A mobile fiber-optic laser-induced breakdown spectrometer(FO-LIBS) prototype was developed to rapidly detect a large quantity of steel material online and quantitatively analyze the trace elements in a large-diameter ...A mobile fiber-optic laser-induced breakdown spectrometer(FO-LIBS) prototype was developed to rapidly detect a large quantity of steel material online and quantitatively analyze the trace elements in a large-diameter steel tube.Twenty-four standard samples and a polynomial fitting method were used to establish calibration curve models.The R^2 factors of the calibration curves were all above 0.99,except for Cu,indicating the elements’ strong self-absorption effect.Five special steel materials were rapidly detected in the steel mill.The average absolute errors of Mn,Cr,Ni,V,Cu,and Mo in the special steel materials were 0.039,0.440,0.033,0.057,0.003,and0.07 wt%,respectively,and their average relative errors fluctuated from 2.9% to 15.7%.The results demonstrated that the performance of this mobile FO-LIBS prototype can be compared with that of most conventional LIBS systems,but the more robust and flexible characteristics of the FO-LIBS prototype provide a feasible approach for promoting LIBS from the laboratory to the industry.展开更多
BACKGROUND: Near-infrared spectroscopy(NIRS) non-invasively monitors muscle tissue oxygen saturation(St O2). It may provide a continuous noninvasive measurement to identify occult hypoperfusion, guide resuscitation, a...BACKGROUND: Near-infrared spectroscopy(NIRS) non-invasively monitors muscle tissue oxygen saturation(St O2). It may provide a continuous noninvasive measurement to identify occult hypoperfusion, guide resuscitation, and predict the development of multiple organ dysfunction(MOD) after severe trauma. We evaluated the correlation between initial St O2 and the development of MOD in multi-trauma patients.METHODS: Patients presenting to our urban, academic, Level I Trauma Center/Emergency Department and meeting standardized trauma-team activation criteria were enrolled in this prospective trial. NIRS monitoring was initiated immediately on arrival with collection of St O2 at the thenar eminence and continued up to 24 hours for those admitted to the Trauma Intensive Care Unit(TICU). Standardized resuscitation laboratory measures and clinical evaluation tools were collected. The primary outcome was the association between initial St O2 and the development of MOD within the f irst 24 hours based on a MOD score of 6 or greater. Descriptive statistical analyses were performed; numeric means, multivariate regression and rank sum comparisons were utilized. Clinicians were blinded from the StO 2 values.RESULTS: Over a 14 month period, 78 patients were enrolled. Mean age was 40.9 years(SD 18.2), 84.4% were male, 76.9% had a blunt trauma mechanism and mean injury severity score(ISS) was 18.5(SD 12.9). Of the 78 patients, 26(33.3%) developed MOD within the first 24 hours. The MOD patients had mean initial St O2 values of 53.3(SD 10.3), signifi cantly lower than those of nonMOD patients 61.1(SD 10.0); P=0.002. The mean ISS among MOD patients was 29.9(SD 11.5), significantly higher than that of non-MODS patients, 12.1(SD 9.1)(P<0.0001). The mean shock index(SI) among MOD patients was 0.92(SD 0.28), also signifi cantly higher than that of non-MODS patients, 0.73(SD 0.19)(P=0.0007). Lactate values were not signifi cantly different between groups.CONCLUSION: Non-invasive, continuous St O2 near-infrared spectroscopy values during initial trauma resuscitation correlate with the later development of multiple organ dysfunction in this patient population.展开更多
