Applications of Chemometrics for Characterization of Macromolecules

Abstract

The possibilities of employing methods of chemometrics in order to characterize macromolecules are described. The review has been limited to chemometric methods concerning multivariate data analysis. Principal component analysis (PCA) has shown to be very useful for pattern recognition problems arising from chromatographic and spectroscopic data. An example of using a classification technique, SIMCA (Soft Independent Modelling of Class Analogy), as a product control method is presented. The suitability of Partial Least Squares (PLS) for relating data of different natures, e.g. chemical data with biological data, is discussed. Moreover, examples ranging from spectroscopic determinations to QSAR (Quantitative Structure Activity Relationships) are illustrated.     

Quantitative spectroscopic analysis of heterogeneous mixtures: the correction of multiplicative effects caused by variations in physical properties of samples

Spectral measurements of complex heterogeneous types of mixture samples are often affected by significant multiplicative effects resulting from light scattering, due to physical variations (e.g., particle size and shape, sample packing, and sample surface, etc.) inherent within the individual samples. Therefore, the separation of the spectral contributions due to variations in chemical compositions from those caused by physical variations is crucial to accurate quantitative spectroscopic analysis of heterogeneous samples. In this work, an improved strategy has been proposed to estimate the multiplicative parameters accounting for multiplicative effects in each measured spectrum and, hence, mitigate the detrimental influence of multiplicative effects on the quantitative spectroscopic analysis of heterogeneous samples. The basic assumption of the proposed method is that light scattering due to physical variations has the same effects on the spectral contributions of each of the spectroscopically active chemical components in the same sample mixture. On the basis of this underlying assumption, the proposed method realizes the efficient estimation of the multiplicative parameters by solving a simple quadratic programming problem. The performance of the proposed method has been tested on two publicly available benchmark data sets (i.e., near-infrared total diffuse transmittance spectra of four-component suspension samples and near-infrared spectral data of meat samples) and compared with some empirical approaches designed for the same purpose. It was found that the proposed method provided appreciable improvement in quantitative spectroscopic analysis of heterogeneous mixture samples. The study indicates that accurate quantitative spectroscopic analysis of heterogeneous mixture samples can be achieved through the combination of spectroscopic techniques with smart modeling methodology.     

Elastic net grouping variable selection combined with partial least squares regression (EN-PLSR) for the analysis of strongly multi-collinear spectroscopic data

In this paper a novel wavelength region selection algorithm, called elastic net grouping variable selection combined with partial least squares regression (EN-PLSR), is proposed for multi-component spectral data analysis. The EN-PLSR algorithm can automatically select successive strongly correlated prediction variable groups related to the response variable using two steps. First, a portion of the correlated predictors are selected and divided into subgroups by means of the grouping effect of elastic net estimation. Then, a recursive leave-one-group-out strategy is employed to further shrink the variable groups in terms of the root mean square error of cross-validation (RMSECV) criterion. The performance of the algorithm with real near-infrared (NIR) spectroscopic data sets shows that the EN-PLSR algorithm is competitive with full-spectrum PLS and moving window partial least squares (MWPLS) regression methods and it is suitable for use with strongly correlated spectroscopic data.     

Optical tomography by the temporally extrapolated absorbance method

The concept of the temporally extrapolated absorbance method (TEAM) for optical tomography of turbid media has been verified by fundamental experiments and image reconstruction. The TEAM uses the time-resolved spectroscopic data of the reference and object to provide projection data that are processed by conventional backprojection. Optical tomography images of a phantom consisting of axisymmetric double cylinders were experimentally obtained with the TEAM and time-gating and continuous-wave (CW) methods. The reconstructed TEAM images are compared with those obtained with the time-gating and CW methods and are found to have better spatial resolution.     

