The near-infrared spectra and distribution of excited states of electrodeless discharge rubidium vapour lamps

The population ratio between the excited states of rubidium in the electrodeless discharge rubidium vapour lamp is calculated according to the near-infrared spectra in the region of 780-1550 nm. By using a 1529 nm laser, we measure the density of natural rubidium atoms at the 5P3/2 level. The populations of different excited states are then clarified.

Background correction in near-infrared spectra of plant extracts by orthogonal signal correction

In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constituents is difficult because of excessive background, and correction of this problem remains difficult. In this work, the orthogonal signal correction (OSC) method was used to correct excessive background. The method was also compared with several classical background correction methods, such as offset correction, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, de-trending (DT), first derivative, second derivative and wavelet methods. A simulated dataset and a real NIR spectral dataset were used to test the efficiency of different background correction methods. The results showed that OSC is the only effective method for correcting excessive background.

Compression strength prediction of Xylosma racemosum using a transfer learning system based on near-infrared spectral data

A transfer learning system was designed to predict Xylosma racemosum compression strength.Near-infrared(NIR)spectral data for Acer mono and its compression strength values were used to resolve the weak generalization problem caused by using a X.racemosum dataset alone.Transfer component analysis and principal component analysis are domain adaption and feature extraction processes to enable the use of A.mono NIR spectral data to design the transfer learning system.A five-layer neural network relevant to the X.racemosum dataset,was fine-tuned using the A.mono dataset.There were 109 A.mono samples used as the source dataset and 79 X.racemosum samples as the target dataset.When the ratio of the training set to the test set was 1:9,the correlation coeffi cient was 0.88,and mean square error was 8.84.The results show that NIR spectral data of hardwood species are related.Predicting the mechanical strength of hardwood species using multi-species NIR spectral datasets will improve the generalization ability of the model and increase accuracy.

Capillary method for measuring near-infrared spectra of microlitre volume liquids

The present study theoretically explored the feasibility of the capillary method for measuring near-infrared (NIR) spectra of liquid or solution samples with microlitre volume, which was proposed in our previous studies. Lambert-Beer absorb- ance rule was applied to establish a model for the integral absorbance of capillary, which was then implemented in numerical analyses of the effects of capillary on various spectral features and dynamic range of absorption measurement. The theoretical speculations indicated that the capillary method might be used in NIR spectroscopy, which was further supported by the empirical data collected from our experiments by comparison between capillary NIR spectra of several organic solvents and cuvette cell NIR spectra.

Application of Grey-correlated Spectral Region Selection in Analysis of Near-infrared Spectra

The optimal selection method of spectral region based on the grey correlation analysis was applied in the analysis of near-infrared（NIR） spectra. In order to compute ＂characteristic＂ spectral region, 160 samples of tobacco were surveyed by NIR. Next, the whole spectral region was randomly divided into six regions, and the values of association coefficients and correlation orders of different regions were computed for total sugar, reducing sugar and nicotine. Moreover, two regions that owned the largest value of association coefficient were regarded as ＂characteristic＂ spectral region of a model. Finally, the quantitative analysis models of different components were established via the partial least squares method, and the common selection methods of spectral region were compared. The simulation results indicate that the models to choose the spectral region based on grey correlation analysis are more effective than the common selection methods of spectral region, the optimized time of algorithm is shorter, the prediction precision of the models is higher and generalization ability for quantitative analysis results is stronger. This research can provide the support for the quantitative analysis models of NIR spectra and new idea for commercial analysis software of NIR. So, it has a high application value in the analysis of NIR spectra.

The application of near-infrared spectra micro-image in the imaging analysis of biology samples

In this research,suitable imaging methods were used for acquiring single compound images ofbiology samples of chicken pectorales tissue section,tobacco dry leaf,fresh leaf and plantglandular hair,respectively.The adverse effects caused by the high water content and thethermal effect of near infrared(NIR)light were effectively solved during the experiment procedures and the data procesing.PCA algorithm was applied to the NIR micro-image of chickenpectorales tissue.Comparing the loading vector of PC3 with the NIR spectrum of dry albumen,the information of PC3 was confimmed to be provided mainly by protein,i.e.,the 3rd score imagerepresents the distribution trend of protein mainly.PCA algorithm was applied to the NIR micro-image of tobacco dry leaf.The information of PC2 was confimed to be provided by carbohydrateindluding starch mainly.Compared to the 2nd score image of tobacco dry leaf,the comparedcorelation image with the reference spectrum of starch had the same distribution trend as the 2nd score image.The comparative correla tion images with the reference spectra of protein,glucose,fructose and the total plant alkaloid were acquired to confirm the distribution trend ofthese compounds in tobacco dry leaf respectively.Comparative correlation images of fresh leafwith the reference spectra of protein,starch,fructose,ghucose and water were acquired to confim the distribution trend of these compounds in fresh leaf.Chemimap imaging of plant glandularhair was acquired to show the tubular structure clearly.

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.

