Asymmetric Squares Research Articles

Targeted FT-NIR and SERS Detection of Breast Cancer HER-II Biomarkers in Blood Serum Using PCB-Based Plasmonic Active Nanostructured Thin Film Label-Free Immunosensor Immobilized with Directional GNU-Conjugated Antibody.

This work describes our recent PCB-based plasmonic nanostructured platform patent (US 11,828,747B2) for the detection of biomarkers in breast cancer serum (BCS). A 50 nm thin gold film (TGF) was immersion-coated on PCB (i.e., PCB-TGF) and immobilized covalently with gold nanourchin (GNU) via a 1,6-Hexanedithiol (HDT) linkage to produce a plasmonic activated nanostructured thin film (PANTF) platform. A label-free SERS immunosensor was fabricated by conjugating the platform with monoclonal HER-II antibodies (mAb) in a directional orientation via adipic acid dihydrazide (ADH) to provide higher accessibility to overexpressed HER-II biomarkers (i.e., 2+ (early), 3+ (locally advanced), and positive (meta) in BCS. An enhancement factor (EF) of 0.3 × 105 was achieved for PANTF using Rhodamine (R6G), and the morphology was studied by scanning electron microscopy (SEM) and atomic force microscope (AFM). UV-vis spectroscopy showed the peaks at 222, 231, and 213 nm corresponding to ADH, mAb, and HER-II biomarkers, respectively. The functionalization and conjugation were investigated by Fourier Transform Near Infrared (FT-NIR) where the most dominant overlapped spectra of 2+, 3+, and Pos correspond to OH-combination of carbohydrate, RNH2 1st overtone, and aromatic CH 1st overtone of mAb, respectively. SERS data were filtered using the filtfilt filter from scipy.signals, baseline corrected using the Improved Asymmetric Least Squares (isals) function from the pybaselines.Whittaker library. The results showed the common peaks at 867, 1312, 2894, 3026, and 3258 cm-1 corresponding to glycine, alanine ν (C-N-C) assigned to the symmetric C-N-C stretch mode; tryptophan and α helix; C-H antisymmetric and symmetric stretching; NH3+ in amino acids; and N-H stretch primary amide, respectively, with the intensity of Pos > 3+ > 2+. This trend is justifiable considering the stage of each sample. Principal Component Analysis (PCA) and Linear Discrimination Analysis (LDA) were employed for the statistical analysis of data.

Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine

Machine learning has emerged as a leading field in artificial intelligence, demonstrating expert-level performance in various domains. Astronomy has benefited from machine learning techniques, particularly in classifying and identifying stars based on their features. This study focuses on the spectra-based classification of 11,408 B-type and 2422 hot subdwarf stars. The study employs baseline correction using Asymmetric Least Squares (ALS) to enhance classification accuracy. It applies the Pan-Core concept to identify 500 unique patterns or ranges for both types of stars. These patterns are the foundation for creating Support Vector Machine (SVM) models, including the linear (L-SVM), polynomial (P-SVM), and radial basis (R-SVM) kernels. Parameter tuning for the SVM models is achieved through cross-validation. Evaluation of the SVM models on test data reveals that the linear kernel SVM achieves the highest accuracy (87.0%), surpassing the polynomial kernel SVM (84.1%) and radial kernel SVM (80.1%). The average calibrated accuracy falls within the range of 90–95%. These results demonstrate the potential of using spectrum-based classification to aid astronomers in improving and expanding their understanding of stars, with a specific focus on the identification of hot subdwarf stars. This study presents a valuable investigation for astronomers, as it enables the classification of stars based on their spectra, leveraging machine learning techniques to enhance their knowledge and insights in astronomy.

Baseline estimation using optimized asymmetric least squares (O-ALS)

Baseline drift widely exists in the spectral measurements due to instrumental nonlinearity. It negatively affects the qualitative or quantitative analysis of chemical and physical parameters, thus, an accurate baseline estimation is needed. An improved algorithm, optimized asymmetric least squares smoothing (O-ALS), is designed based on the traditional ALS algorithm to achieve effective estimation. First, an appropriate smooth factor is selected according to the number of second-order concave points (NSCP) of a measured spectrum. Then, the asymmetric penalty factor is adaptively optimized in each iteration based on the differences between the estimated baseline and the measured spectrum. Various simulated and real measured data, including Fourier Transform Spectrometer spectrum, chromatogram, and ion mobility spectroscopy, have been used for evaluation. All results achieve a high precision, which suggests that our algorithm is effective, flexible, and robust.

