Functional near-infrared spectroscopy in non-invasive neuromodulation

Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks,which have been widely applied in the field of central neurological diseases,such as stroke,Parkinson’s disease,and mental disorders.Although significant advances have been made in neuromodulation technologies,the identification of optimal neurostimulation paramete rs including the co rtical target,duration,and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits.Moreove r,the neural mechanism unde rlying neuromodulation for improved behavioral performance remains poorly understood.Recently,advancements in neuroimaging have provided insight into neuromodulation techniques.Functional near-infrared spectroscopy,as a novel non-invasive optical brain imaging method,can detect brain activity by measuring cerebral hemodynamics with the advantages of portability,high motion tole rance,and anti-electromagnetic interference.Coupling functional near-infra red spectroscopy with neuromodulation technologies offe rs an opportunity to monitor the cortical response,provide realtime feedbac k,and establish a closed-loop strategy integrating evaluation,feedbac k,and intervention for neurostimulation,which provides a theoretical basis for development of individualized precise neuro rehabilitation.We aimed to summarize the advantages of functional near-infra red spectroscopy and provide an ove rview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation,transcranial electrical stimulation,neurofeedback,and braincomputer interfaces.Furthermore,the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized.In conclusion,functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central pellral reorganization to achieve better functional recovery form central nervous system diseases.

Laser spectroscopy imaging technique coupled withnanomaterials for cancer diagnosis: A review

Laser spectroscopic imaging techniques have received tremendous attention in the-eld of cancer diagnosis due to their high sensitivity,high temporal resolution,and short acquisition time.However,the limited tissue penetration of the laser is still a challenge for the in vivo diagnosis of deep-seated lesions.Nanomaterials have been universally integrated with spectroscopic imaging techniques for deeper cancer diagnosis in vivo.The components,morphology,and sizes of nanomaterials are delicately designed,which could realize cancer diagnosis in vivo or in situ.Considering the enhanced signal emitting from the nanomaterials,we emphasized their combination with spectroscopic imaging techniques for cancer diagnosis,like the surface-enhanced Raman scattering(SERS),photoacoustic,fluorescence,and laser-induced breakdown spectroscopy(LIBS).Applications ofthe above spectroscopic techniques offer new prospectsfor cancer diagnosis.

A rened analytical model for reconstruction problems in diuse reectance spectroscopy

A rened analytical model of spatially resolved diffuse reectance with small source-detector separations(SDSs)for the in vivo skin studies is proposed.Compared to the conventional model developed by Farrell et al.,it accounts for the limited acceptance angle of the detectorber.The rened model is validated in the wide range of optical parameters by Monte Carlo simulations of skin diffuse reectance at SDSs of units of mm.Cases of uniform dermis and two-layered epidermis-dermis structures are studied.Higher accuracy of the rened model compared to the conventional one is demonstrated in the separate,constraint-free reconstruction of absorption and reduced scattering spectra of uniform dermis from the Monte Carlo simulated data.In the case of epidermis-dermis geometry,the recovered values of reduced scattering in dermis are overestimated and the recovered values of absorption are underestimated for both analytical models.Presumably,in the presence of a thin mismatched topical layer,only the effective attenuation coe±cient of the bottom layer can be accurately recovered using a diffusion theorybased analytical model while separate reconstruction of absorption and reduced scattering fails due to the inapplicability of the method of images.These-ndings require implementation of more sophisticated models of light transfer in inhomogeneous media in the recovery algorithms.

In situ infrared, Raman and X-ray spectroscopy for the mechanistic understanding of hydrogen evolution reaction

Hydrogen production by water reduction reactions has received considerable attention because hydrogen is considered a clean-energy carrier,key for a sustainable energy future.Computational methods have been widely used to study the reaction mechanism of the hydrogen evolution reaction(HER),but the calculation results need to be supported by experimental results and direct evidence to confirm the mechanistic insights.In this review,we discuss the fundamental principles of the in situ spectroscopic strategy and a theoretical model for a mechanistic understanding of the HER.In addition,we investigate recent studies by in situ Fourier transform infrared(FTIR),Raman spectroscopy,and X-ray absorption spectroscopy(XAS) and cover new findings that occur at the catalyst-electrolyte interface during HER.These spectroscopic strategies provide practical ways to elucidate catalyst phase,reaction intermediate,catalyst-electrolyte interface,intermediate binding energy,metal valency state,and coordination environment during HER.

