Baseline Correction Method Research Articles

Challenges of Continuous Wave EPR of Broad Signals—The Ferritin Case

The study of continuous wave (cw) electron paramagnetic resonance (EPR) spectra still poses a challenge for very broad signals, especially when the spectrum extends over a large part of the accessible field range. The difficulties derive from instrumental challenges, because of insufficient modulation depth and the need to apply measurement conditions that enhance cavity background. The biggest problem, however, is how to define a baseline such that spectral distortions are minimized. Conventional methods rely on a suitable choice of points outside the range of the signal of interest to perform a polynomial interpolation. These methods are effective in most cases where the signal of interest comprises only a narrow range of magnetic field (narrow features). In this study, a novel method of baseline correction for broad signals is proposed and compared to conventional methods. It takes into account that there are only few anchor points for the baseline. The method is applied to the signal of the iron-storage protein ferritin. The ferritin signal is a broad band that extends from zero to 0.8 T. An approach is developed by which this broad signal is analyzed reliably. The method is also extended to the case where the broad signal is superimposed on narrow signals and enables to extract the parameters of both types of signals in a fitting pipeline.

Influence of velocity pulse directivity on seismic response of cross-fault bridges

Pulse-type ground motions have special destructive effects on structures, and directivity is one of the fundamental characteristics of velocity pulses. In this paper, the impact of the velocity pulse directivity of seismic motions on both sides of the fault on the seismic response of fault-crossing bridges was quantified using the original records from the Chi-Chi earthquake in Taiwan. First, an automatic baseline correction method was applied to restore the permanent ground displacement, and the direction corresponding to the strongest pulse energy on both sides of the fault was identified based on continuous wavelet transformation. On this basis, the variability of the peak intensity of ground motions on the hanging wall and footwall in different directions, as well as the differences in pulse characteristics between the strongest pulse component and the two initial horizontal components, were analysed. Finally, a typical 5 × 30 m continuous girder bridge was considered as the research object to reveal the influence of the velocity pulse directivity on the seismic response of key parts on both sides of the fault. The results showed that the variability in the peak ground-motion intensity on the hanging wall of the fault was higher than that on the footwall. The displacement variability was up to 863 %, velocity was 262 %, and acceleration was 124 %. The pulse characteristics of the strongest pulse component were stronger than those of the parallel and vertical fault components, and its acceleration, velocity, and displacement response spectra were larger. The amplification effects of the velocity pulse directivity on the transverse bending moment, longitudinal bending moment, and torque of the pier bottom reached 1.3, 1.1, and 1.6, respectively. However, the amplification effect of the velocity pulse directivity on the relative displacement of the pier top, displacement of the main beam, and displacement of the bearing was more than 1.6 times, which was more significant than the internal force of the pier bottom.

Baseline offset correction technique for terahertz signals based on improved wavelet multiresolution analysis

During the terahertz nondestructive testing of bonded structures, the incomplete discharge of the capacitance in the photoconductive antenna within the terahertz time-domain spectroscopy system results in a shift of the terahertz baseline produced by the antenna. This baseline shift causes variations in the amplitude information of the detected signals. Consequently, when feature imaging of the detection waveforms is performed, the baseline shift can lead to erroneous detection results. In this study, an improved wavelet multiresolution analysis method was used to eliminate high-frequency noise and baseline offset in terahertz detection. The method is based on the frequency characteristics of the detection waveforms, setting thresholds and using similarity as a measurement standard to determine the number of decomposition layers. Ultimately, this achieves the correction of the baseline offset in terahertz signals. Compared with other baseline correction methods, the method presented in this paper achieves the lowest root mean square error of 0.57%, the highest signal-to-noise ratio of 12.64%, and a defect identification accuracy of 96.27% in two-dimensional visualization results.

Raman spectroscopy for classification of neoplastic and non-neoplastic CAM colon tumors

This paper demonstrates the potential of Raman spectroscopy for differentiating neoplastic from non-neoplastic colon tumors, obtained with the CAM (chicken chorioallantoic membrane) model. For the CAM model two human cell lines were used to generate two types of tumors, the RKO cell line for neoplastic colon tumors and the NCM460 cell line for non-neoplastic colon tumors. The Raman spectra were acquired with a 785 nm excitation laser. The measured Raman spectra from the CAM samples (n = 14) were processed with several methods for baseline correction and to remove artifacts. The corrected spectra were analyzed with PCA (principal component analysis). Additionally, machine learning based algorithms were used to create a model capable of classifying neoplastic and non-neoplastic tumors. The principal component scores showed a clear differentiation between neoplastic and non-neoplastic colon tumors. The classification model had an accuracy of 93 %. Thus, a complete methodology to process and analyze Raman spectra was validated, using a rapid, accessible, and well-established tumor model that mimics the human tumor pathology with minor ethical concerns.

