Pub Date : 2022-04-19DOI: 10.1177/09670335221083448
Belal Gaci, Sílvia Mas García, F. Abdelghafour, J. Adrian, F. Maupas, J. Roger
Phenotyping is essential in the process of varietal selection. In the case of sugar beets, richness (g/100g), that is, sugar content, is the key information. The need to acquire this information in a rapid, non-destructive and cheap manner leads the sugar industry to look for portable solutions that enable the suitable field measurements. In this work, a low-cost handheld and narrow visible-NIR spectral range microspectrometer is assessed for its ability to provide such information. During a two-year campaign from 2017 to 2018, a total of 649 samples of sugar beet were measured. The resulting data, along with the reference values for richness, were used to build a predictive model with partial least squares (PLS) regression. Acceptable performance in the estimation of richness from both 2017 data (SEP = 0.84 g/100 g) and 2018 data (SEP = 0.90 g/100 g) is achieved. This study also shows that updating the spectral database is possible by calibration transfer models. From the different tested transfer strategies, the combination of model update and slope-bias correction achieves the best performance, demonstrating that the use of 2017 model on different years is possible and only 75 new sugar beets are necessary to guarantee a richness error lower than 1.05 g/100 g. This work suggests that the molecular sensor could offer a useful tool for a rapid, low cost and non-destructive prediction of richness in sugar beets.
{"title":"Assessing the potential of a handheld visible-near infrared microspectrometer for sugar beet phenotyping","authors":"Belal Gaci, Sílvia Mas García, F. Abdelghafour, J. Adrian, F. Maupas, J. Roger","doi":"10.1177/09670335221083448","DOIUrl":"https://doi.org/10.1177/09670335221083448","url":null,"abstract":"Phenotyping is essential in the process of varietal selection. In the case of sugar beets, richness (g/100g), that is, sugar content, is the key information. The need to acquire this information in a rapid, non-destructive and cheap manner leads the sugar industry to look for portable solutions that enable the suitable field measurements. In this work, a low-cost handheld and narrow visible-NIR spectral range microspectrometer is assessed for its ability to provide such information. During a two-year campaign from 2017 to 2018, a total of 649 samples of sugar beet were measured. The resulting data, along with the reference values for richness, were used to build a predictive model with partial least squares (PLS) regression. Acceptable performance in the estimation of richness from both 2017 data (SEP = 0.84 g/100 g) and 2018 data (SEP = 0.90 g/100 g) is achieved. This study also shows that updating the spectral database is possible by calibration transfer models. From the different tested transfer strategies, the combination of model update and slope-bias correction achieves the best performance, demonstrating that the use of 2017 model on different years is possible and only 75 new sugar beets are necessary to guarantee a richness error lower than 1.05 g/100 g. This work suggests that the molecular sensor could offer a useful tool for a rapid, low cost and non-destructive prediction of richness in sugar beets.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"122 - 129"},"PeriodicalIF":1.8,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48080345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-18DOI: 10.1177/09670335221078356
E. Dreier, K. Sørensen, Toke Lund-Hansen, B. Jespersen, K. S. Pedersen
Near Infrared hyperspectral imaging (HSI) offers a fast and non-destructive method for seed quality assessment through combining spectroscopy and imaging. Recently, convolutional neural networks (CNN) have shown to be promising tools for red-green-blue (RGB) image or spectral cereal classification. This paper describes the design and implementation of deep CNN models capable of utilizing both the spatial and spectral dimension of HSI data simultaneously for analysis of bulk grain samples with densely packed kernels. Classification of eight grain samples, including six different wheat varieties, were used as a test case. The study shows that the CNN architecture ResNet, originally designed for RGB images, can be adapted to use the full spatio-spectral dimension of the HSI data through adding a linear down sample layer prior to the conventional ResNet architecture. Using traditional spectral pre-processing methods before passing the data to the CNN does not improve the classification accuracy of the networks, while a channel-wise image standardization improves the accuracy significantly. The modified ResNet applied to the full spatio-spectral dimension has a classification accuracy of up to 99.75 ± 0.02%, outperforming both purely spectral (86.5 ± 0.1%) and purely spatial (98.70 ± 0.01%) based methods in terms of accuracy, indicating that utilizing spatio-spectral correlation can improve sample classification, but also that grain classification is primarily solved using spatial information. The findings reported in this paper demonstrate how CNN networks can be designed to leverage spatio-spectral information in hyperspectral data. The combination of HSI and spatio-spectral CNN networks shows a possible method for fast prediction of bulk grain quality parameters where both spectral and spatial properties of the grains are important.
