Noninvasive Detection of Cerebral Blood Flow and Blood Oxygen Based on Inductive Sensing Technology and Near Infrared Spectroscopy

The synchronous monitoring of cerebral blood flow and blood oxygen levels plays a pivotal role in the prevention,diagnosis,and treatment of cerebrovascular diseases.This study introduces a novel noninvasive device utilizing inductive sensing and near-infrared spectroscopy technology to facilitate simultaneous monitoring of cerebral blood flow and blood oxygen levels.The device consists of modules for cerebral blood flow monitoring,cerebral blood oxygen monitoring,control,communication,and a host machine.Through experiments conducted on healthy subjects,it was confirmed that the device can effectively achieve synchronous monitoring and recording of cerebral blood flow and blood oxygen signals.The results demonstrate the device’s capability to accurately measure these signals simultaneously.This technology enables dynamic monitoring of cerebral blood flow and blood oxygen signals with potential clinical applications in preventing,diagnosing,treating cerebrovascular diseases while reducing their associated harm.

Feasibility study of assessing cotton fiber maturity from near infrared hyperspectral imaging technique

Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.

Analysis of Protein Content in Panicum miliaceum L. Using Near Infrared Transmittance Spectroscopy

[Objective] To explore a method for the rapid determination of protein con- tent in grains of Panicum miliaceum L. [Method] The near infrared transmittance spec- troscopy （NITS） was used to build the mathematical models for the quantitative analy- sis of protein content in the grains. Four combinations of treatment that first derivative and second derivative were respectively combined with partial least squares （PLS） and modified partial least squares （MPLS） were set to compare their effects on the original transmission spectrum. [Result] The predicting effects of the 4 combinations were similar. The optimal combination was first derivative with MPLS, in which the average determination coefficient of validation （RSQ） was 0.880 6, correlation coeffi- cient of cross validation （1-VR） was 0.857 0, standard error of calibration （SEC） was 0.342 4, standard error of cross validation （SECV） was 0.375 1, and the standard er- ror of prediction （SEP） was 0.454. [Conclusion] The constructed NITS model is a rapid way for the determination of protein content in grains of P. miliaceum.

Rapid Detection on Quinoa Fiber Based on Near Infrared Spectroscopy

[Objective] To explore a rapid determination method for fiber content in grains of quinoa. [Method] Near infrared spectra of 100 quinoa samples were collected. The predicted models for quantitative analysis of fiber contents in the grains were built using near infrared transmittance spectroscopy （NITS）. [Result] In the wavelength range of 10 000-4 000 cm-1, the near infrared quantitative model of quinoa crude fiber was set up via first derivative ＋ vector normalization preprocessing and combining with the data from chemical methods. The calibration and prediction effect were best, and then the cross validation determination coefficient （FFcv） and external validation determination coefficient （FFval） of fiber by near in- frared quantitative model were 0.884 8 and 0.876 1, respectively. [Conclusion] the model of NITS about complete grains quinoa fiber can be available for fast detecting quinoa fiber content.

Matching Performance among Visible and near Infrared Coating,Low Infrared Emitting Coating and Microwave Absorbing Coating

The matching performance among the visible and near infrared coating.the low infrared emitting coating and the microwave absorbing coating was investigated.Experimental results show that the resulting malerial is characteristic of wideband effect ranging from the visible,near infrared and 3-5μm,8-14μm infrared protion of the spectrum,as well as the radar region from 8 to 18GHz when these three materials form αlayerstructure material system.The microwave absorbing ability of material is hardly changed.The resonance peak moves towards lower frequency as the thickness of the visible,near infrared coating and the low infrared emitting coating increases.This problem can be resolved by controlling the thickness of the matrial.On the other hand, the infrared emissivity εof the material system increases as the thickness of the visible,near infrared coating increases.This can be resolved by increasing infrared transparency of the visible and near infrared topcoating or controlling its thickness.The experimental resulting material system has spectral reflection characteristics in visible and near infrared regions that are similar to those of the natural background.

Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

Simulations of Aerosol Optical Properties to Top of Atmospheric Reflected Sunlight in the Near Infrared CO_2 Weak Absorption Band

Over the Asian continent,high aerosol loading is critical to ensure the high accuracy of CO_2 retrieval in the near infrared absorption band.Simulations were performed to explore the effect of light path modification by aerosol son the atmospheric CO_2 near infrared band(6140-6270 cm^(-1)).The Vector LInearized Discrete Ordinate Radiative Transfer(VLIDORT) model and the Line-By-Line Radiative Transfer Model(LBLRTM) were used for forward calculations.The U.S.standard atmosphere was used for atmospheric profiles.The results indicate that the aerosols caused similar effects to increases in CO_2 in the planetary boundary layer and became more significant with aerosol layer rising while aerosol optical depth was 0.1.This effect will cause an over estimation of the CO_2 mixing ratio in the retrieval process and an under estimation in the aerosol layer.The results also indicate that the effect of urban and industrial aerosols is smaller than that of non-absorbing and dust aerosols because of the nearly constant absorption properties in the near infrared band.