Using visible and near infrared spectroscopy and machine learning for estimating total petroleum hydrocarbons in contaminated soils

Petroleum pollution in soil is very damaging to the areas affected by the accidental release of petroleum hydrocarbons and has destructive impacts on natural resources and environmental health. Therefore, its monitoring and analysis are critical, however, due to the cost and time associated with chemical approaches, finding a quick and cost-effective analytical method is valuable. This study was conducted to evaluate the potential of using visible near infrared (Vis-NIR) spectroscopy to predict total petroleum hydrocarbons (TPH) in polluted soils around the Shadegan ponds, in southern Iran. One hundred soil samples showing various degrees of pollution were randomly collected from topsoil (0–10 cm). The soil samples were analyzed for TPH using Vis-NIR reflectance spectroscopy in the laboratory and then following application of preprocessing transformation, partial least squares PLS regression as well as two machine learning models including random forest (RF), and support vector machine (SVM) were examined. The results showed that the reflectance values at 1725 nm and 2311 nm, respectively, served as indicative TPH reflectance features, exhibiting weaker reflection with rising TPH. Among the preprocessing methods, the baseline correction method indicated the highest performance than the others. According to the evaluation model criteria in the validation dataset, the efficiency of the three selected models was observed in the following order SVM > RF > PLS regression. The SVM model provided the best performance in the validation dataset with r2 = 0.85, root mean of square (RMSEP = 1.59 %, and the ratio of prediction to deviation (RPD) = 2.6. Overall, this study provided strong evidence supporting the considerable potential of Visible-NIR spectroscopy as a rapid and cost-effective technique for estimating TPH levels in oil-contaminated soils, surpassing traditional chemical analytical methods. Applying the mid-infrared spectrum (MIR) in combination with Visible-NIR data is expected to provide more comprehensive and accurate results when assessing soils in polluted areas, thereby enhancing the accuracy and reliability of the results across a diverse range of soil types.

Comparison of baseline correction algorithms for in vivo 1H-MRS.

Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short-echo-time MRS data. In short-echo-time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long-echo-time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi-LASER at TE= 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm-QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm-QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting.

Highly sensitive and label-free protein immunoassay-based biosensor comprising infrared metamaterial absorber inducing strong coupling

A mid-infrared label-free immunoassay-based biosensor is an effective device to help identify and quantify biomolecules. This biosensor employs a surface-enhanced infrared absorption spectroscopy, which is a highly potent sensing technique for detecting minute quantities of analytes. In this study, a biosensor was constructed using a metamaterial absorber, which facilitated strong coupling effects. For maximum coupling effect, it is necessary to enhance the near-field intensity and the spatial and spectral overlap between the optical cavity resonance and the vibrational mode of the analyte. Due to significant peak splitting, conventional baseline correction methods fail to adequately analyze such a coupling system. Therefore, we employed a coupled harmonic oscillation model to analyze the spectral distortion resulting from the peak splitting induced by the strong coupling effect. The proposed biosensor with a thrombin-binding aptamer-based immunoassay could achieve a limit of detection of 267.4 pM, paving the way for more efficient protein detection in clinical practice.

Baseline correction for the infrared spectra of exhaled breath

Infrared spectroscopy appears to be a promising analytical method for the metabolic analysis of breath. However, due to the presence of trace amounts in exhaled breath, the absorption strength of the metabolites remains extremely low. In such low detection limits, the nonlinear detection sensitivity of the infrared detector and electronic noise strongly modify the baseline of the acquired infrared spectra of breath. Fitting the reference molecular spectra with the baseline-modified spectral features of breath metabolites does not provide accurate identification. Therefore, baseline correction of the acquired infrared spectra of breath is the primary requirement for the success of breath-based infrared diagnosis. A selective spectral region-based, simple baseline correction method is proposed for the infrared spectroscopy of breath.

