Multivariate Curve Resolution-alternating Least Squares Research Articles

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.

Application of Chemometrics Tools to the Study of the Fe(III)-Tannic Acid Interaction.

Chemometric techniques were applied to the study of the interaction of iron(III) and tannic acid (TA). Modeling the interaction of Fe(III)–TA is a challenge, as can be the modeling of the metal complexation upon natural macromolecules without a well-defined molecular structure. The chemical formula for commercial TA is often given as C76H52O46, but in fact, it is a mixture of polygalloyl glucoses or polygalloyl quinic acid esters with the number of galloyl moieties per molecule ranging from 2 up to 12. Therefore, the data treatment cannot be based on just the stoichiometric approach. In this work, the redox behavior and the coordination capability of the TA toward Fe(III) were studied by UV-vis spectrophotometry and fluorescence spectroscopy. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Parallel Factor Analysis (PARAFAC) were used for the data treatment, respectively. The pH range in which there is the redox stability of the system Fe(III)–TA was evaluated. The binding capability of TA toward Fe(III), the spectral features of coordination compounds, and the concentration profiles of the species in solution as a function of pH were defined. Moreover, the stability of the interaction between TA and Fe(III) was interpreted through the chemical models usually employed to depict the interaction of metal cations with humic substances and quantified using the concentration profiles estimated by MCR-ALS.

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.

Near infrared spectroscopy coupled to MCR-ALS for the identification and quantification of saffron adulterants: Application to complex mixtures

The adulteration of saffron, due to its high market value, is an old and common practice. Hence, there is a high demand for rapid, easy-to-go, environmentally friendly and low-cost techniques able to perform the simultaneous identification and quantification of saffron adulterants. In this sense, the goal of this work was to use near infrared (NIR) spectroscopy coupled to multivariate curve resolution – alternating least-squares (MCR-ALS) for the identification and quantification of four common adulterants (onion, calendula, pomegranate and turmeric). A total of 38 samples containing all these adulterants were prepared. Several spectral regions and pre-processing techniques were tested in the calibration models to find the best combination. The application of MCR-ALS allowed the identification of the adulterants present in the samples through the comparison between the spectral profiles recovered by MCR-ALS and the respective spectra of pure adulterants. Additionally, the quantification of the adulterants was also successfully achieved in these complex samples with relative percentage errors in concentration predictions (RE) of around 10%, except for onion. Overall, the results obtained in this work demonstrate the suitability of NIR spectroscopy coupled to MCR-ALS analysis to identify and quantify adulterants in a very complex mixture and at low levels. To the best of our knowledge, it was the first time that NIR spectroscopy and MCR-ALS were coupled to simultaneously identify and quantify four adulterants in saffron samples.

THz Spectroscopic Decomposition and Analysis in Mixture Inspection Using Soft Modeling Methods

At present, the terahertz time-domain spectroscopic information of each component in multi-composition compounds detection is comprehensively combined. The lower resolution level of mixed spectra has posed many difficulties in signal analysis due to the overlapping characteristic information in the mixed ones. In this paper, to compare the performances of Nonnegative Matrix Factorization (NMF), Self-modeling Mixture Analysis (SMMA) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on complex systems, a binary mixture and a ternary mixture are employed during THz-TDS testing. The position of the absorption peak (PK) and the correlation coefficient are used to evaluate the decomposition effects. The experimental results show that the component spectra resolved by MCR-ALS demonstrate good consistency with the sample components. Further, MCR-ALS presents excellent results in comparison with NMF and SMMA in terms of decomposing precision and computing speed. MCR-ALS thus appears a promising algorithm to resolve the THz multi-way data, and may be useful for many applications, such as medicine quality assurance and unknown component identification in the general area of terahertz science and technology.

