Application of near-infrared spectroscopy for fast germplasm analysis and classification in multi-environment using intact-seed peanut(Arachis hypogaea L.)

Peanut is a worldwide oilseed crop and the need to assess germplasm in a non-destructive manner is important for seed nutritional breeding.In this study,Near Infrared Spectroscopy(NIRS)was applied to rapidly assess germplasm variability from whole seed of 699 samples,field-collected and assembled in four genetic and environmentbased sets:one set of 300 varieties of a core-collection and three sets of 133 genotypes of an interspecific population,evaluated in three environments in a large spatial scale of two countries,Mbalmayo and Bafia in Cameroon and Nioro in Senegal,under rainfed conditions.NIR elemental spectra were gathered on six subsets of seeds of each sample,after three rotation scans,with a spectral resolution of 16 cm-1over the spectral range of867 nm to 2530 nm.Spectra were then processed by principal component analysis(PCA)coupled with Partial least squares-discriminant analysis(PLS-DA).As results,a huge variability was found between varieties and genotypes for all NIR wavelength within and between environments.The magnitude of genetic variation was particularly observed at 11 relevant wavelengths such as 1723 nm,usually related to oil content and fatty acid composition.PCA yielded the most chemical attributes in three significant PCs(i.e.,eigenvalues>10),which together captured 93%of the total variation,revealing genetic and environment structure of varieties and genotypes into four clusters,corresponding to the four samples sets.The pattern of genetic variability of the interspecific population covers,remarkably half of spectrum of the core-collection,turning out to be the largest.Interestingly,a PLS-DA model was developed and a strong accuracy of 99.6%was achieved for the four sets,aiming to classify each seed sample according to environment origin.The confusion matrix achieved for the two sets of Bafia and Nioro showed 100%of instances classified correctly with 100%at both sensitivity and specificity,confirming that their seed quality was different from each other and all other samples.Overall,NIRS chemometrics is useful to assess and distinguish seeds from different environments and highlights the value of the interspecific population and core-collection,as a source of nutritional diversity,to support the breeding efforts.

Development and prospect of near-infrared spectroscopy-assisted schizophrenia diagnosis based on bibliometrics

In this editorial,we comment on the recent article by Fei et al exploring the field of near-infrared spectroscopy(NIRS)research in schizophrenia from a bibliometrics perspective.In recent years,NIRS has shown unique advantages in the auxiliary diagnosis of schizophrenia,and the introduction of bibliometrics has provided a macro perspective for research in this field.Despite the opportunities brought about by these technological developments,remaining challenges require multidi-sciplinary approach to devise a reliable and accurate diagnosis system for schizo-phrenia.Nonetheless,NIRS-assisted technology is expected to contribute to the division of methods for early intervention and treatment of schizophrenia.

Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.

Discriminant Analysis of Liquor Brands Based on Moving-Window Waveband Screening Using Near-Infrared Spectroscopy

Partial least squares discriminant analysis (PLS-DA) with integrated moving-window (MW) waveband screening was applied to the discriminant analysis of liquor brands with near-infrared (NIR) spectroscopy. Luzhou Laojiao, a popular liquor with strong fragrant flavor, was used as the identified liquor brand (160 samples, negative, 52 vol alcoholicity). Liquors of 10 other brands with strong fragrant flavor were used as the interferential brands (200 samples, positive, 52 vol alcoholicity). The Kennard-Stone algorithm was used for the division of modeling samples to achieve uniformity and representativeness. Based on the MW-PLS-DA, a simplified optimal model set with 157 wavebands was further proposed. This set contained five types of wavebands corresponding to the NIR absorption bands of water, ethanol, and other micronutrients (i.e., acids, aldehydes, phenols, and aromatic compounds) in liquor for practical choice. Using five selected simple models with 4775 - 4239, 7804 - 6569, 6264 - 5844, 9435 - 7896, and 12066 - 10373 cm-1, the validation recognition rates were obtained as 99.3% or higher. Results show good prediction performance and low model complexity, and also provided a valuable reference for designing small dedicated instruments. The proposed method is a promising tool for large-scale inspection of liquor food safety.