Development of algorithm for single channel baseline adjustment using asymmetric least squares smoothing methods

Development of algorithm for single channel baseline adjustment using asymmetric least squares smoothing methods

Cell culture product quality attribute prediction using convolutional neural networks and Raman spectroscopy.

Advanced process control in the biopharmaceutical industry often lacks real-time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real-time. In spite of the ease of training, the accuracy of the PLS model is impacted if it is not used to predict quality attributes for the cell lines it is trained on. To address this issue, a deep convolutional neural network (CNN) is proposed for offline modeling of metabolites using Raman spectroscopy. By utilizing asymmetric least squares smoothing to adjust Raman spectra baselines, a generic training data set is created by amalgamating spectra from various cell lines and operating conditions. This data set, combined with their derivatives, forms a two-dimensional model input. The CNN model is developed and validated for predicting different quality variables against measurements from various continuous and fed-batch experimental runs. Validation results confirm that the deep CNN model is an accurate generic model of the process to predict real-time quality attributes, even in experimental runs not included in the training data. This model is robust and versatile, requiring no recalibration when deployed at different sites to monitor various cell lines and experimental runs.

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.

Multivariate optimization of hyperspectral imaging for adulteration detection of ground beef: Towards the development of generic algorithms to predict adulterated ground beef and for digital sorting

Due to its nutritional value, and taste, minced beef meat (MBM) is highly valuable therefore is susceptible to economically motivated adulteration. High-throughput adulteration detection methods are required to guarantee the authenticity and safety of the MBM. Hyperspectral imaging (HSI) in the 400–1000 nm spectral range, in tandem with multivariate analysis, was evaluated to demonstrate the feasibility of developing generic models to detect minced beef meat adulterated with other types of meats. Partial least squares regression (PLSR) method, the ensemble Monte Carlo variable selection method (EMCVS), a range of spectral pre-treatments, and combinations of any two of them were evaluated to predict the amount of MBM in each prepared sample scanned. The beef prediction model was developed using data from MBM adulterated with minced chicken and turkey meats and validated with data from MBM adulterated with pork meat at adulteration levels ranging from 0 to 51% at approximately 3% increments. Good prediction results were obtained using the EMCVS, on the asymmetric least squares (AsLs) + Standard Normal variate (SNV) pre-treated reflectance spectra (Rp2 0.96, RMSEP 2.9% and RPD 5.4) with 23 selected wavelengths. An independent set of MBM samples adulterated with lamb and duck meat were then prepared similarly at concentrations ranging from 3 to 21% to test the developed method. Good results were obtained in the full range (R2p 0.94, RMSEp 3.92%, RPDp 3.83) and excellent prediction results (R2p 0.95, RMSEp 2.78%, RPDp 5.29) were obtained with 9 optimum wavelengths (484, 508, 593, 596, 602, 736, 739, 924 and 960 nm). PLS discriminant analysis (PLS-DA), the EMCVS and spectral pre-treatments were used to discriminate MBM from other adulterants and achieved almost perfect classification for calibration, cross-validation, and prediction using 12 selected spectral bands. This study developed a high-throughput generic minced meat adulteration prediction and discrimination method based on selected spectral bands that can be used to further develop low-cost portable sensors for the digital sorting of adulterated MBM, and demonstrates the feasibility to develop generic models to detect minced beef meat adulterated with other types of meat.

