Application of Raman multivariate curve resolution to solvation-shell spectroscopy

Raman spectroscopy and multivariate curve resolution (Raman-MCR) are combined to yield a powerful spectroscopic method for identifying solute-induced perturbations of solvent molecules. The principles and applications of the resulting solvation-shell spectroscopy are described and illustrated using both numerical model spectra and experimental Raman spectra, including water in acetone and aqueous OH(-), as well as of both neutral and ionic acetic acid solutions. The results illustrate the quantitative capabilities of Raman-MCR as a solvation-shell spectroscopy, including fundamental limitations arising from "intensity" and "rotational" ambiguities.     

Spectra and concentration profiles throughout the reaction of curing epoxy resins from near-infrared spectroscopy and multivariate curve resolution methods

When applied to near-infrared (NIR) data, multivariate curve resolution methods, in particular alternating least squares (ALS), make it possible to calculate the concentration profiles and the spectra of all species involved in the reaction of curing epoxy resins. In this paper, the model reaction between phenyl glicidyl ether and aniline (2:1) was studied at 95 degrees C. A NIR spectrum was recorded every five minutes throughout the eight-hour reaction process. The data display rank deficiency. This problem was overcome by supplying additional information to the system in the form of known spectra of some reactants. The recovered spectra and concentration profiles satisfactorily reproduced the experimental data. In this way, 99.99% of the variance associated with the experimental matrix was reproduced. A value of 0.87% was obtained for lack of fit while the similarity coefficient r between the spectra recovered and the spectra corresponding to the three pure species involved in the reaction were PGE (r = 0.994), aniline (r = 0.994), and tertiary amine (r = 0.999). The maximum and minimum limits associated with the ALS solutions were calculated, which made it possible to limit to a considerable extent the ambiguity that is characteristic of these curve resolution methods.     

2D correlation spectroscopy and multivariate curve resolution in analyzing pH-dependent evolving systems monitored by FT-IR spectroscopy,a comparative study

Multivariate curve resolution (MCR) and 2D correlation spectroscopy (2D-CoS), including sample-sample correlation, have been applied to the analysis of evolving midinfrared spectroscopic data sets obtained from titrations of organic acids in aqueous solution. In these data sets, well-defined species with significant differences in their spectra are responsible for the spectral variation observed. The two fundamentally different chemometric techniques have been evaluated and discussed on the basis of experimental and supportive simulated data sets. MCR gives information that can be directly related to the chemical species that is of importance from a practical point of view, whereas 2D-CoS results normally require more interpretation. The obtained conclusions are regarded valid for similar evolving data, which are increasingly being encountered in analytical chemistry when multivariate detectors are used to follow dynamic processes, including separations as well as chemical reactions, among others.     

Exploring time-dependent structural changes during the cold crystallization process of isotactic polystyrene by infrared spectroscopy and multivariate curve resolution

The present study attempts an application of Fourier transform infrared (FT-IR) spectroscopy in conjunction with multivariate curve resolution (MCR) techniques to explore the structural evolution of isotactic polystyrene (iPS) during the cold crystallization process. The focus of the present study is placed on the performance of MCR techniques, e.g., orthogonal projection (OP), alternating least squares (ALS), and fixed-size moving window evolving factor analysis (FSMWEFA), and the interpretability of spectral changes in the investigated chemical process. As a result, valuable information and conclusions about the structural evolution of iPS during the crystallization process can be extracted: when the amorphous phase of iPS changes, the ordering of the phenyl rings takes place first, and then the polymer chains adjust their local conformations to form short 3(1) helix structures. Furthermore, according to intensity profiles of the spectral variations, the ordering of the phenyl rings proceeds more intensely than the formation of ordered local chains, and the structural evolution of iPS occurs even during the induction period. The spectral variations resulting from the conformational changes in the 3(1) helical structures depend on the sequence length of the helical chains: the longer the polymer chain is, the smaller the corresponding band variations are. It has been demonstrated that the combination of FTIR spectroscopy and chemometric MCR techniques is very promising for the analysis of the crystallization process of polymers. MCR is a powerful tool for analyzing and visualizing spectral data and integrating them with other information, making spectral intensity variations more amenable to interpretation in order to explore the molecular dynamics of polymers.     