Deep-Learning-Assisted multivariate curve resolution

Gas chromatography-mass spectrometry (GC-MS) is one of the major platforms for analyzing volatile compounds in complex samples. However, automatic and accurate extraction of qualitative and quantitative information is still challenging when analyzing complex GC-MS data, especially for the components incompletely separated by chromatography. Deep-Learning-Assisted Multivariate Curve Resolution (DeepResolution) was proposed in this study. It essentially consists of convolutional neural networks (CNN) models to determine the number of components of each overlapped peak and the elution region of each compound. With the assistance of the predicted elution regions, the informative regions (such as selective region and zero-concentration region) of each compound can be located precisely. Then, full rank resolution (FRR), multivariate curve resolution-alternating least squares (MCR-ALS) or iterative target transformation factor analysis (ITTFA) can be chosen adaptively to resolve the overlapped components without manual intervention. The results showed that DeepResolution has superior compound identification capability and better quantitative performances when comparing with MS-DIAL, ADAP-GC and AMDIS. It was also found that baseline levels, interferents, component concentrations and peak tailing have little influences on resolution result. Besides, DeepResolution can be extended easily when encountering unknown component(s), due to the independence of each CNN model. All procedures of DeepResolution can be performed automatically, and adaptive selection of resolution methods ensures the balance between resolution power and consumed time. It is implemented in Python and available at https://github.com/XiaqiongFan/DeepResolution.

Balanced scaling as a pretreatment step in Multivariate Curve Resolution analysis of noisy data

Analysis of data sets with heteroscedastic error has been a challenging problem in the chemometrics literature. Different methods have been proposed for analyzing this type of data, in particular, using the Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method. The present paper introduces the Balanced Scaling (BS) approach as a pretreatment step combined with the Multivariate Curve Resolution Alternating Least Squares (BS-MCR-ALS) method as an adequate procedure to analyze data with heteroscedastic noise. In particular, for the analysis of environmental data, the Balanced Scaling (BS) method can be a useful approach to provide an optimal individual data scaling. The performance of the BS-MCR-ALS method is compared with the performance of the Maximum Likelihood Principal Component Analysis Multivariate Curve Resolution Alternating Least Squares (MLPCA-MCR-ALS) method, and also with the performance of the traditional Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method in the analysis of data sets with different type of error structures. The results obtained in this comparison revealed that the solutions obtained by BS-MCR-ALS and MLPCA-MCR-ALS were very similar.

Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta

Pasta is mostly composed by wheat flour and water. Nevertheless, flour can be partially replaced by fibers to provide extra nutrients in the diet. However, fiber can affect the technological quality of pasta if not properly distributed. Usually, determinations of parameters in pasta are destructive and time-consuming. The use of Near Infrared-Hyperspectral Imaging (NIR-HSI), together with machine learning methods, is valuable to improve the efficiency in the assessment of pasta quality. This work aimed to investigate the ability of NIR-HSI and augmented Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the evaluation, resolution and quantification of fiber distribution in enriched pasta. Results showed R2V between 0.28 and 0.89, %LOF < 6%, variance explained over 99%, and similarity between pure and recovered spectra over 96% and 98% in models using pure flour and control as initial estimates, respectively, demonstrating the applicability of NIR-HSI and MCR-ALS in the identification of fiber in pasta.

Two- and three-way chemometric analyses for investigation of interactions of acarbose with normal and glycated human serum albumin: Developing a novel biosensing system

In this project, our research group has performed an interesting work in which interactions of acarbose (ACB) with normal human serum albumin (HSA) and glycated HSA (GHSA) have been investigated by chemometrics assisted-electrochemical and spectroscopic techniques. To have a deep insight to the interactions of ACB with HSA and GHSA, different electrochemical and spectroscopic techniques were used to monitor ACB-HSA and ACB-GHSA interactions and analyzed by multivariate curve resolution alternating least squares (MCR-ALS), MCR-BANNDS and EQUISPEC as efficient chemometric algorithms. Then, molecular docking techniques were used to extract more information which helped us to better justify binding of ACB with HSA and GHSA. After obtaining qualitative and quantitative information and justification of the ACB-HSA and ACB-GHSA interactions, a novel electrochemical technique was developed for exploiting second-order advantage from differential pulse voltammetric data for determination of GHSA as a potential biomarker in diabetes in the presence of HSA. Calibration and validation of the developed system showed us that our system was successful in determination of GHSA in synthetic and real samples.