{"title":"Hyperspectral imaging for classification of bulk grain samples with deep convolutional neural networks","authors":"E. Dreier, K. Sørensen, Toke Lund-Hansen, B. Jespersen, K. S. Pedersen","doi":"10.1177/09670335221078356","DOIUrl":"https://doi.org/10.1177/09670335221078356","url":null,"abstract":"Near Infrared hyperspectral imaging (HSI) offers a fast and non-destructive method for seed quality assessment through combining spectroscopy and imaging. Recently, convolutional neural networks (CNN) have shown to be promising tools for red-green-blue (RGB) image or spectral cereal classification. This paper describes the design and implementation of deep CNN models capable of utilizing both the spatial and spectral dimension of HSI data simultaneously for analysis of bulk grain samples with densely packed kernels. Classification of eight grain samples, including six different wheat varieties, were used as a test case. The study shows that the CNN architecture ResNet, originally designed for RGB images, can be adapted to use the full spatio-spectral dimension of the HSI data through adding a linear down sample layer prior to the conventional ResNet architecture. Using traditional spectral pre-processing methods before passing the data to the CNN does not improve the classification accuracy of the networks, while a channel-wise image standardization improves the accuracy significantly. The modified ResNet applied to the full spatio-spectral dimension has a classification accuracy of up to 99.75 ± 0.02%, outperforming both purely spectral (86.5 ± 0.1%) and purely spatial (98.70 ± 0.01%) based methods in terms of accuracy, indicating that utilizing spatio-spectral correlation can improve sample classification, but also that grain classification is primarily solved using spatial information. The findings reported in this paper demonstrate how CNN networks can be designed to leverage spatio-spectral information in hyperspectral data. The combination of HSI and spatio-spectral CNN networks shows a possible method for fast prediction of bulk grain quality parameters where both spectral and spatial properties of the grains are important.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"107 - 121"},"PeriodicalIF":1.8,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43734480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-09DOI: 10.1177/09670335211030617
C. Zhu, Xiaping Fu, Jianyi Zhang, Kai-Wen Qin, Chuanyu Wu
Near infrared (NIR) spectroscopy is a non-destructive detection technology involving NIR spectral data acquisition and chemometric treatment. The use of an NIR spectrometer is evidently crucial in this regard; however, traditional benchtop NIR spectrometers considerably limit usage scenarios. Accordingly, the miniaturization of spectrometers with high level performance has become a research trend. Various commercial products have been developed, and new techniques have been applied to produce more portable NIR spectrometers. This paper reviews the main types of commercial portable NIR spectrometers and summarizes as well as compares their specifications. Moreover, new techniques for promoting miniaturization and the prospects for future development are introduced.
{"title":"Review of portable near infrared spectrometers: Current status and new techniques","authors":"C. Zhu, Xiaping Fu, Jianyi Zhang, Kai-Wen Qin, Chuanyu Wu","doi":"10.1177/09670335211030617","DOIUrl":"https://doi.org/10.1177/09670335211030617","url":null,"abstract":"Near infrared (NIR) spectroscopy is a non-destructive detection technology involving NIR spectral data acquisition and chemometric treatment. The use of an NIR spectrometer is evidently crucial in this regard; however, traditional benchtop NIR spectrometers considerably limit usage scenarios. Accordingly, the miniaturization of spectrometers with high level performance has become a research trend. Various commercial products have been developed, and new techniques have been applied to produce more portable NIR spectrometers. This paper reviews the main types of commercial portable NIR spectrometers and summarizes as well as compares their specifications. Moreover, new techniques for promoting miniaturization and the prospects for future development are introduced.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"51 - 66"},"PeriodicalIF":1.8,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43092578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-03DOI: 10.1177/09670335221078355
R. Magalhães, N. Paiva, J. Ferra, F. Magalhães, J. Martins, L. Carvalho
Amino resins are produced by two main processes: the strong acid process and the alkaline-acid process. Both use formaldehyde and a base (e.g. sodium hydroxide) in their formulation. In this work, Forward Interval Partial Least Squares methodology was applied to create prediction models for the determination of the concentration of formaldehyde and residual methanol (that is present in the formaldehyde solution) used in the production of amino resins. Near infrared (NIR) spectra were acquired at two different temperatures: 18 and 35°C. A Partial Least Squares calibration models were established with the measured values from reference methods: namely, sodium sulfite (formaldehyde) and gas chromatography (methanol). The performances of the best models were compared using the root mean square error of cross validation (RMSECV) and coefficient of determination for prediction (r2). The best results obtained a r2 above 0.994. The RMSECV values obtained were 0.063% (m/m) and 0.031% (m/m) for the formaldehyde and methanol concentration, respectively. External validation was performed using different formaldehyde solution samples. The NIR methodology presented in this work proved to be effective and enables a significant time reduction, when compared to the reference methods, in the measurement of formaldehyde and methanol concentrations.
