Application of Multivariate Calibration Techniques to Quantitative Analysis of Bandpass-Filtered Fourier Transform Infrared Interferogram Data

1997 ◽  
Vol 51 (9) ◽  
pp. 1369-1376 ◽  
Author(s):  
Mutua J. Mattu ◽  
Gary W. Small ◽  
Mark A. Arnold
Keyword(s):  
Fourier Transform ◽  
Quantitative Analysis ◽  
Multivariate Calibration ◽  
Fourier Transform Infrared ◽  
Calibration Model ◽  
Calibration Models ◽  
Background Measurement ◽  
Ft Ir ◽  
Calibration Techniques ◽  
Absorbance Spectra

Multivariate calibration models are developed that allow quantitative analysis of short segments of Fourier transform infrared (FT-IR) interferogram data. Before the interferogram segments are submitted to partial least-squares (PLS) regression analysis, a bandpass digital filter is applied to isolate a narrow range of frequencies that correspond to an absorption band of the target analyte. This adds frequency selectivity to the analysis, thereby overcoming the principal obstacle to the direct use of interferogram data for quantitative analysis. With the optimization of the frequency response function of the filter, as well as the position and length of the interferogram segment employed, calibration models are developed that compare well with those computed with conventional absorbance spectra. This methodology is demonstrated by developing calibration models for determining glucose in an aqueous buffer matrix over the physiologically relevant concentration range of 1–20 mM. Through the use of a time-domain filter designed to isolate the modulated interferogram frequencies corresponding to the glucose C–H combination band at 4400 cm−1, a three-factor PLS calibration model is computed on the basis of interferogram points 601–850. This model is characterized by standard errors of calibration (SEC) and prediction (SEP) of 0.3311 and 0.6950 mM, respectively. The best model obtained in a thorough analysis of the corresponding absorbance spectra was also based on three PLS factors. This model was characterized by values of SEC and SEP of 0.2396 and 0.6115, respectively. In addition to achieving similar calibration and prediction results to the spectral-based model, the interferogram-based method has the advantage of requiring no background measurement of the sample matrix. Furthermore, since the analysis is based on only a 250-point segment of the interferogram, a reduction in the instrumentation and data collection requirements is realized.

Quantitative Analysis of Sulfur Dioxide with Passive Fourier Transform Infrared Remote Sensing Interferogram Data

2000 ◽  
Vol 54 (3) ◽  
pp. 341-348 ◽  
Author(s):  
Mutua J. Mattu ◽  
Gary W. Small ◽  
Roger J. Combs ◽  
Robert B. Knapp ◽  
Robert T. Kroutil
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Sulfur Dioxide ◽  
Temperature Variation ◽  
Multivariate Calibration ◽  
Fourier Transform Infrared ◽  
Calibration Models ◽  
Ft Ir ◽  
Infrared Frequencies ◽  
The Impact

Multivariate calibration models are developed for the determination of sulfur dioxide (SO2) by passive Fourier transform infrared (FT-IR) remote sensing measurements. In a series of experiments designed to simulate the measurement of SO2 from industrial stack emissions, low-angle sky backgrounds are viewed through the windows of a heated flow-through gas cell. With this apparatus, infrared emission from the hot SO2 is measured against the cold background of the sky. The FT-IR interferogram data collected are analyzed directly in the construction of the calibration models. Bandpass digital filters are applied to the interferograms to isolate the modulated infrared frequencies corresponding to either the asymmetric or symmetric S–O stretching vibrations at 1361 and 1151 cm−1, respectively. Quantitative calibration models are constructed by submitting short segments of the filtered interferograms to partial least-squares regression analysis. The experimental design allows the impact of variation in the temperature of the SO2 to be evaluated for its effect on the calibration models. Three data sets are constructed consisting of data with increasing temperature variation. When the temperature variation in the data is less than 30 °C, the calibration models are able to achieve a cross-validation standard error of prediction (CV-SEP) of approximately 27 ppm-m across the 185 to 727 ppm-m range of density-corrected, path-averaged concentration. These calibration models are applied to an interferogram segment of only 250 points, and do not require any separate measurement of the infrared background. A comparison of the results from the interferogram-based analyses with those obtained in an analysis of single-beam spectral data reveals similar performances for the models computed with both types of data.

scholarly journals Atmospheric particulate matter characterization by Fourier Transform Infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks

2018 ◽  
Author(s):  
Satoshi Takahama ◽  
Ann M. Dillner ◽  
Andrew T. Weakley ◽  
Matteo Reggente ◽  
Charlotte Bürki ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Particulate Matter ◽  
Fourier Transform Infrared ◽  
Atmospheric Particulate Matter ◽  
Calibration Model ◽  
Vibrational Modes ◽  
Atmospheric Particulate ◽  
Calibration Models ◽  
Ft Ir ◽  
Statistical Calibration

