian cell Raman hyperspectra and the results were used to develop an enhanced procedure for implementing NNMF. We demonstrated the utility of this procedure using a Raman hyperspectral data set measured from human islet cells to recover the spectra of insulin and glucagon. This was compared to the relatively inferior PCA of these data. [ABSTRACT FROM AUTHOR]

The objective of the present work is development of joint approximate diagonalization of eigenmatrices (JADE) as a member of independent component analysis (ICA) family, for the analysis of gas chromatography-mass spectrometry (GC–MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC–MS) data to address incomplete separation problem occurred during the analysis of complex sample matrices. In this regard, simulated GC–MS and GC × GC–MS data sets with different number of components, different degree of overlap and noise were evaluated. In the case of simultaneous analysis of multiple samples, column-wise augmentation for GC–MS and column-wise super-augmentation for GC × GC–MS was used before JADE analysis. The performance of JADE was evaluated in terms of statistical parameters of lack of fit (LOF), mutual information (MI) and Amari index as well as analytical figures of merit (AFOMs) obtained from calibration curves. In addition, the area of feasible solutions (AFSs) was calculated by two different approaches of MCR-BANDs and polygon inflation algorithm (FACPACK). Furthermore, JADE performance was compared with multivariate curve resolution-alternating least squares (MCR-ALS) and other ICA algorithms of mean-field ICA (MFICA) and mutual information least dependent component analysis (MILCA). In all cases, JADE could successfully resolve the elution and spectral profiles in GC–MS and GC × GC–MS data with acceptable statistical and calibration parameters and their solutions were in AFSs. To check the applicability of JADE in real cases, JADE was used for resolution and quantification of phenanthrene and anthracene in aromatic fraction of heavy fuel oil (HFO) analyzed by GC × GC–MS. Surprisingly, pure elution and spectral profiles of target compounds were properly resolved in the presence of baseline and interferences using JADE. Once more, the performance of JADE was compared with MCR-ALS in real case. On this matter, the mutual information (MI) values were 1.01 and 1.13 for resolved profiles by JADE and MCR-ALS, respectively. In addition, LOD values (μg/mL) were respectively 1.36 and 1.24 for phenanthrene and 1.26 and 1.09 for anthracene using MCR-ALS and JADE which showed outperformance of JADE over MCR-ALS. [ABSTRACT FROM AUTHOR]

Tryptophan (Trp) and tyrosine (Tyr) are important amino acids that usually coexist with each other in biological fluids. Therefore the simultaneous determination of them is very important because of their very nearly properties. On the other hand, differential pulse voltammetry studies show a high degree overlapping of voltammograms for oxidation of Trp and Tyr. Multivariate curve resolution-alternating least squares (MCR-ALS) with electrochemical methods have been applied to overcome this limitation for simultaneous determination of Trp and Tyr. Electrochemical sensor has been developed by utilizing the ZnFe 2 O 4 nanoparticles modified carbon paste electrode. The characterization of the modified electrode was studied using different techniques such as field emission scanning electron microscopy, X-ray diffraction, electrochemical impedance spectroscopy, differential pulse and cyclic voltammetry methods. Operating conditions were optimized with central composite rotatable design (CCRD) associated with the response surface methodology (RSM). The proposed method under the optimized conditions was used to determine Trp and Tyr with linear ranges of 0.1–200.0 μM and 0.4–175.0 μM, detection limits of 0.04 μM and 0.10 μM (S/N = 3), respectively. The method was applied for simultaneous determination of Trp and Tyr in spiked human serum and urine samples. [ABSTRACT FROM AUTHOR]