Internal quality evaluation of ‘Fuji’ apples during storage based on bulk optical properties or diffuse reflection and transmission spectra

The purpose of this study is to compare the performance of prediction models based either on the bulk optical properties (BOP) or with models based on the diffuse reflection and transmission spectra to assess the soluble solids content (SSC) or fruit firmness (FF) of ‘Fuji’ apples. Diffuse reflection and transmission spectra of 240 apples in 500–1000 nm were acquired using a self-constructed single integrating sphere system, from which the absorption coefficient (μa) and reduced scattering coefficient (μ′s) were determined. The relationships of μa, μ′s, and the reflection and transmission spectra with the SSC and FF were analyzed, and detection models were established using partial least squares regression coupled with characteristic wavelength selection. Results showed that neither the SSC nor the FF changed obviously during 80-days storage, and the μa and μ′s showed no monotonic increase or decrease pattern. The SSC prediction model based on μa spectra combined with the baseline correction method performed the best (R2p = 0.79, RMSEP = 0.62), while the model based on μ′s spectra was the best for FF (R2p = 0.67, RMSEP = 0.35 N). Overall, the accuracy of the model based on BOP in predicting internal quality was better than that based on reflection and transmission spectra from the same measurement.

Enhancing real-time cell culture monitoring: Automated Raman model optimization with Taguchi method.

Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial-and-error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25(56). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky-Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal-to-noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all-combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.

ELM combined with differential Raman spectroscopy for the detection of microplastics in organisms

Aiming at the problems of low extraction efficiency, high false detection rate, weak Raman signal and serious interference by fluorescence signal in the detection of microplastics in marine organisms, this paper establishes a set of rapid detection methods for microplastics in organisms, including confocal Raman spectroscopy, fluorescence imaging, differential Raman spectroscopy, and rapid identification of microplastics based on the ELM modeling assistance. Firstly, to address the problem of low extraction efficiency of microplastics, we explored and optimized the digestion method of tissues, which effectively improved the digestion effect of fish tissues and excluded the influence of tissues on microplastics detection. Aiming at the problems of high misdetection rate and low pre-screening efficiency of microplastics, fluorescence imaging technology is adopted to realize the visualization and detection of microplastics, which effectively improves the detection efficiency and precision of microplastics. Based on the confocal microscopy Raman spectroscopy detection system built independently in the laboratory, using 784/785 nm as the excitation light, the differential Raman spectroscopy technique effectively excludes the interference of fluorescence signals in the Raman spectra, and improves the signal-to-noise ratio of the Raman spectra, and the recovery rate of the Raman characteristic peaks in the differential Raman spectroscopy reaches 100 % compared to the traditional baseline correction method, which is 33.3 % higher than that of the baseline correction method. Finally, a microplastic identification model is constructed based on ELM to assist in realizing the rapid and accurate identification of microplastics. The more complete detection method of microplastics in marine organisms proposed in this paper can realize the rapid and nondestructive, efficient and accurate detection of microplastics in fish, which can help to further promote the development of marine microplastics monitoring technology.

A baseline correction and noise suppression method based on fitting neural network for CH4/C2H6 dual gas sensing system

We designed a methane/ethane (CH4/C2H6) dual-gas sensor on the basis of direct absorption spectroscopy (DAS) technology and explored the potential for low power consumption and miniaturization of the detection system using the effective optical range is only 25 cm for absorption cell and an uncooled photodetector. The problem of baseline drift introduced by uncooled photodetectors has been improved by using of a fitting neural network approach, and absorbance spectrum denoising has been realized. We established the mathematical relationship between absorbance and concentration for each gas of CH4/C2H6 through experimental detection, which shows strong linearity. And the correlation coefficients (R2) reached 0.99995 and 0.99998, demonstrating the robustness of the sensor. Through 3 h of measured and evaluated system, we obtained the minimum detectable column densities of 0.588 ppm⋅m and 0.128 ppm⋅m for CH4 and C2H6, respectively, which proved that the sensor has high sensitivity, strong stability, and has broad application prospects.