Abstract. Atmospheric particulate matter (PM) is a complex mixture of many different substances, and requires a suite of instruments for chemical characterization. Fourier Transform Infrared (FT-IR) spectroscopy is a technique that can provide quantification of multiple species provided that accurate calibration models can be constructed to interpret the acquired spectra. In this capacity, FT-IR has enjoyed a long history in monitoring gas-phase constituents in the atmospher and in stack emissions. However, application to PM poses a different set of challenges as the condensed-phase spectrum has broad, overlapping absorption peaks and contributions of scattering to the mid-infrared spectrum. Past approaches have used laboratory standards to build calibration models for prediction of inorganic substances or organic functional groups and predicting their concentration in atmospheric PM mixtures by extrapolation. In this work, we review recent studies pursuing an alternate strategy, which is to build statistical calibration models for mid- IR spectra of PM using collocated ambient measurements. Focusing on calibrations with organic carbon (OC) and elemental carbon (EC) reported from thermal optical reflectance (TOR), this synthesis serves to consolidate our knowledge for extending FT-IR to provide TOR-equivalent OC and EC measurements to new PM samples when TOR measurements are not available. We summarize methods for model specification, calibration sample selection, and model evaluation for these substances at several sites in two US national monitoring networks: 7 sites in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network for the year 2011, and 10 sites in the Chemical Speciation Network (CSN) for the year 2013. We then describe application of the model in an operational context for the IMPROVE network for samples collected in 2013 at 6 of the same sites as 2011, and 11 additional sites. In addition to extending the evaluation to samples from a different year and different sites, we describe strategies for error anticipation due to precision and biases from the calibration model to assess model applicability for new spectra a priori. We conclude with a discussion regarding past work and future strategies for recalibration. In addition to targeting numerical accuracy, we encourage model interpretation to facilitate understanding of the underlying structural composition related to operationally-defined quantities of TOR OC and EC from the vibrational modes in mid-IR deemed most informative for calibration. The paper is structured such that the life cycle of a statistical calibration model for FT-IR can be envisioned for any substance with IR-active vibrational modes, and more generally for instruments requiring ambient calibrations.

scholarly journals Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks

2019 ◽  
Vol 12 (1) ◽  
pp. 525-567 ◽  
Author(s):  
Satoshi Takahama ◽  
Ann M. Dillner ◽  
Andrew T. Weakley ◽  
Matteo Reggente ◽  
Charlotte Bürki ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Particulate Matter ◽  
Ir Spectroscopy ◽  
Atmospheric Particulate Matter ◽  
Calibration Model ◽  
Vibrational Modes ◽  
Calibration Models ◽  
Ft Ir ◽  
Ft Ir Spectroscopy ◽  
Statistical Calibration

Abstract. Atmospheric particulate matter (PM) is a complex mixture of many different substances and requires a suite of instruments for chemical characterization. Fourier transform infrared (FT-IR) spectroscopy is a technique that can provide quantification of multiple species provided that accurate calibration models can be constructed to interpret the acquired spectra. In this capacity, FT-IR spectroscopy has enjoyed a long history in monitoring gas-phase constituents in the atmosphere and in stack emissions. However, application to PM poses a different set of challenges as the condensed-phase spectrum has broad, overlapping absorption peaks and contributions of scattering to the mid-infrared spectrum. Past approaches have used laboratory standards to build calibration models for prediction of inorganic substances or organic functional groups and predict their concentration in atmospheric PM mixtures by extrapolation. In this work, we review recent studies pursuing an alternate strategy, which is to build statistical calibration models for mid-IR spectra of PM using collocated ambient measurements. Focusing on calibrations with organic carbon (OC) and elemental carbon (EC) reported from thermal–optical reflectance (TOR), this synthesis serves to consolidate our knowledge for extending FT-IR spectroscopy to provide TOR-equivalent OC and EC measurements to new PM samples when TOR measurements are not available. We summarize methods for model specification, calibration sample selection, and model evaluation for these substances at several sites in two US national monitoring networks: seven sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) network for the year 2011 and 10 sites in the Chemical Speciation Network (CSN) for the year 2013. We then describe application of the model in an operational context for the IMPROVE network for samples collected in 2013 at six of the same sites as in 2011 and 11 additional sites. In addition to extending the evaluation to samples from a different year and different sites, we describe strategies for error anticipation due to precision and biases from the calibration model to assess model applicability for new spectra a priori. We conclude with a discussion regarding past work and future strategies for recalibration. In addition to targeting numerical accuracy, we encourage model interpretation to facilitate understanding of the underlying structural composition related to operationally defined quantities of TOR OC and EC from the vibrational modes in mid-IR deemed most informative for calibration. The paper is structured such that the life cycle of a statistical calibration model for FT-IR spectroscopy can be envisioned for any substance with IR-active vibrational modes, and more generally for instruments requiring ambient calibrations.