Automatic peak annotation and area estimation of glycan map peaks directly from chromatograms

AbstractThe present bottleneck in biosimilar bioprocess development has become evaluation of analytical results, due to recent advances in analytics, such as automated sample preparation and development of high‐throughput methods. Currently automated chromatogram integration and annotation is only efficient for simple chromatograms. In an ever more competitive field of biosimilars, this represents a serious drawback because chromatographic analytical methods that provide some of the most valuable physicochemical quality attributes of the product also require careful chromatogram integration and annotation. This work focuses on the glycan mapping analytical method as utilized in development of monoclonal antibody biosimilars, evaluating more than 2000 chromatograms spanning the life cycle of multiple biosimilar development projects. It proposes a modified workflow by implementing automatic machine learning algorithms to determine the proportion of specific relevant glycan species in a sample directly from the chromatogram. Data preparation and analysis is performed using a pipeline approach. Pipeline is a modular design of data processing where signal “travels” through various active modules in a series. Each module performs a specific function or transformation on the signal and propagates the transformed signal to the next module. The pipeline is designed in a way that modules can be independently improved and exchanged. Module functions currently implemented are chromatogram resampling by spline interpolation, baseline removal by asymmetric least squares, peak alignment using parametric time warping, and quantification of the relative proportion of a glycan species using partial least squares regression. Hyper‐parameters of the pipeline are then optimized using the Nelder–Mead method. The approach stands out for its ability to accommodate a broad landscape of samples, covering multiple different proteins in different stages of biosimilar development, analyzed using different adaptations of the glycan map analytical method. The pipeline presents an intuitive, flexible, and creatively simple method design capable of providing reliable results for a wide range of glycan species essential for biosimilar development. It enables transparent, faster, and less subjective evaluation of analytic raw data (from sample to result). Furthermore, our automated approach maintained an accuracy comparable with manual integration thus demonstrating its readiness for implementation in the conservative and highly regulated environment. The presented methodology reduces the cost and time of biosimilar development and should be applicable for any chromatogram‐based analytical method.

Double-sided asymmetric method for automated fetal heart rate baseline calculation.

The fetal heart rate (FHR) signal is used to assess the well-being of a fetus during labor. Manual interpretation of the FHR is subject to high inter- and intra-observer variability, leading to inconsistent clinical decision-making. The baseline of the FHR signal is crucial for its interpretation. An automated method for baseline determination may reduce interpretation variability. Based on this claim, we present the Auto-Regressed Double-Sided Improved Asymmetric Least Squares (ARDSIAsLS) method as a baseline calculation algorithm designed to imitate expert obstetrician baseline determination. As the FHR signal is prone to a high rate of missing data, a step of gap interpolation in a physiological manner was implemented in the algorithm. The baseline of the interpolated signal was determined using a weighted algorithm of two improved asymmetric least squares smoothing models and an improved symmetric least squares smoothing model. The algorithm was validated against a ground truth determined from annotations of six expert obstetricians. FHR baseline calculation performance of the ARDSIAsLS method yielded a mean absolute error of 2.54bpm, a max absolute error of 5.22bpm, and a root mean square error of 2.89bpm. In a comparison between the algorithm and 11 previously published methods, the algorithm outperformed them all. Notably, the algorithm was non-inferior to expert annotations. Automating the baseline FHR determination process may help reduce practitioner discordance and aid decision-making in the delivery room.

Robust optimal subsampling based on weighted asymmetric least squares

Robust optimal subsampling based on weighted asymmetric least squares

Automatic background correction method for laser-induced breakdown spectroscopy

In laser-induced breakdown spectroscopy (LIBS), fluctuations in laser energy, fluctuations in laser-sample or laser-plasma interactions, and environmental noise cause the acquired spectral to contain different backgrounds. The background has a substantial impact on the analysis of spectra. The aim of this work is to present a method for automatic estimation of diverse spectral backgrounds. The proposed method utilizes the ideas of window functions and differentiation, combined with a piecewise cubic Hermite interpolating polynomial (Pchip), to achieve automatic removal of spectral backgrounds. In simulation experiments on background correction, it has been observed that this method outperforms asymmetric least squares (ALS) and Model-free background correction in terms of effectiveness. It exhibits the highest signal-to-background ratio (SBR). This method effectively eliminates the baseline that makes the spectrum elevated and some of the white noise. It also demonstrates stability in the background baseline jumps and in the dense region of the characteristic spectral lines. By using this approach to correct the spectral backgrounds of seven different aluminum alloys, there is a notable enhancement in the linear correlation coefficient between spectral intensity and the concentration of Mg element. In the experiment of measuring Mg concentration in aluminum alloys,the linear correlation coefficients between the predicted and actual concentrations of Mg in aluminum alloys were substantially improved after correction of the spectral background. The values of linear correlation coefficients after background correction using asymmetric least squares (ALS) and model-free methods were 0.9913 and 0.9926, respectively. The correlation coefficients between predicted and actual concentrations of Mg in aluminum alloys were improved from 0.9154 to 0.9943 by using the method proposed in this study. In contrast to ALS and Model-free methods, the proposed method showed more significant improvement on this experiment with higher linear correlation and smaller error. Both simulation and quantitative experimental findings affirm that this method achieves high accuracy and can be effectively employed for background correction prior to quantitative analysis.