Application of multivariate curve resolution to the analysis of yeast genome-wide screens

In this work, the application of Multivariate Curve Resolution to the analysis of yeast genome-wide screens obtained by means of DNA microarray technology is shown. In order to perform the analysis of this type of data, two algorithms based on Alternating Least Squares (MCR-ALS) and on its maximum likelihood weighted projection (MCR-WALS) variant are compared. The utilization of the modified weighted alternating least (WALS) squares algorithm is motivated by the rather poor quality, uncertainties and experimental noise associated to DNA microarray data. Moreover, a large number of missing values are usually present in these data sets and the weighted WALS approach allowed circumventing this problem. Two different experimental datasets were used for this comparison. In the first dataset, gene expression values in budding yeast were monitored in-response to glucose limitation. In the second dataset, the changes in the gene expression caused by the daunorubicin drug were monitored as a function of time. Results obtained by application of Multivariate Curve Resolution in the two cases allowed a good recovery of the evolving gene expression profiles and the identification of metabolic pathways and individual genes involved in these gene expression changes.     

Application of two-dimensional correlation spectroscopy to chemometrics: self-modeling curve resolution analysis of spectral data sets

This paper demonstrates the use of two-dimensional (2D) correlation spectroscopy in conjunction with alternating least squares (ALS) based self-modeling curve resolution (SMCR) analysis of spectral data sets. This iterative regression technique utilizes the non-negativity constraints for spectral intensity and concentration. ALS-based SMCR analysis assisted with 2D correlation was applied to Fourier transform infrared (FT-IR) spectra of a polystyrene/methyl ethyl ketone/deuterated toluene (PS/MEK/d-toluene) solution mixture during the solvent evaporation process to obtain the pure component spectra and then the time-dependent concentration profiles of these three components during the evaporation process. We focus the use of asynchronous 2D correlation peaks for the identification of pure variables needed for the initial estimates of the ALS process. Choosing the most distinct bands via the positions of asynchronous 2D peaks is a viable starting point for ALS iteration. Once the pure variables are selected, ALS regression can be used to obtain the concentration profiles and pure component spectra. The obtained pure component spectra of MEK, d-toluene, and PS matched well with known spectra. The concentration profiles for components looked reasonable.     

Statistical process monitoring using an empirical Bayes multivariate process control chart

In this paper, we describe the theory underlying an empirical Bayesian approach to monitoring two or more process characteristics simultaneously. If the data is continuous and multivariate in nature, often the multivariate normal distribution can be used to model the process. Then, using Bayesian theory, we develop techniques to implement empirical Bayes process monitoring of the multivariable process. Lastly, an example is given to illustrate the use of our techniques. Copyright © 2001 John Wiley & Sons, Ltd.     

Application of a multivariate curve resolution procedure to the analysis of second-order melting data of synthetic and natural polynucleotides

The melting behavior of several synthetic polynucleotides and a mixture of natural tRNAs was studied by monitoring the changes in the whole UV absorbance spectrum at several pH values. The second-order absorbance data were analyzed with a soft-modeling multivariate curve resolution procedure that allows the determination of the number of different species or conformations present along the melting experiment and the calculation of the melting profile and the pure spectrum for each chemical species or conformation. The melting temperature, T(m), for each thermal transition can be calculated from the melting profiles, and structural information on the different species or conformations can be obtained from their pure spectra. The multistranded species formed at certain pH conditions show several sharp thermal transitions related to the loss of the initial highly ordered structure. For these transitions, the mixture of species obtained in the denaturing process can be resolved when several data matrices, each giving additional information, are analyzed simultaneously with the mathematical procedure proposed.     

Contribution made by multivariate curve resolution applied to gel permeation chromatography-Fourier transform infrared data for an in-depth characterization of styrene-butadiene rubber blends

We evaluate the contribution made by multivariate curve resolution-alternating least squares (MCR-ALS) for resolving gel permeation chromatography-Fourier transform infrared (GPC-FT-IR) data collected on butadiene rubber (BR) and styrene butadiene rubber (SBR) blends in order to access in-depth knowledge of polymers along the molecular weight distribution (MWD). In the BR-SBR case, individual polymers differ in chemical composition but share almost the same MWD. Principal component analysis (PCA) gives a general overview of the data structure and attests to the feasibility of modeling blends as a binary system. MCR-ALS is then performed. It allows resolving the chromatographic coelution and validates the chosen methodology. For SBR-SBR blends, the problem is more challenging since the individual elastomers present the same chemical composition. Rank deficiency is detected from the PCA data structure analysis. MCR-ALS is thus performed on column-wise augmented matrices. It brings very useful insight into the composition of the analyzed blends. In particular, a weak change in the composition of individual SBR in the MWD's lowest mass region is revealed.     

Application of multivariate curve resolution for analysis of FT-IR microspectroscopic images of in situ plant tissue

The chemometric techniques of multivariate curve resolution (MCR) are aimed at extracting the spectra and concentrations of individual components present in mixtures using a minimum set of initial assumptions. We present results from the application of alternating least squares (ALS) based MCR to the analysis of hyperspectral images of in situ biological material. The spectra of individual pure components were mathematically extracted and then identified by searching the spectra against a commercial library. No prior information about the chemical composition of the material was used in the data analysis. The spectra recovered by ALS-MCR analysis of an FT-IR microspectroscopic image of an 8-micron-cornkernel section matched very well the spectra of the corn storage protein, zein, and starch. Through the application of MCR, we were able to show the presence of a second spectrally different protein, which could not be easily seen using univariate analysis. These results demonstrate the value of multivariate curve resolution techniques for the analysis of biological tissue. The value of principal components analysis (PCA) for hyperspectral image analysis is also discussed.     