{"title":"Prediction of formaldehyde and residual methanol concentration in formalin using near infrared spectroscopy","authors":"R. Magalhães, N. Paiva, J. Ferra, F. Magalhães, J. Martins, L. Carvalho","doi":"10.1177/09670335221078355","DOIUrl":"https://doi.org/10.1177/09670335221078355","url":null,"abstract":"Amino resins are produced by two main processes: the strong acid process and the alkaline-acid process. Both use formaldehyde and a base (e.g. sodium hydroxide) in their formulation. In this work, Forward Interval Partial Least Squares methodology was applied to create prediction models for the determination of the concentration of formaldehyde and residual methanol (that is present in the formaldehyde solution) used in the production of amino resins. Near infrared (NIR) spectra were acquired at two different temperatures: 18 and 35°C. A Partial Least Squares calibration models were established with the measured values from reference methods: namely, sodium sulfite (formaldehyde) and gas chromatography (methanol). The performances of the best models were compared using the root mean square error of cross validation (RMSECV) and coefficient of determination for prediction (r2). The best results obtained a r2 above 0.994. The RMSECV values obtained were 0.063% (m/m) and 0.031% (m/m) for the formaldehyde and methanol concentration, respectively. External validation was performed using different formaldehyde solution samples. The NIR methodology presented in this work proved to be effective and enables a significant time reduction, when compared to the reference methods, in the measurement of formaldehyde and methanol concentrations.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"160 - 168"},"PeriodicalIF":1.8,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48244888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-28DOI: 10.1177/09670335211054299
Martina Marečková, Veronika Danková, L. Zelený, P. Suran
Non-invasive flesh firmness prediction using near infrared spectroscopy has been perfected and validated on three apple varieties. Three novel calibration models were developed following three year's of repeated large-scale sampling of stored commercial apple varieties ‘Gala’, ‘Red Jonaprince’ and ‘Jonagored’. The spectroscopic results were compared directly with those obtained using the invasive method. Increased accuracy of calibration models was achieved with the newly established data collection model. The results exhibited coefficient of determination for calibration, R2, and ratio of prediction to deviation (RPD) in excess of 0.91 and 2.3, respectively, thus enabling excellent prediction of flesh firmness via a non-invasive and fast spectroscopic approach. The highest R2 obtained was 0.94, RPD 2.6, root mean square error of calibration 5.87 N, and root mean square error of cross-validation (internal) 6.75 N for variety ‘Red Jonaprince’. Our complex long-term study provided excellent external validated calibration models and the approach can help developing calibration models for commercial sorting lines using near infrared spectroscopy.
