The non-linear relationships between the contents of ginsenoside Rg 1, Rb 1, Rd and Panax notoginseng saponins(PNS) in Panax notoginseng root herb and the near infrared(NIR) diffuse reflectance spectra of the herb wer...The non-linear relationships between the contents of ginsenoside Rg 1, Rb 1, Rd and Panax notoginseng saponins(PNS) in Panax notoginseng root herb and the near infrared(NIR) diffuse reflectance spectra of the herb were established by means of artificial neural networks(ANNs). Four three-layered perception feed-forward networks were trained with an error back-propagation algorithm. The significant principal components of the NIR spectral data matrix were utilized as the input of the networks. The networks architecture and parameters were selected so as to offer less prediction errors. Relative prediction errors for Rg 1, Rb 1, Rd and PNS obtained with the optimum ANN models were 8.99%, 6.54%, 8.29%, and 5.17%, respectively, which were superior to those obtained with PLSR methods. It is verified that ANN is a suitable approach to model this complex non-linearity. The developed method is fast, non-destructive and accurate and it provides a new efficient approach for determining the active components in the complex system of natural herbs.展开更多
Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analysed to predict the spatial soil organic carbon (SOC) content using multivariate...Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analysed to predict the spatial soil organic carbon (SOC) content using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyperspectral image, field and laboratory scale data sets (350 - 2500 nm) were generated which consisted of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflectance data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and all three datasets (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods had a great potential for estimating and mapping spatial SOC content. The study concluded that ANN model was potential tools in predicting SOC distribution in agricultural field using hyper-spectral remote sensing data at image-scale, field-scale and lab-scale.展开更多
Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alt...Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material. This process has not been extensively used commercially because of its complex dependency on process parameters. In this paper, a selective flocculation process, using synthetic mixtures of hematite and kaolinite in different ratios, was attempted, and the ad-sorption mechanism was investigated by Fourier transform infrared （FTIR） spectroscopy. A three-layer artificial neural network （ANN） model （4？4？3） was used to predict the separation performance of the process in terms of grade, Fe recovery, and separation efficiency. The model values were in good agreement with experimental values.展开更多
Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse r...Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.展开更多
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 ap...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.展开更多
A novel method for rapid,accurate and nondestructive determination of trimethoprim in complex matrix was presented.Near-infrared spectroscopy coupled with multivariate calibration（partial least-squares and artificial...A novel method for rapid,accurate and nondestructive determination of trimethoprim in complex matrix was presented.Near-infrared spectroscopy coupled with multivariate calibration（partial least-squares and artificial neural networks） was applied in the experiment.The variable selection process based on a modified genetic algorithm with fixed number of selected variables was proceeded,which can reduce the training time and enhance the predictive ability when coupled with artificial neural network model.展开更多
Rapid and accurate detection of microorganisms is critical to clinical diagnosis.As Raman spectroscopy promises label-free and culture-free detection of biomedical objects,it holds the potential to rapidly identify mi...Rapid and accurate detection of microorganisms is critical to clinical diagnosis.As Raman spectroscopy promises label-free and culture-free detection of biomedical objects,it holds the potential to rapidly identify microorganisms in a single step.To stabilize the microorganism for spectrum collection and to increase the accuracy of real-time identification,we propose an optofluidic method for single microorganism detection in microfluidics using optical-tweezing-based Raman spectroscopy with artificial neural network.A fiber optical tweezer was incorporated into a microfluidic channel to generate op-tical forces that trap different species of microorganisms at the tip of the tweezer and their Raman spectra were simultaneously collected.An artificial neural network was designed and employed to classify the Raman spectra of the microorganisms,and the identification accuracy reached 94.93%.This study provides a promising strategy for rapid and accurate diagnosis of mi-crobial infection on a lab-on-a-chip platform.展开更多
微生物细胞的傅里叶变换近红外光谱(Fourier transform near infrared spectroscopy,FT-NIR)反映了细胞成分的分子振动信息,具有的高度特异性,为寻求一种基于FT-NIR的微生物快速鉴定方法提供了可能。文章通过采集1株酵母和5株细菌标准...微生物细胞的傅里叶变换近红外光谱(Fourier transform near infrared spectroscopy,FT-NIR)反映了细胞成分的分子振动信息,具有的高度特异性,为寻求一种基于FT-NIR的微生物快速鉴定方法提供了可能。文章通过采集1株酵母和5株细菌标准菌株的近红外漫反射光谱,采用主成分分析法对光谱数据进行了分析,构建了基于FT-NIR的微生物快速鉴定模型。分析结果表明:①光谱鉴别指数Dy1y2值范围为1.61±1.05～10.97±6.65,重现性良好;②建立的基于线性判别分析模型的鉴定准确率为100%,基于人工神经网络模型的预测结果平均相对误差为5.75%,预测准确率高。研究结果证实该方法可以实现基于FT-NIR结合多元数学统计方法的微生物快速鉴定,并具有广阔的产业应用前景。展开更多
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 a...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.展开更多
Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data se...Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.展开更多
Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mod...Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Wines (n=90) were randomly split into two sets, calibration set (n=54) and validation set (n=36). Discriminant analysis models were developed using BP neural network and discriminant partial least-squares discriminant analysis (PLS-DA). The prediction performance of calibration models in different wavelength range was also investigated. BP neural network models and PLS-DA models correctly classified 100% of the wines in calibration set. When used to predict wines in validation set, BP neural network models correctly classified 100%, 81.8%, and 90.9% of the wines from Changli, Huailai, and Yantai respectively, and PLS-DA models correctly classified 100% of all samples. The results demonstrated that NIRS could be used to discriminate Chinese grape wines as a rapid and reliable method.展开更多
Optical parameters(properties)of tissue-mimicking phantoms are determined through nonin-vasive optical imaging.Objective of this study is to decompose obtained difuse reflectance into these optical properties such as ...Optical parameters(properties)of tissue-mimicking phantoms are determined through nonin-vasive optical imaging.Objective of this study is to decompose obtained difuse reflectance into these optical properties such as absorption and scattering coefficients.To do so,transmission spectroscopy is firstly used to measure the coefficients via an experimental setup.Next,the optical properties of each characterized phantom are input for Monte Carlo(MC)simulations to get diffuse reflectance.Also,a surface image for each single phantom with its known optical properties is obliquely captured due to reflectance-based geometrical setup using CMOS camera that is positioned at 5°angle to the phantoms.For the illumination of light,a laser light source at 633 nm wavelength is preferred,because optical properties of different components in a biological tissue on that wavelength are nonoverlapped.During in vitro measurements,we prepared 30 different mixture samples adding clinoleic intravenous lipid emulsion(CILE)and evans blue(EB)dye into a distilled water.Finally,all obtained difuse reflectance values are used to estimate the optical coefficients by artificial neural networks(ANNs)in inverse modeling.For a biological tissue it is found that the simulated and measured values in our results are in good agreement.展开更多
文摘The non-linear relationships between the contents of ginsenoside Rg 1, Rb 1, Rd and Panax notoginseng saponins(PNS) in Panax notoginseng root herb and the near infrared(NIR) diffuse reflectance spectra of the herb were established by means of artificial neural networks(ANNs). Four three-layered perception feed-forward networks were trained with an error back-propagation algorithm. The significant principal components of the NIR spectral data matrix were utilized as the input of the networks. The networks architecture and parameters were selected so as to offer less prediction errors. Relative prediction errors for Rg 1, Rb 1, Rd and PNS obtained with the optimum ANN models were 8.99%, 6.54%, 8.29%, and 5.17%, respectively, which were superior to those obtained with PLSR methods. It is verified that ANN is a suitable approach to model this complex non-linearity. The developed method is fast, non-destructive and accurate and it provides a new efficient approach for determining the active components in the complex system of natural herbs.
文摘Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analysed to predict the spatial soil organic carbon (SOC) content using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyperspectral image, field and laboratory scale data sets (350 - 2500 nm) were generated which consisted of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflectance data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and all three datasets (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods had a great potential for estimating and mapping spatial SOC content. The study concluded that ANN model was potential tools in predicting SOC distribution in agricultural field using hyper-spectral remote sensing data at image-scale, field-scale and lab-scale.
基金the funding given by Council of Scientific and Industrial Research(CSIR)India through project NWP-31 for this project
文摘Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material. This process has not been extensively used commercially because of its complex dependency on process parameters. In this paper, a selective flocculation process, using synthetic mixtures of hematite and kaolinite in different ratios, was attempted, and the ad-sorption mechanism was investigated by Fourier transform infrared （FTIR） spectroscopy. A three-layer artificial neural network （ANN） model （4？4？3） was used to predict the separation performance of the process in terms of grade, Fe recovery, and separation efficiency. The model values were in good agreement with experimental values.
基金Supported by the Science Technology Development Project of Jilin Province,China(No.20020503-2).
文摘Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.
基金supported by the National Natural Science Foundation of China (Nos. 60778024 and 30825027)the National Basic Re-search Program (973) of China (No. 2006BAD11A12)
文摘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.
基金Supported by the National Natural Science Foundation of China（No.21207047）, the State Major Project for Science and Technology Development, China（No.2011YQ14015001）, the Basic Research Foundation of Jilin University, China （Nos.201103096, 201103102） and the Science-Technology Development Project of Jilin Province of China（Nos.201105008, 20126018, 20130206014GX）.
