[Objectives]This study was conducted to realize the rapid and nondestructive identification of blueberry producing areas and protect benefits of high-quality blueberry brands.[Methods]Five types of blueberries from di...[Objectives]This study was conducted to realize the rapid and nondestructive identification of blueberry producing areas and protect benefits of high-quality blueberry brands.[Methods]Five types of blueberries from different regions were selected as experimental subjects,and spectral analysis techniques were combined with deep learning.Firstly,standard normal variable transform(SNV)and convolutional smoothing(SG)were used to deal with scattering noise and other issues in original spectral data.Secondly,due to a large amount of redundant information and high correlation between adjacent wavelengths in the collected spectra,continuous projection algorithm(SPA)and partial least squares regression(PLS)were combined for screening of features with RMSE as the indicator,and 40 feature variables were obtained.Finally,a convolutional network model CNN-SE integrating a Squeeze and Excitation(SE)attention mechanism module was constructed and compared with convolutional neural network(CNN),support vector machine(SVM),and BP neural network.[Results]The CNN-SE model had the best effect,with the accuracy and precision of the test set reaching 95%and 94.56%,respectively,and the recall and F 1 score reaching 93.94%and 94.24%,respectively.[Conclusions]The CNN-SE convolution network model can realize rapid,nondestructive and high-throughout identification of blueberry producing areas.展开更多
The cyanine dyes represented by IR780 can achieve synergistic photodynamic therapy(PDT)and photothermal therapy(PTT)under the stimulation of near-infrared(NIR)light(commonly 808 nm).Unfortunately,the stability of NIR-...The cyanine dyes represented by IR780 can achieve synergistic photodynamic therapy(PDT)and photothermal therapy(PTT)under the stimulation of near-infrared(NIR)light(commonly 808 nm).Unfortunately,the stability of NIR-excited cyanine dyes is not satisfactory.These cyanine dyes can be attacked by self-generated reactive oxygen species(ROS)during PDT processes,resulting in structural damage and rapid degradation,which is fatal for phototherapy.To address this issue,a novel non-cyanine dye(IR890)was elaborately designed and synthesized by our team.The maximum absorption wavelength of IR890 was located in the deep NIR region(ca.890 nm),which was beneficial for further improving tissue penetration depth.Importantly,IR890 exhibited good stability when continuously illuminated by deep NIR light.To improve the hydrophilicity and biocompatibility,the hydrophobic IR890 dye was grafted onto the side chain of hydrophilic polymer(POEGMA-b-PGMA-g-C≡CH)via click chemistry.Then,the synthesized POEGMA-b-PGMA-g-IR890 amphiphilic polymerwas utilized to prepare P-IR890 nano-photosensitizer via self-assembly method.Under irradiation with deep NIR light(850 nm,0.5 W/cm^(2),10 min),the dye degradation rate of P-IR890 was less than 5%.However,IR780 was almost completely degraded with the same light output power density and irradiation duration.In addition,P-IR890 could stably generate a large number of ROS and heat at the same time.It was rarely reported that the stable synergistic combination therapy of PDT and PTT could be efficiently performed by a single photosensitizer via irradiation with deep NIR light.P-IR890 exhibited favorable anti-tumor outcomes through apoptosis pathway.Therefore,the P-IR890 could provide a new insight into the design of photosensitizers and new opportunities for synergistic combination therapy of PDT and PTT.展开更多
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 uti...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.展开更多
[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 mo...[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.展开更多
[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 c...[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.展开更多
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 laborat...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.展开更多
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 char...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.展开更多
We have developed a set of chemometric methods to address two critical issues in quality control of a precious traditional Chinese medicine （TCM）, Dong＇e Ejiao （DEE J）. Based on near infrared （NIR） spectra of m...We have developed a set of chemometric methods to address two critical issues in quality control of a precious traditional Chinese medicine （TCM）, Dong＇e Ejiao （DEE J）. Based on near infrared （NIR） spectra of multiple samples, the genuine manufacturer of DEE J, e.g. Dong＇e Ejiao Co., Ltd., was accurately identified among 21 suppliers by the fingerprint method using Hotelling T2, distance to Model X （DModX）, and similarity match value （SMV） as dis- criminate criteria. Soft independent modeling of the class analogy algorithm led to a misjudgment ratio of 6.2%, suggesting that the fingerprint method is more suitable for manufacturer identification. For another important feature related to clinical efficacy of DEE J, storage time, the partial least squares-discriminant analysis （PLS-DA） method was applied with a satisfactory misjudgment ratio （15.6%） and individual prediction error around 1 year. Our results demonstrate that NIR spectra comprehensively reflect the essential quality information of DEE J, and with the aid of proper chemometric algorithms, it is able to identify genuine manufacturer and determine accurate storage time. The overall results indicate the promising potential of NIR spectroscopy as an effective quality control tool for DEEJ and other precious TCM products.展开更多
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 mi...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.展开更多
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 ...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.展开更多