scholarly journals Application of Fourier Transform Infrared Spectroscopy for the Oxidation and Peroxide Value Evaluation in Virgin Walnut Oil

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Pengjuan Liang ◽  
Chaoyin Chen ◽  
Shenglan Zhao ◽  
Feng Ge ◽  
Diqiu Liu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Peroxide Value ◽  
Edible Oils ◽  
Fourier Transform Infrared ◽  
Calibration Model ◽  
Walnut Oil ◽  
Value Evaluation ◽  
Ft Ir

Recent developments in Fourier transform infrared spectroscopy-partial least squares (FTIR-PLSs) extend the application of this strategy to the field of the edible oils and fats research. In this work, FT-IR spectroscopy was used as an effective analytical tool to determine the peroxide value of virgin walnut oil (VWO) samples undergone during heating. The spectra were recorded from a film of pure oil between two disks of KBr for each sample at frequency regions of 4000–650 cm−1. Changes in the values of the frequency of most of the bands of the spectra were observed and used to build the calibration model. PLS model correlates the actual and FT-IR estimated value of peroxide value with a correlation coefficient of 0.99, and the root mean square error of the calibration (RMSEC) value is 0.4838. The methodology has potential as a fast and accurate way for the quantification of peroxide value of the edible oils.

scholarly journals Rapid Quantitative Analysis of the Major Components in Soymilk Using Fourier-Transform Infrared Spectroscopy (FT-IR)

2004 ◽  
Vol 10 (2) ◽  
pp. 137-142 ◽  
Author(s):  
Katsuhiko NAKASATO ◽  
Tomotada ONO ◽  
Takahiro ISHIGURO ◽  
Michihiko TAKAMATSU ◽  
Chigen TSUKAMOTO ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Fourier Transform Infrared Spectroscopy ◽  
Fourier Transform Infrared ◽  
Ft Ir

Examinations of the Matrix Isolation Fourier Transform Infrared Spectra of Organic Compounds: Part XVII

1989 ◽  
Vol 43 (8) ◽  
pp. 1424-1427 ◽  
Author(s):  
W. M. Coleman ◽  
Bert M. Gordon
Keyword(s):  
Fourier Transform ◽  
Quantitative Analysis ◽  
Matrix Isolation ◽  
Complex Mixture ◽  
Fourier Transform Infrared ◽  
Mass Range ◽  
Infrared Spectrometry ◽  
Flame Ionization ◽  
The Matrix ◽  
Ft Ir

Qualitative and quantitative analysis of compounds in a complex mixture by gas chromatography/matrix isolation/Fourier transform infrared spectrometry (GC/MI/FT-IR) is described. The carbon-deuterium stretching mode was characterized and used for analysis since it has a unique position in the infrared spectrum. Compounds of varying functionalities were examined over a concentration range from 6 to 50 ng. Linear responses over this mass range were obtained. Flame ionization detection was used for collaborative detection in establishing the linearity of the responses. These results represent the first use of GC/MI/FT-IR for quantitative analysis of compounds in a complex mixture using deuterium-labeled analogues.

Quantitative Fourier-Domain Analysis. Part I: Analysis of Raw FT-IR Interferograms

1992 ◽  
Vol 46 (11) ◽  
pp. 1699-1710 ◽  
Author(s):  
Stephen L. Monfre ◽  
Steven D. Brown
Keyword(s):  
Fourier Transform ◽  
Quantitative Analysis ◽  
Digital Filter ◽  
Fourier Transform Infrared ◽  
Domain Analysis ◽  
Spectral Information ◽  
Fourier Domain ◽  
Recursive Digital Filter ◽  
Ft Ir ◽  
Quantitative Results

A new procedure for performing quantitative analysis in the Fourier domain has been developed. The procedure involves the use of a linear, recursive digital filter for the analysis of raw interferogram data from a Fourier transform infrared (FT-IR) spectrometer. It was discovered that the analysis of raw interferograms permitted the use of spectral information that is generally neglected when conventional quantitative Fourier-domain procedures are used. Furthermore, a significant reduction in the computation requirements for Fourier-domain quantitative analysis procedures is seen. It was also discovered during the analysis of the raw interferograms that instrument stability was one factor which affected the accuracy of the concentration estimates. Tests were performed to demonstrate the required stability of the instrumentation, and a procedure is described for maintaining the required instrument stability throughout the analysis. Once instrument stability was achieved, the quantitative results which were obtained were similar to results obtained from the conventional Fourier-domain analysis algorithms.

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