Mathematic Models for Analysis of Quality Components in Sugarcane Juice with Fourier Transform Near Infrared Spectroscopy

With the technique of Fourier transform near infrared (FT-NIR) spectroscopy, the calibration models for quantitative analysis of sucrose and polarization in sugarcane juice were developed by using transmission mode and calibrating with partial least square (PLS) algorithm. The determination coefficients (R2) of the predicted models for sucrose and polarization in juice were 0. 9980 and 0. 9979 respectively; the root mean square errors of cross validation (RMSECV) were 0. 143 and 0. 155% for sucrose and polarization in juice respectively. The predictive errors measured by FT-NIR were close to those by routine laboratory methods. The results demonstrated that the FT-NIR methods had high accuracy and they were able to replace the routine laboratory analysis. It was also demonstrated that as a rapid and accurate measurement, the FT-NIR technique had potential applications in quality control of mill sugarcane, establishment of payment system based on sugarcane quality, and selection of clones in sugarcane breeding.

Rapid Non-destructive Detection for Molds Colony of Paddy Rice Based on Near Infrared Spectroscopy

Near infrared spectrometer technology under a wavelength range of 918-1045 nm was used to rapidly detect paddy rice that was stored at 5℃, 15℃ and 25℃. A total of 121 paddy rice samples were collected from artificial infection with moulds to build the calibration models to calculate the total number colony of moulds based on the principal component regression method and multiple linear regression method. The results of statistical analysis indicated that multiple linear regression method was applicable to the detection of the total number colony of moulds. The correlation of calibration data set was 0.943. The correlation of prediction data set was 0.897. Therefore, the result showed that near infrared spectroscopy could be a useful instrumental method for determining the total number colony of moulds in paddy rice. The near infrared spectroscopy methodology could be applied for monitoring mould contamination in postharvest paddy rice during storage and might become a powerful tool for monitoring the safety of the grain.

Development of Near Infrared Spectroscopy for Rapid Quality Assessment of Red Ginseng

Near infrared spectroscopy（NIRS） was developed as a rapid analysis method for the qualitative and quantitative assessment of the quality of red ginseng. Discriminant analysis（DA） based on principal component analysis and Mahalanobis distance was used to distinguish red ginseng from counterfeits non-destructively. The result shows that the proposed method could distinguish red ginseng from counterfeits correctly and no misclassified sample was found in both training and test sets. The partial least squares（PLS） algorithm was used to predict the sum of ginsenosides Re and Rgl and the content of ginsenoside Rb1. Two calibration models were developed to correlate NIR spectra with the reference values determined by HPLC method. The correlation coefficient（R）, the root mean square error of calibration（RMSEC） and the root mean square error of prediction（RMSEP） were as follows： R=0.9827, RMSEC=0.0163%, RMSEP=0.0250% for the sum of ginsenosides Re and Rgl; R=0.9869, RMSEC=0.0156%, RMSEP=0.0256% for content of ginsenoside Rb1. The overall results demonstrate that NIRS coupled with chemometrics could be successfully applied as a rapid, precise and cost-effective method not only to identify the red ginseng from counterfeits but also to determine simultaneously some chemical compositions in red ginseng.

Near-Infrared Imaging Using a High-Speed Monitoring Near Infrared Hyperspectral Camera(Compovision)

This review paper reports near-infrared(NIR)imaging studies using a newly-developed NIR camera,Compovision.Compovision can measure a significantly wide area of 150mm×250mm at high speed of between 2and 5s.It enables a wide spectral region measurement in the 1 000~2 350nm range at 6nm intervals.We investigated the potential of Compovision in the applications to industrial problems such as the evaluation of pharmaceutical tablets and polymers.Our studies have demonstrated that NIR imaging based on Compovision can solve several issues such as long acquisition times and relatively low sensitivity of detection.NIR imaging with Compovision is strongly expected to be applied not only to pharmaceutical tablet monitoring and polymer characterization but also to various applications such as those to food products,biomedical substances and organic and inorganic materials.

Spectra selection methods:A novel optimization way for treating dynamic spectra and in-line near infrared modeling

Near infrared(NIR)spectroscopy is now widely used influidized bed granulation.However,there are still some demerits that should be overcome in practice.Valid spectra selection during modeling process is now a hard nut to crack.In this study,a novel NIR sensor and a cosine distance method were introduced to solve this problem in order to make thefluidized process into"visualization".A NIR sensor wasfixed on the side of the expansion chamber to acquire the NIR spectra.Then valid spectra were selected based on a cosine distance method to reduce the influence of dynamic disturbances.Finally,spectral pretreatment and wavelength selection methods were investigated to establish partial least squares(PLS)models to monitor the mois-ture content.The results showed that the root mean square error of prediction(RMSEP)was 0.124%for moisture content model,which was much lower than that without valid spectra selection treatment.All results demonstrated that with the help of valid spectra selection treatment,NIR sensor could be used for real-time determination of critical quality attributes(CQAs)more accurately.It makes the manufacturing easier to understand than the process parameter control.

