Improved Sensitivity of Infrared Spectroscopy by the Application of Least Squares Methods

1980 ◽  
Vol 34 (5) ◽  
pp. 539-548 ◽  
Author(s):  
David M. Haaland ◽  
Robert G. Easterling
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Trace Gases ◽  
Region Of Interest ◽  
Detection Limits ◽  
Trace Gas ◽  
Reference Spectrum ◽  
Baseline Shift ◽  
Data Points ◽  
The Individual

Improved sensitivity and precision in the quantitative analysis of trace gases by Fourier transform infrared spectroscopy have been achieved by the application of new spectral least squares methods. By relating all of the spectral information present in the reference spectrum of a trace gas to that of the unknown sample and by appropriately fitting the baseline, detections of trace gases can be obtained even though the individual spectral features may lie well below the noise level. Four least squares methods incorporating different baseline assumptions were investigated and compared using calibrated gases of CO, N2O, and CO2 in dry air. These methods include: (I) baseline known, (II) baseline linear over the spectral region of interest, (III) baseline linear over each spectral peak, and (IV) negligible baseline shift between successive data points. Methods III and IV were found to be most reliable for the gases studied. When method III is applied to the spectra of these trace gases, detection limits improved by factors of 5 to 7 over conventional methods applied to the same data. “Three sigma” detection limits are equal to 0.6, 0.2, and 0.08 ppm for CO, N2O, and CO2, respectively, when a 10-cm pathlength at a total pressure of 640 Torr is used with a ∼35 min measurement time at 0.06 cm−1 resolution.

scholarly journals Recent improvements of long-path DOAS measurements: impact on accuracy and stability of short-term and automated long-term observations

2019 ◽  
Vol 12 (8) ◽  
pp. 4149-4169 ◽  
Author(s):  
Jan-Marcus Nasse ◽  
Philipp G. Eger ◽  
Denis Pöhler ◽  
Stefan Schmitt ◽  
Udo Frieß ◽  
...  
Keyword(s):  
Light Source ◽  
Optical Density ◽  
Measurement Accuracy ◽  
Trace Gases ◽  
Detection Limits ◽  
Trace Gas ◽  
Light Sources ◽  
Light Path ◽  
Mixing Ratios ◽  
Spectral Residual

Abstract. Over the last few decades, differential optical absorption spectroscopy (DOAS) has been used as a common technique to simultaneously measure abundances of a variety of atmospheric trace gases. Exploiting the unique differential absorption cross section of trace-gas molecules, mixing ratios can be derived by measuring the optical density along a defined light path and by applying the Beer–Lambert law. Active long-path (LP-DOAS) instruments can detect trace gases along a light path of a few hundred metres up to 20 km, with sensitivities for mixing ratios down to ppbv and pptv levels, depending on the trace-gas species. To achieve high measurement accuracy and low detection limits, it is crucial to reduce instrumental artefacts that lead to systematic structures in the residual spectra of the analysis. Spectral residual structures can be introduced by most components of a LP-DOAS measurement system, namely by the light source, in the transmission of the measurement signal between the system components or at the level of spectrometer and detector. This article focuses on recent improvements by the first application of a new type of light source and consequent changes to the optical setup to improve measurement accuracy. Most state-of-the-art LP-DOAS instruments are based on fibre optics and use xenon arc lamps or light-emitting diodes (LEDs) as light sources. Here we present the application of a laser-driven light source (LDLS), which significantly improves the measurement quality compared to conventional light sources. In addition, the lifetime of LDLS is about an order of magnitude higher than of typical Xe arc lamps. The small and very stable plasma discharge spot of the LDLS allows the application of a modified fibre configuration. This enables a better light coupling with higher light throughput, higher transmission homogeneity, and a better suppression of light from disturbing wavelength regions. Furthermore, the mode-mixing properties of the optical fibre are enhanced by an improved mechanical treatment. The combined effects lead to spectral residual structures in the range of 5-10×10-5 root mean square (rms; in units of optical density). This represents a reduction of detection limits of typical trace-gas species by a factor of 3–4 compared to previous setups. High temporal stability and reduced operational complexity of this new setup allow the operation of low-maintenance, automated LP-DOAS systems, as demonstrated here by more than 2 years of continuous observations in Antarctica.

