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.

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.

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.

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.

Near Infrared Spectroscopy (NIRS) Model-Based Prediction for Protein Content in Cowpea

Cowpea (Vigna unguiculata L. Walp) is a multi-purpose legume with high quality protein for human consumption and livestock. The objective of this work was to develop near-infrared spectroscopy (NIRS) prediction models to estimate protein content in cowpea. A total of 116 cowpea breeding lines with a wide range of protein contents (19.28 % to 32.04%) were selected to build the model using whole seed and ground seed samples. Partial least-squares discriminant analysis (PLS-DA) regression technique with different pre-treatments (derivatives, standard normal variate, and multiplicative scatter correction) were carried out to develop the protein prediction model. Results showed: 1) spectral plots of both the whole seed and ground seed showed higher spectral scatter at higher wavelengths (>1450 nm), 2) data pre-processing affects prediction accuracy for bot whole seed and ground seed samples, 3) prediction using ground seed samples (0.64 R2 0.85) is better than the whole seed (0.33 R2 0.78), and 4) the data pre-processing second derivative with standard normal variate has the best prediction (R2_whole seed = 0.78, R2_ground seed = 0.85). The results will be of interest in cowpea breeding programs aimed at improving total seed protein content.

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.

Synchronous measurements of prefrontal activity and pulse rate variability during online video game playing with functional near-infrared spectroscopy

Interactions between the central nervous system(CNS)and autonomic nervous system(ANS)play a crucial role in modulating perception,cognition,and emotion production.Previous studies on CNS–ANS interactions,or heart–brain coupling,have often used heart rate variability(HRV)metrics derived from electrocardiography(ECG)recordings as empirical measurements of sympathetic and parasympathetic activities.Functional near-infrared spectroscopy(fNIRS)is a functional brain imaging modality that is increasingly used in brain and cognition studies.The fNIRS signals contain frequency bands representing both neural activity oscillations and heartbeat rhythms.Therefore,fNIRS data acquired in neuroimaging studies can potentially provide a single-modality approach to measure task-induced responses in the brain and ANS synchronously,allowing analysis of CNS–ANS interactions.In this proof-of-concept study,fNIRS was used to record hemodynamic changes from the foreheads of 20 university students as they each played a round of multiplayer online battle arena(MOBA)game.From the fNIRS recordings,neural and heartbeat frequency bands were extracted to assess prefrontal activities and shortterm pulse rate variability(PRV),an approximation for short-term HRV,respectively.Under the experimental conditions used,fNIRS-derived PRV metrics showed good correlations with ECG-derived HRV golden standards,in terms of absolute measurements and video game playing(VGP)-related changes.It was also observed that,similar to previous studies on physical activity and exercise,the PRV metrics closely related to parasympathetic activities recovered slower than the PRV indicators of sympathetic activities after VGP.It is concluded that it is feasible to use fNIRS to monitor concurrent brain and ANS activations during online VGP,facilitating the understanding of VGP-related heart–brain coupling.

Assessment of cerebral oxygenation response to hemodialysis using near-infrared spectroscopy (NIRS):Challenges and solutions

To date,the clinical use of functional near-infrared spectroscopy(NIRS)to detect cerebral ischemia has been largely limited to surgical settings,where motion artifacts are minimal.In this study,we present novel techniques to address the challenges of using NIRS to monitor ambu-latory patients with kidney disease during approximately eight hours of hemodialysis(HD)treatment.People with end-stage kidney disease who require HD are at higher risk for cognitive impairment and dementia than age-matched controls.Recent studies have suggested that HD-related declines in cerebral blood flow might explain some of the adverse outcomes of HD treatment.However,there are currently no established paradigms for monitoring cerebral per-fusion in real-time during HD treatment.In this study,we used NIRS to assess cerebral hemo-dynamic responses among 95 prevalent HD patients during two consecutive HD treatments.We observed substantial signal attenuation in our predominantly Black patient cohort that required probe modifications.We also observed consistent motion artifacts that we addressed by devel-oping a novel NIRS methodology,called the HD cerebral oxygen demand algorithm(HD-CODA),to identify episodes when cerebral oxygen demand might be outpacing supply during HD treatment.We then examined the association between a summary measure of time spent in cerebral deoxygenation,derived using the HD-CODA,and hemodynamic and treatment-related variables.We found that this summary measure was associated with intradialytic mean arterial pressure,heart rate,and volume removal.Future studies should use the HD-CODA to implement studies of real-time NIRS monitoring for incident dialysis patients,over longer time frames,and in other dialysis modalities.