New Baseline Correction Method for Near-Fault Ground-Motion Records Based on Continuous Wavelet Transform

Abstract On 6 February 2023, a magnitude 7.8 earthquake struck south-central Türkiye. It was followed by many aftershocks, the largest of which was a magnitude 7.5 aftershock. This earthquake caused considerable loss of life and property. Numerous near-fault ground-motion records were collected during the 2023 Türkiye earthquake. Most existing baseline correction methods for near-fault ground motions disregard the commonly used filtering techniques due to their potential elimination of actual permanent displacement. To address this, a new automatic baseline correction method is proposed based on the continuous wavelet transform, which incorporates filtering while preserving permanent displacement. This method is compatible with existing filtering techniques and demonstrates good applicability. In this approach, the raw record is decomposed into a pulse signal containing permanent displacement and a nonpulse signal. The nonpulse signal is high-pass filtered to obtain the corrected nonpulse signal, and then the true ground motion is obtained by combining the pulse signal with the corrected nonpulse signal. The effectiveness of the proposed method is evaluated across five aspects using the 1999 Chi-Chi earthquake records: time histories, baseline offsets, response spectra, peak ground velocities, and peak ground displacements. Furthermore, the method is applied to 36 station records (108 components) of the 2023 Türkiye earthquake. The analysis results indicate that the corrected displacement time histories exhibit sustained flatness in their tails and demonstrate a closer agreement with the Global Navigation Satellite System (GNSS) measurements than the previous methods. This method can also incorporate static GNSS offsets to accurately determine true ground motions with precise permanent displacements. The corrected results can be utilized for nonlinear seismic calculations and structural damage analysis of fault-crossing structures such as bridges and tunnels.

Rapid diagnosis of systemic lupus erythematosus by Raman spectroscopy combined with spiking neural network

Multiple organs are affected by the autoimmune inflammatory connective tissue disease known as systemic lupus erythematosus (SLE). If not diagnosed and treated in a timely manner, it can lead to nephritis and damage to the blood system in severe cases, resulting in the patient's death. Therefore, correct and timely diagnosis and treatment are essential for patients. In this study, a framework based on neural network algorithm and Raman spectroscopy technique was established to diagnose SLE patients. Firstly, we pre-processed the obtained Raman data by three methods: baseline correction, smoothing processing and normalization methods, before using it as input for the model, and then ANN, ResNet and SNN classification models were established. The respective classification accuracies for SLE patients were 89.61%, 85.71%, and 95.65% for the three models, with corresponding AUC values of 0.8772, 0.8100, and 0.9555. The results of the experimental indicate that SNN possesses a good classification effect, and the number of model parameters is only 525,826, which is 414,221 less than that of ResNet model. Since the network only uses 0 and 1 to transmit information, and only has basic operations such as summation, compared with the second-generation artificial neural network, which simplifies the product operation of floating point numbers into multiple addition operations, the network has low energy consumption and is suitable for embedding portable Raman spectrometer for clinical diagnosis. This research highlights the significant potential for quick and precise SLE patient discrimination offered by Raman spectroscopy in conjunction with spiking neural networks.

A LIBS spectrum baseline correction method based on the non-parametric prior penalized least squares algorithm.

Laser-induced breakdown spectroscopy (LIBS) has become a popular element analysis technique because of its real-time multi-element detection and non-damage advantages. However, due to factors such as laser-substance interaction and the experimental environment, the measured LIBS spectrum signal contains a continuous background, severely influencing spectrum analysis. In this paper, we propose a LIBS spectrum baseline correction method based on the non-parametric prior penalized least squares (NPPPLS) algorithm. Compared with the traditional Penalized Least Squares (PLS) method, improvements have been made in two aspects. On the one hand, a new weight method with faster convergence is proposed. On the other hand, we combine the Adam algorithm and introduce the RMSE of the baseline correction result at the previous time to constrain the update of the balance parameter, which enables the balance parameter to be adjusted adaptively and no parameter prior is required. The simulation results show that the proposed NPPPLS algorithm can achieve excellent correction results, even with no parametric priors. In addition, the performance of the NPPPLS algorithm is not affected by the initial value of the balance parameter, and the stability and robustness are significantly improved. Finally, we conducted baseline correction of the experimental LIBS spectrum and performed univariate and multivariate analyses. The results show that the quantitative analysis accuracy is improved after baseline correction, and the correlation coefficient R2 of different elements obtained by the extreme learning machine method of multivariate analysis can reach 0.99, demonstrating a better quantitative analysis result. The simulation and experimental results verify the excellent performance of the proposed NPPPLS algorithm, which can be effectively used to improve the accuracy of quantitative analysis. In addition, this method is also expected to be used for baseline correction of the Raman spectrum, near-infrared spectrum and so on.