Near infrared（NIR） spectroscopy technique has shown great power and gained wide acceptance for analyzing complicated samples.The present work is to distinguish different brands of tobacco products by using on-line N...Near infrared（NIR） spectroscopy technique has shown great power and gained wide acceptance for analyzing complicated samples.The present work is to distinguish different brands of tobacco products by using on-line NIR spectroscopy and pattern recognition techniques.Moreover,since each brand contains a large number of samples,an improved dendrogram was proposed to show the classification of different brands.The results suggest that NIR spectroscopy combined with principal component analysis （PCA） and hierarchical cluster analysis（HCA） performs well in discrimination of the different brands,and the improved dendrogram could provide more information about the difference of the brands.展开更多
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 an...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.展开更多
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 artifici...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.展开更多
A near infrared universal quantitative analysis model was established to determinate the effective ingredient content in pesticide EC （hikemalisation） by the PLS （partial least squares） algorithm, the model predic...A near infrared universal quantitative analysis model was established to determinate the effective ingredient content in pesticide EC （hikemalisation） by the PLS （partial least squares） algorithm, the model predictive ability was evaluated by the external inspection method. The model was established among samples containing the same active ingredient from five different companies, and the model determination coefficient R2 and RMSECV （root mean square error of cross validation） were 0.9997 and 0.0223, respectively, the relative error between predicted value and chemical value of the testing set samples was between -2.71% and 3.36%, which indicated that the method to determinate the effective ingredient content in pesticide EC by the established universal model can meet the need of pesticide market monitoring.展开更多
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 analy...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.展开更多
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 enab...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.展开更多
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 ...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.展开更多
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 s...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.展开更多
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...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.展开更多
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...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.展开更多
基金Supported by Natural Science Foundation of Heilongjiang Province(LH2022E099)Daqing Guidance Fund for Science and Technology Planning Project(zd-2023-63)San Heng San Zong Support Program of Heilongjiang Bayi Agricultural University(ZRCPY202216).
文摘[Objectives]This study was conducted to realize the rapid and nondestructive identification of blueberry producing areas and protect benefits of high-quality blueberry brands.[Methods]Five types of blueberries from different regions were selected as experimental subjects,and spectral analysis techniques were combined with deep learning.Firstly,standard normal variable transform(SNV)and convolutional smoothing(SG)were used to deal with scattering noise and other issues in original spectral data.Secondly,due to a large amount of redundant information and high correlation between adjacent wavelengths in the collected spectra,continuous projection algorithm(SPA)and partial least squares regression(PLS)were combined for screening of features with RMSE as the indicator,and 40 feature variables were obtained.Finally,a convolutional network model CNN-SE integrating a Squeeze and Excitation(SE)attention mechanism module was constructed and compared with convolutional neural network(CNN),support vector machine(SVM),and BP neural network.[Results]The CNN-SE model had the best effect,with the accuracy and precision of the test set reaching 95%and 94.56%,respectively,and the recall and F 1 score reaching 93.94%and 94.24%,respectively.[Conclusions]The CNN-SE convolution network model can realize rapid,nondestructive and high-throughout identification of blueberry producing areas.
基金This project was supported by National Natural Science Foundation of China(Grant No.82271629 and 82301790)Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang(Grant No.2023R01002)Ningbo Natural Science Foundation(Grant No.2023J054).
文摘The cyanine dyes represented by IR780 can achieve synergistic photodynamic therapy(PDT)and photothermal therapy(PTT)under the stimulation of near-infrared(NIR)light(commonly 808 nm).Unfortunately,the stability of NIR-excited cyanine dyes is not satisfactory.These cyanine dyes can be attacked by self-generated reactive oxygen species(ROS)during PDT processes,resulting in structural damage and rapid degradation,which is fatal for phototherapy.To address this issue,a novel non-cyanine dye(IR890)was elaborately designed and synthesized by our team.The maximum absorption wavelength of IR890 was located in the deep NIR region(ca.890 nm),which was beneficial for further improving tissue penetration depth.Importantly,IR890 exhibited good stability when continuously illuminated by deep NIR light.To improve the hydrophilicity and biocompatibility,the hydrophobic IR890 dye was grafted onto the side chain of hydrophilic polymer(POEGMA-b-PGMA-g-C≡CH)via click chemistry.Then,the synthesized POEGMA-b-PGMA-g-IR890 amphiphilic polymerwas utilized to prepare P-IR890 nano-photosensitizer via self-assembly method.Under irradiation with deep NIR light(850 nm,0.5 W/cm^(2),10 min),the dye degradation rate of P-IR890 was less than 5%.However,IR780 was almost completely degraded with the same light output power density and irradiation duration.In addition,P-IR890 could stably generate a large number of ROS and heat at the same time.It was rarely reported that the stable synergistic combination therapy of PDT and PTT could be efficiently performed by a single photosensitizer via irradiation with deep NIR light.P-IR890 exhibited favorable anti-tumor outcomes through apoptosis pathway.Therefore,the P-IR890 could provide a new insight into the design of photosensitizers and new opportunities for synergistic combination therapy of PDT and PTT.