Application of Visible/Near-Infrared Spectra in Modeling of Soil Total Phosphorus

Overabundance of phosphorus (P) in soils and water is of great concern and has received much attention in Florida, USA. Therefore, it is essential to analyze and predict the distribution of P in soils across large areas. This study was undertaken to model the variation of soil total phosphorus (TP) in Florida. A total of 448 soil samples were collected from different soil types. Soil samples were analyzed by chemical reference method and scanned in the visible/near-infrared (VNIR) region of 350-2 500 nm. Partial least squares regression (PLSR) calibration model was developed between chemical reference values and VNIR values. The coefficient of determination (R2) and the root mean squares error (RMSE) of calibration and validation sets, and the residual prediction deviation (RPD) were used to evaluate the models. The R2in calibration and validation for log-transformed TP (log TP) were 0.69 and 0.65, respectively, indicating that VNIR calibration obtained in this study accounted for at least 65% of the variance in log TP using only VNIR spectra, and the high RPD of 2.82 obtained suggested that the spectral model derived in this study was suitable and robust to predict TP in a wide range of soil types, being representative of Florida soil conditions.

A variable importance criterion for variable selection in near-infrared spectral analysis

Variable selection is a universal problem in building multivariate calibration models, such as quantitative structure-activity relationship(QSAR) and quantitative relationships between quantity or property and spectral data. Significant improvement in the prediction ability of the models can be achieved by reducing the bias induced by the uninformative variables. A new criterion,named as C, is proposed in this study to evaluate the importance of the variables in a model. The value of C is defined as the average contribution of a variable to the model, which is calculated by the statistics of the models built with different combinations of the variables. In the calculation, a large number of partial least squares(PLS) models are built using a subset of variables selected by randomly re-sampling. Then, a vector of the prediction errors, in terms of root mean squared error of cross validation(RMSECV), and a matrix composed of 1 and 0 indicating the selected and unselected variables can be obtained. If multiple linear regression(MLR) is employed to model the relationship between the RMSECVs and the matrix, the coefficients of the MLR model can be used as a criterion to evaluate the contribution of a variable to the RMSECV. To enhance the efficiency of the method, a multi-step shrinkage strategy was used. Comparison with Monte Carlo-uninformative variables elimination(MC-UVE), randomization test(RT) and competitive adaptive reweighted sampling(CARS) was conducted using three NIR benchmark datasets. The results show that the proposed criterion is effective for selecting the informative variables from the spectra to improve the prediction ability of models.

Influence of HITRAN Database Updates on Retrievals of Atmospheric CO_2 from Near-Infrared Spectra

The line-transition parameters of the High Resolution Transmission （HITRAN） 2008 database have been updated relative to previous editions. The transmission spectra and sensitivity to changes in CO2 concentrations using line parameters from the HITRAN 2004 and HITRAN 2008 databases are compared to evaluate the effect of the database updates on retrievals of carbon dioxide vertical columns from nearinfrared reflected sunlight. This comparison is done in three spectral regions covering the 2.06-, 1.61-, and 1.58-μm CO2 bands used by the Greenhouse Gases Observatory Satellite （GOSAT） instrument and the planned successor to the Orbiting Carbon Observatory （OCO）. The updates to the HITRAN database have the largest effects on the transmittance and the off-line to on-line transmittance ratio in the 2.06-μm region and the smallest effects on these parameters in the 1.58-μm region. The influence of the updates to the HITRAN database on the off-line to on-line ratio calculation in the narrow spectral region 4855-4880 cm^-1 could be equivalent to a change in CO2 of more than 50 ppmv. Use of the HITRAN 2004 database will lead to an underestimate of the column CO2 abundance in the 2.06- and 1.61-pro spectral regions, whereas it will lead to an overestimate of the column CO2 abundance in the 1.58-μm spectral region.

Chemometric methods for extracting information from temperature-dependent near-infrared spectra

Temperature-dependent near-infrared(NIR) spectroscopy is a new technique for measuring the NIR spectra of a sample at different temperatures. Taking the advantage of the temperature effect, the technique has shown its potential in both quantitative and qualitative analysis. The technique has been proved to be powerful in determination of the analytes in complex samples,particularly in studying the functions of water in aqueous systems due to the significant effect of temperature on the NIR spectra of water. Because of the complicated interactions in the samples and the overlapping of the broad peaks in NIR spectra, it is difficult to extract the temperature-dependent information from the spectra. Chemometric methods, therefore, have been developed for improving the spectral resolution and extracting the temperature-induced spectral information. In this review, recent advances in the studies of chemometric methods and the applications in resolution, quantitative and structural analysis of temperature-dependent NIR spectra were summarized.

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.

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.