Prediction of Crude Protein in Ramie by Near-Infrared Spectroscopy(NIR)

[Objective] This study was conducted to evaluate the feasibility of deter- mining crude protein in ramie using near-infrared （NIR） spectrometer. [Method] Par- tial least square regression （PLSR） was performed to establish a calibration model based on 50 samples for predicting the crude protein content in ramie, and the model was validated with data in the validation set consisting of 10 samples. [Result] The correlation coefficient of the model was 0.98. There was a good correla- tion between the predicted values by the near-infrared prediction model and the measured values by chemical analysis, and the relative error was 3.54% on aver- age between the predicted and the measured values. [Conclusion] The results showed that it is feasible to determine crude protein content in ramie using NIR spectroscopy-based prediction model.

Correlation between LIFG and Autonomic Activation during Stressful Tasks:A Functional Near-infrared Spectroscopy (fNIRS) Study

It remains unclear whether language tasks in one＇s first （L1） or second （L2） language can cause stress responses and whether frontal, autonomic and behavioral responses to stressful tasks are correlated. In this study, we studied 22 Chinese subjects whose L2 was English and measured the cerebral blood oxygenation in their frontal lobe by using functional near-infrared spectroscopy （fNIRS） as par- ticipants engaged in a mental arithmetic task （MAT） and verbal fluency tasks （VFTs） in L1 （Chinese） and L2 （English）. To examine the activated cortical areas, we estimated the channel location based on Montreal Neurological Institute （MNI） standard brain space by using a-probabilistic estimation method. We evaluated heart rate （HR） changes to analyze autonomic nervous system （ANS） functioning. We found that the MAT and VFTs induced greater increases in HR than did the control （Ctrl） task. Further- more, subjects developed greater increases in HR in the MAT and VFTt~ than they did in the VFTL1. Compared with the Ctrl task, the MAT and both VFTLland VFTL2 produced robust and widespread bi- lateral activation of the frontal cortex. Interestingly, partial correlation analysis indicated that the activity in the left inferior frontal gyrus （LIFG） [Brodmarm＇s area （BA） 47] was consistently correlated with the increases in HR across the three tasks （MAT, VFTL2, and VFTL1）, after controlling for the performance data. The present results suggested that a VFT in L2 may be more stressful than in L1. The LIFG may affect the activation of the sympathetic system induced by stressful tasks, includin~ MATs and VFTs.

Investigation of Prefrontal Cortex Activity in University Students with Presenteeism: A Near-Infrared Spectroscopy (NIRS) Study

Presenteeism refers to impaired performance attributed to attending work with health problems. There has been no study examining the state of presenteeism with objective measures. We compared cerebral hemodynamic changes, measured by near-infrared spectroscopy (NIRS), during neuropsychological tests conducted by university students with presenteeism and healthy controls. Twenty-two university students participated in the study;11 of them with impaired performance caused by mental health problem were allocated to the presenteeism group and 11 without health problems to the control group. Presenteeism was assessed by the Presenteeism Scale for Students. To evoke hemodynamics changes, the participants completed a Word Fluency Test (WFT) and a Trail Making Test (TMT). The NIRS probes were located over the bilateral prefrontal area. Students with presenteeism had significantly higher incidences of depression than controls. However, there was no significant difference in behavioral performance examinations between the two groups. With regard to hemodynamics changes, the repeated measures analysis of covariance of the NIRS signals revealed significant interactions between group and task activation. Although we observed a significant increase in oxygenated hemoglobin concentration during the WFT among controls (simple main effect;left channel, F(1, 19) = 27.34, P F(1, 19) = 22.05, P < 0.001), no changes were found in students with presenteeism during either the WFT (simple main effect;left channel, F(1, 19) = 0.12, P F(1, 19) = 0.08, P t = ﹣0.94, P with Bonferroni correction = 0.745;right channel, t = ﹣2.19, P with Bonferroni correction < 0.113). This is the first study to reveal differences in activity in the cerebral cortex associated with presenteeism. The fact that students with presenteeism have prefrontal dysfunction might reinforce the concept of presenteeism.