How Could Increasing Temperature Scenarios Alter the Risk of Terrorist Acts in Different Historical Squares? A Simulation-Based Approach in Typological Italian Squares

Squares in the urban historical built environment are public open spaces prone to the risk of terrorist acts, essentially because they are ideal soft targets and attract significant user densities. Risk assessment methods should consider how users behave in them, both before and during an accident (i.e., the evacuation process). In addition to squares’ morphology and layout, and considering that urban areas are more and more prone to the effects of increasing temperatures, outdoor climate conditions can alter the initial scenario. In fact, such conditions can lead users to gather in specific outdoor areas, where they can look for shadows and shelter. This work hence proposes a simulation-based approach to assess how differences in users’ behaviours in response to increasing temperatures and squares’ morphology can alter the risk of terrorist acts in an emergency evacuation. An agent-based model is developed to simulate the interactions between users, hazards and the historical built environment. The work considers four typological squares prone to terrorist acts since they host a special building attracting users in front of it. These squares are derived from the analysis of Italian historical contexts within the BE S2ECURe project. Users are generated in the public open space (thus, before the terrorist act) depending on the intended uses of the square and on the outdoor temperature, which is affected by the square’s morphology. Three different users’ behaviours are modelled to consider (or not) the effects of the outdoor temperature on users’ thermal acceptability levels in an increasing temperature situation. Then, two evacuation scenarios are simulated: (a) a general evacuation process, without attackers, as the baseline for the risk assessment; and (b) an armed assault with cold weapons, to define one of the most probable attack situations in open spaces. Evacuation performance indicators are developed to assess users’ risk. Preliminary verifications demonstrate the capabilities of the approach. The results show that higher differences in evacuation indicators are noticed in large and asymmetric squares, since their conditions highly affect the variability of users’ behaviours in response to increasing temperatures. At the same time, stronger safety behaviours in response to increasing temperatures could reduce emergency issues because they allow users to be more dispersed and initially placed farther from the attack area. Decision-makers could take advantage of the proposed approach and simulation tool, moving towards an effectiveness analysis of solutions to increase the thermal comfort of users in respect of the risk levels during an evacuation. Finally, applications to real-world scenarios are thus encouraged to compare such idealized results with effective conditions.

Baseline correction for Raman spectra using a spectral estimation-based asymmetrically reweighted penalized least squares method.

Baseline correction is necessary for the qualitative and quantitative analysis of samples because of the existence of background fluorescence interference in Raman spectra. The asymmetric least squares (ALS) method is an adaptive and automated algorithm that avoids peak detection operations along with other user interactions. However, current ALS-based improved algorithms only consider the smoothness configuration of regions where the signals are greater than the fitted baseline, which results in smoothing distortion. In this paper, an asymmetrically reweighted penalized least squares method based on spectral estimation (SEALS) is proposed. SEALS considers not only the uniform distribution of additive noise along the baseline but also the energy distribution of the signal above and below the fitted baseline. The energy distribution is estimated using inverse Fourier and autoregressive models to create a spectral estimation kernel. This kernel effectively optimizes and balances the asymmetric weight assigned to each data point. By doing so, it resolves the issue of local oversmoothing that is typically encountered in the asymmetrically reweighted penalized least squares method. This oversmoothing problem can negatively impact the iteration depth and accuracy of baseline fitting. In comparative experiments on simulated spectra, SEALS demonstrated a better baseline fitting performance compared to several other advanced baseline correction methods, both under moderate and strong fluorescence backgrounds. It has also been proven to be highly resistant to noise interference. When applied to real Raman spectra, the algorithm correctly restored the weak peaks and removed the fluorescence peaks, demonstrating the effectiveness of this method. The computation time of the proposed method was approximately 0.05s, which satisfies the real-time baseline correction requirements of practical spectroscopy acquisition.