Determination of Protein and Starch Content in Whole Maize Kernel by Near Infrared Reflectance Spectroscopy

Using 128 bulk-kernel samples of inbred lines and hybrids, a study was conducted toinvestigate the feasibility and method of measuring protein and starch contents inintact seeds of maize by near infrared reflectance spectroscopy (NIRS). The chemometricalgorithms of partial least square (PLS) regression was used. The results indicated thatthe calibration models developed by the spectral data pretreatment of firstderivative+multivariate scattering correction within the spectral region of 10000-4000cm-1, and first derivative + straight line subtraction in 9000-4000cm-1 were thebest for protein and starch, respectively. All these models yielded coefficients ofdetermination of calibration (R2cal) above 0.97, while R2cv and R2val of cross and externalvalidation ranged from 0.92 to 0.95, respectively; however, the root of mean squareerrors of calibration, cross and external validation (RMSEE, RMSECV and RMSEP) werebelow 1(ranged 0.3-0.7),respectively. This study demonstrated that it is feasible touse NIRS as a rapid, accurate, and none-destructive technique to predict protein andstarch contents of whole kernel in the maize quality improvement program.

Species authentication and geographical origin discrimination of herbal medicines by near infrared spectroscopy:A review

Near infrared （NIR） spectroscopy as a rapid and nondestructive analytical technique, integrated with chemometrics, is a powerful process analytical tool for the pharmaceutical industry and is becoming an attractive complementary technique for herbal medicine analysis. This review mainly focuses on the recent applications of NIR spectroscopy in species authentication of herbal medicines and their geo- graphical origin discrimination.

Deep belief network-based drug identification using near infrared spectroscopy

Near infrared spectroscopy(NIRS)analysis technology,combined with chemometrics,can be effectively used in quick and nondestructive analysis of quality and category.In this paper,an effective drug identification method by using deep belief network(DBN)with dropout mecha-nism(dropout-DBN)to model NIRS is introduced,in which dropout is employed to overcome the overfitting problem coming from the small sample.This paper tests proposed method under datasets of different sizes with the example of near infrared diffuse refectance spectroscopy of erythromycin ethylsuccinate drugs and other drugs,aluminum and nonaluminum packaged.Meanwhile,it gives experiments to compare the proposed method's performance with back propagation(BP)neural network,support vector machines(SVMs)and sparse denoising auto-encoder(SDAE).The results show that for both binary classification and multi-classification,dropout mechanism can improve the classification accuracy,and dropout-DBN can achieve best classification accuracy in almost all cases.SDAE is similar to dropout-DBN in the aspects of classification accuracy and algorithm stability,which are higher than that of BP neural network and SVM methods.In terms of training time,dropout-DBN model is superior to SDAE model,but inferior to BP neural network and SVM methods.Therefore,dropout-DBN can be used as a modeling tool with effective binary and multi-class classification performance on a spectrum sample set of small size.

SYNTHESIS AND CHARACTERIZATIONS OF NEAR INFRARED ABSORBING POLYMERS

A series of near infrared (NIR) absorbing dinuclear ruthenium dicarbonylhydrazine complexes (DCH-Ru),[{Ru(bpy)_2)_2μ-DCH]^(n+) (where bpy = 2,2'-bipyridinc and n = 2, 3 or 4), were prepared. The DCH-Ru complexes areelectrochromic in the NIR region with a high absorption coefficient at 1550-1600 nm typically over 10000 M^(-1)cm^(-1). DCH-Ru complex polymers with good NIR electrochromic properties were also obtained and processed to make a device foroptical attenuation at a wavelength of 1550 nm. The potential of these DCH-Ru polymers for use in a variable opticalattenuator has been demonstrated with an attenuating power at the 1550-nm telecommunication wavelength over 7.0 dB permicron of polymer film thickness. Other classes of NIR active materials are the pentacenediquinones and the correspondingpoly(ether pentacenediquinone)s. These polymers can be electrochemically reduced to the corresponding semiquinone(radical anion) having NIR absorption within a telecom window (e. g., 1310 nm).