Optimization of human plasma 1H NMR spectroscopic data processing for high-throughput metabolic phenotyping studies and detection of insulin resistance related to type 2 diabetes

Optimizing NMR experimental parameters for high-throughput metabolic phenotyping requires careful examination of the total biochemical information obtainable from (1)H NMR data, which includes concentration and molecular dynamics information. Here we have applied two different types of mathematical transformation (calculation of the first derivative of the NMR spectrum and Gaussian shaping of the free-induction decay) to attenuate broad spectral features from macromolecules and enhance the signals of small molecules. By application of chemometric methods such as principal component analysis (PCA), orthogonal projections to latent structures discriminant analysis (O-PLS-DA) and statistical spectroscopic tools such as statistical total correlation spectroscopy (STOCSY), we show that these methods successfully identify the same potential biomarkers as spin-echo (1)H NMR spectra in which broad lines are suppressed via T2 relaxation editing. Finally, we applied these methods for identification of the metabolic phenotype of patients with type 2 diabetes. This "virtual" relaxation-edited spectroscopy (RESY) approach can be particularly useful for high-throughput screening of complex mixtures such as human plasma and may be useful for extraction of latent biochemical information from legacy or archived NMR data sets for which only standard 1D data sets exist.     

Improved understanding of factors contributing to quantification of anhydrate/hydrate powder mixtures

Different spectroscopic approaches have proved to be excellent analytical tools for monitoring process-induced transformations of active pharmaceutical ingredients during pharmaceutical unit operations. In order to use these tools effectively, it is necessary to build calibration models that describe the relationship between the amount of each solid-state form of interest and the spectroscopic signal. In this study, near-infrared (NIR) and Raman spectroscopic methods have been evaluated for the quantification of hydrate and anhydrate forms in pharmaceutical powders. Process type spectrometers were used to collect the data and the role of the sampling procedure was examined. Multivariate regression models were compared with traditional univariate calibrations and special emphasis was placed on data treatment prior to multivariate modeling by partial least squares (PLS). It was found that the measured sample volume greatly affected the performance of the model whereby the calibrations were significantly improved by utilizing a larger sampling area. In addition, multivariate regression did not always improve the predictability of the data compared to univariate analysis. The data treatment prior to multivariate modeling had a significant influence on the quality of predictions with standard normal variate transformation generally proving to be the best preprocessing method. When the appropriate sampling techniques and data analysis methods were utilized, both NIR and Raman spectroscopy were found to be suitable methods for the quantification of anhydrate/hydrate in powder systems, and thus the method of choice will depend on the conditions in the process under investigation.     

Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets

We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.     

Analysis of variance in spectroscopic imaging data from human tissues

The analysis of cell types and disease using Fourier transform infrared (FT-IR) spectroscopic imaging is promising. The approach lacks an appreciation of the limits of performance for the technology, however, which limits both researcher efforts in improving the approach and acceptance by practitioners. One factor limiting performance is the variance in data arising from biological diversity, measurement noise or from other sources. Here we identify the sources of variation by first employing a high throughout sampling platform of tissue microarrays (TMAs) to record a sufficiently large and diverse set data. Next, a comprehensive set of analysis of variance (ANOVA) models is employed to analyze the data. Estimating the portions of explained variation, we quantify the primary sources of variation, find the most discriminating spectral metrics, and recognize the aspects of the technology to improve. The study provides a framework for the development of protocols for clinical translation and provides guidelines to design statistically valid studies in the spectroscopic analysis of tissue.     

Ionization kinetics in a supersonic xenon plasma flow entering electric field

We have studied xenon plasma moving in a supersonic diffuser in external electric and magnetic fields. The main physical parameters of the plasma (electron temperature and density) were determined using specially developed methods based on the theory of continuous optical emission from inert gas atoms. These experimental data are compared to the results of theoretical calculations. Based on an analysis of the results of spectroscopic measurements, a mechanism of plasma ionization is established which is capable of maintaining a high degree of ionization in the supersonic xenon plasma flow.     

Application of two-dimensional band-target entropy minimization to fluorescence data: implications for the recovery of patterns arising from only bilinear and not trilinear structures

Pattern recognition in two-dimensional (2D) spectroscopy, without recourse to spectral libraries, etc., has a number of important potential applications. In the present contribution, two blind source separation techniques (spectral reconstruction) are applied to sets of 2D fluorescence data possessing both Rayleigh scattering and Raman scattering. The two methods used are (1) two-dimensional band-target entropy minimization (2D-BTEM), which models data as a bilinear form (in terms of a weighted sum of 2D patterns) and (2) parallel factor analysis (PARAFAC), which models data as a trilinear form. In addition, an a priori estimate of the number of patterns present is not required by 2D-BTEM but is required in PARAFAC. Both 2D-BTEM and PARAFAC are successfully applied to the real three-component data, and good 2D spectral reconstructions of the three amino acids are achieved. Moreover, 2D-BTEM was also able to recover the 2D Raman scattering directly, whereas PARAFAC did not recover the 2D Raman scatter (the Raman scatter does not possess a trilinear form). The present results suggest that 2D-BTEM can be useful in a wide range of spectroscopic applications for the recovery of underlying 2D patterns.     

