Wavelet transform based on the optimal wavelet pairs for tunable diode laser absorption spectroscopy signal processing

Quantitative analysis of SO2, NO2 and NO mixed gases based on ultraviolet absorption spectrum

SO2, NO2 and NO are the main atmospheric pollutants produced by the combustion of fossil fuel. Detecting these gases is of great significance for atmospheric protection and the online concentration detection of pollutants. In this study, the concentration retrieval methods of NO, NO2 and SO2 and their mutual effects were studied in the wavelength range of 192.3-254.4 nm. In this band, NO, NO2 and SO2 have large absorption cross-sections; however, their spectrum superpositions were serious. A novel method was proposed to separate the superposed absorption spectra of NO and SO2 or NO2. The advantage of this method is that it can remove the influence of SO2 and NO2 on NO concentration retrieval. The fast Fourier transform (FFT) amplitude method was used to calculate the concentrations of SO2 and NO2, and the direct absorption spectroscopy method was used to calculate NO concentration. Via these methods, the gas concentrations of SO2, NO2 and NO can be calculated in ternary-gas mixtures. The experimental results show that these methods can effectively remove the mutual interferences between the concentration retrieval of NO, NO2 and SO2. The maximum absolute values of the relative deviations for the concentration retrieval of SO2, NO2 and NO in ternary-gas mixtures are 3.868%, 4.740% and 5.008%, respectively. These methods have high detection precision and good adaptability and are suitable for online flue detection equipment.

Study on the concentration retrieval of SO2 and NO2 in mixed gases based on the improved DOAS method

NO₂ and SO₂ are important components of air pollutants, and their absorption spectra are superimposed at 193-253 nm. The superposed spectra affect the gas concentration retrieval based on the ultraviolet differential optical absorption spectroscopy (DOAS) method. In this study, a suitable wavelength band was chosen for concentration retrieval, moreover, the characteristics of the quasi-periodic variation of absorption cross-section with wavelength was given sufficient attention and then the superposed spectra were separated by the Fast Fourier transform (FFT) method. The concentration of the gases to be measured was calculated according to the relationship between the amplitude of absorbance after FFT and the gas concentration. The experimental results prove that by using an absorption cell with a 700 mm optical path, the relative deviation absolute value of the retrieval concentration of SO₂ in a NO₂ and SO₂ gas mixture is less than 1.471%, and that of NO₂ in a NO₂ and SO₂ gas mixture is less than 7.207%. The method has good adaptability, high detection precision, whether single SO₂, NO₂ or a mixture of both, and important reference value for the development of DOAS and future research on the high-precision detection of more types of mixed gases in the ultraviolet band, such as gas mixtures of NO₂, SO₂ and NO in flue gas.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

Highly sensitive methane detection based on light-induced thermoelastic spectroscopy with a 2.33 µm diode laser and adaptive Savitzky-Golay filtering

In this manuscript, a highly sensitive methane (CH₄) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2.33 µm diode laser with high power is demonstrated for the first time. A quartz tuning fork (QTF) with an intrinsic resonance frequency of 32.768 kHz was used to detect the light-induced thermoelastic signal. A Herriot multi-pass cell with an effective optical path of 10 m was adopted to increase the laser absorption. The laser wavelength modulation depth and concentration response of this CH₄-LITES sensor were investigated. The sensor showed excellent long term stability when Allan deviation analysis was performed. An adaptive Savitzky-Golay (S-G) filtering algorithm with χ2 statistical criterion was firstly introduced to the LITES technique. The SNR of this CH₄-LITES sensor was improved by a factor of 2.35 and the minimum detection limit (MDL) with an integration time of 0.1 s was optimized to 0.5 ppm. This reported CH₄-LITES sensor with sub ppm-level detection ability is of great value in applications such as environmental monitoring and industrial safety.

SGCS: a signal reconstruction method based on Savitzky-Golaysgz filtering and compressed sensing for wavelength modulation spectroscopy

The residual oxygen concentration in pharmaceutical glass vial variously threatens the aseptic properties of encapsulated agents. The demodulated 2nd harmonic signals in the wavelength modulation spectroscopy (WMS) detection system, the data basis of the inversion of oxygen concentration, are inevitably destroyed by various time-varying industrial noises. In this work, we propose a signal reconstruction method based on self-correcting Savitzky-Golaysgz filter and compressed sensing (namely SGCS) for the urgent signal denoising task, which is a dual-step lightweight denoising scheme. First, in order to avoid the influence of glitch noise on sparse signal reconstruction, Savitzky-Golay (S-G) filter is used to smooth the 2nd harmonic signal while retaining the change information effectively. Then, the well-tuned measurement matrix of compressed-sensing (CS) is applied to aggressively fetch the sparse principal components while bypassing most residual dynamic noises. Finally, the orthogonal matching pursuit (OMP) is used to reconstruct the 2nd harmonic signal according to sparsity constrain and the sparse principal components. Experimental results show that the performance of SGCS method is superior. Compared with other competitive methods the operation time of SGCS is the shortest. When the normalized SNR is 1, the average correct discrimination rate is 98.57%. Even if SNR reduces from 1 to 0.55, the WMS detection system still survives well, with the highest average correct discrimination rate of 89.34%.

Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection

A non-local patch regression (NLPR) denoising-enhanced differential broadband photoacoustic (PA) sensor was developed for the high-sensitive detection of multiple trace gases. Using the edge preservation index (EPI) and signal-to-noise ratio (SNR) as a dual-criterion, the fluctuation was dramatically suppressed while the spectral absorption peaks were maintained by the introduction of a NLPR algorithm. The feasibility of the broadband framework was verified by measuring the C2H2 in the background of ambient air. A normalized noise equivalent absorption (NNEA) coefficient of 6.13 × 10−11 cm−1·W·Hz−1/2 was obtained with a 30-mW globar source and a SNR improvement factor of 23. Furthermore, the simultaneous multiple-trace-gas detection capability was determined by measuring C2H2, H2O, and CO2. Following the guidance of single-component processing, the NLPR processed results showed higher EPI and SNR compared to the spectra denoised by the wavelet method and the non-local means algorithm. The experimentally determined SNRs of the C2H2, H2O, and CO2 spectra were improved by a factor of 20. The NNEA coefficient reached a value of 7.02 × 10−11 cm−1·W·Hz−1/2 for C2H2. The NLPR algorithm presented good performance in noise suppression and absorption peak fidelity, which offered a higher dynamic range and was demonstrated to be an effective approach for trace gas analysis.

High-precision methane isotopic abundance analysis using near-infrared absorption spectroscopy at 100 Torr

A near-infrared methane (CH₄) sensor system for carbon isotopic abundance analysis was developed based on laser absorption spectroscopy (LAS). For good thermal stability, two CH₄ absorption lines with a similar low-state energy level were selected to realize relative weak temperature dependence. Wavelet denoising (WD) was employed for a pre-treatment of the direct absorption spectral (DAS) signal to perform a preliminary suppression of high-frequency noise. Due to the abnormal ¹³CH₄ profile caused by superimposition of multiple lines, two statistical analysis algorithms including linear regression and neural network prediction were respectively employed on the retrieval of molecule fractions instead of the traditionally used standard absorption line fitting method. Performance assessment and a comparison between the two methods were carried out. Compared with the concentration deducing method based on the maximum absorbance in rough data, the linear regression and the neural network prediction obtained a sensitivity enhancement by ∼2 times and ∼10 times, respectively. A simultaneous measurement of pressure and concentration was performed using the neural network, which indicated a good potential of the technique for multi-parameter analysis using a single LAS-based sensor system.

ISREA: An Efficient Peak-Preserving Baseline Correction Algorithm for Raman Spectra

A critical step in Raman spectroscopy is baseline correction. This procedure eliminates the background signals generated by residual Rayleigh scattering or fluorescence. Baseline correction procedures relying on asymmetric loss functions have been employed recently. They operate with a reduced penalty on positive spectral deviations that essentially push down the baseline estimates from invading Raman peak areas. However, their coupling with polynomial fitting may not be suitable over the whole spectral domain and can yield inconsistent baselines. Their requirement of the specification of a threshold and the non-convexity of the corresponding objective function further complicates the computation. Learning from their pros and cons, we have developed a novel baseline correction procedure called the iterative smoothing-splines with root error adjustment (ISREA) that has three distinct advantages. First, ISREA uses smoothing splines to estimate the baseline that are more flexible than polynomials and capable of capturing complicated trends over the whole spectral domain. Second, ISREA mimics the asymmetric square root loss and removes the need of a threshold. Finally, ISREA avoids the direct optimization of a non-convex loss function by iteratively updating prediction errors and refitting baselines. Through our extensive numerical experiments on a wide variety of spectra including simulated spectra, mineral spectra, and dialysate spectra, we show that ISREA is simple, fast, and can yield consistent and accurate baselines that preserve all the meaningful Raman peaks.