Visible and Near-Infrared Spectroscopy with Multi-Parameters Optimization of Savitzky-Golay Smoothing Applied to Rapid Analysis of Soil Cr Content of Pearl River Delta

Using visible and near-infrared (Vis-NIR) spectroscopy combined with partial least squares (PLS) regression, the rapid reagent-free analysis model for chromium (Cr) content in tideland reclamation soil in the Pearl River Delta, China was established. Based on Savitzky-Golay (SG) smoothing and PLS regression, a multi-parameters optimization platform (SG-PLS) covering 264 modes was constructed to select the appropriately spectral preprocessing mode. The optimal SG-PLS model was determined according to the prediction effect. The selected optimal parameters d, p, m and LV were 2, 6, 23 and 8, respectively. Using the validation samples that were not involved in modeling, the root mean square error (SEPV), relative root mean square error (R-SEPV) and correlation coefficients (RP, V) of prediction were 11.66 mg·kg-1, 10.7% and 0.722, respectively. The results indicated that the feasibility of using Vis-NIR spectroscopy combined with SG-PLS method to analyze soil Cr content. The constructed multi-parameters optimization platform with SG-PLS is expected to be applied to a wider field of analysis. The rapid detection method has important application values to large-scale agricultural production.

Application of near-infrared spectroscopy for the rapid analysis of Lonicerae Japonicae Flos solution extracted by water

A rapid quantitative analytical method for three components of Lonicerae Japornicae Flos solution(Lonicera Japonica Thumb.)extracted by water was developed using near-infrared(NIR)spectroscopy and the partial least-squares(PLS)method.The NIR spectra of 81 samples collected from a production line were obtained.The concentrations of secologanic acid,chlorogenicacid and galuteolin were detemmined by using high-performance liquid chromatography-diodearray detection as the reference method.Several pretreatment methods for the NIR spectra wereusedi during PLS calibration.The most appropriate latent variable number of the PLS factor wasselected based on the standard error of cross-validation(SECV).The performance of the finalPLS models was evaluated according to SECV,standard error of predliction(SEP)and deter-mination coeficient(R^(2)).The compounds secologanic acid,chlorogenic acid and galuteolin hadSEP values of 0.030,0.061 and 1.668μg/mL,respectively and R^(2) values over 0.85.This workshows that NIR spectroscopy is a rapid and convenient method for the analysis of LoniceraeJaponicae Flos solution extracted by water.The proposed method can help in the application ofprocs analytical technology in the pha maceutical industry,particularly in tra ditional Chinesemedicine injections.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Brand Identification of Wine

High-end wine brand is made through the use of high-quality grape variety and yeast strain, and through a unique process. Not only is it rich in nutrients, but also it has a unique taste and a fragrant scent. Brand identification of wine is difficult and complex because of high similarity. In this paper, visible and near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to explore the feasibility of wine brand identification. Chilean Aoyo wine (2016 vintage) was selected as the identification brand (negative, 100 samples), and various other brands of wine were used as interference brands (positive, 373 samples). Samples of each type were randomly divided into the calibration, prediction and validation sets. For comparison, the PLS-DA models were established in three independent and two complex wavebands of visible (400 - 780 nm), short-NIR (780 - 1100 nm), long-NIR (1100 - 2498 nm), whole NIR (780 - 2498 nm) and whole scanning (400 - 2498 nm). In independent validation, the five models all achieved good discriminant effects. Among them, the visible region model achieved the best effect. The recognition-accuracy rates in validation of negative, positive and total samples achieved 100%, 95.6% and 97.5%, respectively. The results indicated the feasibility of wine brand identification with Vis-NIR spectroscopy.

Near-infrared spectroscopy method for rapid proximate quantitative analysis of nutrient composition in Pacific oyster Crassostrea gigas

Glycogen,amino acids,fatty acids,and other nutrient components affect the flavor and nutritional quality of oysters.Methods based on near-infrared reflectance spectroscopy(NIRS)were developed to rapidly and proximately determine the nutrient content of the Pacific oyster Crassostreagigas.Samples of C.gigas from 19 costal sites were freeze-dried,ground,and scanned for spectral data collection using a Fourier transform NIR spectrometer(Thermo Fisher Scientific).NIRS models of glycogen and other nutrients were established using partial least squares,multiplication scattering correction first-order derivation,and Norris smoothing.The R_(C) values of the glycogen,fatty acids,amino acids,and taurine NIRS models were 0.9678,0.9312,0.9132,and 0.8928,respectively,and the residual prediction deviation(RPD)values of these components were 3.15,2.16,3.11,and 1.59,respectively,indicating a high correlation between the predicted and observed values,and that the models can be used in practice.The models were used to evaluate the nutrient compositions of 1278 oyster samples.Glycogen content was found to be positively correlated with fatty acids and negatively correlated with amino acids.The glycogen,amino acid,and taurine levels of C.gigas cultured in the subtidal and intertidal zones were also significantly different.This study suggests that C.gigas NIRS models can be a cost-effective alternative to traditional methods for the rapid and proximate analysis of various slaughter traits and may also contribute to future genetic and breeding-related studies in Pacific oysters.