基金National Natural Science Foundation of China(No.51977214)Science and Technology Research Project of Chongqing Education Commission(No.KJQN202212805)Special funding project of Army Medical University(No.2021XJS08)。
文摘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.
基金Supported by the National Key Technology R&D Program of China (2006BAD02B07)the National Mordern Agricultural Industry System of China(CARS-07-12.5-A12)~~
文摘[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.
基金Supported by the Collection and Arrangement of Crop Germplasm Resources in Shanxi Province(2016zzcx-17)the Special Fund for the Protection and Utilization of Crop Germplasm Resources of the Ministry of Agriculture(2015NWB030-07)+1 种基金the Project of the National Science and Technology Infrastructure of the Ministry of Science and Technology and the Ministry of Finance(NICGR2015-026)the Special Fund for Seed Industry of Shanxi Province(2016zyzx41)~~
文摘[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.
基金supported partially by the USDA-ARS Research Project#6054-44000-080-00D.
文摘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.
文摘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.
基金Project supported by the National Science and Technology Major Project(No.2011ZX09201-201-10),China
文摘We have developed a set of chemometric methods to address two critical issues in quality control of a precious traditional Chinese medicine （TCM）, Dong＇e Ejiao （DEE J）. Based on near infrared （NIR） spectra of multiple samples, the genuine manufacturer of DEE J, e.g. Dong＇e Ejiao Co., Ltd., was accurately identified among 21 suppliers by the fingerprint method using Hotelling T2, distance to Model X （DModX）, and similarity match value （SMV） as dis- criminate criteria. Soft independent modeling of the class analogy algorithm led to a misjudgment ratio of 6.2%, suggesting that the fingerprint method is more suitable for manufacturer identification. For another important feature related to clinical efficacy of DEE J, storage time, the partial least squares-discriminant analysis （PLS-DA） method was applied with a satisfactory misjudgment ratio （15.6%） and individual prediction error around 1 year. Our results demonstrate that NIR spectra comprehensively reflect the essential quality information of DEE J, and with the aid of proper chemometric algorithms, it is able to identify genuine manufacturer and determine accurate storage time. The overall results indicate the promising potential of NIR spectroscopy as an effective quality control tool for DEEJ and other precious TCM products.
基金supported by the projects under the Innovation Team of the Safety Standards and Testing Technology for Agricultural Products of Zhejiang Province, China (Grant No.2010R50028)the National Key Technologies R&D Program of China during the 11th Five-Year Plan Period (Grant No.2006BAK02A18)
文摘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.
基金funded by the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues (Grant No. XDA05040200)the National High Techonology Research and Development Program of China (863 Program,Grant No. 2011AA12A104)
文摘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.
基金supported by National Natural Science Foundation of China(No.20835002)
文摘Near infrared（NIR） spectroscopy technique has shown great power and gained wide acceptance for analyzing complicated samples.The present work is to distinguish different brands of tobacco products by using on-line NIR spectroscopy and pattern recognition techniques.Moreover,since each brand contains a large number of samples,an improved dendrogram was proposed to show the classification of different brands.The results suggest that NIR spectroscopy combined with principal component analysis （PCA） and hierarchical cluster analysis（HCA） performs well in discrimination of the different brands,and the improved dendrogram could provide more information about the difference of the brands.
文摘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.
基金Supported by the National 12th Five-year Plan for Science&Technology Support Fund(2012BAK08B04-02)the Heilongjiang Science and Technology Plan(GC12B404)
文摘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.
基金supported by the National Natural Science Foundation of China(No.20575076)Chinese Universities Scientific Fund(No.2012QJ028)
文摘A near infrared universal quantitative analysis model was established to determinate the effective ingredient content in pesticide EC （hikemalisation） by the PLS （partial least squares） algorithm, the model predictive ability was evaluated by the external inspection method. The model was established among samples containing the same active ingredient from five different companies, and the model determination coefficient R2 and RMSECV （root mean square error of cross validation） were 0.9997 and 0.0223, respectively, the relative error between predicted value and chemical value of the testing set samples was between -2.71% and 3.36%, which indicated that the method to determinate the effective ingredient content in pesticide EC by the established universal model can meet the need of pesticide market monitoring.
文摘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.
文摘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.
基金financial support from the National Natural Science Foundation of China(no.81373926)
文摘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.
文摘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.
基金the financial support of the Natural Science Foundation of Shandong Province of China(No.ZR2017MB012)Major In-novation Project of Shandong Province of China(2018CXGC1405)
文摘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.
基金the National Natural Science Foundation of China(Grant Nos.21365008 and 61562013)Natural Science.Foundation of Guangxi(Grant No.2013GXNSFBA019279)Innovation Project of GUET Graduate.Education(Grant Nos.GDYCSZ201474 and GDYCSZ201478).
文摘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.