A transfer learning system was designed to predict Xylosma racemosum compression strength.Near-infrared(NIR)spectral data for Acer mono and its compression strength values were used to resolve the weak generalization ...A transfer learning system was designed to predict Xylosma racemosum compression strength.Near-infrared(NIR)spectral data for Acer mono and its compression strength values were used to resolve the weak generalization problem caused by using a X.racemosum dataset alone.Transfer component analysis and principal component analysis are domain adaption and feature extraction processes to enable the use of A.mono NIR spectral data to design the transfer learning system.A five-layer neural network relevant to the X.racemosum dataset,was fine-tuned using the A.mono dataset.There were 109 A.mono samples used as the source dataset and 79 X.racemosum samples as the target dataset.When the ratio of the training set to the test set was 1:9,the correlation coeffi cient was 0.88,and mean square error was 8.84.The results show that NIR spectral data of hardwood species are related.Predicting the mechanical strength of hardwood species using multi-species NIR spectral datasets will improve the generalization ability of the model and increase accuracy.展开更多
Nitrogen(N)monitoring is essential in nurseries to ensure the production of high-quality seedlings.Nearinfrared spectroscopy(NIRS)is an instantaneous,nondestructive method to monitor N.Spectral data such as NIRS can a...Nitrogen(N)monitoring is essential in nurseries to ensure the production of high-quality seedlings.Nearinfrared spectroscopy(NIRS)is an instantaneous,nondestructive method to monitor N.Spectral data such as NIRS can also provide the basis for developing a new vegetation spectral index(VSI).Here,we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emargiaata and developed a new VSI for this task.Plants were grown in a hydroponics system with 0,2.75,5.5or 11 mM N for 45 days.Then we measured gas exchange,chlorophylla fluorescence,and pigments in leaves;analyzed complete leaf nutrients,and recorded spectral data for leaves at 966 to 1685 nm using NIRS.With a statistical learning approach,the dimensionality of the spectral data was reduced,then models were generated using two classes(N deficiency,N)or four classes(0,2.75,5.5,11 mM N).The best combination of techniques for dimensionality reduction and classification,respectively,was stepwise regression(PROC STEPDISC)and linear discriminant function.It was possible to detect N deficiency in seedlings leaves with 100%precision,and the four N concentrations with93.55%accuracy before photosynthetic damage to the plant occurred.Thereby,NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of A.emarginata.展开更多
[目的]提高近红外光谱技术在线检测柚子糖度的精度。[方法]采用自主研发的柚子在线无损检测设备采集3种光照区域的柚子的漫透射光谱数据,在650~950 nm的波长范围内采用标准正交变量变换(SNV)、多元散射校正(MSC)、归一化(normalize)、S...[目的]提高近红外光谱技术在线检测柚子糖度的精度。[方法]采用自主研发的柚子在线无损检测设备采集3种光照区域的柚子的漫透射光谱数据,在650~950 nm的波长范围内采用标准正交变量变换(SNV)、多元散射校正(MSC)、归一化(normalize)、SG一阶求导(savitzky-golay first order derivative,SG-1st)对原始数据进行预处理,使用自适应性加权算法(CARS)筛选反映柚子糖度的光谱特征,建立了偏最小二乘回归(PLSR)模型。使用未参与到建模的30个柚子样本进行在线验证。[结果]光照区域C结合SNV-CARS-PLSR方法的建模效果最优。其预测集的决定系数为0.95,均方根误差为0.30°Brix。在线验证时决定系数为0.90,均方根误差为0.58°Brix。模型对于柚子糖度具有较强的在线检测能力。[结论]在光斑直径为70 mm且位于柚子赤道上方20 mm的光照区域C的条件下采集的柚子光谱数据所建立的预测模型能更有效地实现柚子糖度的在线预测。展开更多
应用近红外光谱分析技术结合化学计量学方法,建立了中药清开灵注射液中间体总氮和栀子苷含量测定的新方法.首先采用Kernard-Stone法对训练集样本和预测集样品进行分类,然后应用组合的间隔偏最小二乘法(Synergy interval partial least s...应用近红外光谱分析技术结合化学计量学方法,建立了中药清开灵注射液中间体总氮和栀子苷含量测定的新方法.首先采用Kernard-Stone法对训练集样本和预测集样品进行分类,然后应用组合的间隔偏最小二乘法(Synergy interval partial least squares,siPLS)对所得近红外透射光谱进行有效谱段范围的选择以及二者定量校正模型的建立,并对光谱预处理方法进行了详细的讨论.所建立的总氮和栀子苷校正模型的预测相关系数(R)分别为0.999和0.708;交叉验证误差均方根(RMSECV)均为0.023;预测误差均方根(RMSEP)分别为0.074和0.159;预测结果表明,本实验所建方法快速、无损且可靠,可推广并应用于中药注射液中间体的在线质量控制.展开更多