Characterization of methanol-water and acetonitrile-water association using multivariate curve resolution methods

The solvatochromic comparison method has been used to probe the interactions of solutes with binary solvent mixtures of methanol-water and acetonitrile-water. The solute spectra recorded in these mixtures are composed of the additive spectral contributions of the different solvated species of the solute, i.e., the water-solvated species, the cosolvent-solvated species, and the species solvated by water-solvent complexes. Multivariate curve resolution-alternating least squares has been used to model the solvation of the solutes as a function of the composition of the binary solvent mixture. Spectra and concentration profiles of the dye surrounded by the different solvation environments have been isolated. For the first time, solute spectra solvated exclusively by methanol-water and acetonitrile-water complexes have been obtained, and the solvatochromic parameters of dipolarity/polarizability and hydrogen-bonding acidity have been estimated for these complex species.     

Nondestructive determination of solids and carotenoids in tomato products by near-infrared spectroscopy and multivariate calibration

Tomato is an important player in the agricultural market. It is the second most consumed vegetable in the world and is a source of important micronutrients such as lycopene and beta-carotene. Recent research has demonstrated that these carotenoids can act as free-radical quenchers in the body and prevent aging, tissue damage, heart disease, and certain cancers. Besides these microcomponents, tomato is composed of soluble and insoluble solids. In industry, these solids govern factory yield and play a major role in the tomato trade. Nowadays, standard methods for determining tomato solids and carotenoids are time and labor consuming. In this work, we present the development of a simultaneous and nondestructive method for determining total and soluble solids, as well as lycopene and beta-carotene, in tomato products by near-infrared spectroscopy. PLS-1 was the calibration technique chosen. For spectra preprocessing, MSC and second derivative were applied. As variable selection techniques, the correlogram cutoff, the successive projections algorithm, the dimension wise selection, and spectra splitting approach were applied. Best models presented satisfactory prediction abilities evaluated through its RMSEP and r values: total solids 0.4157, 0.9998; soluble solids 0.6333, 0.9996; lycopene 21.5779, 0.9996; beta-carotene 0.7296, 0.9981.     

Modeling temperature-dependent protein structural transitions by combined near-IR and mid-IR spectroscopies and multivariate curve resolution

The combination of near- and midinfrared spectroscopies (NIR and MIR) is proposed to monitor temperature-dependent transitions of proteins. These techniques offer a high discriminating power to distinguish among protein structural conformations but, in temperature-dependent processes, present the drawback associated with the intense and evolving absorption of the deuterium oxide, used as a solvent in the protein solutions. Multivariate curve resolution-alternating least squares (MCR-ALS) is chosen as the data analysis technique able to unravel the contributions of the pure protein and deuterium oxide species from the mixed raw experimental measurements. To do so, MCR-ALS works by analyzing simultaneously experiments from MIR and NIR on pure deuterium oxide solutions and protein solutions in D2O. This strategy has proven to be effective for modeling the protein process in the presence of D2O and, therefore, for avoiding the inclusion of artifacts in the data stemming from inadequate baseline corrections. The use of MIR and NIR and MCR-ALS has been tested in the study of the temperature-dependent evolution of beta-lactoglobulin. Only the combined use of these two infrared techniques has allowed for the distinction of the three pure conformations involved in the process in the working thermal range: native, R-type state, and molten globule.     

Real-time monitoring of distillations by near-infrared spectroscopy

A simple device is described to couple a fast-scanning acoustooptic tunable filter-based NIR spectrophotometer to a distillation apparatus for monitoring the condensed vapor in real time. The device consists of a small funnel whose glass neck (2-mm diameter) is bent into an "U" format to produce a flow cell of approximately 150-microL inner volume. A pair of optical fibers is used to deliver the monochromatic light and to collect the fraction passing through the glass tube. The end of the condenser of the distillation head touches the wall of the small funnel. The condensed liquid flows uncoupled from pressure changes in the interior of the distillation head. Absorbance spectra were obtained, during the distillation, as averages of 50 scans (4 s) every 5 s in the spectral range 950-1800 nm with nominal resolution of 2.0 nm. In the first experiments, the distillations were performed at constant power supplied to the sample (25 mL) in a microdistillation apparatus working without any type of reflux column. The usefulness of the real-time monitoring of distillation is demonstrated using some prepared binary mixtures and by comparing the distillation behavior of adulterated and regular gasoline samples. Data analysis and interpretation are facilitated by employing principal component analysis. The system accesses the composition of the condensate, which can separate and concentrate one or more compounds present in the original sample.     