Spatial sensitivity to absorption changes for various near-infrared spectroscopy methods:A compendium review

This compendium review focuses on the spatial distribution of sensitivity to localized absorption changes in optically diffuse media,particularly for measurements relevant to near-infrared spectroscopy.The three temporal domains,continuous wave,frequency domain,and time domain,each obtain different optical data types whose changes may be related to effective homogeneous changes in the absorption coefficient.Sensitivity is the relationship between a localized perturbation and the recovered effective homogeneous absorption change.Therefore,spatial sensitivity maps representing the perturbation location can be generated for the numerous optical data types in the three temporal domains.The review first presents a history of the past 30 years of work investigating this sensitivity in optically diffuse media.These works are experimental and theoretical,presenting one-,two-,and three-dimensional sensitivity maps for different Near-Infrared Spectroscopy methods,domains,and data types.Following this history,we present a compendium of sensitivity maps organized by temporal domain and then data type.This compendium provides a valuable tool to compare the spatial sensitivity of various measurement methods and parameters in one document.Methods for one to generate these maps are provided in Appendix A,including the code.This historical review and comprehensive sensitivity map compendium provides a single source researchers may use to visualize,investigate,compare,and generate sensitivity to localized absorption change maps.

Solid-state impedance spectroscopy studies of dielectric properties and relaxation processes in Na_(2)O–V_(2)O_(5)–Nb_(2)O_(5)–P_(2)O_(5) glass

Solid-state impedance spectroscopy(SS-IS)was used to investigate the influence of structural modifications resulting from the addition of Nb2O5 on the dielectric properties and relaxation processes in the quaternary mixed glass former(MGF)system 35Na_(2)O–10V_(2)O_(5)–(55-x)P_(2)O_(5)–xNb_(2)O_(5)(x=0–40,mol%).The dielectric parameters,including the dielectric strength and dielectric loss,are determined from the frequency and temperature-dependent complex permittivity data,revealing a significant dependence on the Nb2O5 content.The transition from a predominantly phosphate glass network(x<10,region I)to a mixed niobate–phosphate glass net-work(10≤x≤20,region II)leads to an increase in the dielectric parameters,which correlates with the observed trend in the direct-cur-rent(DC)conductivity.In the predominantly niobate network(x≥25,region III),the highly polarizable nature of Nb5+ions leads to a fur-ther increase in the dielectric permittivity and dielectric strength.This is particularly evident in Nb-40 glass-ceramic,which contains Na_(13)Nb_(35)O_(94) crystalline phase with a tungsten bronze structure and exhibits the highest dielectric permittivity of 61.81 and the lowest loss factor of 0.032 at 303 K and 10 kHz.The relaxation studies,analyzed through modulus formalism and complex impedance data,show that DC conductivity and relaxation processes are governed by the same mechanism,attributed to ionic conductivity.In contrast to glasses with a single peak in frequency dependence of imaginary part of electrical modulus,M″(ω),Nb-40 glass-ceramic exhibits two distinct contributions with similar relaxation times.The high-frequency peak indicates bulk ionic conductivity,while the additional low-fre-quency peak is associated with the grain boundary effect,confirmed by the electrical equivalent circuit(EEC)modelling.The scaling characteristics of permittivity and conductivity spectra,along with the electrical modulus,validate time-temperature superposition and demonstrate a strong correlation with composition and modification of the glass structure upon Nb_(2)O_(5) incorporation.

Cortical activity in patients with high-functioning ischemic stroke during the Purdue Pegboard Test:insights into bimanual coordinated fine motor skills with functional near-infrared spectroscopy

