Willem Windig

Infrared Chemical Micro-Imaging Assisted by Interactive Self-Modeling Multivariate Analysis

In the analytical environment, spectral data resulting from analysis of samples often represent mixtures of several components. Extraction of information about pure components from that kind of mixture is a major problem, especially when reference spectra are not available. Self-modeling multivariate mixture analysis has been developed for this type of problem. In this paper, two examples will be used to show the potential of the technique for vibrational spectroscopy. Infrared microspectroscopic chemical imaging has been employed to improve spatial resolution for distinguishing differences between adjacent, nonidentical materials. The resolution of a 2- to 3-μm-thick inner layer, from a four-layer polymer laminate, has been achieved. The same approach has been utilized to extract pure component spectra out of a KBr pellet of a mixture of three compounds.

Biochemical analysis of wood and wood products by pyrolysis-mass spectrometry and multivariate analysis☆

Abstract Pyrolysis—mass spectrometry in combination with discriminant analysis and a newly development non-supervised mixture analysis approach is shown to provide valuable information about the biochemical composition of wood and wood products. The application of this technique to the chemotaxonomy of sagebrush (Artemisia) species as well as to the quality control of wood pulping processes is discussed. With regard to the latter application strong correlations are found between classical lignin and xylan determinations and direct pyrolysis—mass spectrometric analysis results.

Biochemical differences observed in pyrolysis mass spectra of range grasses with different resistance to Labops hesperius Uhler attack

Pyrolysis mass spectrometry in combination with discriminant analysis is shown to be a promising approach for the chemical differentiation of range grass species and interspecific hybrids with different susceptibilities to Labop: hesperius damage. A clear correlation between susceptibility and pyrolysis patterns believed to represent isoprenoids, phospholipids, proteins and glycosides with phenolic aglycones was found.

Quantitative analysis of rubber triblends by pyrolysis-mass spectrometry

Abstract Quantitative analysis of the components of rubber vulcanizates is a difficult, yet very important, process in industrial polymer chemistry. Infrared (IR) and nuclear magnetic resonance (NMR) spectroscopy are the techniques most often used. These methods generally involve time consuming pretreatment steps, but in favorable cases these methods yield results with 1–5% accuracy. In a preliminary study, we acquired Curie-point pyrolysis-mass spectra for several uncured rubber blends as well as some compounded vulcanizates. Qualitative examination of the data indicated the potential for the derivation of quantitative data from the spectra. Now we have examined a series of rubber triblend samples by pyrolysis-mass spectrometry (Py-MS) to assess the quantitative aspects. The three components were styrene-butadiene rubber (SBR), cis-polybutadiene rubber (BR), and natural (polyisoprene) rubber (NR). The blends were analyzed in triplicate or quadruplicate by Curie-point Py-MS from toluene suspensions of samples obtained by prolonged grinding under liquid nitrogen. The spectra were normalized with the NORMA program and subsequently analyzed by target rotation factor analysis using the SPSS program. Finally, response factors were calculated for each of the components, and these were used to predict the concentrations of the individual rubber components in various training or test sets of data. The response factors were found to be relatively constant for each of the blends, indicating that interactions between components were minimal. The average errors for the three components were in the 2–5% range. The error for BR was slightly larger since the butadiene monomer is common to both SBR and BR. The results demonstrate the feasibility of the Py-MS approach to polymer blend analysis. The results are comparable in accuracy to those obtained by IR and NMR spectroscopy.

Characterization of U.S. lignites by pyrolysis mass spectrometry and multivariate analysis

Abstract Sixteen Texas (Gulf Province) lignite samples and six Montana and Wyoming (Northern Great Plains Province) lignitic coals were obtained from the Pennsylvania State University Coal Bank and analyzed in triplicate by pyrolysis mass spectrometry (Py-MS) using Curie-point pyrolysis (equilibrium temp. 610°C) in combination with low-voltage (12 eV) electron ionization. The spectra obtained were evaluated by means of factor analysis, followed by discriminant analysis using only factors with eigenvalue ⩾ 1 and regarding each set of triplicate spectra as a separate category. The discriminant analysis results showed a definite separation between lignites from the two provinces as well as some clustering of samples from the same seam field or region. Six additional lignite samples obtained from an independent source and representing other regions of the Gulf Province were found to cluster with the Texas lignite samples when treated as “unknowns” in the discriminant analysis procedure. Chemical interpretation of the spectral differences underlying the clustering behavior of the lignite samples in the discriminant analysis procedure was attempted using a newly developed, unsupervised numerical extraction method for chemical components in complex spectra. This procedure, the Variance Diagram (VARDIA) technique, revealed the presence of six major chemical component axes. Examination of the spectral patterns corresponding to these component axes showed a softwood lignin-like component (high in Northern Great Plains lignitic coals) and an alipathic (algal?) hydrocarbon component (high in Gulf lignites) to be primarily responsible for the differences between the two provinces. In addition, two biomarker patterns, namely a terpenoid resin-like component and an unknown component, were shown to be highly characteristic for the Northern Great Plains and Gulf Province samples, respectively. Two other component axes were found to consist largely of sulfur-containing ion series, one of which appeared to represent an obvious marine influence on the South Texas region of the Gulf Province. Furthermore, a set of seventeen conventional coal parameters, including petrographic, ultimate and proximate analysis data as well as sulfur content, calorific value and vitrinite reflectance, obtained from the Pennsylvania State University coal data bank on all twenty-two samples, was also submitted to factor analysis. Comparison of the scores of the first two factors from this set with the scores of the first two discriminant functions of the Py-MS data set revealed an overall similarity in clustering behavior of the samples from the two provinces. Subsequently, canomical variate analysis was used to rotate both the conventional and Py-MS data sets to a common set of vectors describing the correlating (“overlapping”) portions of both data sets. Examination of the first two pairs of canonical variate functions revealed strong correlations between the conventional data and the Py-MS data, e.g., with regard to alipathic vs. aromatic or hydrocarbon vs. heteroatomic tendencies, as well as sulfur containing moieties. This enabled a final, tentative synthesis of all the data into several highly simplified schemes relating compositional aspects to depositional environments.