Rapid Analysis of Soil Copper Content for the Pearl River Delta Based on Near-Infrared Spectroscopy Combined with SG-PLS

Using near-infrared (NIR) spectroscopy combined with an optimal method for Savitzky-Golay (SG) smoothing and partial least squares (PLS) regression, a rapid analysis method was established for copper content in the beach reclamation soil samples from Pearl River Delta in China. A framework with calibration, prediction and validation was established by considering randomness and stability. The parameters were optimized according to the comprehensive index (SEP+) to produce modeling stability. The validation results show that, based on the SG-PLS model in long-NIR region (1100 - 2498 nm) with first-order derivative, fifth degree polynomial, seven smoothing points and six PLS factors, the corresponding root mean square error (SEP), correlation coefficient of prediction (RP) and average relative error (ARE) were 0.31 mg·kg-1, 0.924 and 4.5%, respectively. The result indicates high prediction accuracy. The relevant parameter selection can also provide a reference for designing small and dedicated spectrometer.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Identification of Soy Sauce Adulteration

The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spectroscopy combined with standard normal variate-partial least squares-discriminant analysis (SNV-PLS-DA) was used to establish the discriminant analysis models for adulterated and brewed soy sauces. Chubang soy sauce was selected as an identification brand (negative, 70). The adulteration samples (positive, 72) were prepared by mixing Chubang soy sauce and blended soy sauce with different adulteration rates. Among them, the “blended soy sauce” sample was concocted of salt water (NaCl), monosodium glutamate (C5H10NNaO5) and caramel color (C6H8O3). The rigorous calibration-prediction-validation sample design was adopted. For the case of 1 mm, five waveband models (visible, short-NIR, long-NIR, whole NIR and whole scanning regions) were established respectively;in the case of 10 mm, three waveband models (visible, short-NIR and visible-short-NIR regions) for unsaturated absorption were also established respectively. In independent validation, the models of all wavebands in the cases of 1 mm and 10 mm have achieved good discrimination effects. For the case of 1 mm, the visible model achieved the optimal validation effect, the validation recognition-accuracy rate (RARV) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RARV = 100%). The detection method does not require reagents and is fast and simple, which is easy to promote the application. The results can provide valuable reference for designing small dedicated spectrometers with different measurement modals and different spectral regions.

Analysis of Some Important Forage Quality Attributes of Southeastern Wildrye (Elymus glabriflorus) Using Near-Infrared Reflectance Spectroscopy

Southeastern wildrye (Elymus glabriflorus, Vasey ex L.H. Dewey) is a cool-season, perennial grass native to southeastern United States. Recently, there is a growing interest in its development as a grazing and haying forage crop due to its wide area of adaptation across this region. Consequently, there is a great need for the evaluation of its forage quality by rapid, but accurate analytical methods like Near-Infrared Reflectance Spectroscopy (NIRS). In this study, acceptable NIRS calibration models were developed for: dry matter, DM (n = 113, R2 = 0.904, RSCD = 2.54, RSCIQ = 4.65);crude protein, CP (n = 113, R2 = 0.974, RSCD = 5.16, RSCIQ = 5.92);acid detergent fiber, ADF (n = 116, R2 = 0.896, RSCD = 2.35, RSCIQ = 1.28);neutral detergent fiber, NDF (n = 118, R2 = 0.934, RSCD = 2.53, RSCIQ = 3.38);digestible dry matter, DDM (n = 116, R2 = 0.895, RSCD = 2.36, RSCIQ = 1.35);dry matter intake, DMI (n = 115, R2 = 0.924, RSCD = 2.40, RSCIQ = 2.53);and relative feed value, RFV (n = 114, R2 = 0.932, RSCD = 2.94, RSCIQ = 2.81). Prediction of independent validation sets yielded good agreement between the NIRS predicted values and the laboratory reference values for each of: DM (n = 53, R2 = 0.831, RPD = 2.45, RPIQ = 4.24);CP (n = 57, R2 = 0.967, RPD = 5.37, RPIQ = 7.16);ADF (n = 49, R2 = 0.895, RPD = 2.97, RPIQ = 1.51);NDF (n = 53, R2 = 0.928, RPD = 3.75, RPIQ = 4.22);digestible dry matter, DDM (n = 55, R2 = 0.860, RSCD = 265, RSCIQ = 1.15);dry matter intake, DMI (n = 156, R2 = 0.845, RSCD = 2.48, RSCIQ = 2.11);and relative feed value, RFV (n = 55, R2 = 0.916, RSCD = 3.45, RSCIQ = 3.04) contents, indicating that all seven calibration models had good quantitative information. Therefore, precise, accurate, and rapid analysis of these important forage quality attributes of southeastern wildrye can be routinely done using the developed NIRS calibration models.