Hydrophilic interaction liquid chromatography on bare silica presents some benefits for analysis and purification of ionizable basic alkaloids. This mode was used to separate five major opium alkaloids: morphine, codeine, thebaine, papaverine, and noscapine. Central composite design based on response surface methodology was applied for experimental design, modeling, and optimization in a single-step gradient method. The main effects and their interactions (initial percentage of modifier, changing range of modifier in run time, pH of buffer, and its concentration) were investigated in 30 experiments. Multivariate curve resolution-alternating least squares, by resolving overlapped curves, helped in the accurate calculation of baseline resolution factors to be modeled and optimized more accurately. Then three crucial resolution factors besides elution time were modeled in quadratic and cubic equations and optimized. In addition to the four factors, five extra logarithmic, and nonlogarithmic factors extracted from the four factors to give nine factors overall were inspected on mechanism of retention. It was shown that a linear combination consist of four independence variables successfully describes morphinans retentivity in a single-step gradient method. [ABSTRACT FROM AUTHOR]

Both extraction and chemical cleanup are steps performed when the official AOAC 2007.01 QuEChERS method is used. The chemical cleanup step is required because many co-extractives may still be present after the whole samples extraction; however, it may reduce recovery of certain analytes due to non-selectivity of this step. In addition, unexpected constituents may still interfere with the analysis even when chemical cleanup is carried out, impairing univariate quantification. A chemometric cleanup using MCR-ALS is proposed as an alternative to the QuEChERS chemical cleanup. The performance of the proposed approach was demonstrated through quantification of seven pesticide residues (Carbendazim, Thiabendazole, Fuberidazole, Carbofuran, Carbaryl, 1-naphthol, and Flutriafol) in four non-spiked vegetable samples using HPLC-DAD. With the proposed strategy, it was possible to perform a reliable quantification despite the presence of co-eluted constituents (analytes and interferents), peak shift, band shape changes and avoiding the chemical cleanup with low analyte losses and LOD. [ABSTRACT FROM AUTHOR]

In this study, optimisation of the photocatalytic behaviour of crystal violet (CV) by thiourea (Tu)-codoped TiO2 thin film in fixed bed photoreactor was investigated by central composite designs (CCDs). The effective variables were pH, the concentration of CV dye, flow rate and reaction time. The results of the CCD model showed a good agreement with experimental results, with R² = 0.9680 (p < 0.0001) and maximum degradation efficiency was obtained at the optimum conditions: dye concentration 8.5 mg/L, pH 9, flow rate 6 mL/min and reaction time 80 min. Subsequently, three absorbing chemical compounds presented in the degradation reaction were obtained by using singular value decomposition (SVD) method and evolving factor analysis (EFA). Then a multivariate curve resolution with alternating least squares (MCR-ALS) was performed to achieve the concentration and spectral profiles for each component. Finally, a hard modelling method was applied to determine the kinetic constants of distinct reactions occurred in the photocatalytic degradation process. The reaction rate constants were calculated for the first and second steps as k1 = 0.08327 (SD = ±0.0015)/min and k2 = 0.045 (SD = ±0.0006)/min, respectively. [ABSTRACT FROM AUTHOR]

Receptor models apportion the measured mass of an ambient particulate matter (PM) sample at a given site, called the receptor, to its emission sources by using multivariate factor analysis. In this work, a general workflow for PM data quality assessment, measurements uncertainty calculations, receptor modeling and error in model parameters estimation is proposed. Maximum likelihood principal component analysis - multivariate curve resolution–alternating least squares (MLPCA-MCR-ALS) is proposed for general bilinear receptor modeling of noisy environmental datasets and compared with other approaches used in the field. Equations to obtain PM-source contribution estimates (PM-SCE) and contribution-to-species to identify emission sources and their attributes are proposed. Propagation of experimental uncertainties in the parameters of the receptor model is obtained using extensive computer resampling methods. Results are shown for source apportionment of a particulate matter (PM 10 ) air monitoring data set obtained under Fairmode-WG3 project. [ABSTRACT FROM AUTHOR]