RAPID DETERMINATION OF PROTEIN IN MILLET BY FOURIER TRANSFORM NEAR-INFRARED(FTNIR)DIFFUSE REFLECTANCE SPECTROSCOPY

In this paper,the Fourier transform near-infrared(FTNIR)diffuse reflectance spectroscopy is applied for the rapid determination of protein in millet.The partial least-squares(PLS)regression is successfully used as an effective multivariate calibration technique.The calibration set is composed of 20 standard millet samples that the protein contents were determined by the traditional Kjeldahl method.The optimal model dimension is found to be 5 by cross-validation.22 millet samples were determined by the proposed FTNIR-PLS method.The correlation coefficient between the concentration values obtained by the FTNIR-PLS method and the traditional Kjeldahl method is 0.9805.The standard error of prediction(SEP)is 0.28% and the mean recovery is 100.2%.The proposed method has been successfully applied for the routine analysis of protein in about 10,000 grain samples.

Correlation of prefrontal activity measured by near-infrared spectroscopy (NIRS) with mood, BDNF genotype and serum BDNF level in healthy individuals

Depression has been known to reduce the prefrontal activity associated with the execution of certain cognitive tasks, although whether a temporarily depressed or anxious mood in healthy individuals affects the prefrontal blood oxygen level during cognitive tasks is unknown. Combining the measurement of prefrontal activity with near-infrared spectroscopy (NIRS) and the two cognitive tasks, namely the letter version of the verbal fluency test (VFT-l) and the Stroop test, we measured the effect of a depressed or anxious mood and gender on the changes in the prefrontal oxygenated hemoglobin (Oxy-Hb) levels during those cognitive tests in healthy individuals. Depressed mood or anxious mood was assessed by the Hospital Anxiety and Depression Scale (HADS). Thereby we aimed to explore the possibility of NIRS measurement for detecting the early subclinical manifestation of major depression. Moreover, we examined the possible relationships between prefrontal activation and the functional Val66Met polymorphisms of the brain derived neurotropic factor (BDNF) gene and serum BDNF level. As a result, the increased prefrontal Oxy-Hb levels during cognitive tasks were significantly correlated with the severity of depressed mood in males. The course of the prefrontal Oxy-Hb increase was different depending on the cognitive tasks, i.e., the VFT-l or the Stroop test, in both genders. Correlations of BDNF genotype and serum BDNF level with the prefrontal Oxy-Hb levels during those cognitive tasks were negative. Our results suggest that the early subclinical manifestation of depressed mood in males might be detected by the NIRS measurement, which is not correlated with the individual properties of BDNF.

Construction of universal quantitative models for the determination of cefoperazone sodium/sulbactam sodium for injection from different manufacturers using near-infrared reflectance spectroscopy

To develop near-infrared （NIR） reflectance spectroscopic methods for the quantitative analysis of cefoperazone sodium/ sulbactam sodium from different manufacturers for injection powder medicaments. Various powders of cefoperazone sodium/ sulbactam sodium were directly analyzed by non-destructive NIR reflectance spectroscopy using the spectrometer EQUINOX55. Two quantitative methods via integrating sphere （IS） and fiberoptic probe （FOP） models were explored from 6 batches of commercial samples and 42 batches of laboratory samples at a content ranging from 30% to 70% for cefoperazone and 60% to 20% for sulbactam. The root mean square errors of cross validation （RMSECV） and the root mean square errors of prediction （RMSEP） of IS were 1.79% and 2.85%, respectively, for cefoperazone sodium, and were 1.86% and 3.08%, respectively, for sulbactam sodium; and those of FOP were 2.93% and 2.92%, respectively, for cefoperazone sodium, and were 2.23% and 3.01%, respectively, for sulbactam sodium. Based on the ICH guidelines and Ref. 12, the quantitative models were then evaluated in terms of specificity, linearity, accuracy, precision, robustness and model transferability. The non-destructive quantitative NIR methods used in this study are applicable for rapid analysis of injectable powdered drugs from different manufacturers.