Chemical imaging of latent fingerprint residues

In situ attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectroscopic imaging has been used to obtain chemical images of fingerprints under controlled humidity and temperature. The distribution of lipid and amino acid components in the fingerprints from different donors left on the surface of the ZnSe crystal has been studied using an in situ FT-IR spectroscopic imaging approach under a controlled environment and studied as a function of time. Univariate and multivariate analyses were employed to analyze the spectroscopic dataset. Changes in the spectra of lipids with temperature and time have been detected. This information is needed to understand aging of the fingerprints. The ATR-FT-IR spectroscopic imaging offers a new and complementary means for studying the chemistry of fingerprints that are left pristine for further analysis. This study demonstrates the potential for visualizing the chemical changes of fingerprints for forensic applications by spectroscopic imaging.     

Determination and resolution of non-UV-visible absorptive component during a kinetic process with modified soft modeling methods

Soft modeling (SM) methods can be used to resolve spectroscopic data from complicated reaction processes with unknown kinetics, with the exception of data containing a component for which there is no spectroscopic information available, such as an intermediate that does not absorb in the UV-visible region. In this work, modified SM methods were developed to resolve these undetectable components. Based on the mass balance principle, the mass balance error (MBE) method was first applied to determine whether the undetectable component existed. Next, the evolving error analysis (EEA) method was developed to search the local mass balance region (LMBR) where the concentration of the non-absorptive component was low enough to be neglected. In the LMBR, the concentration profiles of all absorptive components were scaled according to least squares regression. Subsequently, more reliable results were obtained using the evolving time region iteration (ETRI) method. Based on the mass balance principle, the concentration profile of the undetectable component was resolved for the entire time period. Both simulated and experimental data from an autocatalytic reaction were used to demonstrate the feasibility of the proposed method. In the autocatalytic oxidation of sodium oxalate by acidic potassium permanganate, the product Mn(II) was determined to be non-absorptive. Using the methods described above, the pure spectra of three other absorptive components and the scaled concentration profiles of four Mn species, including two intermediates, were all resolved. As a result, the mechanism of the reaction was more clearly described.     

Distinguishability of biological material by use of ultraviolet multispectral fluorescence

Recent interest in the detection and analysis of biological samples by spectroscopic methods has led to questions concerning the degree of distinguishability and biological variability of the UV fluorescent spectra from such complex samples. We show that the degree of distinguishability of such spectra is readily determined numerically. As a practical example of this technique, we show its application to the analysis of UV fluorescence spectra taken of E. coli, S. aureus, and S. typhimurium. The use of this analysis to determine the degree of biological variability and also to verify that measurements are being made in a linear regime in which analytic methods such as multivariate analysis are valid is discussed.     

Sampling and Raman confocal microspectroscopic analysis of airborne particulate matter using poly(dimethylsiloxane) solid-phase microextraction fibers

Commercial poly(dimethylsiloxane) (PDMS) 7-microm solid-phase microextraction (SPME) fibers were used for sampling and Raman spectroscopic analysis of a tailpipe diesel exhaust, candle smoke, cigarette smoke, and asbestos dust. Samples were collected via direct exposure of the SPME fiber to contaminated air. The mass loading for SPME fibers was varied by changing the sampling time. Results indicate that PDMS-coated fibers provide a simple, fast, reusable, and cost-effective air sampling tool for airborne particulates. The PDMS coating was stable; Raman bands of the PDMS coating were observed exactly at the same wavenumber positions before and after air sampling. Raman spectroscopic analysis resulted in identification of several characteristic bands allowing chemical speciation of particulates. The advantage of the SPME fiber is the open bed geometry allowing for application of various spectroscopic methods of particulate analysis. This paper describes the first-ever combined application of SPME technology with Raman confocal microspectroscopy for sampling and analysis of airborne particulates. Advantages of the combination of solid-phase microextraction and Raman microspectroscopy for airborne particulate analysis are discussed. Challenges associated with combined SPME sampling and Raman analysis of single particles are also described.     