{"title":"Non-destructive near infrared spectroscopy externally validated using large number sets for creation of robust calibration models enabling prediction of apple firmness","authors":"Martina Marečková, Veronika Danková, L. Zelený, P. Suran","doi":"10.1177/09670335211054299","DOIUrl":"https://doi.org/10.1177/09670335211054299","url":null,"abstract":"Non-invasive flesh firmness prediction using near infrared spectroscopy has been perfected and validated on three apple varieties. Three novel calibration models were developed following three year's of repeated large-scale sampling of stored commercial apple varieties ‘Gala’, ‘Red Jonaprince’ and ‘Jonagored’. The spectroscopic results were compared directly with those obtained using the invasive method. Increased accuracy of calibration models was achieved with the newly established data collection model. The results exhibited coefficient of determination for calibration, R2, and ratio of prediction to deviation (RPD) in excess of 0.91 and 2.3, respectively, thus enabling excellent prediction of flesh firmness via a non-invasive and fast spectroscopic approach. The highest R2 obtained was 0.94, RPD 2.6, root mean square error of calibration 5.87 N, and root mean square error of cross-validation (internal) 6.75 N for variety ‘Red Jonaprince’. Our complex long-term study provided excellent external validated calibration models and the approach can help developing calibration models for commercial sorting lines using near infrared spectroscopy.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"97 - 104"},"PeriodicalIF":1.8,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49006724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-25DOI: 10.1177/09670335211057234
Meng-hong Li, Tianhong Pan, Yang Bai, Qi Chen
Development of qualitative or quantitative models is essential to exploit the full potential of near infrared (NIR) spectroscopy. In tandem with one-dimensional convolutional neural network (1D-CNN), a data-driven model is developed using NIR spectroscopy to estimate organic contents. First, the 1D-CNN model is designed to capture the features of the NIR spectra by means of several convolutional and pooling operations. Then, the suitable hyper-parameters of 1D-CNN are obtained by using the grid search algorithm to achieve the optimal performance. Furthermore, the dropout operation is added into the 1D-CNN to suppress the overfitting problem by means of removing some neurons, and the probability distribution of throwing follows the Bernoulli distribution. The developed framework is validated by the application in the sugar content estimation of Huangshan Maofeng tea. The experimental results demonstrate that the key features of the NIR spectra are successfully extracted by the proposed strategy; thereby, a new effective scheme for analyzing NIR spectra is provided for food analysis.
{"title":"Development of a calibration model for near infrared spectroscopy using a convolutional neural network","authors":"Meng-hong Li, Tianhong Pan, Yang Bai, Qi Chen","doi":"10.1177/09670335211057234","DOIUrl":"https://doi.org/10.1177/09670335211057234","url":null,"abstract":"Development of qualitative or quantitative models is essential to exploit the full potential of near infrared (NIR) spectroscopy. In tandem with one-dimensional convolutional neural network (1D-CNN), a data-driven model is developed using NIR spectroscopy to estimate organic contents. First, the 1D-CNN model is designed to capture the features of the NIR spectra by means of several convolutional and pooling operations. Then, the suitable hyper-parameters of 1D-CNN are obtained by using the grid search algorithm to achieve the optimal performance. Furthermore, the dropout operation is added into the 1D-CNN to suppress the overfitting problem by means of removing some neurons, and the probability distribution of throwing follows the Bernoulli distribution. The developed framework is validated by the application in the sugar content estimation of Huangshan Maofeng tea. The experimental results demonstrate that the key features of the NIR spectra are successfully extracted by the proposed strategy; thereby, a new effective scheme for analyzing NIR spectra is provided for food analysis.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"89 - 96"},"PeriodicalIF":1.8,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41851585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-24DOI: 10.1177/09670335211061842
Zhen Wang, R. Künnemeyer, A. McGlone, Jason Sun, J. Burdon, M. Cree
As a physiological disorder, chilling injury in kiwifruit may develop when the fruit are stored for long periods at a low storage temperature of 0–1°C. Presence of the disorder, inconsistent with marketing requirements for high-quality fruit, may lead to substantial financial and reputational losses. Thus, early detection or removal of chill-damaged fruit is desirable. This study demonstrates a novel dual-laser scanning system which has potential to be developed into a fast online system for the detection of chilling injury in Actinidia chinensis var. chinensis ‘Zesy002’ kiwifruit. The system consists of two laser modules at 730 and 880 nm wavelengths, a scanning mechanism and two detectors at partial (90°) and full (180°) light transmission. A sample of 231 kiwifruit was used to prove the concept, including 80 sound and 151 chill-damaged fruit of three different severity categories (slight, moderate and severe). A principal component analysis – back propagation neural network was used to classify fruit with 5-fold cross-validation. A comparison was made with standard visible-near infrared (Vis-NIR) interactance spectroscopy used to classify the same fruit using the same modelling algorithm. The dual-laser scanning system showed a slightly higher binary classification accuracy than the Vis-NIR spectroscopy, with an average accuracy of 95% for distinguishing sound and chill-damaged fruit. The classification error rate was 0% for severe damaged fruit. These experimental results demonstrate the potential of this dual-laser scanning system for the detection of chill-damaged fruit. The setup using only two wavelengths, its unique scanning operation and flexible system layout make it practical and attractive for future development for application on high-speed fruit graders.