Comparison of three chemometric methods for processing HPLC-DAD data with time shifts: Simultaneous determination of ten molecular targeted anti-tumor drugs in different biological samples

Three intelligent chemometric multi-way calibration methods including alternating trilinear decomposition (ATLD), alternating trilinear decomposition assisted multivariate curve resolution (ATLD-MCR) and multivariate curve resolution-alternating least squares (MCR-ALS) combined with high performance liquid chromatography-diode array detection (HPLC-DAD) were used to quantify ten molecular targeted anti-tumor drugs in three complex biological matrices (plasma, urine and cell culture media matrices). All analytes can be successfully eluted in 6.5 min. In this experiment, various degrees of time shifts occurred in different samples. While slight time shifts exist in the chromatographic analysis, satisfactory results can be obtained by the three proposed methods. When the time shift was large (5.6 s), the average spiked recoveries obtained by ATLD analysis were in the range of 58.9%–116.5%, which was less than satisfactory. However, the average recoveries obtained by MCR-ALS and ATLD-MCR analysis were 89.8%–114.8% and 84.5%–106.1% respectively, and more satisfactory results were obtained. For further research, ATLD-MCR and MCR-ALS methods were compared, and the results were evaluated by statistical tests. Accuracies of concentrations obtained by them were considered to be no significant difference. In addition, compared with other methods currently published, the proposed chemometric methods combined with the HPLC-DAD can rapidly, simultaneously and accurately determine varieties of molecular targeted anti-tumor drugs in different complex biological matrices even in the presence of severe peak overlaps, severe time shifts, slight baseline drifts and different unknown background interferences.

Joint selection of essential pixels and essential variables across hyperspectral images

An approach is proposed and illustrated for the joint selection of essential samples and essential variables of a data matrix in the frame of spectral unmixing. These essential features carry the signals required to linearly recover all the information available in the rows and columns of a data set. Working with hyperspectral images, this approach translates into the selection of essential spectral pixels (ESPs) and essential spatial variables (ESVs). This results in a highly-reduced data set, the benefits of which can be minimized computational effort, meticulous data mining, easier model building as well as better problem understanding or interpretation. Working with both simulated and real data, we show that (i) reduction rates of over 99% can be typically obtained, (ii) multivariate curve resolution – alternating least squares (MCR-ALS) can be easily applied on the reduced data sets and (iii) the full distribution maps and spectral profiles can be readily obtained from the reduced profiles and the reduced data sets (without using the full data matrix).

Detection of Penicillin G Produced by Penicillium chrysogenum with Raman Microspectroscopy and Multivariate Curve Resolution-Alternating Least-Squares Methods.

Raman microspectroscopy is a minimally invasive technique that can identify molecules without labeling. In this study, we demonstrate the detection of penicillin G inside Penicillium chrysogenum KF425 fungal cells. Raman spectra acquired from the fungal cells had highly overlapped spectroscopic signatures and hence were analyzed with multivariate curve resolution by alternating least-squares (MCR-ALS) to extract the spectra of individual molecular constituents. In addition to detecting spatial distribution of multiple constituents such as proteins and lipids inside the fungal body, we could also observe the subcellular localization of penicillin G. This methodology has the potential to be employed in screening the production of bioactive compounds by microorganisms.