Chemometric study of excitation-emission matrix fluorescence data: quantitative analysis of petrol-kerosene mixtures

Products of petroleum crude are multifluorophoric in nature due to the presence of a mixture of a variety polycyclic aromatic hydrocarbons (PAHs). The use of excitation-emission matrix fluorescence (EEMF) spectroscopy for the analysis of such multifluorophoric samples is gaining progressive acceptance. In this work, EEMF spectroscopic data is processed using chemometric multivariate methods to develop a reliable calibration model for the quantitative determination of kerosene fraction present in petrol. The application of the N-way partial least squares regression (N-PLS) method was found to be very efficient for the estimation of kerosene fraction. A very good degree of accuracy of prediction, expressed in terms of root mean square error of prediction (RMSEP), was achieved at a kerosene fraction of 2.05%.     

Band-target entropy minimization (BTEM) applied to hyperspectral Raman image data

Band-target entropy minimization (BTEM) has been applied to extraction of component spectra from hyperspectral Raman images. In this method singular value decomposition is used to calculate the eigenvectors of the spectroscopic image data set. Bands in non-noise eigenvectors that would normally be used for recovery of spectra are examined for localized spectral features. For a targeted (identified) band, information entropy minimization or a closely related algorithm is used to recover the spectrum containing this feature from the non-noise eigenvectors, plus the next 5-30 eigenvectors, in which noise predominates. Tests for which eigenvectors to include are described. The method is demonstrated on one synthesized Raman image data set and two bone tissue specimens. By inclusion of small amounts of signal that would be unused in other methods, BTEM enables the extraction of a larger number of component spectra than are otherwise obtainable. An improvement in signal/noise ratio of the recovered spectra is also obtained.     

Nonintrusive optical measurements of aircraft engine exhaust emissions and comparison with standard intrusive techniques

Nonintrusive systems for the measurement on test rigs of aeroengine exhaust emissions required for engine certification (CO, NO(x), total unburned hydrocarbon, and smoke), together with CO(2) and temperature have been developed. These results have been compared with current certified intrusive measurements on an engine test. A spectroscopic database and data-analysis software has been developed to enable Fourier-transform Infrared measurement of concentrations of molecular species. CO(2), CO, and NO data showed agreement with intrusive techniques of approximately ?30%. A narrow-band spectroscopic device was used to measure CO(2) (with deviations of less than ?10% from the intrusive measurement), whereas laser-induced incandescence was used to measure particles. Future improvements to allow for the commercial use of the nonintrusive systems have been identified and the methods are applicable to any measurement of combustion emissions.     

Introduction and application of secured principal component regression for analysis of uncalibrated spectral features in optical spectroscopy and chemical sensing

In this study, a novel chemometric algorithm for improved evaluation of analytical data is presented and applied to three spectroscopic data sets obtained by different analytical methods. This so-called secured principal component regression (sPCR) was developed for detecting and correcting uncalibrated spectral features newly emerging in spectra after finalizing the PCR calibration, which may result in major concentration errors. Hence, detection and correction of uncalibrated features is essential. Furthermore, detected uncalibrated features provide qualitative information for sensing and process monitoring applications indicating problems in the process flow. After conventional PCR calibration, sPCR analyzes measurement data in two steps: The first step investigates whether the obtained data set is consistent with the calibration model or not. If spectroscopic features are found that cannot be modeled by the principal components, they are extracted from the measurement spectrum. This corrected spectrum is then evaluated by conventional PCR. In the Experimental Section, sPCR was successfully applied to three data sets obtained by different spectroscopic measurements in order to corroborate general applicability of the proposed concept. For each data set, one of several substances was excluded from the calibration acting in the sPCR assessment as uncalibrated absorber. The test sets consisted of disturbed and undisturbed samples. A total of 109 out of 110 test samples were correctly classified as disturbed or undisturbed by an uncalibrated absorber. It was confirmed that the extracted disturbance spectra are in accordance with the spectra of the uncalibrated analytes. The concentration results obtained with sPCR were found to be equivalent to conventional PCR results in the case of undisturbed samples and more precise for disturbed samples.