An ultra-sensitive detection system for sulfur dioxide and nitric oxide based on improved differential optical absorption spectroscopy method

A highly sensitive detection system for sulfur dioxide (SO₂) and nitric oxide (NO) was developed via deep ultraviolet differential optical absorption spectroscopy (DUV-DOAS). The wavelength range of 200-230 nm was used which was rarely used before as result of severe cross sensitivity to SO₂ and NO, in this work, this problem was overcame. A system detection limit (DL) of 60 ppb for SO₂ has been reached which was among the best ones. Meanwhile, a novel method based on spectrum superposition theory was proposed to decompose the differential optical density (DOD) of NO from that of gas mixture in cross sensitive band. The advantage of this method is that the most sensitive absorption peak of NO was used, which cannot be used by conventional methods due to the cross sensitive to SO₂. A system DL of 7 ppb for NO has been achieved which is among the best ones reported before. Furthermore, the effect of gas temperature and humidity on concentration retrieval has also been studied, gas temperature and humidity compensation models have also been proposed. The experimental results show that the compensation models succeed in compensating the deviation caused by gas temperature and humidity. The environmental adaptability of the system has been enhanced. This work achieves the aim of monitoring ultra-low concentration of SO₂ and NO in a complex environment simultaneously.

An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network applied for simultaneously detection of exhaled CO and N2O

A compact high-resolution spectroscopic sensor using a thermoelectrically (TE) cooled continuous-wave (CW) room temperature (RT) quantum cascade laser (QCL) was demonstrated for simultaneous measurements of exhaled carbon monoxide (CO) and nitrous oxide (N₂O). The sampling pressure was optimized to improve the sensitivity, the optimal pressure was determined to be 150 mbar based on an optical density analysis of simulated and measured absorption spectra. An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network was developed and proposed for real-time exhaled breath analysis in order to perform fast and high precision on-line measurements. The detection limits (1σ) of 1.14 ppb and 1.12 ppb were experimentally achieved for CO and N₂O detection, respectively. Typical concentrations of exhaled CO and N₂O from smokers and non-smokers were analyzed. The experimental results indicated that the state-of-the-art CW-QCL based sensor has a great potential for non-invasive, on-line identification and quantification of biomarkers in human breath.

Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization

Laser absorption spectroscopy (LAS) is a promising diagnostic method capable of providing high-bandwidth, species-specific sensing, and highly quantitative measurements. This review aims at providing general guidelines from the perspective of LAS sensor system design for realizing quantitative species diagnostics in combustion-related environments. A brief overview of representative detection limits and bandwidths achieved in different measurement scenarios is first provided to understand measurement needs and identify design targets. Different measurement schemes including direct absorption spectroscopy (DAS), wavelength modulation spectroscopy (WMS), and their variations are discussed and compared in terms of advantages and limitations. Based on the analysis of the major sources of noise including electronic, optical, and environmental noises, strategies of noise reduction and design optimization are categorized and compared. This addresses various means of laser control parameter optimization and data processing algorithms such as baseline extraction, in situ laser characterization, and wavelet analysis. There is still a large gap between the current sensor capabilities and the demands of combustion and engine diagnostic research. This calls for a profound understanding of the underlying fundamentals of a LAS sensing system in terms of optics, spectroscopy, and signal processing.

Mathematical Methods and Algorithms for Improving Near-Infrared Tunable Diode-Laser Absorption Spectroscopy

Tunable diode laser absorption spectroscopy technology (TDLAS) has been widely applied in gaseous component analysis based on gas molecular absorption spectroscopy. When dealing with molecular absorption signals, the desired signal is usually interfered by various noises from electronic components and optical paths. This paper introduces TDLAS-specific signal processing issues and summarizes effective algorithms so solve these.

Goldindec: A Novel Algorithm for Raman Spectrum Baseline Correction

Raman spectra have been widely used in biology, physics, and chemistry and have become an essential tool for the studies of macromolecules. Nevertheless, the raw Raman signal is often obscured by a broad background curve (or baseline) due to the intrinsic fluorescence of the organic molecules, which leads to unpredictable negative effects in quantitative analysis of Raman spectra. Therefore, it is essential to correct this baseline before analyzing raw Raman spectra. Polynomial fitting has proven to be the most convenient and simplest method and has high accuracy. In polynomial fitting, the cost function used and its parameters are crucial. This article proposes a novel iterative algorithm named Goldindec, freely available for noncommercial use as noted in text, with a new cost function that not only conquers the influence of great peaks but also solves the problem of low correction accuracy when there is a high peak number. Goldindec automatically generates parameters from the raw data rather than by empirical choice, as in previous methods. Comparisons with other algorithms on the benchmark data show that Goldindec has a higher accuracy and computational efficiency, and is hardly affected by great peaks, peak number, and wavenumber.