Absorption bands in this region are generally due to intramolecular phenomena and are highly specific to each material. The specificity of these bands allows computerized data searches within reference libraries to identify a material. Quantitation - Quantitative concentration of a compound can be determined from the area under the curve in characteristic regions of the IR spectrum.
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Concentration calibration is obtained by establishing a standard curve from spectra for known concentrations. Sample requirements vary depending on the sample form and instrument. Samples may be in liquid, solid or gaseous form. Thin organic films on a reflective surface e. Download PDF Print this page. Since the ambient concentrations likely fluctuate from place to place e. Up to now, only three studies continuously measured real-time ambient concentration to logically cross validate quantitative methods and data qualities under fluctuating environmental factors e.
The soils were classified as Drummer silty clay loam fine-silty, mixed, mesic Typic Endoaquoll with a bulk density of 1. A mid-IR beam in the spectrometer passed through the atmosphere along an optical path and returned to the telescope after reflection from a retroreflector to collect spectra that included information about the gas of interest. The humidity, air temperature, and wind information were measured from the weather station.
Ambient concentrations of N 2 O and CO 2 were also determined independently to assess the bias and precision. An S-OPS consisted of 9. The inlet flow rates were adjusted by critical orifices to 0. The recorded data were telemetered to the on-site instrumentation trailer. A spectra range of The IFGs were converted to single-beam SB spectra using a zero-filling factor of 1, triangular apodization, and Mertz phase correction.
Each sampled SB spectrum was stray-light corrected by subtracting the stray-light SB spectrum from the sampled SB spectrum before converting to the absorbance spectrum. The IFGs and corresponding SB spectra were influenced by ambient factors that included wind-derived vibrations, scintillation induced by air mixing, water vapour content, dust accumulation, and condensation on the retroreflector. The maximum and minimum of the IFG centre bursts were controlled between approximately 0. To calculate a concentration for a given solute, a stray-light corrected SB spectrum is ratioed against an SB background spectrum GHG-free to produce an absorbance spectrum from which the gas concentration is determined using the Beer—Lambert law.
Two different approaches were used in this study to overcome this constraint. Both methods required a normal SB spectrum corresponding to the path length of interest that was then mathematically manipulated to produce a background spectrum. This is illustrated for the N 2 O region of interest in Fig. For the zap-bkg method, one quality SB spectrum was selected to create a zap-bkg each day, and all of the sampled SB spectra collected from one day were converted to absorbance spectra using this zap-bkg.
In this case, numerous points in the non-absorbing region of the SB spectrum were selected as base points, and a high-order fitting function was used to construct a background spectrum. Six points within The mathematically manipulated SB spectra were used as background files to convert the sampled SB spectra into absorbance spectra Fig.
For the syn-bkg method, all data points were stored as one data file, and this file was applied to each sampled SB spectrum to create its syn-bkg. Since the selected points determined the curvature of the syn-bkg SB spectrum, it is critical to choose the data points that do not introduce any distortion e. In general, we avoided selecting data points within the absorption feature of interest e.
Adding too many data points may lead to artificial distortion in a syn-bkg. Because the syn-bkg is one of the recommended methods for spectral analysis ASTM, , it was used to assess the feasibility of the zap-bkg method. Based on the Beer—Lambert law, we used reference spectra to predict gas concentrations from field absorbance spectra.
The details of these two methods are described as follows.
CLS prediction model. Each of the reference spectra used in the CLS model contained only one gas component e. The non-linear function between the actual and predicted gas concentrations of the reference spectra was selected in the CLS model in both quantitative packages. PLS prediction model. Each of the reference spectra used in the PLS model consisted of multiple gas components e. Certified N 2 O was diluted with ultra-pure N 2 gas using a diluter series , Environics Inc, Tolland, CT , and the water vapour content was controlled by a Nafion tube Perma Pure, Lakewood, NJ contained within a sealed container of saturated water vapour.
Spectra were acquired at 0.
In order to avoid overfitting the models, the optimum set of factors used in PLS models was determined by cross validation and justified by the prediction of residual error sum of squares PRESS function. The correlation between known and PLS-predicted concentrations was used to quantify N 2 O from the field absorbance spectrum converted by syn-bkg within given spectral windows. Spectral window selections. The window selection Fig. While a broader window contained more information of the gas of interest and potentially improved the spectral fit between the modelled and sampled spectra and the quantitative accuracy, it also included more features of water vapour and led to biases in gas quantifications.
On the other hand, a narrow window can minimize the interfering effect of the uninteresting gases but may reduce the spectral information of the targeted gas, which lead to biases in gas calculations e. The window used for N 2 O quantifications was For CO 2 , the spectral windows of Multiple windows W C 1—3 shown in Fig.
Both SB background methods zap- and syn-bkg were used to convert the sampled SB spectra to absorbance spectra for gas quantifications.
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Spectral windows experiencing less water vapour interference generally improved the accuracy of N 2 O quantification. In the CLS model, N 2 O concentrations calculated from the absorbance spectra converted by zap-bkg were underestimated by This bias was reduced using W N 2 Likewise, N 2 O concentrations derived from the absorbance spectra converted by syn-bkg were underestimated by 8.
This bias was reduced using W N 3 Although interferences of water vapour can be mitigated by narrowing down spectral windows, the narrowest window W N 4: The narrowed window also lost N 2 O absorption features and presumably increased biases if the analytical window was over-confined. The P-branch feature of N 2 O extended from In the CLS model, the window of As previously mentioned, it was important to generate a reasonable background for the spectral analysis.
The syn-bkg method coupled with the integrated window of For CO 2 estimations, three spectral windows were used in the The accuracy of CO 2 quantification was also improved by narrowing down spectral windows Fig. In the CLS model, CO 2 concentrations calculated from the absorbance spectra converted by zap-bkg were underestimated by 6. This bias was reduced by the narrowed window of W C 2 The bias of the calculated CO 2 concentrations was - 4.
The most confined window W C 3: Thus, the range from Since the absorbance feature of CO 2 at Therefore, the calculated bias showed that there was no significant difference between zap- and syn-bkg methods for CO 2 concentration calculations using the W C 2 Fig. Chambers, T. Staal, J.
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Fourier Transform Infrared Spectroscopy (FTIR) Analysis
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