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 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.

The Nature of Cation–Anion Interactions in Magnetic Ionic Liquids as Revealed Using High-Pressure Fourier Transform Infrared (FT-IR) Spectroscopy

2019 ◽  
Vol 73 (5) ◽  
pp. 511-519 ◽  
Author(s):  
Christopher M. Burba ◽  
Hai-Chou Chang
Keyword(s):  
High Pressure ◽  
Fourier Transform ◽  
Ionic Liquids ◽  
Ir Spectroscopy ◽  
Ir Spectra ◽  
Ambient Pressure ◽  
Vibrational Modes ◽  
Magnetic Ionic Liquids ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

Magnetic ionic liquids are a group of magneto-responsive compounds that typically possess high ionic conductivities and low vapor pressures. In spite of the general interest in these materials, a number of questions concerning the fundamental interactions among the ions remain unanswered. We used vibrational spectroscopy to gain insight into the nature of these interactions. Intramolecular vibrational modes of the ions are quite sensitive to their local potential energy environments, which are ultimately defined by cation–anion coordination schemes present among the ions. Ambient pressure Fourier transform infrared (FT-IR) spectroscopy indicates comparable interaction motifs for 1-ethyl-3-methylimidazolium tetrachloroferrate(III), [emim]FeCl4, and 1-ethyl-3-methylimidazolium tetrabromoferrate(III), [emim]FeBr4, magnetic ionic liquids. However, the vibrational modes of [emim]FeCl4 generally occur at slightly higher frequencies than those of [emim]FeBr4. These differences reflect different interaction strengths between the [emim]+ cations and [Formula: see text] or [Formula: see text] anions. This conclusion is supported by gas-phase ab initio calculations of single [emim]FeCl4 and [emim]FeBr4 ion pairs that show longer C–H···Br–Fe interaction lengths compared to C–H···Cl–Fe. Although the IR spectra of [emim]FeCl4 and [emim]FeBr4 are comparable at ambient pressure, a different series of spectroscopic changes transpire when pressure is applied to these compounds. This suggests [emim]+ cations experience different types of interaction with the anions under high-pressure conditions. The pressure-dependent FT-IR spectra highlights the critical role ligands attached to the tetrahalogenoferrate(III) anions play in modulating cation–anion interactions in magnetic ionic liquids.

scholarly journals Detection of coconut (Cocos nucivera) sugar adulteration in palm (Arenga pinnata Merrill) sugar by Fourier Transform Infrared (FT-IR) Spectroscopy

Food Research ◽  
2021 ◽  
Vol 5 (S2) ◽  
pp. 31-36
Author(s):  
F. Roosmayanti ◽  
K. Rismiwindira ◽  
R.E. Masithoh
Keyword(s):  
Fourier Transform ◽  
Ir Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Calibration Model ◽  
Mean Square ◽  
Palm Sugar ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

Palm sugar which is also named brown sugar is powdered sugar produced from palm extract. Due to the high price of palm sugar, its contamination of materials that are cheap or low quality is inevitable. Usually, adulteration detection is done by conventional methods such as HPLC, TLC, or NMR which are time-consuming and require high-priced equipment, thus impractical for routine and large sample analysis. The aim of this research was to detect adulteration in palm sugar using Fourier Transform Infrared (FT-IR) spectroscopy. The samples used in this study were palm sugar as the main ingredient and coconut sugar as the adulterant. Two chemometric methods namely principal component analysis (PCA) and partial least squares regression (PLSR) were used for analysis. The absorbance data were taken at wavenumber 4000-650 cm-1 . Several concentrations of coconut sugar as an adulterant ranging from 0 to 100% were added to palm sugar. A total of 110 spectra of both pure and adulterated palm sugar samples were divided into two groups, i.e. 73 samples for developing calibration model and 37 samples for developing prediction model. The spectral obtained were pre-processed and analyzed using The Unscrambler X version 10.4. a total of six pre-processing methods were used, i.e., Normalization, Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Baseline. Results showed that PCA was able to classify palm sugar based on adulterant concentrations. PLSR calibration model with a coefficient of determination (Rc2 ) of 0.94 and root mean square error of calibration (RMSEC) of 8% was obtained by applying the MSC method. The model was able to predict coconut sugar adulteration in palm sugar with Rp2 of 0.89 and root mean square error of prediction (RMSEP) of 10.68%. The results confirmed the potential of FT-IR spectroscopy for detecting adulteration in palm sugar.

scholarly journals Bacterial Typing and Identification Based on Fourier Transform Infrared Spectroscopy

2020 ◽  
Author(s):  
Huayan Yang ◽  
Fangling Wu ◽  
Fuxin Xu ◽  
Keqi Tang ◽  
Chuanfan Ding ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Ir Spectroscopy ◽  
Bacterial Culture ◽  
Clinical Laboratory ◽  
Fourier Transform Infrared ◽  
Identification Accuracy ◽  
Bacterial Typing ◽  
Typical Experiment ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

Abstract Fourier transform infrared (FT-IR) spectroscopy is a label-free and highly sensitive technique that provides complete information on the chemical composition of biological samples. The bacterial FT-IR signals are extremely specific and highly reproducible fingerprint-like patterns, making FT-IR an efficient tool for bacterial typing and identification. Due to the low cost and high flux, FT-IR has been widely used in hospital hygiene management for infection control, epidemiological studies, and routine bacterial determination of clinical laboratory values. However, the typing and identification accuracy could be affected by many factors, and the bacterial FT-IR data from different laboratories are usually not comparable. A standard protocol is required to improve the accuracy of FT-IR-based typing and identification. Here, we detail the principles and procedures of bacterial typing and identification based on FT-IR spectroscopy, including bacterial culture, sample preparation, instrument operation, spectra collection, spectra preprocessing, and mathematical data analysis. Without bacterial culture, a typical experiment generally takes <2 h.

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.

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