{"title":"Non-destructive detection of chilling injury in kiwifruit using a dual-laser scanning system with a principal component analysis - back propagation neural network","authors":"Zhen Wang, R. Künnemeyer, A. McGlone, Jason Sun, J. Burdon, M. Cree","doi":"10.1177/09670335211061842","DOIUrl":"https://doi.org/10.1177/09670335211061842","url":null,"abstract":"As a physiological disorder, chilling injury in kiwifruit may develop when the fruit are stored for long periods at a low storage temperature of 0–1°C. Presence of the disorder, inconsistent with marketing requirements for high-quality fruit, may lead to substantial financial and reputational losses. Thus, early detection or removal of chill-damaged fruit is desirable. This study demonstrates a novel dual-laser scanning system which has potential to be developed into a fast online system for the detection of chilling injury in Actinidia chinensis var. chinensis ‘Zesy002’ kiwifruit. The system consists of two laser modules at 730 and 880 nm wavelengths, a scanning mechanism and two detectors at partial (90°) and full (180°) light transmission. A sample of 231 kiwifruit was used to prove the concept, including 80 sound and 151 chill-damaged fruit of three different severity categories (slight, moderate and severe). A principal component analysis – back propagation neural network was used to classify fruit with 5-fold cross-validation. A comparison was made with standard visible-near infrared (Vis-NIR) interactance spectroscopy used to classify the same fruit using the same modelling algorithm. The dual-laser scanning system showed a slightly higher binary classification accuracy than the Vis-NIR spectroscopy, with an average accuracy of 95% for distinguishing sound and chill-damaged fruit. The classification error rate was 0% for severe damaged fruit. These experimental results demonstrate the potential of this dual-laser scanning system for the detection of chill-damaged fruit. The setup using only two wavelengths, its unique scanning operation and flexible system layout make it practical and attractive for future development for application on high-speed fruit graders.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"67 - 73"},"PeriodicalIF":1.8,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49233715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-20DOI: 10.3760/cma.j.cn501120-20211025-00366
The development of burn units in our country is now undergoing a trend of geographic centralization and regionalization. To solve the problems like severe burn patients are too far away from burn units, overloaded operation in regional burn centers when mass burn accidents happen, and growing requirement for aeromedical transportation, etc., it is now the top priority to improve national aeromedical transportation system for burn patients. Expert teams from Chinese Burn Association, National Aeromedical Rescue Base, and China Association for Disaster & Emergency Rescue Medicine discussed and reached a consensus on the key points of aeromedical transportation of burn patients, including organizational structure, staff and materials, and three links before, during, and after aeromedical transportation. The consensus aims to provide guidance for a safe, efficient, and standardized operation of aeromedical transportation for burn patients in China.
{"title":"[National expert consensus on the aeromedical trans- portation of burn patients (2022 version)].","authors":"","doi":"10.3760/cma.j.cn501120-20211025-00366","DOIUrl":"10.3760/cma.j.cn501120-20211025-00366","url":null,"abstract":"<p><p>The development of burn units in our country is now undergoing a trend of geographic centralization and regionalization. To solve the problems like severe burn patients are too far away from burn units, overloaded operation in regional burn centers when mass burn accidents happen, and growing requirement for aeromedical transportation, etc., it is now the top priority to improve national aeromedical transportation system for burn patients. Expert teams from Chinese Burn Association, National Aeromedical Rescue Base, and China Association for Disaster & Emergency Rescue Medicine discussed and reached a consensus on the key points of aeromedical transportation of burn patients, including organizational structure, staff and materials, and three links before, during, and after aeromedical transportation. The consensus aims to provide guidance for a safe, efficient, and standardized operation of aeromedical transportation for burn patients in China.</p>","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"3 1","pages":"101-108"},"PeriodicalIF":0.0,"publicationDate":"2022-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88333044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-17DOI: 10.1177/09670335211047959
Jhon Buendia Garcia, J. Gornay, M. Lacoue-Nègre, Sílvia Mas García, Jihane Er-Rmyly, R. Bendoula, Jean-Michel Roger
This study uses a novel analysis methodology based on the Hierarchical Clustering Analysis (HCA) to determine the effectiveness of different preprocessing methods in minimizing undesired spectral variability in near infrared spectroscopy due to both the consecutive and repetitive acquisition of the spectrum and the sample temperature. Nine preprocessing methods and different combinations of them were evaluated in four case studies: reproducibility, repeatability, sample temperature, and combination of the before mentioned cases. Eighty-four spectra acquired on seven different hydrocarbon samples from catalytic conversion processes have been selected as the real case study to illustrate the potential of the mentioned methodology. The approach proposed allows a more detailed discriminatory analysis compared to the classical methods for comparing the between-class and the within-class variances, such as the Wilks’ lambda criterion, and hence constitutes a powerful tool to determine adequate spectral preprocessing strategies. This study also proves the potential of the discrimination analysis methodology as a general scheme to identify atypical behaviors either in the spectrum acquisition or in the measured samples.