After stroke,even high-functioning individuals may experience compromised bimanual coordination and fine motor dexterity,leading to reduced functional independence.Bilateral arm training has been proposed as a promising intervention to address these deficits.However,the neural basis of the impairment of functional fine motor skills and their relationship to bimanual coordination performance in stroke patients remains unclear,limiting the development of more targeted interventions.To address this gap,our study employed functional near-infrared spectroscopy to investigate cortical responses in patients after stroke as they perform functional tasks that engage fine motor control and coordination.Twenty-four high-functioning patients with ischemic stroke(7 women,17 men;mean age 64.75±10.84 years)participated in this cross-sectional observational study and completed four subtasks from the Purdue Pegboard Test,which measures unimanual and bimanual finger and hand dexterity.We found significant bilateral activation of the sensorimotor cortices during all Purdue Pegboard Test subtasks,with bimanual tasks inducing higher cortical activation than the assembly subtask.Importantly,patients with better bimanual coordination exhibited lower cortical activation during the other three Purdue Pegboard Test subtasks.Notably,the observed neural response patterns varied depending on the specific subtask.In the unaffected hand task,the differences were primarily observed in the ipsilesional hemisphere.In contrast,the bilateral sensorimotor cortices and the contralesional hemisphere played a more prominent role in the bimanual task and assembly task,respectively.While significant correlations were found between cortical activation and unimanual tasks,no significant correlations were observed with bimanual tasks.This study provides insights into the neural basis of bimanual coordination and fine motor skills in high-functioning patients after stroke,highlighting task-dependent neural responses.The findings also suggest that patients who exhibit better bimanual performance demonstrate more efficient cortical activation.Therefore,incorporating bilateral arm training in post-stroke rehabilitation is important for better outcomes.The combination of functional near-infrared spectroscopy with functional motor paradigms is valuable for assessing skills and developing targeted interventions in stroke rehabilitation.

Application of a neural network model with multimodal fusion for fluorescence spectroscopy

In energy-dispersive X-ray fluorescence spectroscopy,the estimation of the pulse amplitude determines the accuracy of the spectrum measurement.The error generated by the amplitude estimation of the pulse output distorted by the measurement system leads to false peaks in the measured spectrum.To eliminate these false peaks and achieve an accurate estimation of the distorted pulse amplitude,a composite neural network model is proposed,which embeds long and short-term memory(LSTM)into the UNet structure.The UNet network realizes the fusion of pulse sequence features and the LSTM model realizes pulse amplitude estimation.The model is trained using simulated pulse datasets with different amplitudes and distortion times.For the pulse height estimation,the average relative error of the trained model on the test set was approximately 0.64%,which is 27.37% lower than that of the traditional trapezoidal shaping algorithm.Offline processing of a standard iron source further validated the pulse height estimation performance of the UNet-LSTM model.After estimating the amplitude of the distorted pulses using the model,the false peak area was reduced by approximately 91% over the full spectrum and was corrected to the characteristic peak region of interest(ROI).The corrected peak area accounted for approximately 1.32%of the characteristic peak ROI area.The results indicate that the model can accurately estimate the height of distorted pulses and has substantial corrective effects on false peaks.

A seven-crystal spectrometer for high-energy resolution X-ray spectroscopy at Shanghai Synchrotron Radiation Facility

A Johann-type X-ray spectrometer was successfully developed at the hard X-ray branch(in-vacuum undulator with a 24-mm periodic length)of the energy material beamline(E-line)at the Shanghai Synchrotron Radiation Facility(SSRF).This spectrometer was utilized to implement X-ray emission spectroscopy(XES),high-energy resolution fluorescence-detected X-ray absorption spectroscopy(HERFD-XAS),and resonant inelastic X-ray scattering.Seven spherically bent crystals were positioned on the respective vertical 500-mm-diameter Rowland circles,adopting an area detector to increase the solid angle to 1.75%of 4πsr,facilitating the study of low-concentrate systems under complex reaction conditions.Operated under the atmosphere pressure,the spectrometer covers the energy region from 3.5 to 18 keV,with the Bragg angle ranging from 73°to 86°during vertical scanning.It offers a promised energy resolution of sub-eV(XES)and super-eV(HERFD-XAS).Generally,these comprehensive core-level spectroscopy methods based on hard X-rays at the E-line with an extremely high photon flux can meet the crucial requirements of a green energy strategy.Moreover,they provide substantial support for scientific advances in fundamental research.