Synthesis and Spectral Properties of Novel Water-soluble Near-infrared Fluorescent Indocyanines

Two fluorescent pentamethine and a squarylium indocyanines containing at least one p-carboxybenzyl group on N atoms in the heterocyclic rings were synthesized. They had good water solubility and photostability. Their maximum absorption and maximum emission were 600-700 nm in water. When it was anchored onto nanostructured TiO2 electrode, compared with in water, the squaraine showed double absorption peaks (one blue shifted and another red shifted) and absorption intensity of the red shift peak increased with the increase of the time of irradiation. The intensity of the blue one decreased simultaneously. We proposed that the presence of two electronic charge forms of squaraine anchored on the TiO2 film might be the reason.

A highly sensitive ratiometric near-infrared nanosensor based on erbium-hyperdoped silicon quantum dots for iron(Ⅲ) detection

Ratiometric fluorescent detection of iron(Ⅲ)(Fe^(3+))offers inherent self-calibration and contactless analytic capabilities.However,realizing a dual-emission near-infrared(NIR)nanosensor with a low limit of detection(LOD)is rather challenging.In this work,we report the synthesis of water-dispersible erbium-hyperdoped silicon quantum dots(Si QDs:Er),which emit NIR light at the wavelengths of 810 and 1540 nm.A dual-emission NIR nanosensor based on water-dispersible Si QDs:Er enables ratiometric Fe^(3+)detection with a very low LOD(0.06μM).The effects of pH,recyclability,and the interplay between static and dynamic quenching mechanisms for Fe^(3+)detection have been systematically studied.In addition,we demonstrate that the nanosensor may be used to construct a sequential logic circuit with memory functions.

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.

Rapid Determination of Hemicellulose Content in Corn Stalks by Near-infrared Spectroscopy Based on Dung Beetle Optimizer

Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new materials.Hemicellulose is an important component in corn stalks,and it is very important to determine its content in corn stalks.In this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was studied.In order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of NIRS.Its modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm(GA)and binary particle swarm optimization(BPSO),and it was found that the characteristic wavelength selection performance of DBO was excellent,and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms.

Understanding the thermal stability of human serum proteins with the related near-infrared spectral variables selected by Monte Carlo-uninformative variable elimination

Understanding the thermal stability of the proteins in human serum is essential since human serum is the important source of pharmaceutical proteins. Near-infrared（NIR） spectroscopy was applied to the investigation of thermal changes in secondary structure and hydration of human serum proteins.However, as a multicomponent system, the overlap of the broad NIR bands makes the structural analysis very difficult directly using the spectra of serum samples. Therefore, continuous wavelet transform（CWT） was used to improve the resolution of NIR spectra, and Monte Carlo-uninformative variable elimination（MC-UVE） method was applied to the selection of the variables associated with the proteins for the structural analysis. The variables（5956, 5867, 5815, 5747, 4525, 4401, 4359 and 4328 cm^-1） related to protein secondary structures and those（7074, 6951, 6827 and 6700 cm 1） connected with water species were selected. Then, the thermal stability was analyzed through the intensity variations of the selected variables with temperature from 30℃ to 80 ℃. It was found that the variation of the spectral variables related to both a-helix and b-sheet changes apparently around 60 ℃, indicating the beginning of the thermal denaturation and the transition from a-helix to b-sheet. Moreover, an obvious change was found around 60℃for the content of the water specie S3, i.e., the water cluster containing three hydrogen bonds. The result demonstrates that MC-UVE can identify the protein-related NIR spectral variables, and the water species may be a marker for investigation of the structural change of proteins in biochemical systems.

Near-infrared cholangiography with intragallbladder indocyanine green injection in minimally invasive cholecystectomy

Laparoscopic cholecystectomy(LC)remains one of the most commonly performed procedures in adult and paediatric populations.Despite the advances made in intraoperative biliary anatomy recognition,iatrogenic bile duct injuries during LC represent a fatal complication and consist an economic burden for healthcare systems.A series of methods have been proposed to prevent bile duct injury,among them the use of indocyanine green(ICG)fluorescence.The most commonly reported method of ICG injection is the intravenous administration,while literature is lacking studies investigating the direct intragallbladder ICG injection.This narrative mini-review aims to assess the potential applications,usefulness,and limitations of intragallbladder ICG fluorescence in LC.Authors screened the available international literature to identify the reports of intragallbladder ICG fluorescence imaging in minimally invasive cholecystectomy,as well as special issues regarding its use.Literature search retrieved four prospective cohort studies,three case-control studies,and one case report.In the three case-control studies selected,intragallbladder near-infrared cholangiography(NIRC)was compared with standard LC under white light,with intravenous administration of ICG for NIRC and with standard intraoperative cholangiography(IOC).In total,133 patients reported in the literature have been administered intragallbladder ICG administration for biliary mapping during LC.Literature includes several reports of intragallbladder ICG administration,but a standardized technique has not been established yet.Published data suggest that NIRC with intragallbladder ICG injection is a promising method to achieve biliary mapping,overwhelming limitations of IOC including intervention and radiation exposure,as well as the high hepatic parenchyma signal and time interval needed in intravenous ICG fluorescence.Evidence-based guidelines on the role of intragallbladder ICG fluorescence in LC require the assessment of further studies and multicenter data collection into large registries.

Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra

Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.