Multivariate curve resolution (MCR) of absorption spectra is now a ubiquitously used tool. However, MCR methods, which use ordinary least squares (OLS) approach, assume that the measurement uncertainties are unbiased and homoscedastic. This is not true for absorption measurements, in which uncertainty variance and bias both increase as the true absorbance increases. The bias produces a well-known flattening/saturation of the peaks at high optical densities, which makes the data nonlinear and unsuitable for OLS-based MCR analysis. This problem can be reduced by using weighted least squares (WLS). In the present paper, the ability of WLS-based MCR to handle simulated and real datasets with realistic optical noise and flattening was assessed. Three weighting schemes were tested: OLS (unity weights), weights based on the maximum likelihood principle (MLP) and the physics of absorption measurement, and weights based on empirical cutoff (zero weights for saturated data points). The abilities of MCR to recover the true profiles and to evaluate rotational ambiguity of the solutions were compared for the 3 weighting schemes. MLP- and cutoff-based WLS-MCR produced better resolution of flattened data than OLS, but the success of the extension to strongly flattened spectra depended on data structure. MLP-based MCR was general and stable, while cutoff-based MCR was more sensitive to the data but could recover unbiased profiles. Generally, the use of WLS can expand MCR functionality to the analysis of flattened spectra. The specifics of finding WLS bilinear solutions and approaches to migrate factor-based MCR methods from OLS to WLS are also discussed. [ABSTRACT FROM AUTHOR]

Investigation of a sample covered by an interfering layer is required in many fields, e.g., for process control, biochemical analysis, and many other applications. This study is based on the analysis of spectra collected by near-infrared (NIR) diffuse reflectance spectroscopy. Each spectrum is a composition of a useful, target spectrum and a spectrum of an interfering layer. To recover the target spectrum, we suggest using a new phenomenological approach, which employs the multivariate curve resolution (MCR) method. In general terms, the problem is very complex. We start with a specific problem of analyzing a system, which consists of several layers of polyethylene (PE) film and underlayer samples with known spectral properties. To separate information originating from PE layers and the target, we modify the system versus both the number of the PE layers as well as the reflectance properties of the target sample. We consider that the interfering spectrum of the layer can be modeled using three components, which can be tentatively called transmission, absorption, and scattering contributions. The novelty of our approach is that we do not remove the reflectance and scattering effects from the spectra, but study them in detail aiming to use this information to recover the target spectrum. [ABSTRACT FROM AUTHOR]

In this study, we consider the reconstruction of a diffuse reflectance near-infrared spectrum of an object (target spectrum) in case the object is covered by an interfering absorbing and scattering layer. Recovery is performed using a new empirical method, which was developed in our previous study. We focus on a system, which consists of several layers of polyethylene (PE) film and underlayer objects with different spectral features. The spectral contribution of the interfering layer is modeled by a three-component two-parameter multivariate curve resolution (MCR) model, which was built and calibrated using spectrally flat objects. We show that this model is applicable to real objects with non-uniform spectra. Ultimately, the target spectrum can be reconstructed from a single spectrum of the covered target. With calculation methods, we are able to recover quite accurately the spectrum of a target even when the object is covered by 0.7mm of PE. [ABSTRACT FROM AUTHOR]

In this work, the combination of chemometric techniques with kinetic-spectroscopic data allowed quantifying two dyes (tartrazine and carminic acid) in complex matrices as mustard, ketchup, asparagus soup powder, pumpkin soup powder, plum jam and orange-strawberry juice. Quantitative analysis was performed without the use of tedious sample pretreatment, due to the achievement of the second-order advantage. The results obtained showed an improvement in simplicity, speed and cost with respect to usual separation techniques, allowing to properly quantifying these dyes obtaining limits of detection below 0.6 mg L −1 . In addition, to the best of our knowledge, is the first time that kinetic-spectroscopic data are obtained from the action of laccase for analytical purposes. [ABSTRACT FROM AUTHOR]