Analysis of sticky cotton by near-infrared spectroscopy

"Stickiness" in cotton is a major problem affecting throughput in cotton gins and spinning mills alike. Stickiness is thought to be caused by the deposition of sugars by insects, principally aphid and whitefly, on the open boll. Fourier transform near-infrared (FT-NIR) spectroscopy was used to develop models for sugar content from high-pressure liquid chromatography (HPLC), thermodetector, and mini-card data. A total of 457 cotton samples were selected to represent both Upland and Pima varieties and cotton processing before and after ginning. The Unscrambler was used to develop the models. A successful model was made to determine the mini-card value and successfully detect "stickiness". The standard error of cross-validation (SECv) was 0.26 with an R2 of 0.96. The model was not improved by increasing the range of "stickiness" as measured by the mini-card from the usual 0-3 scale to a scale of 0-8. If a value is determined to be greater than 1 it will be difficult to blend bales at a spinning plant "opening line" to allow for maximum efficiency of spinning.     

Background correction and multivariate curve resolution of online liquid chromatography with infrared spectrometric detection

The use of multivariate curve resolution-alternating least-squares (MCR-ALS) in liquid chromatography-infrared detection (LC-IR) is troublesome due to the intense background absorption changes during gradient elution. Its use has been facilitated by previous removal of a significant part of the solvent background IR contributions due to common mobile phase systems employed during reversed phase gradient applications. Two straightforward background correction approaches based on simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and principal component analysis (PCA) are proposed and evaluated on reversed phase gradient LC-IR data sets obtained during the analysis of carbohydrate and nitrophenol mixtures. After subtraction of the calculated background signal, MCR-ALS provided improved signal-to-noise ratios, removed remaining mobile phase and background signal contributions, and resolved overlapping chromatographic peaks. The present approach tends to enable easy-to-use background correction to facilitate the use of MCR-ALS in online LC-IR, even in challenging situations when gradient conditions are employed and only poor chromatographic resolution is achieved. It, therefore, shows great potential to facilitate the full exploitation of the advantages of simultaneous quantification and identification of a vast amount of analytes employing online IR detection, making new exciting applications more accessible.     

Combination of mid-infrared spectroscopy and curve resolution method to follow the antioxidant action of alkylated diphenylamines

The inhibition of the oxidation of lubricating base oils by dinonyldiphenylamine has been studied by combined Fourier transform infrared (FTIR) spectroscopy and multivariate curve resolution-alternating least squares (MCR-ALS) method. This allowed to extract chemical information during the induction period. The substituted diphenylamines are progressively converted to a degraded form which can be followed with the chemometric analysis. When concentration of dinonyldiphenylamine is too low, the induction period ends and the oxidation of the base oils begins.     

Application of near-infrared spectroscopy to optimize dissolution profiles of tablets according to the granulation mechanism

We developed a method for the optimization of dissolution properties of solid oral dosage forms manufacturing using high shear wet granulation (HSWG) by using near-infrared spectroscopy (NIRS) with chemometrics in small-scale experiments. The changes in rheology and NIR spectra of the granules were monitored to verify the granulation mechanism and determine the suitable water amount for model formulation during the HSWG. Tablets were manufactured by altering the added water amount to investigate the impact of the granulation mechanism on drug product qualities. Model formulation granules were prepared with 10–20% w/w water in a funicular state, corresponding to the plateau region in score plots obtained by principal component analysis (PCA). The dissolution rate of model formulation tablets manufactured with more than 20% w/w of water was significantly delayed while tablets manufactured with 15% w/w water showed 100% dissolution at 15?min. NIRS and PCA are applicable to the optimization of dissolution properties via the process understanding of HSWG at the early formulation development stage and could facilitate drug development.     

In-line reaction monitoring of a methyl methacrylate and N, N-dimethylacrylamide copolymerization reaction using near-infrared spectroscopy

Fast and accurate monitoring of monomer concentration during copolymerization reactions is of much interest. It is known that near-infrared spectroscopy (NIRS) can be used to monitor polymerization reactions. Here, a free radical solution copolymerization reaction between methyl methacrylate and N,N-dimethylacrylamide is considered. NIR spectra were measured in-line with a transflectance probe. The spectra of both involved monomers are very similar, making monitoring with NIRS challenging. It is shown that the NIRS calibration can be set up with only a few (5) off-line measured mixtures. Several validation methods for such a NIRS calibration model are discussed and tested. NIRS is used to follow conversion of the two monomers in a copolymerization reaction on-line.