Employing complementary multivariate methods for a designed nontarget LC-HRMS screening of a wastewater-influenced river

Environmental pollution issues, such as the impact of wastewater treatment plants (WWTPs) to contaminate the recipient waters, are multivariate problems in which several variables with varying correlation values contribute. Chemometrics-based methods can play a critical role to study the occurrence and fate of contaminants of emerging concern (CECs) in an environmental pollution question. This study performed nontarget screening (NTS) with liquid chromatography-high resolution mass spectrometry (LC-HRMS) for the investigation of pollution patterns of river water samples affected by the effluent of a WWTP (upstream/downstream) at three sampling times. The complex data sets collected in a designed experiment were analyzed using the ROIMCR approach, using data selection from MS regions of interest (ROI). Then, multivariate curve resolution alternating least-squares (MCR-ALS) was applied for simultaneous resolution of 18 upstream/downstream river water samples. The resolved and cleaned matrix of peak areas were subjected to group-wise ANOVA-simultaneous component analysis (GASCA) for interpretation of the variations induced by location and time factors in a sparse principal component analysis way and to uncover the groups of pollutants co-occurring in the environment. The resolved groups of significant features based on sampling site factor were compared with the prioritized features using partial least squares-discriminant analysis (PLS-DA) least squares-discriminant analysis ures using partial least-squares-discriminant analysis (PLS-DA). Here, we focused on the environmental relevance of groups of compounds with significant variations for a few tentatively identified contaminants. This study clearly shows that employing complementary chemometric tools for NTS can improve the current knowledge on exposure trends of CECs in water bodies for further investigation.

Imaging of PD-L1 in single cancer cells by SERS-based hyperspectral analysis.

We developed a hyperspectral imaging tool based on surface-enhanced Raman spectroscopy (SERS) probes to determine the expression level and visualize the distribution of PD-L1 in individual cells. Electron-microscopic analysis of PD-L1 antibody - gold nanorod conjugates demonstrated binding the cell surface and internalization into endosomal vesicles. Stimulation of cells with IFN-γ or metformin was used to confirm the ability of SERS probes to report treatment-induced changes. The multivariate curve resolution-alternating least squares (MCR-ALS) analysis of spectra provided a greater signal-noise ratio than single peak mapping. However, single peak mapping allowed a systematic subtraction of background and the removal of non-specific binding and endocytic SERS signals. The mean or maximum peak height in the cell or the mean peak height in the area of specific PD-L1 positive pixels was used to estimate the PD-L1 expression levels in single cells. The PD-L1 levels were significantly up-regulated by IFN-γ and inhibited by metformin in human lung cancer cells from the A549 cell line. In conclusion, the method of analyzing hyperspectral SERS imaging data together with systematic and comprehensive removal of non-specific signals allows SERS imaging to be a quantitative tool in the detection of the cancer biomarker, PD-L1.

Monitoring biodiesel and its intermediates in transesterification reactions with multivariate curve resolution alternating least squares calibration models

The monitoring of biodiesel production processes is currently performed by methods requiring high reagent consumption and long response times. Aiming to overcome these drawbacks, this article developed chemometric models combining principal component analysis (PCA), multivariate curve resolution alternating least squares (MCR-ALS) and mid infrared spectroscopy to determine simultaneously the concentrations of the main components, methyl esters and triglycerides, along transesterification reactions. This methodology also tried to model the intermediates (fatty acids plus monoglycerides, and diglycerides), thus providing semi-quantitative predictions for these components. These reactions were carried out in different experimental conditions according to a factorial design with two factors, the batch of soybean oil (raw material) and the alcohol:oil molar ratio. The concentration values estimated by high performance liquid chromatography (HPLC), as the reference method, were the inputs for the correlation constraint used as a key step in the multivariate calibration MCR-ALS models. The spectral profiles recovered for the methyl esters and for the triglycerides have shown high correlations (greater than 0.985) in comparison to the reference spectra. In addition, the spectra calculated for fatty acids, monoglycerides and diglycerides showed absorption bands characteristic of functional groups present in these molecules, mainly the hydroxyl and carbonyl stretching bands. The root mean square errors of calibration and prediction estimated for the triglycerides and for the methyl esters were within the range from 1.9% to 6.3%.