A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud

As one chemical composition,nicotine content has an important influence on the quality of tobacco leaves.Rapid and nondestructive quantitative analysis of nicotine is an important task in the tobacco industry.Near-infrared(NIR)spectroscopy as an effective chemical composition analysis technique has been widely used.In this paper,we propose a one-dimensional fully convolutional network(1D-FCN)model to quantitatively analyze the nicotine composition of tobacco leaves using NIR spectroscopy data in a cloud environment.This 1D-FCN model uses one-dimensional convolution layers to directly extract the complex features from sequential spectroscopy data.It consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information loss.Cloud computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and accessing.Experimental results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other approaches.This research provides a deep learning foundation for quantitative analysis of NIR spectral data in the tobacco industry.

Near-Infrared Spectroscopy Combined with Absorbance Upper Optimization Partial Least Squares Applied to Rapid Analysis of Polysaccharide for Proprietary Chinese Medicine Oral Solution

Near-infrared (NIR) spectroscopy was applied to reagent-free quantitative analysis of polysaccharide of a brand product of proprietary Chinese medicine (PCM) oral solution samples. A novel method, called absorbance upper optimization partial least squares (AUO-PLS), was proposed and successfully applied to the wavelength selection. Based on varied partitioning of the calibration and prediction sample sets, the parameter optimization was performed to achieve stability. On the basis of the AUO-PLS method, the selected upper bound of appropriate absorbance was 1.53 and the corresponding wavebands combination was 400 - 1880 & 2088 - 2346 nm. With the use of random validation samples excluded from the modeling process, the root-mean-square error and correlation coefficient of prediction for polysaccharide were 27.09 mg·L-1 and 0.888, respectively. The results indicate that the NIR prediction values are close to those of the measured values. NIR spectroscopy combined with AUO-PLS method provided a promising tool for quantification of the polysaccharide for PCM oral solution and this technique is rapid and simple when compared with conventional methods.

Wavelength Selection for Near-Infrared Spectroscopic Analysis of Teicoplanin

Teicoplanin (TCP) is a multiple drug-resistant lipoglycopeptide antibiotic produced by fermenting Actinoplanes teichomyceticus. In this study, a mixture of TCP with the Tris-HCl buffer (TCP-Tris- HCl) was used to simulate TCP fermentation broth. The reagent-free, rapid and simultaneous quantitative analysis models for TCP and Tris in the TCP-Tris-HCl mixtures were established by near-infrared (NIR) spectroscopy. The equidistant combination partial least squares (EC-PLS) method and the equivalent model sets were proposed, the simplest equivalent model with the smallest number of wavelengths were further selected. The initial wavelength, number of wavelengths, number of wavelength gaps, number of PLS factors were 1520 nm, 28, 5, 5 for TCP and 1084 nm, 13, 6, 4 for Tris, respectively. Compared with the optimal EC-PLS models, the simplest equivalent models adopted fewer wavelengths. Thus, the redundant wavelengths were removed, the models were further simplified. The root-mean-square errors (SEP) and correlation coefficients (RP) for prediction were 0.043 mg·mL-1 and 0.9998 for TCP, and 0.222 mg·mL-1 and 0.9989 for Tris, respectively. The results indicate that NIR method can be applied to highly accurate quantitative analysis for TCP and provide valuable references for further application to TCP fermentation broth.