基金supported by National Natural Science Foundation of China(Nos.61705064,11647122)the Natural Science Foundation of Hubei Province(Nos.2018CFB773,2018CFB672)the Project of the Hubei Provincial Department of Education(No.T201617)。
文摘A mobile fiber-optic laser-induced breakdown spectrometer(FO-LIBS) prototype was developed to rapidly detect a large quantity of steel material online and quantitatively analyze the trace elements in a large-diameter steel tube.Twenty-four standard samples and a polynomial fitting method were used to establish calibration curve models.The R^2 factors of the calibration curves were all above 0.99,except for Cu,indicating the elements’ strong self-absorption effect.Five special steel materials were rapidly detected in the steel mill.The average absolute errors of Mn,Cr,Ni,V,Cu,and Mo in the special steel materials were 0.039,0.440,0.033,0.057,0.003,and0.07 wt%,respectively,and their average relative errors fluctuated from 2.9% to 15.7%.The results demonstrated that the performance of this mobile FO-LIBS prototype can be compared with that of most conventional LIBS systems,but the more robust and flexible characteristics of the FO-LIBS prototype provide a feasible approach for promoting LIBS from the laboratory to the industry.
文摘BACKGROUND: Near-infrared spectroscopy(NIRS) non-invasively monitors muscle tissue oxygen saturation(St O2). It may provide a continuous noninvasive measurement to identify occult hypoperfusion, guide resuscitation, and predict the development of multiple organ dysfunction(MOD) after severe trauma. We evaluated the correlation between initial St O2 and the development of MOD in multi-trauma patients.METHODS: Patients presenting to our urban, academic, Level I Trauma Center/Emergency Department and meeting standardized trauma-team activation criteria were enrolled in this prospective trial. NIRS monitoring was initiated immediately on arrival with collection of St O2 at the thenar eminence and continued up to 24 hours for those admitted to the Trauma Intensive Care Unit(TICU). Standardized resuscitation laboratory measures and clinical evaluation tools were collected. The primary outcome was the association between initial St O2 and the development of MOD within the f irst 24 hours based on a MOD score of 6 or greater. Descriptive statistical analyses were performed; numeric means, multivariate regression and rank sum comparisons were utilized. Clinicians were blinded from the StO 2 values.RESULTS: Over a 14 month period, 78 patients were enrolled. Mean age was 40.9 years(SD 18.2), 84.4% were male, 76.9% had a blunt trauma mechanism and mean injury severity score(ISS) was 18.5(SD 12.9). Of the 78 patients, 26(33.3%) developed MOD within the first 24 hours. The MOD patients had mean initial St O2 values of 53.3(SD 10.3), signifi cantly lower than those of nonMOD patients 61.1(SD 10.0); P=0.002. The mean ISS among MOD patients was 29.9(SD 11.5), significantly higher than that of non-MODS patients, 12.1(SD 9.1)(P<0.0001). The mean shock index(SI) among MOD patients was 0.92(SD 0.28), also signifi cantly higher than that of non-MODS patients, 0.73(SD 0.19)(P=0.0007). Lactate values were not signifi cantly different between groups.CONCLUSION: Non-invasive, continuous St O2 near-infrared spectroscopy values during initial trauma resuscitation correlate with the later development of multiple organ dysfunction in this patient population.