Simultaneous Determination of Medicinal Drugs with Overlapping Profiles Contained in Low Chromatographic Resolution Data using HPLC-DAD and Multivariate Curve Resolution

Background: The increasing demand of effective pharmaceutical products directed to fight against malaria lead to the combination of at least two antimalarial drugs. This combination aims to minimize the Plasmodium falciparum resistance which is found when the most frequently used drugs are taken individually. Within this context, proguanil hydrochloride and chloroquine phosphate which have independent modes of action are taken together to prevent malaria. This paper aims to develop a fast and powerful analytical method for the simultaneous determination of proguanil hydrochloride and chloroquine phosphate in the commercial Paludrine/Avloclor dosage forms using a multi-way chromatographic calibration based on high-performance liquid chromatography with diode array detection (HPLC-DAD) and multivariate curve resolution – alternating leastsquares (MCR-ALS). Methods: A rapid and powerful analytical method based on HPLC-DAD and MCR-ALS was developed for the simultaneous quantification of proguanil hydrochloride and chloroquine phosphate in the commercial Paludrine/Avloclor antimalarial drugs. An isocratic mobile phase composed by 0.2 M ammonium acetate, acetonitrile, and methanol (40:25:35) and a flow rate of 1.2 mL min-1 were employed in the chromatographic runs with an elution time about 5 min. Results: This approach demonstrates that chromatographic analysis may become considerably simpler and economical in terms of time, cost, and organic solvent consumption when coupled to multiway calibration models such as MCR-ALS. In fact, this multi-way chromatographic calibration based on second-order HPLC-DAD data matrices (with extremely low chromatographic resolution) and MCR-ALS allows the development of greener analytical methods for complex samples. The proposed analytical method allowed the simultaneous quantification of two antimalarial APIs present in the commercial Paludrine/Avloclor drugs with low REP values below 8% for the simultaneous determination of proguanil hydrochloride and chloroquine phosphate. Conclusion: The proposed multi-way chromatographic strategy can be used for routine control of pharmaceutical dosage forms. It should be highlighted that MCR-ALS allowed to: (a) achieve the second- order advantage and the quantification of analytes in the presence of uncalibrated compounds such as coeluted profile measured in different magnitude of the signal in each successive chromatographic run and significant overlapping profiles and (b) separate the contribution of several components from chromatographic runs with extremely low separation of peaks through the deconvolution of the signal obtained, performing the so-called mathematical chromatography.

A chemometric comparison of different models in fluorescence analysis of dabigatran etexilate and dabigatran.

In this paper, a simple, rapid, low-cost and potential method was established for the simultaneous quantitative analysis of dabigatran etexilate (DABE) and dabigatran (DAB) in spiked biological fluids. It combined excitation-emission matrix fluorescence (EEMF) with different second-order calibration methods, including the self-weighted alternating normalized residue fitting (SWANRF) algorithm based on trilinear decomposition model, the multivariate curve resolution - alternating least-squares (MCR-ALS) based on bilinear decomposition model and the unfolded partial least-square coupled with residual bilinearization (U-PLS/RBL) based on latent variables model. The proposed method showed "second-order advantage", that is, satisfactory quantitative results were successfully obtained even in the presence of unknown interferences and serious spectral overlap. The recoveries of DABE and DAB in spiked biological fluids were 91.7%-101.7% for SWANRF, 95.9%-117.8% for MCR-ALS, 83.0%-109.6% for U-PLS/RBL, respectively. Figures of merit and other statistical parameters were also calculated to assess the performance of the proposed method. Moreover, the modeling procedures and characteristics of three different models in EEMF analysis were discussed and compared.