Reliability of online near-infrared spectroscopy analysis of the ex-traction process of Huatan Qushi decoction for phlegm-dampness constitution

OBJECTIVE: To establish a quantitative model for the online analysis of hesperidin content in the extraction process of Huatan Qushi decoction for individuals with phlegm-dampness constitution by using near-infrared(NIR) spectroscopy and partial least squares(PLS) methods.METHODS: The reference content of hesperidin, an effective ingredient of Huatan Qushi decoction,was determined using high-performance liquid chromatography(HPLC). The modeling band was selected using synergy interval PLS(Si PLS) algorithms, and a PLS model demonstrating the relationship between NIR predictive values and HPLC reference values was established. The optimal modeling route was selected through global optimization of process trace parameters.RESULTS: The root mean square error of cross-validation was 9.8410. The coefficients of determination of the calibration set and prediction set were0.9860(R2 cal) and 0.9458(R_(cal)~2), respectively. The residual predictive deviation was 3.0115. The parameters demonstrated high predictive capability and reliability. The band was selected via the Si PLS method. There were seven latent variables.CONCLUSION: This study provides a favorable comprehensive evaluation method for variable selection and quality control of the Traditional Chinese Medicine extraction process.

Near-Infrared Spectroscopy Coupled with Kernel Partial Least Squares-Discriminant Analysis for Rapid Screening Water Containing Malathion

Near-infrared spectroscopy coupled with kernel partial least squares-discriminant analysis was used to rapidly screen water containing malathion. In the wavenumber of 4348 cm-1 to 9091 cm-1, the overall correct classification rate of kernel partial least squares-discriminant analysis was 100% for training set, and 100% for test set, with the lowest concentration detected malathion residues in water being 1 μg·ml-1. Kernel partial least squares-discriminant analysis was able to have a good performance in classifying data in nonlinear systems. It was inferred that Near-infrared spectroscopy coupled with the kernel partial least squares-discriminant analysis had a potential in rapid screening other pesticide residues in water.

DISCRIMINATIVE ANALYSIS OF FUNCTIONAL NEAR-INFRARED SPECTROSCOPY SIGNALS FOR DEVELOPMENT OF NEUROIMAGING BIOMARKERS OF ELDERLY DEPRESSION

Functional near-infrared spectroscopy(fNIRS)is a neuroimaging technology which is suitable for psychiatric patients.Several fNIRS studies have found abnormal brain activations during cognitive tasks in elderly depression.In this paper,we proposed a discriminative model of multivariate pattern classification based on fNIRS signals to distinguish elderly depressed patients from healthy controls.This model used the brain activation patterns during a verbal fluency task as features of classification.Then Pseudo-Fisher Linear Discriminant Analysis was performed on the feature space to generate discriminative model.Using leave-one-out(LOO)cross-validation,our results showed a correct classification rate of 88%.The discriminative model showed its ability to identify people with elderly depression and suggested that fNIRS may be an efficient clinical tool for diagnosis of depression.This study may provide the first step for the development of neuroimaging biomarkers based on fNIRS in psychiatric disorders.

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.

Near-infrared spectroscopy in schizophrenia:A bibliometric perspective

BACKGROUND Compared with current methods used to assess schizophrenia,near-infrared spectroscopy(NIRS)has the advantages of providing noninvasive and real-time monitoring of functional activities of the brain and providing direct and objective assessment information.AIM To explore the research field of NIRS in schizophrenia from the perspective of bibliometrics.METHODS The Web of Science Core Collection was used as the search tool,and the last search date was April 21,2024.Bibliometric indicators,such as the numbers of publications and citations,were recorded.Bibliometrix and VOS viewer were used for visualization analysis.RESULTS A total of 355 articles from 105 journals were included in the analysis.The overall trend of the number of research publications increased.Schizophrenia Research was identified as an influential journal in the field.Kasai K was one of the most influential and productive authors in this area of research.The University of Tokyo and Japan had the highest scientific output for an institution and a country,respectively.The top ten keywords were“schizophrenia”,“activation”,“near-infrared spectroscopy”,“verbal fluency task”,“cortex”,“brain,performance”,“workingmemory”,“brain activation”,and“prefrontal cortex”.CONCLUSION Our study reveals the evolution of knowledge and emerging trends in the field of NIRS in schizophrenia.the research focus is shifting from underlying disease characteristics to more in-depth studies of brain function and physiological mechanisms.