Wavelet-based classification and influence matrix analysis method for the fast discrimination of Chinese herbal medicines according to the geographical origins with near infrared spectroscopy

A discriminant analysis technique using wavelet transformation(WT)and influence matrixanalysis(CAIMAN)method is proposed for the near infrared(NIR)spectroscopy classifi-cation.In the proposed methodology,NIR spectra are decomposed by WT for data com-pression and a forward feature selection is further employed to extract the relevant informationfrom the wavelet coefficients,reducing both classification errors and model complexity.Adiscriminant-CAIMAN(D-CAIMAN)method is utilized to build the classification model inwavelet domain on the basis of reduced wavelet coefficients of spectral variables.NIR spectradata set of 265 salviae miltiorrhizae radia samples from 9 different geographical origins is usedas an example to test the classification performance of the algorithm.For a comparison,k-nearest neighbor(KNN),linear discriminant analysis(LDA)and quadratic discriminant analysis(QDA)methods are also employed.D-CAIMAN with wavelet-based feature selection(WD-CAIMAN)method shows the best performance,achieving the total classification rate of ioo%in both cross-validation set and prediction set.It is worth noting that the WD-CAIMANclassifier also shows improved sensitivity,selectivity and model interpretability in thecla.ssifications.

A"donor–acceptor"structured semiconductor polymer for near infrared fluorescence imaging guided photodynamic therapy

Near infrared(NIR)fluorescence imaging guided photodynamic therapy(PDT)is a technique which has been developed in many clinical trials due to its advantage of real-time optical monitoring,specific spatiotemporal selectivity,and minimal invasiveness.For this,photosensitizers with NIR fluorescence emission and high^(1)O_(2)generation quantum yield are highly desirable.Herein,we designed and synthesized a"donor-acceptor"(D-A)structured semiconductor polymer(SP),which was then wrapped with an amphiphilic compound(Pluronic■F127)to prepare water-soluble nanoparticles(F-SP NPs).The obtained F-SP NPs exhibit good water solubility,excellent particle size stability,strong absorbance at deep red region,and strong NIR fluorescent emission characteristics.The maximal mass extinction coe±cient and fluorescence quantum yield of these F-SPs were calculated to be 21.7 L/(g·cm)and 6.5%,respectively.Moreover,the^(1)O_(2)quantum yield of 89%for F-SP NPs has been achieved under 635 nm laser irradiation,which is higher than Methylene Blue,Ce6,and PpIX.The outstanding properties of these F-SP NPs originate from their unique D-A molecular characteristic.This work should help guide the design of novel semiconductor polymer for NIR fluorescent imaging guided PDT applications.

Moisture content in raw rubber sheet analyzed by transflectance near infrared spectroscppy

Moisture content is an important trait for rubber sheet trading system.Therefore,a calibrationequation for predicting moisture content was created by near infrared(NIR)technique in order todevelop a more fair trading system in Thailand.Spectra were recorded in two systems.One wasmeasurement on each rubber sheet and the other was on a pile of sheets.Both were[measured by ahandheld NIR spectrometer in the short wavelength region(700-1100 nm)in the transflectancemode using Teflon as a diffuse refector.The spectra showed the peak at about 900 nm whichbelongs to isoprene,the major component of rubber sheet.Pretreatment with second derivativewas applied to remove baseline shift effect occurring due to thickness differences on each rubber sheet.From validation results,moisture contents predicted by single sheet system were moreaccurate than a pile of sheet system with standard error of prediction(SEP)=0.39 %and bias of-0.07%,and they were not significantly diferent from the actual values at 95%confidence.As aresult,determining moisture content in each rubber sheet by a handheld NIR,spectrometerprovided accurate values,easy and rapid operation.

Application of near infrared spectroscopy in monitoring the moisture content in freeze-drying process of human coagulation factor VIII

As an important process analysis tool,near infrared spectroscopy(NIRS)has been widely used in process monitoring.In the present work,the feasibility of NIRS for monitoring the moisture content of human coagulation factor VIII(FVIII)in freeze-drying process was investigated.A partial least squares regression(PLS-R)model for moisture content determination was built with 88 samples.Different pre-processing methods were explored,and the best method found was standard normal variate(SNV)transformation combined with 1st derivation with Savitzky–Golay(SG)15 point smoothing.Then,four different variable selection methods,including uninformative variable elimination(UVE),interval partial least squares regression(iPLS),competitive adaptive reweighted sampling(CARS)and manual method,were compared for eliminating irrelevant variables,and iPLS was chosen as the best variable selection method.The correlation coe±cient(R),correlation coe±cient of calibration set(Rcal),correlation coefficient of validation set(Rval),root mean square errors of cross-validation(RMSECV)and root mean square errors of prediction(RMSEP)of PLS model were 0.9284,0.9463,0.8890,0.4986% and 0.4514%,respectively.The results showed that the model for moisture content determination has a wide range,good linearity,accuracy and precision.The developed approach was demonstrated to be a potential for monitoring the moisture content of FVIII in freeze-drying process.