Second Derivative Infrared Spectroscopy

1981 ◽  
Vol 35 (1) ◽  
pp. 93-95 ◽  
Author(s):  
Michael R. Whitbeck
Keyword(s):  
Infrared Spectroscopy ◽  
Infrared Spectrum ◽  
Trace Gases ◽  
Numerical Differentiation ◽  
Gas Analysis ◽  
Trace Gas ◽  
Second Derivative ◽  
Trace Gas Analysis

The analysis of trace gases by infrared spectroscopy is enhanced by computing the second derivative of the spectrum. The peak maxima are increased for sharp bands while contributions from broad, structureless, continua are reduced. For weak absorbers the peak maxima are linear in concentration. The advantages of second derivative infrared spectroscopy in trace gas analysis are illustrated, and an algorithm is described for numerical differentiation of a digitized infrared spectrum.

Uncertainty of a Least Squares Curve Fit through Points with Known Uncertainties

1995 ◽  
Vol 23 (4) ◽  
pp. 315-326
Author(s):  
Ronald D. Flack
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Fourth Order ◽  
Simulated Data ◽  
Higher Order ◽  
Individual Data ◽  
Linear Quadratic ◽  
Curve Fit ◽  
Data Points ◽  
The Individual

Uncertainties in least squares curve fits to data with uncertainties are examined. First, experimental data with nominal curve shapes, representing property profiles between boundaries, are simulated by adding known uncertainties to individual points. Next, curve fits to the simulated data are achieved and compared to the nominal curves. By using a large number of different sets of data, statistical differences between the two curves are quantified and, thus, the uncertainty of the curve fit is derived. Studies for linear, quadratic, and higher-order nominal curves with curve fits up to fourth order are presented herein. Typically, curve fits have uncertainties that are 50% or less than those of the individual data points. These uncertainties increase with increasing order of the least squares curve fit. The uncertainties decrease with increasing number of data points on the curves.

scholarly journals Recent improvements of Long-Path DOAS measurements: impact on accuracy and stability of short-term and automated long-term observations

2019 ◽  
Author(s):  
Jan-Marcus Nasse ◽  
Philipp G. Eger ◽  
Denis Pöhler ◽  
Stefan Schmitt ◽  
Udo Frieß ◽  
...  
Keyword(s):  
Light Source ◽  
Optical Density ◽  
Measurement Accuracy ◽  
Trace Gases ◽  
Detection Limits ◽  
Trace Gas ◽  
Light Sources ◽  
Light Path ◽  
Mixing Ratios ◽  
Spectral Residual

Abstract. Over the last decades, Differential Optical Absorption Spectroscopy (DOAS) has been used as a common technique to simultaneously measure abundances of a variety of atmospheric trace gases. Exploiting the unique differential absorption cross section of trace gas molecules, mixing ratios can be derived by measuring the optical density along a defined light path and by applying the Beer-Lambert law. Active long-path (LP-DOAS) instruments can detect trace gases along a light path of a few hundred metres up to 20 km with sensitivities for mixing ratios down to ppbv and pptv levels, depending on the trace gas species. To achieve high measurement accuracy and low detection limits, it is crucial to reduce instrumental artefacts that lead to systematic structures in the residual spectra of the analysis. Spectral residual structures can be introduced by most components of a LP-DOAS measurement system, namely by the light source, in the transmission of the measurement signal between the system components or at the level of spectrometer and detector. This article focuses on recent improvements by the first application of a new type of light source and consequent changes to the optical setup to improve measurement accuracy. Most state-of-the-art LP-DOAS instruments are based on fibre optics and use xenon arc lamps or light emitting diodes (LEDs) as light sources. Here we present the application of a Laser Driven Light Source (LDLS), which significantly improves the measurement quality compared to conventional light sources. In addition the lifetime of LDLS is about an order of magnitude higher than of typical Xe-arc lamps. The small and very stable plasma discharge spot of the LDLS allows the application of a modified fibre configuration. This enables a better light coupling with higher light throughput, higher transmission homogeneity, and a better suppression of light from disturbing wavelength regions. Furthermore, the mode mixing properties of the optical fibre are enhanced by an improved mechanical treatment. The combined effects lead to spectral residual structures in the range of 5–10 · 10−5 RMS (in units of optical density). This represents a reduction of detection limits of typical trace gas species by a factor of 3–4 compared to previous setups. High temporal stability and reduced operational complexity of this new setup allow the operation of low-maintenance automated LP-DOAS systems as demonstrated here by more than two years of continuous observations in Antarctica.