Comparing baseline correction algorithms in discriminating brownish soils from five proximity locations based on UPLC and PLS-DA methods.

UPLC data of soil interfere with baseline drifts.BC can improve the quality of the pixel-level UPLC data.MW emerges as the most desired algorithm in improving the quality of UPLC data of soil.

An Iterative Curve-Fitting Baseline Correction Method for Raman Spectra Driven by Neural Network.

Baseline correction is a vital part of spectral preprocessing, especially for Raman spectra. Iterative polynomial fitting is an easy but less accurate way to find baselines compared to other methods such as wavelet transform and penalized least squares (PLS) methods. The polynomial fitting methods can also get distorted results in certain conditions. In this paper, a neural network model for detecting the trend of the baseline was proposed to improve the correction accuracy of the fitting methods. The model selects the function basis according to the baseline trend instead of using a fixed polynomial function to match the baseline for a more precise fit. We also propose a way to generate simulation data, these data can be used to train the neural network model. The model provides reliable results for real spectral data with noise. Our method provides a new idea to correct the baseline with a strange shape. In addition, we examine the limitations of conventional iterative polynomial fitting, adaptive iteratively reweighted PLS and explain why our approach surpasses these methods.

Permanent displacement estimation method based on variational mode decomposition

In the near-field area of strong earthquakes, the information recorded by a strong seismometer includes ground motion information and complex low-frequency noise. This noise results in the presence of an “extra” acceleration component in the record that is independent of ground motion, and these components are integrated into the velocity and displacement time history to cause offset. In order to solve this problem, this paper adopts a baseline correction method based on variational mode decomposition (VMD), which decomposes seismic signals into modal functions of different frequencies and then extracts the low-frequency modal functions. It is worth noting that these low-frequency modal function components contain linear trend terms, a property that allows us to perform baseline corrections efficiently. The results show that the permanent displacement obtained by this method is in good agreement with the coseismic displacement observed by GPS stations nearby. This method provides an efficient approach for estimation permanent displacement which helps revealing the focal mechanism of an earthquake.

Accessing the intrinsic factors of carbonyl index of microplastics: Physical and spectral properties, baseline correction, calculation methods, and their interdependence

Carbonyl index (CI) is a measure of the degradation status of microplastics. While many studies address environmental factors of microplastic degradation, intrinsic factors like physical properties, spectral properties, baseline correction, and CI calculation methods are less explored. This research focused on these aspects using surface seawater samples. We found that color and shape have limited dependence on particle size or signal-to-noise ratio (SNR). Baseline correction can significantly alter CI values, with the direction of the shift depending on the methods used. Additionally, most CI values before and after baseline correction and those calculated using different methods tend to be strongly correlated. Using the selected CI calculation methods, we found that CI values varied significantly by shape and color. CI's relation to the similarity between the sample and its pristine form suggests an alternative degradation measure. Our findings emphasize the need for standardized CI calculation methods.

Simultaneously determining amino acid contents using near-infrared reflectance spectroscopy improved by pre-processing method in rice

Rice, the most consumed in Asian and constitute important source of amino acids. The present study evaluated the suitability of near-infrared reflectance spectroscopy to predict amino acid contents (AACs) using 1210 rice samples representing a wide range of grain qualities and genetic background. Calibration models established by combining different spectral derivatives, scatter and baseline correction methods, and regression algorithm were compared using full cross validation. A modified partial least square model (MPLS) with the spectral derivative treatment “2, 3, 3, 1” (2nd order derivative computed based on 3 data points, and 3 and 1 data points in the 1st and 2nd smoothing, respectively) and weighted multiplicative scattering correction (WMSC) method was identified as the best model for simultaneously measurement of 14 AACs in brown rice flour. The high model quality is reflected by RSQ of 0.914∼0.969, standard error of calibration (SEC) of 0.0187%∼0.0553%, 1-VR of 0.901∼0.955, SECV of 0.0214%∼0.0579%, and residual prediction deviation (RPD) of 2.58∼3.85. These results indicated that NIRS coupled with an appropriate pre-processing, multivariate regression could be a rapid and reliable method for quantification of AACs in rice breeding programs and quality control in the food industry. Factors that would influence model quality were investigated and discussed.