A cocrystallization process may involve several molecular species, which are generally solid under ambient conditions. Thus, accurate monitoring of different components that might appear during the reaction is necessary, as well as quantification of the final product. This work reports for the first time the synthesis of carbamazepine-nicotinamide cocrystal in aqueous media with a full conversion. The reactions were monitored by Raman spectroscopy coupled with Multivariate Curve Resolution - Alternating Least Squares, and the quantification of the final product among its coformers was performed using Raman spectroscopy and Partial Least Squares regression. The slurry reaction was made in four different conditions: room temperature, 40 °C, 60 °C and 80 °C. The slurry reaction at 80 °C enabled a full conversion of initial substrates into the cocrystal form, using water as solvent for a greener method. The employment of MCR-ALS coupled with Raman spectroscopy enabled to observe the main steps of the reactions, such as drug dissolution, nucleation and crystallization of the cocrystal. The PLS models gave mean errors of cross validation around 2.0 (% wt/wt), and errors of validation between 2.5 and 8.2 (% wt/wt) for all components. These were good results since the spectra of cocrystals and the physical mixture of the coformers present some similar peaks. [ABSTRACT FROM AUTHOR]

Although multivariate curve resolution (MCR) has been applied to the analysis of Fourier transform infrared (FTIR) imaging, it is still problematic to determine the number of components. The reported methods at present tend to cause the components of low concentration missed. In this paper a new idea was proposed to resolve this problem. First, MCR calculation was repeated by increasing the number of components sequentially, then each retrieved pure spectrum of as-resulted MCR component was directly compared with a real-world pixel spectrum of the local high concentration in the corresponding MCR map. One component was affirmed only if the characteristic bands of the MCR component had been included in its pixel spectrum. This idea was applied to attenuated total reflection (ATR)/FTIR mapping for identifying the trace additives in blind polymer materials and satisfactory results were acquired. The successful demonstration of this novel approach opens up new possibilities for analyzing additives in polymer materials. [ABSTRACT FROM AUTHOR]

In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the ¹H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different ¹H NMR metabolomics datasets. In a first study, ¹H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by ¹H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using ¹H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique. [ABSTRACT FROM AUTHOR]

Multivariate curve resolution (MCR) methods as MCR-ALS, ReactLab, the peak group analysis and SVD-based hard-modeling methods differ in their algorithms and the underlying optimization procedures. These differences include variants in the implementation of the algorithms and a differing weighting of the constraints. Depending on the MCR method different computational results can be obtained for the same data set. The area of feasible solutions (AFS) comprises all possible outcomes of MCR methods. It represents all nonnegative factors of a given spectral data set. It therefore offers an unbiased view of the problem. In a comparative study we present within the AFS the various MCR results for a model data set and for experimental FTIR data. For the model data we observe that the spread of the MCR results correlates with the so-called purity of the spectral data. [ABSTRACT FROM AUTHOR]

Organic inputs from plant establishment are part of the rehabilitation process in restoring coal minesoil function. Soil organic matter (SOM) content could therefore potentially be an indicator of rehabilitation success. Rehabilitated coal minesoils contain inherited coal and char that complicate the attribution of measured total C to recent SOM inputs. We provide proof-of-principle tests of ramped combustion thermal analyses combined with chemometrics (multivariate curve resolution–alternate least squares, MCR-ALS) to quantitatively distinguish between geogenic C, pyrogenic C and SOM C in model mixtures and a coal minesoil from Queensland, Australia. MCR-ALS successfully separated coal and char, and initial estimate thermograms improved the model fits. Geogenic C represented a small proportion of total organic C in the tested unknown minesoils (mean = 9.2%), and a larger contribution from pyrogenic C (mean = 32.4%). Trends in SOM were also consistent with Walkley-Black measurements of organic C. Our combined thermal-chemometric approach represents a robust means of quantifying SOM, distinct from coal and char, though resolution could be improved using multi-element thermograms and additional end-members and soils. [ABSTRACT FROM AUTHOR]