Classification of Guizhou Aspidistra Plants by Near-infrared Diffuse Reflectance Spectroscopy

[Objective] This study was conducted to establish a near-infrared diffuse reflectance spectroscopy of Guizhou Aspidistra plants. [Method] Twenty three batch- es of Guizhou Aspidistra plants including A. chishuiensis, A. spinula, A. Caespitosa, A. sichuanensis, A. ebianensis, A. retusa, A. guizhouensis and A. liboensis were subjected to drying, pulverization and sieving and then directly determined for near- infrared reflectance spectrums; and the plants in this genus were classified by clus- ter analysis and principal component analysis （PCA）. [Result] The near-infrared re- flectance spectrums of the 23 batches of Guizhou Aspidistra plants showed very high similarity. The spectrums were processed by first derivative method, and the spectral range of 4 000-7 500 cm-1 was selected as the analytical range. Cluster analysis and PCA were employed to mass spectrum variables of plants in Aspidis- tra, fewer new variables became the linear combination of primary variables, and small differences between different varieties were enlarged, thereby facilitating intu- itive classification of plants in this genus. [Conclusion] Near-infrared diffuse re- flectance spectroscopy is nondestructive and rapid for determination of solid sam- pies, and provides a new method for the classification of Guizhou Aspidistra plants combined by information processing techniques.

Application of near-infrared spectroscopy to predict sweetpotato starch thermal properties and noodle quality

Sweetpotato starch thermal properties and its noodle quality were analyzed using a rapid predictive method based on near-infrared spectroscopy （NIRS）. This method was established based on a total of 93 sweetpotato genotypes with diverse genetic background. Starch samples were scanned by NIRS and analyzed for quality properties by reference methods. Results of statistical modelling indicated that NIRS was reasonably accurate in predicting gelatinization onset temperature （To） （standard error of prediction SEP=2.014 ℃, coefficient of determination RSQ=0.85）, gelatinization peak temperature （Tp） （SEP=-1.371 ℃, RSQ=0.89）, gelatinization temperature range （Tr） （SEP=2.234 ℃, RSQ=0.86）, and cooling resistance （CR） （SEP=0.528, RSQ=0.89）. Gelatinization completion temperature （To）, enthalpy of gelatinization （△H）, cooling loss （CL） and swelling degree （SWD）, were modelled less well with RSQ between 0.63 and 0.84. The present results suggested that the NIRS based method was sufficiently accurate and practical for routine analysis of sweetpotato starch and its noodle quality.

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.

Rapid determination of production date for green tea by near-infrared spectroscopy

Coupled with partial least squares(PLS),near infrared(NIR)spectroscopy was applied to develop a fast and nondestructive method to identify the production date of Rizhao green tea aiming at the deficiencies of the existing methods.In the modeling process,the raw spectra were first processed by five-point smoothing and first derivative.And then,moving window back propagation artificial neural network(MW-BP-ANN)was applied to select the characteristic spectral variables.After that,the calibration model was built by PLS,and the optimum model was achieved when 9 principal component scores(PCs)were included.The performances of the calibration models were evaluated according to root mean square error of predictionεRMSEP,correlation coefficient(C p)and residual prediction deviation(σRPD).The optimum results of the calibration model was achieved,andεRMSEP=19.965,C p=0.943 andσRPD=3.07.The overall results sufficiently demonstrate that NIR spectroscopy combined with PLS can be efficiently applied in the rapid identification of green tea production date.

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.

Measurement and analysis of soil nitrogen and organic matter content using near-infrared spectroscopy techniques

Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic matter （OM） content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP （standard error of prediction） were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.