Influence of pyrolytic techniques on mass spectra of biopolymers with chemical ionization at atmospheric pressure

Abstract Three pyrolytic techniques were compared and contrasted for production of mass spectral information from the biopolymers deoxyribonucleic acid, glycogen and bovine serum albumin (BSA) in combination with an atmospheric pressure chemical ionization source of a triple quadrupole mass spectrometer. The mass spectral features of certain biopolymers were affected under oxidative conditions according to the type of pyrolysis probe, pulsed or programmed heating rate, near ambient/ambient ion source pressures (0.97–0.98 atm) and the presence/absence of an air flow over the heating zone. These factors significantly influenced the mass spectral appearance of the DNA nitrogenous base and deoxyribose moiety species and the glucose monomer, dehydration and other pyrolysis products. However, similar mass spectra were obtained for BSA with various source condition combinations and heating probes.

Angle-Constrained Alternating Least Squares

When resolving mixture data sets using self-modeling mixture analysis techniques, there are generally a range of possible solutions. There are cases, however, in which a unique solution is possible. For example, variables may be present (e.g., m/z values in mass spectrometry) that are characteristic for each of the components (pure variables), in which case the pure variables are proportional to the actual concentrations of the components. Similarly, the presence of pure spectra in a data set leads to a unique solution. This paper will show that these solutions can be obtained by applying angle constraints in combination with non-negativity to the solution vectors (resolved spectra and resolved concentrations). As will be shown, the technique goes beyond resolving data sets with pure variables and pure spectra by enabling the analyst to selectively enhance contrast in either the spectral or concentration domain. Examples will be given of Fourier transform infrared (FT-IR) microscopy of a polymer laminate, secondary ion mass spectrometry (SIMS) images of a two-component mixture, and energy dispersive spectrometry (EDS) of alloys.

Curie-point pyrolysis atmospheric pressure chemical ionization mass spectrometry as a probe on the effect of sodium chloride on biopolymers

Abstract The combination of Curie-point wire pyrolysis and atmospheric pressure chemical ionization mass spectrometry (APCI-MS) is described and applied to study the effect of sodium chloride on DNA, bovine serum albumin and glycogen biopolymers. Characteristic fragments are observed for the biopolymers in the pyrolysis-APCI technique. The presence of salt has a moderate effect on peak intensities in the DNA spectrum but reduces the relative intensities of a majority of the oxygen-containing glycogen fragments as well as of alkanenitrile albumin fragments. Multivariate analysis reveals significant effects of salt on the reproducibility (inner-variance) and separation or selectivity (outer-variance) of the biopolymer mass spectra. The presence of salt produced evidence of mass spectral information loss to the original biopolymer mass spectra, and a variable effect is noted on mass spectral reproducibility.

Multivariate analysis of time-resolved mass spectral data

Abstract Multvariate analysis of time-resolved pyrolysis/mass spectrometric data is described. The approach is based on the variance diagram (VARDIA), a recently developed technique that quantifies the clustering of variables in two-dimensional factor analysis (sub)-spaces in a rotational scanning procedure. A maximum in the VARDIA plot indicates a correlated behavior of the mass variables, indicating a common origin. This common origin is generally caused by a change in the concentration of a chemical component. With this information the “factor spectrum” and the scores of the component can be retrieved. For time-resolved serial data, consideration of the clustering behavior of the variables as a function of time is more appropriate than a rotational scanning procedure. Adaptation of the VARDIA for serial data, such as time-resolved data, is described. This approach has the advantage that all the factors can be used. It will be shown that the resolution of the obtained curve than the total ion current curve as a function of time. Examples will be given for time-resolved data of coal, rubber and wood samples.

Effects of low temperature air oxidation (weathering) reactions on the pyrolysis mass spectra of US coals

Abstract Two high volatile bituminous coals (Upper Freeport and Hiawatha seams), a subbituminous coal (Adaville seam) and a lignite (Anderson seam) were used to investigate the effect of low temperature air oxidation (‘weathering’) on the yield and composition of vacuum pyrolysis products. Fresh coal samples were exposed to air at 80 and 100°C for up to 10 days under controlled laboratory conditions. Curie-point pyrolysis mass spectrometry combined with computerized data analysis was applied to study the weathering induced changes in a series of samples weathered for various lengths of time. It was found that the abundance of small oxygen-containing molecules such as carbon monoxide, carbon dioxide and aliphatic carboxylic acids increased in the pyrolysate of all samples, whereas phenols and dihydroxybenzenes showed decreased yields, especially in the two lowest rank coals. Besides phenols, alkylnaphthalenes and alkyltetralins showed decreased abundances in the pyrolysis mass spectra of the two high volatile bituminous coals. An attempt is made to explain the difference in pyrolysis patterns observed before and after weathering experiments in terms of underlying structural changes.