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.

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.

scholarly journals Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: sparse methods for statistical selection of relevant absorption bands

2016 ◽  
Vol 9 (7) ◽  
pp. 3429-3454 ◽  
Author(s):  
Satoshi Takahama ◽  
Giulia Ruggeri ◽  
Ann M. Dillner
Keyword(s):  
Infrared Spectra ◽  
Atmospheric Aerosols ◽  
Quantitative Prediction ◽  
Calibration Model ◽  
Vibrational Modes ◽  
Aerosol Composition ◽  
Absorption Bands ◽  
Calibration Models ◽  
Ft Ir ◽  
Selection Of

Abstract. Various vibrational modes present in molecular mixtures of laboratory and atmospheric aerosols give rise to complex Fourier transform infrared (FT-IR) absorption spectra. Such spectra can be chemically informative, but they often require sophisticated algorithms for quantitative characterization of aerosol composition. Naïve statistical calibration models developed for quantification employ the full suite of wavenumbers available from a set of spectra, leading to loss of mechanistic interpretation between chemical composition and the resulting changes in absorption patterns that underpin their predictive capability. Using sparse representations of the same set of spectra, alternative calibration models can be built in which only a select group of absorption bands are used to make quantitative prediction of various aerosol properties. Such models are desirable as they allow us to relate predicted properties to their underlying molecular structure. In this work, we present an evaluation of four algorithms for achieving sparsity in FT-IR spectroscopy calibration models. Sparse calibration models exclude unnecessary wavenumbers from infrared spectra during the model building process, permitting identification and evaluation of the most relevant vibrational modes of molecules in complex aerosol mixtures required to make quantitative predictions of various measures of aerosol composition. We study two types of models: one which predicts alcohol COH, carboxylic COH, alkane CH, and carbonyl CO functional group (FG) abundances in ambient samples based on laboratory calibration standards and another which predicts thermal optical reflectance (TOR) organic carbon (OC) and elemental carbon (EC) mass in new ambient samples by direct calibration of infrared spectra to a set of ambient samples reserved for calibration. We describe the development and selection of each calibration model and evaluate the effect of sparsity on prediction performance. Finally, we ascribe interpretation to absorption bands used in quantitative prediction of FGs and TOR OC and EC concentrations.

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 Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques

Foods ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 232
Author(s):  
Hanim Z. Amanah ◽  
Salma Sultana Tunny ◽  
Rudiati Evi Masithoh ◽  
Myoung-Gun Choung ◽  
Kyung-Hwan Kim ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Prediction Models ◽  
Soybean Seed ◽  
Fourier Transform Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model ◽  
Spectroscopic Techniques ◽  
Ft Ir

The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to predict total as well as an individual type of isoflavones and oligosaccharides using intact soybean samples. A partial least square regression method was performed to develop models based on the spectral data of 310 soybean samples, which were synchronized to the reference values evaluated using a conventional assay. Furthermore, the obtained models were tested using soybean varieties not initially involved in the model construction. As a result, the best prediction models of FT-NIR were allowed to predict total isoflavones and oligosaccharides using intact seeds with acceptable performance (R2p: 0.80 and 0.72), which were slightly better than the model obtained based on FT-IR data (R2p: 0.73 and 0.70). The results also demonstrate the possibility of using FT-NIR to predict individual types of evaluated components, denoted by acceptable performance values of prediction model (R2p) of over 0.70. In addition, the result of the testing model proved the model’s performance by obtaining a similar R2 and error to the calibration model.

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 Long-Term Strategy for Assessing Carbonaceous Particulate Matter Concentrations from Multiple Fourier Transform Infrared (FT-IR) Instruments: Influence of Spectral Dissimilarities on Multivariate Calibration Performance

2018 ◽  
Vol 73 (3) ◽  
pp. 271-283 ◽  
Author(s):  
Bruno Debus ◽  
Satoshi Takahama ◽  
Andrew T. Weakley ◽  
Kelsey Seibert ◽  
Ann M. Dillner
Keyword(s):  
Fourier Transform ◽  
Particulate Matter ◽  
Hybrid Model ◽  
Spectral Response ◽  
Fourier Transform Infrared ◽  
Calibration Transfer ◽  
Ft Ir ◽  
Matching Procedure ◽  
The Impact