Alternative fixed-effects panel model using weighted asymmetric least squares regression

Alternative fixed-effects panel model using weighted asymmetric least squares regression

Action potential metrics and automated data analysis pipeline for cardiotoxicity testing using optically mapped hiPSC-derived 3D cardiac microtissues.

Recent advances in human induced pluripotent stem cell (hiPSC)-derived cardiac microtissues provide a unique opportunity for cardiotoxic assessment of pharmaceutical and environmental compounds. Here, we developed a series of automated data processing algorithms to assess changes in action potential (AP) properties for cardiotoxicity testing in 3D engineered cardiac microtissues generated from hiPSC-derived cardiomyocytes (hiPSC-CMs). Purified hiPSC-CMs were mixed with 5-25% human cardiac fibroblasts (hCFs) under scaffold-free conditions and allowed to self-assemble into 3D spherical microtissues in 35-microwell agarose gels. Optical mapping was performed to quantify electrophysiological changes. To increase throughput, AP traces from 4x4 cardiac microtissues were simultaneously acquired with a voltage sensitive dye and a CMOS camera. Individual microtissues showing APs were identified using automated thresholding after Fourier transforming traces. An asymmetric least squares method was used to correct non-uniform background and baseline drift, and the fluorescence was normalized (ΔF/F0). Bilateral filtering was applied to preserve the sharpness of the AP upstroke. AP shape changes under selective ion channel block were characterized using AP metrics including stimulation delay, rise time of AP upstroke, APD30, APD50, APD80, APDmxr (maximum rate change of repolarization), and AP triangulation (APDtri = APDmxr-APD50). We also characterized changes in AP metrics under various ion channel block conditions with multi-class logistic regression and feature extraction using principal component analysis of human AP computer simulations. Simulation results were validated experimentally with selective pharmacological ion channel blockers. In conclusion, this simple and robust automated data analysis pipeline for evaluating key AP metrics provides an excellent in vitro cardiotoxicity testing platform for a wide range of environmental and pharmaceutical compounds.

An Improved Dual Asymmetric Penalized Least Squares Baseline Correction Method for High-Noise Spectral Data Analysis

Baseline drift and noise can blur or even drown out a signal and affect analysis results, especially in multivariate analysis. To address the problem of spectrum denoising and baseline correction, this paper proposes an improved dual asymmetric penalized least squares (IDAPLS) baseline correction method. The proposed method first changes the single parameter λ used for balancing fidelity and roughness in the traditional penalty least squares (PLS) method into a new diagonal matrix Λ and uses the fast convergent inverse tangent S-type penalty function to iteratively estimate the noise level. Then, the diagonal matrix Ψ is introduced into the fidelity of the updated energy spectrum, and the element ψi is updated iteratively by using the inverse tangent S-type penalty function. Finally, the baseline of the original signal is obtained when a preset number of iterations or termination criteria are reached. Compared with other methods, IDAPLS solves the problem of underfitted curves when dealing with additive noise that the asymmetric least squares method and adaptive iterative reweighted penalized least squares method would get. The proposed method also retains the advantage of fast PLS and realizes the further approximation of the fitting baseline to the real baseline. Especially, in the case of high noise, this method reduces the error of the traditional PLS method from 30% to less than 5%, which gives a useful reference for nuclear data analysis.

Asymmetric and robust loss function driven least squares support vector machine

Least squares support vector machine (LSSVM) considerably simplifies problem solving, however, there are restrictions. The first is that it treats samples on both sides of the proximal hyperplane equally and does not differentiate them based on locations; the second is that it is sensitive to noise and outliers. To address the aforementioned issues, this paper proposes an asymmetric robust least squares support vector machine termed QTLS by combining Quadratic Type Squared Error Loss Function (QTSELF) with LSSVM, in which QTSELF is introduced into machine learning for the first time. On the one hand, QTLS can impose various penalties on samples according to their locations and place greater emphasis on samples that are susceptible to misclassification. On the other hand, it improves the model robustness by imposing a tiny penalty on noise or outliers located far from the proximal hyperplane. Using Rademacher complexity theory, we investigate the generalization capacity of QTLS. The RMSProp technique is utilized to solve both linear and nonlinear QTLS. Extensive experiments indicate the effectiveness of QTLS in addressing binary classification problems.