Comprehensive two-dimensional liquid chromatography (LC × LC) has been gaining popularity for the analysis of complex samples in a wide range of fields including metabolomics, environmental analysis, and food analysis. While LC × LC can provide greater chromatographic resolution than one-dimensional LC (1D-LC), overlapping peaks are often still present in separations of complex samples, a problem that can be alleviated by chemometric curve resolution techniques such as multivariate curve resolution-alternating least squares (MCR-ALS). MCR-ALS has also been previously shown to assist in the quantitative analysis of LC x LC data by isolating pure analyte signals from background signals which are often present at higher levels in LC x LC compared to 1D-LC. In this work we present the analysis of a dataset from the LC × LC analyses of parsley, parsnip and celery samples for the presence and concentrations of 14 furanocoumarins. Several MCR-ALS implementations are compared for the analysis of LC × LC data. These implementations include analyzing the LC x LC chromatogram alone, analyzing the one-dimensional chromatogram alone, as well as two hybrid approaches that make use of both the first and second dimension chromatograms. Furthermore, we compared manual integration of resolved chromatograms versus a simple summation approach, using the resolved chromatographic peaks in both cases. It is found that manual integration of the resolved LC × LC chromatograms provides the best quantification as measured by the consistency between replicate injections. If the summation approach is desired for automation, the choice of MCR-ALS implementation has a large effect on the precision of the analysis. Based on these results, the concentrations of the 14 furanocoumarins are determined in the three apiaceous vegetable types by analyzing the LC × LC chromatograms with MCR-ALS and manual integration for peak area determination. The concentrations of the analytes are found to vary greatly between samples, even within a single vegetable type. [ABSTRACT FROM AUTHOR]

Influence of magnesium(II) and calcium(II) ions on the autoxidation of hematoxylin in weakly alkaline aqueous solution was studied by UV-vis. spectrophotometry. Multivariate curve resolution-alternating least square analysis of spectrophotometric data enabled determination of spectra and concentration profiles of individual absorbing species involved in the autoxidation processes. In weakly alkaline aqueous solution in the absence of metal ions only formation of hematein was detected during the 40 min of autoxidation, while during the 25 min of autoxidation in the presence of magnesium(II) and calcium(II) ions further transformation of hematein to dimeric and even oligomeric/polymeric products was observed. The presence of both magnesium(II) and calcium(II) ions greatly enhanced the autoxidation rate of hematoxylin, but there were also notable differences between these two ions. Hematoxylin autoxidation in the presence of magnesium(II) ion proceeded at a slower rate due to its more pronounced stabilising effect on hematoxylin and initial autoxidation products caused by the higher ionic potential in comparison to calcium(II) ion. The results of this study may also contribute to the better understanding of interactions of magnesium(II) and calcium(II) ions with natural phenolic compounds which are important because of their various biological activities. [ABSTRACT FROM AUTHOR]

In this work, multivariate statistical techniques are employed to determine patterns and conversion curves from time-resolved X-ray powder diffraction data. For these purposes, time-window statistical total correlation spectroscopy is introduced for the pattern matching of the crystalline phase and is shown to be effective even in the case of overlapping peaks. When combined with evolving factor analysis and multivariate curve resolution-alternating least squares, this technique allows a definite estimation of patterns and conversion curves. The procedure is applied to in situ synchrotron powder diffraction patterns to monitor the setting reaction of magnesium potassium phosphate ceramic (MKP) from magnesia (MgO) and potassium dihydrogen phosphate. It is shown that the phases involved in the reaction are clearly distinguished and their evolution is correctly described. The conversion curves estimated with the proposed procedure are compared with the ones determined with the peak integration method, leading to an excellent agreement (Pearson's correlation coefficient equal to 0.9995 and 0.9998 for MgO and MKP, respectively). The approach also allows for the detection and description of the evolution of amorphous phases that cannot be described through conventional analysis of powder diffraction data. [ABSTRACT FROM AUTHOR]