Matching the spectral response between multiple spectrometers is a mandatory procedure when developing robust calibrations whose prediction is independent of instrument-related signal variations. A viable alternative to complex calibration transfer methods consists of matching the instrument spectral response by controlling a set of key instrumental and environmental parameters. This paper discusses the applicability of such an approach to three Fourier transform infrared (FT-IR) spectrometers used for the routine assessment of carbonaceous particulate matter concentrations in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) speciation network. The effectiveness of the proposed matching procedure is evaluated by comparing the spectral response for each individual instrument in order to characterize the extent, and nature, of the remaining inter-instrument spectral dissimilarities. Instrument-related contributions to the signal were determined to be small compared with the spectral variability induced by the filter type used for sample collection. The impact of spectral differences on prediction was addressed through the comparison of model performance derived from multiple calibration scenarios. A hybrid model yielding accurate and homogeneous prediction regardless of the instrument was proposed for organic carbon (OC) and elemental carbon (EC), two major constituents of atmospheric particulate matter. Coefficients of determination of 0.98 (OC) and 0.90 (EC) with median biases not exceeding 0.20 µg (OC) and 0.07 µg (EC) are reported. The long-term stability, assessed from weekly measurements of reference samples, shows a deviation in predicted concentrations of less than ±5% over a 2.5-year period for most of the data collected. Extending OC and EC hybrid models to the prediction of ambient samples collected during the two subsequent years provides satisfactory performance. The proposed instrument matching procedure coupled with the relative simplicity of the hybrid model is an alternative to computationally advanced calibration transfer methodologies for the characterization of carbonaceous particulate matter using multiple FT-IR instruments.

scholarly journals Analysis of Raspberry Ketone in Nutraceutical Formulation Using Fourier Transform Infrared Spectrophotometric Method

2019 ◽  
Vol 7 (1) ◽  
pp. 8-13
Author(s):  
Hassan Y. Aboul-Enein ◽  
Oana Mihaela Antochi ◽  
Gheorghe Nechifor ◽  
Andrei A. Bunaciu
Keyword(s):  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Infrared Region ◽  
Spectrometric Method ◽  
Calibration Model ◽  
Ir Spectrometry ◽  
Raspberry Ketone ◽  
Ft Ir ◽  
Analytical Quantification

Aims: A Fourier Transform Infrared (FT-IR) spectrometric method was developed for the rapid, direct measurement of Raspberry Ketone (RK) and Caffeine (CAF) in a nutraceutical formulation. Methods: Conventional KBr-spectra and KBr+0.5 mg Microcrystalline Cellulose (MCC)-spectra were used as the basis for a better determination of active substances in the nutraceutical formulation. A calibration model was developed using caffeine and raspberry ketone standards of varying concentrations in the mid-infrared region (4000-400 cm-1). The Beer-Lambert law was used in data processing. Results: The results indicate that FT-IR spectrometry is applicable to the analytical quantification of RK and CAF in the nutraceutical formulation. Conclusion: The method proposed is simple, precise and not time-consuming compared to the chromatographic methods that are cited in the literature. Quantification is performed in about 10-15 minutes, including sample preparation and spectral acquisition.

FR-IR Molecular Microanalysis System

1990 ◽  
Vol 48 (2) ◽  
pp. 270-271
Author(s):  
John A. Reffner ◽  
William T. Wihlborg
Keyword(s):  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Integrated System ◽  
Morphological Examination ◽  
Fully Integrated ◽  
Ir Microscopy ◽  
Normal Functions ◽  
Infrared Microspectroscopy ◽  
Infrared Spectral ◽  
Ft Ir

The IRμs™ is the first fully integrated system for Fourier transform infrared (FT-IR) microscopy. FT-IR microscopy combines light microscopy for morphological examination with infrared spectroscopy for chemical identification of microscopic samples or domains. Because the IRμs system is a new tool for molecular microanalysis, its optical, mechanical and system design are described to illustrate the state of development of molecular microanalysis. Applications of infrared microspectroscopy are reviewed by Messerschmidt and Harthcock.Infrared spectral analysis of microscopic samples is not a new idea, it dates back to 1949, with the first commercial instrument being offered by Perkin-Elmer Co. Inc. in 1953. These early efforts showed promise but failed the test of practically. It was not until the advances in computer science were applied did infrared microspectroscopy emerge as a useful technique. Microscopes designed as accessories for Fourier transform infrared spectrometers have been commercially available since 1983. These accessory microscopes provide the best means for analytical spectroscopists to analyze microscopic samples, while not interfering with the FT-IR spectrometer’s normal functions.

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