Application of Gaussian process regression and asymmetric least squares baseline algorithm on the determination of electrochemical sensor characteristics: A case study on SARS-CoV-2 glucometer

In this study, it was aimed to determine limit of detection, limit of quantification, and signal to noise ratio by the use of SARS-CoV-2 glucometer response data in "Singh, N·K., Ray, P., Carlin, A.F., Morgan, S·C., Magallanes, C., Laurent, L.C., Aronoff-Spencer, E.S., Hall, D.A., 2021b. Dataset on optimization and development of a point-of-care glucometer-based SARS-CoV-2 detection assay using aptamers. Data Brief 38, 107,278.” and by Bayesian optimization and asymmetric least squares baseline algorithm which was used for prediction of response curve and baseline. It was seen that the predicted limit of detection and limit of quantification reached a certain level after a certain number of training observations and ranged between 0.9-16 pM and 2.8–44 pM respectively.

Employing Auto-Machine Learning Algorithms for Predicting the Cold Filter Plugging and Kinematic Viscosity at 40 ºC in Biodiesel Blends using Vibrational Spectroscopy Data

This work aims to develop an auto-machine learning method using Mid-Infrared (MIR) spectroscopy data to determine the cold filter plugging point (CFPP) and kinematic viscosity at 40 ºC of biodiesel, diesel, and mixtures samples. The biodiesel was obtained by the transesterification reaction and later purified. The first dataset was composed of 108 blends (biodiesel obtained from different biomass such as soy, corn, sunflower, and canola) with binary, ternary and quaternary mixtures. The second dataset was composed of 227 blends of diesel-biodiesel and diesel-biodiesel-ethanol, respectively. The physical properties of the samples were obtained according to ABNT NBR 14747 and ABNT NBR 10441, respectively. The MIR Spectroscopy data were acquired from 7,800 to 450 cm-1, with a 4 cm-1 resolution and 20 scans. The spectra' baseline alignment was carried out using the asymmetric least squares method. A Savitzky–Golay filter was applied to a set of digital data points to smooth the data. This work used a first-order polynomial and a zero derivative function to smooth the spectra. The dataset was split into training and test sets using the function CreateDataPartition from the caret package. It was adopted 70% for training and 30% for test sets. In this work, the model training process was carried out using the open-source Python library LazyPredict. The LazyPredict returns the trained models and their performance metrics. The kinematic viscosity at 40 ºC of the biodiesel samples and their blends could be modeled using the MIR Spectroscopy dataset using different auto-machine learning algorithms. The RMSEP (Root Mean Square Error of Prediction) (≤ 0.02 mm2 s-1) was similar to the experimental error obtained after log transformation. The CFPP of the biodiesel samples and their blends could be modeled using the MIR Spectroscopy dataset by different auto-machine learning algorithms with an RMSEP (≤ 1.6 ºC) similar to the experimental error obtained by traditional methodology. Based on the lower computational time and the same performance observed by the RMSEP and R2 (coefficient of determination) values from different algorithms, it is recommended to use Ridge or Ridge Cross-Validation Regression models for both physical properties using MIR Spectroscopy data.

Single convolutional neural network model for multiple preprocessing of Raman spectra

Raman spectra suffer from the interferences of noises, baseline drifts, and cosmic rays, which leads to errors in the subsequent analysis of the spectra. The commonly applied spectral preprocessing methods, such as wavelet transform (WT), Savitzky-Golay smooth (SG smooth), asymmetric least squares (AsLS) can only correspondingly reduce a single interference term, and the completion of preprocessing based on these traditional methods requires a series of tedious trials. Especially, each scheme can only be used for a specific data set. Convolutional neural network (CNN) is commonly used for object recognition, image super-resolution, and natural language processing. CNN has the potential to be applied to the preprocessing of chemical signals to solve problems intelligently. In this paper, we developed a method to remove all the interferences from multiple Raman spectra data sets collected from food and wastewater samples, using only a single CNN model. By optimization of hyperparameters, activity functions, and loss functions in CNN, the CNN model completely removed noises, spikes, baselines, and cosmic rays from Raman spectra and simplified the preprocessing of Raman spectra.