{"title":"A novel methodology for determining effectiveness of preprocessing methods in reducing undesired spectral variability in near infrared spectra","authors":"Jhon Buendia Garcia, J. Gornay, M. Lacoue-Nègre, Sílvia Mas García, Jihane Er-Rmyly, R. Bendoula, Jean-Michel Roger","doi":"10.1177/09670335211047959","DOIUrl":"https://doi.org/10.1177/09670335211047959","url":null,"abstract":"This study uses a novel analysis methodology based on the Hierarchical Clustering Analysis (HCA) to determine the effectiveness of different preprocessing methods in minimizing undesired spectral variability in near infrared spectroscopy due to both the consecutive and repetitive acquisition of the spectrum and the sample temperature. Nine preprocessing methods and different combinations of them were evaluated in four case studies: reproducibility, repeatability, sample temperature, and combination of the before mentioned cases. Eighty-four spectra acquired on seven different hydrocarbon samples from catalytic conversion processes have been selected as the real case study to illustrate the potential of the mentioned methodology. The approach proposed allows a more detailed discriminatory analysis compared to the classical methods for comparing the between-class and the within-class variances, such as the Wilks’ lambda criterion, and hence constitutes a powerful tool to determine adequate spectral preprocessing strategies. This study also proves the potential of the discrimination analysis methodology as a general scheme to identify atypical behaviors either in the spectrum acquisition or in the measured samples.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"74 - 88"},"PeriodicalIF":1.8,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46656160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-14DOI: 10.1177/09670335211061841
Xiaojie Ouyang, Shu-Yi Zhan, Min Tang, Shumei Wang, S. Liang, Fei Sun
Real time release testing (RTRT) has been applied in the pharmaceutical process to ensure the high quality of finished products. Near infrared (NIR) spectroscopy is one of the primary analytical methods to implement RTRT. In this study, an NIR quantitative method was developed to determine the content of total flavonol glycosides in Shuxuening injection and validated by the accuracy profile approach. Combining the NIR validation with quality specification limits, a reliable RTRT method was constructed. Shuxuening injection samples of different concentrations were prepared and characterized by NIR spectroscopy. A first-order Savitzky–Golay derivative was used to pretreat the NIR spectra, and the competitive adaptive reweighted sampling method was used to select the feature variables. Partial least squares (PLS) regression was used to build the NIR quantitative model. The trueness, precision, and accuracy of the developed NIR models were validated by accuracy profile, and the measurement uncertainty was also estimated. Finally, the unreliability graph as a decision tool was established to avoid risk, enabling correct decision making to release of Shuxuening injection. The root mean square error of calibration, root mean square error of cross validation, root mean square error of prediction, and the ratio of prediction to deviation of the PLS model were 19.6 μg·mL−1, 20.9 μg·mL−1, 29.9 μg·mL−1, and 12.2, respectively, indicating the NIR quantitative model had good predictive performance. The validation results prove that the precision, trueness, and accuracy of the NIR quantitative model were within the acceptable limits. Based on the unreliability graph, the decision to release Shuxuening injection was satisfied, if the prediction of total flavonol glycosides fell into the range from 783 μg·mL−1 to 900 μg·mL−1. The RTRT method for Shuxuening injection based on NIR spectroscopy and accuracy profile can improve the efficiency and accuracy of quality control.
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