Molecular structure characterization of middle-high rank coal via^(13)C NMR,XPS,and FTIR spectroscopy

Elemental analysis,nuclear magnetic resonance carbon spectroscopy(^(13)C-NMR),X-ray photoelectron spectroscopy(XPS)and Fourier transform infrared spectroscopy(FTIR)experiments were carried out to determine the existence of aromatic structure,heteroatom structure and fat structure in coal.MS(materials studio)software was used to optimize and construct a 3D molecular structure model of coal.A method for establishing a coal molecular structure model was formed,which was“determination of key structures in coal,construction of planar molecular structure model,and optimization of three-dimensional molecular structure model”.The structural differences were compared and analyzed.The results show that with the increase of coal rank,the dehydrogenation of cycloalkanes in coal is continuously enhanced,and the content of heteroatoms in the aromatic ring decreases.The heteroatoms and branch chains in the coal are reduced,and the structure is more orderly and tight.The stability of the structure is determined by theπ-πinteraction between the aromatic rings in the nonbonding energy EN.Key Stretching Energy The size of EB determines how tight the structure is.The research results provide a method and reference for the study of the molecular structure of medium and high coal ranks.

Characteristics of laser-induced breakdown spectroscopy of liquid slag

Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350℃, 1400℃, and 1450℃. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350℃, 1400℃, and 1450℃.The normalized full spectrum combined with partial least squares(PLS) quantification modeling was used, using the Ca Ⅱ 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450℃ has been found to yield superior results compared to both 1350℃ and 1400℃, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.

Emission and capture characteristics of deep hole trap in n-GaN by optical deep level transient spectroscopy

Emission and capture characteristics of a deep hole trap(H1)in n-GaN Schottky barrier diodes(SBDs)have been investigated by optical deep level transient spectroscopy(ODLTS).Activation energy(Eemi)and capture cross-section(σ_(p))of H1 are determined to be 0.75 eV and 4.67×10^(−15)cm^(2),respectively.Distribution of apparent trap concentration in space charge region is demonstrated.Temperature-enhanced emission process is revealed by decrease of emission time constant.Electricfield-boosted trap emission kinetics are analyzed by the Poole−Frenkel emission(PFE)model.In addition,H1 shows point defect capture properties and temperature-enhanced capture kinetics.Taking both hole capture and emission processes into account during laser beam incidence,H1 features a trap concentration of 2.67×10^(15)cm^(−3).The method and obtained results may facilitate understanding of minority carrier trap properties in wide bandgap semiconductor material and can be applied for device reliability assessment.

Quantifying the chemical composition of weathering products of Hainan basalts with reflectance spectroscopy and its implications for Mars

With the development of the hyperspectral remote sensing technique,extensive chemical weathering profiles have been identified on Mars.These weathering sequences,formed through precipitation-driven leaching processes,can reflect the paleoenvironments and paleoclimates during pedogenic processes.The specific composition and stratigraphic profiles mirror the mineralogical and chemical trends observed in weathered basalts on Hainan Island in south China.In this study,we investigated the laboratory reflectance spectra of a 53-m-long drilling core of a thick basaltic weathering profile collected from Hainan Island.We established a quantitative spectral model by combining the genetic algorithm and partial least squares regression(GA-PLSR)to predict the chemical properties(SiO2,Al2O3,Fe2O3)and index of laterization(IOL).The entire sample set was divided into a calibration set of 25 samples and a validation set of 12 samples.Specifically,the GA was used to select the spectral subsets for each composition,which were then input into the PLSR model to derive the chemical concentration.The coefficient of determination(R2)values on the validation set for SiO2,Al2O3,Fe2O3,and the IOL were greater than 0.9.In addition,the effects of various spectral preprocessing techniques on the model accuracy were evaluated.We found that the spectral derivative treatment boosted the prediction accuracy of the GA-PLSR model.The improvement achieved with the second derivative was more pronounced than when using the first derivative.The quantitative model developed in this work has the potential to estimate the contents of similar weathering basalt products,and thus infer the degree of alteration and provide insights into paleoclimatic conditions.Moreover,the informative bands selected by the GA can serve as a guideline for designing spectral channels for the next generation of spectrometers.