基金fully funded by the Program of National Natural Science Foundation of China(CN)(31700643)Fundamental Research Funds for the Central Universities(2572015AB24)。
文摘A transfer learning system was designed to predict Xylosma racemosum compression strength.Near-infrared(NIR)spectral data for Acer mono and its compression strength values were used to resolve the weak generalization problem caused by using a X.racemosum dataset alone.Transfer component analysis and principal component analysis are domain adaption and feature extraction processes to enable the use of A.mono NIR spectral data to design the transfer learning system.A five-layer neural network relevant to the X.racemosum dataset,was fine-tuned using the A.mono dataset.There were 109 A.mono samples used as the source dataset and 79 X.racemosum samples as the target dataset.When the ratio of the training set to the test set was 1:9,the correlation coeffi cient was 0.88,and mean square error was 8.84.The results show that NIR spectral data of hardwood species are related.Predicting the mechanical strength of hardwood species using multi-species NIR spectral datasets will improve the generalization ability of the model and increase accuracy.
基金a scholarship from Capes(Coordena??o de Aperfei?oamento de Pessoal de Nível Superior)-Brazil(Award number:001)for the first author。
文摘Nitrogen(N)monitoring is essential in nurseries to ensure the production of high-quality seedlings.Nearinfrared spectroscopy(NIRS)is an instantaneous,nondestructive method to monitor N.Spectral data such as NIRS can also provide the basis for developing a new vegetation spectral index(VSI).Here,we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emargiaata and developed a new VSI for this task.Plants were grown in a hydroponics system with 0,2.75,5.5or 11 mM N for 45 days.Then we measured gas exchange,chlorophylla fluorescence,and pigments in leaves;analyzed complete leaf nutrients,and recorded spectral data for leaves at 966 to 1685 nm using NIRS.With a statistical learning approach,the dimensionality of the spectral data was reduced,then models were generated using two classes(N deficiency,N)or four classes(0,2.75,5.5,11 mM N).The best combination of techniques for dimensionality reduction and classification,respectively,was stepwise regression(PROC STEPDISC)and linear discriminant function.It was possible to detect N deficiency in seedlings leaves with 100%precision,and the four N concentrations with93.55%accuracy before photosynthetic damage to the plant occurred.Thereby,NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of A.emarginata.
文摘[目的]提高近红外光谱技术在线检测柚子糖度的精度。[方法]采用自主研发的柚子在线无损检测设备采集3种光照区域的柚子的漫透射光谱数据,在650~950 nm的波长范围内采用标准正交变量变换(SNV)、多元散射校正(MSC)、归一化(normalize)、SG一阶求导(savitzky-golay first order derivative,SG-1st)对原始数据进行预处理,使用自适应性加权算法(CARS)筛选反映柚子糖度的光谱特征,建立了偏最小二乘回归(PLSR)模型。使用未参与到建模的30个柚子样本进行在线验证。[结果]光照区域C结合SNV-CARS-PLSR方法的建模效果最优。其预测集的决定系数为0.95,均方根误差为0.30°Brix。在线验证时决定系数为0.90,均方根误差为0.58°Brix。模型对于柚子糖度具有较强的在线检测能力。[结论]在光斑直径为70 mm且位于柚子赤道上方20 mm的光照区域C的条件下采集的柚子光谱数据所建立的预测模型能更有效地实现柚子糖度的在线预测。
文摘应用近红外光谱分析技术结合化学计量学方法,建立了中药清开灵注射液中间体总氮和栀子苷含量测定的新方法.首先采用Kernard-Stone法对训练集样本和预测集样品进行分类,然后应用组合的间隔偏最小二乘法(Synergy interval partial least squares,siPLS)对所得近红外透射光谱进行有效谱段范围的选择以及二者定量校正模型的建立,并对光谱预处理方法进行了详细的讨论.所建立的总氮和栀子苷校正模型的预测相关系数(R)分别为0.999和0.708;交叉验证误差均方根(RMSECV)均为0.023;预测误差均方根(RMSEP)分别为0.074和0.159;预测结果表明,本实验所建方法快速、无损且可靠,可推广并应用于中药注射液中间体的在线质量控制.