Interval estimation in multivariate curve resolution by exploiting the principles of error propagation in linear least squares

Interval estimation in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is a challenging problem. Several algorithms including Bayesian, Monte-Carlo, bootstrap and jackknife resampling approaches have been proposed previously to address this problem in MCR. In the present contribution, constructing the confidence intervals (CIs) in MCR-ALS resolved profiles using the principles of error propagation in linear least squares (LS) parameter estimation is proposed. In MCR-ALS, every set of profiles in the row subspace (scores) can be considered as coefficients for estimating their counterparts in the columns subspace (loadings) and vice versa. Therefore, it should be possible to calculate the CIs for these coefficients (scores or loadings) using the principles of error propagation in LS. The confidence intervals of the coefficients in linear regression are directly related to the inverse of the matrix of sums of squares and sums of cross products of independent variables and to the standard deviation of the residuals. This idea has been successfully applied in this work for the construction of the CIs of the resolved profiles in the MCR-ALS algorithm. The proposed approach is named ‘Confidence Intervals based on Least Squares’ (CILS). The weighted version of this approach has also been implemented and named as CIWLS. The latter method can be used for handling datasets with a known type of error structure. The performances of the CILS and CIWLS approaches are evaluated in this work for the calculation of the CIs for several simulated three component LC-MS and LC-DAD datasets, with different homo- and heteroscedastic noise levels. The CIs obtained by CILS and CIWLS are compared with those obtained by the Monte-Carlo noise addition method. The empirical coverage probabilities of the CIs are also computed to check if the calculated CIs achieve the nominal confidence level. The average standard errors (ASE) are used as measures of the level of uncertainty (precision) in the recovered profiles. The results obtained in this work revealed that the CIs obtained by the CILS method are in agreement with those obtained by the Monte-Carlo approach. The main advantage of the CILS method is that the calculations are faster and require less computation time. Finally, the performance of the CIWLS algorithm was assessed in the analysis of a real environmental dataset for the source apportionment of particulate matter (PM) in air samples collected in Northern Spain. The results obtained by the CIWLS method were in agreement with those previously reported for this real dataset and the CIs of the contribution and composition profiles of the PM10 sources were properly estimated.

Detection of illegal additives in Brazilian S-10/common diesel B7/5 and quantification of Jatropha biodiesel blended with diesel according to EU 2015/1513 by MIR spectroscopy with DD-SIMCA and MCR-ALS under correlation constraint

This short communication presents a proper detection of illegal additives in Brazilian S-10/common diesel B7/5 using mid-infrared (MIR) spectroscopy and data driven soft independent modeling of class analogy (DD-SIMCA). The one-class classification chemometric model based on DD-SIMCA with MIR spectroscopic data was applied in the following analytical systems: (a) authentic Brazilian S-10 diesel B7 (BX is the amount of biodiesel blended) used as members of the unique target class and Brazilian S-10 diesel B7 adulterated by residual automotive lubricant oil (RALO) and residual solvent used in a dry wash (RSUDW) as non-members of the target class and (b) authentic Brazilian common diesel B5 used as a unique target class and Brazilian common diesel B5 adulterated by RALO, soybean oil and used frying oil (UFO) and contaminated with gasoline. DD-SIMCA model in data set described in (a) was able to properly classify all samples of Brazilian S-10 diesel B7 adulterated as non-members of the target class in the validation phase with a specificity of 100% and also all samples of authentic Brazilian S-10 diesel B7 were correctly accepted as members of the target class with test sensitivity equal 100%, although in the training phase one of the samples was rejected at 95% tolerance of the acceptance area, achieving training sensitivity equal 95%. In the data set described in (b) the modern DD-SIMCA model also achieved excellent results with 100% training sensitivity, test sensitivity and specificity. Finally, multivariate curve resolution-alternating least-squares (MCR-ALS) with the area correlation constraint and first-order MIR spectroscopic data was able to provide pure profiles such as: (i) concentration profiles allowed to quantify the amount of Jatropha biodiesels added in Brazilian S-10 diesel and (ii) spectral profiles allowed to identify the feedstock used in biodiesel production present in the biodiesel/diesel blend, which can provide further investigation for the European Union during the implementation of EU 2015/1513.

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 (Ka) was 2.139×104L·mol-1 at 298K. 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 5ns, and the particle size (Rg) 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.