Ultrafast carrier dynamics in GeSn thin film based on time-resolved terahertz spectroscopy

We measure the time-resolved terahertz spectroscopy of GeSn thin film and studied the ultrafast dynamics of its photo-generated carriers.The experimental results show that there are photo-generated carriers in GeSn under femtosecond laser excitation at 2500 nm,and its pump-induced photoconductivity can be explained by the Drude–Smith model.The carrier recombination process is mainly dominated by defect-assisted Auger processes and defect capture.The firstand second-order recombination rates are obtained by the rate equation fitting,which are(2.6±1.1)×10^(-2)ps^(-1)and(6.6±1.8)×10^(-19)cm^(3)·ps^(-1),respectively.Meanwhile,we also obtain the diffusion length of photo-generated carriers in GeSn,which is about 0.4μm,and it changes with the pump delay time.These results are important for the GeSn-based infrared optoelectronic devices,and demonstrate that Ge Sn materials can be applied to high-speed optoelectronic detectors and other applications.

Research on batch multielement rapid quantitative analysis based on the standard curve-assisted calibration-free laserinduced breakdown spectroscopy method

This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve(SC)and calibration-free laser-induced breakdown spectroscopy(CF-LIBS)technology to achieve synchronous,rapid,and accurate measurement of elements in a large number of samples,namely,SC-assisted CF-LIBS.Al alloy standard samples,divided into calibration and test samples,were applied to validate the proposed method.SC was built based on the characteristic line of Pb and Cr in the calibration sample,and the contents of Pb and Cr in the test sample were calculated with relative errors of 6%and 4%,respectively.SC built using Cr with multiple characteristic lines yielded better calculation results.The relative contents of ten elements in the test sample were calculated using CF-LIBS.Subsequently,the SC-assisted CF-LIBS was executed,with the majority of the calculation relative errors falling within the range of 2%-5%.Finally,the Al and Na contents of the Al alloy were predicted.The results demonstrate that it effectively enables the rapid and accurate quantitative analysis of multiple elements after a single-element SC analysis of the tested samples.Furthermore,this quantitative analysis method was successfully applied to soil and Astragalus samples,realizing an accurate calculation of the contents of multiple elements.Thus,it is important to advance the LIBS quantitative analysis and its related applications.

Hot carrier cooling in lead halide perovskites probed by two-pulse photovoltage correlation spectroscopy

The next-generation hot-carrier solar cells,which can overcome the Shockley-Queisser limit by harvesting excessenergy from hot carriers,are receiving increasing attention.Lead halide perovskite(LHP)materials are considered aspromising candidates due to their exceptional photovoltaic properties,good stability and low cost.The cooling rate of hotcarriers is a key parameter influencing the performance of hot-carrier solar cells.In this work,we successfully detected hotcarrier dynamics in operando LHP devices using the two-pulse photovoltage correlation technique.To enhance the signalto-noise ratio,we applied the delay-time modulation method instead of the traditional power modulation.This advancementallowed us to detect the intraband hot carrier cooling time for the organic LHP CH_(3)NH_(3)PbBr_(3),which is as short as 0.21 ps.In comparison,the inorganic Cs-based LHP CsPbBr_(3)exhibited a longer cooling time of around 0.59 ps due to differentphonon contributions.These results provide us new insights into the optimal design of hot-carrier solar cells and highlightthe potential of LHP materials in advancing solar cell technology.

Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.

Differential diagnosis of Crohn’s disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning

BACKGROUND Crohn’s disease(CD)is often misdiagnosed as intestinal tuberculosis(ITB).However,the treatment and prognosis of these two diseases are dramatically different.Therefore,it is important to develop a method to identify CD and ITB with high accuracy,specificity,and speed.AIM To develop a method to identify CD and ITB with high accuracy,specificity,and speed.METHODS A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB.Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.RESULTS The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm^(-1) and 1234 cm^(-1) bands,and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy,specificity,and sensitivity of 91.84%,92.59%,and 90.90%,respectively,for the differential diagnosis of CD and ITB.CONCLUSION Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level,and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

Development and Application of Ultrafast Circular Dichroism Spectroscopy Techniques

Chirality hold broad applications in life sciences,quantum devices,and various other areas.Traditionally,molecular chirality can be characterized by using steady-state circular dichroism spectroscopy.However,the techniques that can characterize excited state chirality are progressively capturing the public interest as it can provide the dynamic information for chirality generation and transfer.In this review,we focus on the theoretical background and the developmental history of femtosecond time-resolved circular dichroism spectroscopy(TRCD)techniques around the world.Additionally,we provide examples to showcase the utility of these techniques in the analysis of the dynamical molecular chemical structures,the investigation of molecular chirality generation,and the detection of electron spin dynamics in semiconductor quantum dots.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.