文摘A novel method for rapid,accurate and nondestructive determination of trimethoprim in complex matrix was presented.Near-infrared spectroscopy coupled with multivariate calibration（partial least-squares and artificial neural networks） was applied in the experiment.The variable selection process based on a modified genetic algorithm with fixed number of selected variables was proceeded,which can reduce the training time and enhance the predictive ability when coupled with artificial neural network model.
基金National Natural Science Foundation of China,Grant/Award Numbers:11874183,62135005,82171637The Shenzhen Project of Science,Grant/Award Number:JCYJ20190809094407602+3 种基金The Science and Technology Program of Guangzhou,Grant/Award Number:202002030179Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2021B1515020046The Scientific Research Foundation of Peking University Shenzhen Hospital,Grant/Award Number:KYQD2021104The Fundamental Research Funds for the Central Universities in Jinan University,Grant/Award Number:21621107。
文摘Rapid and accurate detection of microorganisms is critical to clinical diagnosis.As Raman spectroscopy promises label-free and culture-free detection of biomedical objects,it holds the potential to rapidly identify microorganisms in a single step.To stabilize the microorganism for spectrum collection and to increase the accuracy of real-time identification,we propose an optofluidic method for single microorganism detection in microfluidics using optical-tweezing-based Raman spectroscopy with artificial neural network.A fiber optical tweezer was incorporated into a microfluidic channel to generate op-tical forces that trap different species of microorganisms at the tip of the tweezer and their Raman spectra were simultaneously collected.An artificial neural network was designed and employed to classify the Raman spectra of the microorganisms,and the identification accuracy reached 94.93%.This study provides a promising strategy for rapid and accurate diagnosis of mi-crobial infection on a lab-on-a-chip platform.
文摘微生物细胞的傅里叶变换近红外光谱(Fourier transform near infrared spectroscopy,FT-NIR)反映了细胞成分的分子振动信息,具有的高度特异性,为寻求一种基于FT-NIR的微生物快速鉴定方法提供了可能。文章通过采集1株酵母和5株细菌标准菌株的近红外漫反射光谱,采用主成分分析法对光谱数据进行了分析,构建了基于FT-NIR的微生物快速鉴定模型。分析结果表明:①光谱鉴别指数Dy1y2值范围为1.61±1.05～10.97±6.65,重现性良好;②建立的基于线性判别分析模型的鉴定准确率为100%,基于人工神经网络模型的预测结果平均相对误差为5.75%,预测准确率高。研究结果证实该方法可以实现基于FT-NIR结合多元数学统计方法的微生物快速鉴定,并具有广阔的产业应用前景。
基金Supported by the National Natural Science Foundation of China(No.50635030)the Key Project of Jilin Provincial De-partment of Science & Technology, China(Nos.20060902-02, 200705C07)
文摘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.
基金supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA040608)National Natural Science Foundation of China(Nos.61473279,61004131)the Development of Scientific Research Equipment Program of Chinese Academy of Sciences(No.YZ201247)
文摘Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.
文摘Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Wines (n=90) were randomly split into two sets, calibration set (n=54) and validation set (n=36). Discriminant analysis models were developed using BP neural network and discriminant partial least-squares discriminant analysis (PLS-DA). The prediction performance of calibration models in different wavelength range was also investigated. BP neural network models and PLS-DA models correctly classified 100% of the wines in calibration set. When used to predict wines in validation set, BP neural network models correctly classified 100%, 81.8%, and 90.9% of the wines from Changli, Huailai, and Yantai respectively, and PLS-DA models correctly classified 100% of all samples. The results demonstrated that NIRS could be used to discriminate Chinese grape wines as a rapid and reliable method.
基金the Scientific and Technological Research Council of Turkey(TUBI-TAK),under Grant No.113E771.
文摘Optical parameters(properties)of tissue-mimicking phantoms are determined through nonin-vasive optical imaging.Objective of this study is to decompose obtained difuse reflectance into these optical properties such as absorption and scattering coefficients.To do so,transmission spectroscopy is firstly used to measure the coefficients via an experimental setup.Next,the optical properties of each characterized phantom are input for Monte Carlo(MC)simulations to get diffuse reflectance.Also,a surface image for each single phantom with its known optical properties is obliquely captured due to reflectance-based geometrical setup using CMOS camera that is positioned at 5°angle to the phantoms.For the illumination of light,a laser light source at 633 nm wavelength is preferred,because optical properties of different components in a biological tissue on that wavelength are nonoverlapped.During in vitro measurements,we prepared 30 different mixture samples adding clinoleic intravenous lipid emulsion(CILE)and evans blue(EB)dye into a distilled water.Finally,all obtained difuse reflectance values are used to estimate the optical coefficients by artificial neural networks(ANNs)in inverse modeling.For a biological tissue it is found that the simulated and measured values in our results are in good agreement.