Sampling of Stratospheric Trace Constituents

1974 ◽  
Vol 52 (8) ◽  
pp. 1510-1518 ◽  
Author(s):  
D. H.Ehhalt
Keyword(s):  
Trace Gases ◽  
Detection Methods ◽  
Trace Gas ◽  
Air Samples ◽  
Selective Sampling ◽  
Data Points ◽  
Trace Constituents ◽  
Selective Collection ◽  
Trapping Device ◽  
Stratospheric Trace Gases

The measurement of stable atmospheric trace constituents is often separated into two steps: (a) the collection of a representative sample and (b) the analysis of the trace constituents in the laboratory. The main advantage of this approach is that the most sensitive detection methods can be used. The main disadvantage is that only a relatively limited number of data points can be taken along one sampling profile.Basically two sampling techniques are used: (a) the collection of whole air samples or (b) the selective collection of one or more trace gases on a suitable trapping device. Whole air samples are taken either in the form of grab samples or by compression of air into pressure tanks or by condensing air in cryogenically cooled vessels. Stratospheric trace gases which have been measured by these methods include: CO2, CH4, CO, H2O, H2, N2O, and the noble gases. Selective sampling is accomplished by passing air through molecular sieve beds, impregnated filters, or liquid nitrogen cooled traps. Trace gases retained and measured this way include CO2, H2O, N2O, HNO3.Whole air samplers have been flown on high-flying aircraft, balloons, and rockets. The maximum altitude to which this technique can be usefully extended is 80 km. Selective trace gas samplers have been operated aboard aircraft and on balloons up to an altitude of about 35 km. Examples of the resulting trace gas profiles will be reported.

EXPRESS: Baseline Correction Based on a Search Algorithm from Artificial Intelligence

2020 ◽  
pp. 000370282097751
Author(s):  
Xin Wang ◽  
Xia Chen
Keyword(s):  
Artificial Intelligence ◽  
Objective Function ◽  
Least Squares ◽  
Least Squares Method ◽  
Search Algorithm ◽  
Absolute Error ◽  
Search Problem ◽  
Baseline Correction ◽  
Current Spectrum ◽  
Data Points

Many spectra have a polynomial-like baseline. Iterative polynomial fitting (IPF) is one of the most popular methods for baseline correction of these spectra. However, the baseline estimated by IPF may have substantially error when the spectrum contains significantly strong peaks or have strong peaks located at the endpoints. First, IPF uses temporary baseline estimated from the current spectrum to identify peak data points. If the current spectrum contains strong peaks, then the temporary baseline substantially deviates from the true baseline. Some good baseline data points of the spectrum might be mistakenly identified as peak data points and are artificially re-assigned with a low value. Second, if a strong peak is located at the endpoint of the spectrum, then the endpoint region of the estimated baseline might have significant error due to overfitting. This study proposes a search algorithm-based baseline correction method (SA) that aims to compress sample the raw spectrum to a dataset with small number of data points and then convert the peak removal process into solving a search problem in artificial intelligence (AI) to minimize an objective function by deleting peak data points. First, the raw spectrum is smoothened out by the moving average method to reduce noise and then divided into dozens of unequally spaced sections on the basis of Chebyshev nodes. Finally, the minimal points of each section are collected to form a dataset for peak removal through search algorithm. SA selects the mean absolute error (MAE) as the objective function because of its sensitivity to overfitting and rapid calculation. The baseline correction performance of SA is compared with those of three baseline correction methods: Lieber and Mahadevan–Jansen method, adaptive iteratively reweighted penalized least squares method, and improved asymmetric least squares method. Simulated and real FTIR and Raman spectra with polynomial-like baselines are employed in the experiments. Results show that for these spectra, the baseline estimated by SA has fewer error than those by the three other methods.

scholarly journals Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1460
Author(s):  
Jinming Liu ◽  
Changhao Zeng ◽  
Na Wang ◽  
Jianfei Shi ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Square ◽  
Biochemical Methane Potential ◽  
Methane Potential

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

scholarly journals Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 666
Author(s):  
Rafael Font ◽  
Mercedes del Río-Celestino ◽  
Diego Luna ◽  
Juan Gil ◽  
Antonio de Haro-Bailón
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cicer Arietinum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Neutral Detergent Fiber ◽  
Acid Detergent Fiber ◽  
Cicer Arietinum L

The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

Sign in / Sign up

Export Citation Format

Share Document