Multivariate Calibration of Infrared Spectra for Quantitative Analysis Using Designed Experiments

Cahn, Frederick; Compton, Senja V.

doi:10.1366/0003702884428978

Cited by 32 publications

(8 citation statements)

References 13 publications

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“…), beam scattering, sample inhomogeneities, differences in geometry between samples and standards, and system effects (e.g., alignment, accessory effects, and system optics) (Haaland et al 1985). Several statistical models have been developed to help address quantitative non-linearities in simple binary mixtures, especially those arising from intermolecular interactions (Li-shi and Levine 1989;Cahn and Compton 1988;Fredericks et al 1985aFredericks et al , 1985bHaaland et al 1985). Calibration curves typically contain nonzero y-intercepts (e.g., Pollard et al 1990) due to infrared (IR) beam scattering and absorption by system optics (Haaland et al 1985).…”

Section: Introductionmentioning

confidence: 99%

Issues in the Quantitation of Functional Groups by FTIR Spectroscopic Analysis of Impactor-Collected Aerosol Samples

Blando¹,

Porcja²,

Turpin³

2001

Aerosol Science and Technology

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Fourier Transform Infrared (FTIR) spectroscopy is used to complement total carbon and molecular level measurements in the study of organic particulate matter because it is a relatively simple analytical procedure that provides chemically useful details (i.e., functional group and bond information) missing in total carbon measurements. FTIR characterizes the entire aerosol, whereas molecular methods identify only a small fraction of the organic mass. The need to measure the size-resolved chemical composition of ambient aerosols often necessitates the collection of aerosol samples with a single-jet impactor, such as a Hering low pressure impactor (LPI). Single-jet impactor samples have a nonuniform (conical) deposit geometry that results in a variable and inconsistent path length across the sample deposit and may also result in the enhancement of infrared beam scattering. This makes quantitation of LPI samples by traditional procedures based on Beer's Law impossible. Therefore we applied a ratio method developed by Cliff and Lorimer (1972) for energy dispersive spectroscopy to FTIR analyses of LPI samples. In this study the prospects and limitations for quantitative measurement of functional groups by FTIR spectroscopic analysis of impactor-collected aerosol samples is examined using laboratory-generated standards and size-segregated samples collected with a LPI in the Smoky Mountains. FTIR spectroscopy provides semiquantitative estimates of functional group concentrations (precision » 50%) in aerosol samples. However, Infrared (IR) beam scattering due to the physical geometry of impactor deposits is a signi cant source of uncertainty, and concentrations of some functional groups appear to be substantially underestimated due to the "clipping" of high wave number IR peaks by rising baselines. The rising baselines result from increases in IR beam scattering with wave number and are related to the

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Section: Introductionmentioning

confidence: 99%

Issues in the Quantitation of Functional Groups by FTIR Spectroscopic Analysis of Impactor-Collected Aerosol Samples

Blando¹,

Porcja²,

Turpin³

2001

Aerosol Science and Technology

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“…Recently, quantitative spectroscopy has been greatly improved by the use of a variety of multivariate statistical methods (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16). Multivariate calibrations are useful in spectral analyses because the simultaneous inclusion of multiple spectral intensities can greatly improve the precision and applicability of quantitative spectral analyses.…”

Section: Introductionmentioning

confidence: 99%

“…Often, only limited and subjective comparisons between the various multivariate calibration methods have been made in the literature. When detailed comparisons have been made, the comparisons generally were based on a single data set or a small number of data sets (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16). Observed differences between methods applied to a given data set often have not been assessed for 0003-2700/90/0362-1091S02.50/0 statistical significance.…”

Section: Introductionmentioning

confidence: 99%

Comparison of multivariate calibration methods for quantitative spectral analysis

Thomas¹,

Haaland²

1990

Anal. Chem.

457

177

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The quantitative prediction abilities of four multivariate calibration methods for spectral analyses are compared by using extensive Monte Carlo simulations. The calibration methods compared Include Inverse least-squares (ILS), classical least-squares (CLS), partial least-squares (PLS), and principal component regression (PCR) methods. ILS is a frequencylimited method while the latter three are capable of fullspectrum calibration. The simulations were performed assuming Beer's law holds and that spectral measurement errors and concentration errors associated with the reference method are normally distributed. Eight different factors that could affect the relative performance of the calibration methods were varied In a two-level, eight-factor experimental design In order to evaluate their effect on the prediction abilities of the four methods. It Is found that each of the three full-spectrum methods has its range of superior performance.The frequency-limited ILS method was never the best method, although In the presence of relatively large concentration errors it sometimes yields comparable analysis precision to the full-spectrum methods for the major spectral component. The Importance of each factor In the absolute and relative performances of the four methods is compared. A relatively simple model Involving the mean squared prediction errors Is developed for estimating the prediction errors for each calibration method over the range of variation of the factors considered. These results offer the analyst guidelines to be used In evaluating which multivariate calibration method will provide the best predictions when applied to a given spectral data set. In the absence of specific Information about the data set, we would recommend the use of PLS since It Is usually optimal or close to optimal.

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“…The first method uses a set of gravimetrically prepared standards the latter uses spectra from samples that are directly obtained from the sample and analyzed by a suitable reference analysis. For a robust model the composition of the samples need to cover the whole design space and, therefore, the reference samples are planned by a design of experiments (DoE) 42. With the concept of DoE a maximum of information can be obtained with a minimum amount of samples and equipment 43.…”

Section: Strategies For Data Evaluation and Modelingmentioning

confidence: 99%

Quantitative Online NMR Spectroscopy in a Nutshell

Zientek

Meyer

Kern

et al. 2016

Chemie Ingenieur Technik

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Online NMR spectroscopy is an excellent tool to study complex reacting multicomponent mixtures and gain process insight and understanding. For online studies under process conditions, flow NMR probes can be used in a wide range of temperature and pressure. This paper compiles the most important aspects towards quantitative process NMR spectroscopy in complex multicomponent mixtures and provides examples. After NMR spectroscopy is introduced as an online method and for technical samples without sample preparation in deuterated solvents, influences of the residence time distribution, pre-magnetization, and cell design are discussed. NMR acquisition and processing parameters as well as data preparation methods are presented and the most practical data analysis strategies are introduced.

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Multivariate Calibration of Infrared Spectra for Quantitative Analysis Using Designed Experiments

Cited by 32 publications

References 13 publications

Issues in the Quantitation of Functional Groups by FTIR Spectroscopic Analysis of Impactor-Collected Aerosol Samples

Issues in the Quantitation of Functional Groups by FTIR Spectroscopic Analysis of Impactor-Collected Aerosol Samples

Comparison of multivariate calibration methods for quantitative spectral analysis

Quantitative Online NMR Spectroscopy in a Nutshell

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