Pub Date : 2022-08-01DOI: 10.1177/09670335221110013
Xiaoxue Zhang, Xinyu Chen, Zhi-xin Xiong, H. Siesler, Long Liang
In order to reduce the time and cost for near infrared (NIR) model development and maintenance, the transfer of NIR spectra measured on four different portable spectrometers (one master and three target instruments) for predicting the lignin content of pulp wood is investigated in this work. Eighty-two wood samples were prepared by chipping and grinding, and their NIR spectra were recorded with four spectrometers. Calibration models for the determination of lignin in pulp wood have been developed by partial least squares (PLS) regression, while average Mahalanobis distances (AMD) and average differences of spectra (ADS) were used to quantify the spectral differences. Then piecewise direct standardization (PDS) has been applied, and compared to direct standardization (DS), slope/bias correction (SBC) and canonical correlation analysis (CCA). The accuracy of the models has been evaluated by comparing their prediction performance. The results indicated that the prediction performances of the three target instruments are greatly improved by using the three algorithms. The advantage of the PDS algorithm is that fewer samples are required for the transfer sets, which means lower model maintenance cost for practical applications. When it comes to window size setting procedure, it was found that if there are large spectral differences between the master and the target spectrometer, a large window size should be used and if the spectral difference is a significant lateral shift, an asymmetric window with appropriate window size is necessary to ensure a good transfer performance for the PDS algorithm.
{"title":"Transfer of a calibration model for the prediction of lignin in pulpwood among four portable near infrared spectrometers","authors":"Xiaoxue Zhang, Xinyu Chen, Zhi-xin Xiong, H. Siesler, Long Liang","doi":"10.1177/09670335221110013","DOIUrl":"https://doi.org/10.1177/09670335221110013","url":null,"abstract":"In order to reduce the time and cost for near infrared (NIR) model development and maintenance, the transfer of NIR spectra measured on four different portable spectrometers (one master and three target instruments) for predicting the lignin content of pulp wood is investigated in this work. Eighty-two wood samples were prepared by chipping and grinding, and their NIR spectra were recorded with four spectrometers. Calibration models for the determination of lignin in pulp wood have been developed by partial least squares (PLS) regression, while average Mahalanobis distances (AMD) and average differences of spectra (ADS) were used to quantify the spectral differences. Then piecewise direct standardization (PDS) has been applied, and compared to direct standardization (DS), slope/bias correction (SBC) and canonical correlation analysis (CCA). The accuracy of the models has been evaluated by comparing their prediction performance. The results indicated that the prediction performances of the three target instruments are greatly improved by using the three algorithms. The advantage of the PDS algorithm is that fewer samples are required for the transfer sets, which means lower model maintenance cost for practical applications. When it comes to window size setting procedure, it was found that if there are large spectral differences between the master and the target spectrometer, a large window size should be used and if the spectral difference is a significant lateral shift, an asymmetric window with appropriate window size is necessary to ensure a good transfer performance for the PDS algorithm.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"208 - 218"},"PeriodicalIF":1.8,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46226095","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 : 2022-07-20DOI: 10.1177/09670335221110011
Yi-Hui Wu, Dan Li
This paper presents an application of near infrared spectroscopy associated with partial least squares regression calibration, for ash content analysis of prepreg in the manufacturing progress of copper clad laminate for printed circuited boards. The performance of the model was assessed by cross validation and external validation. The correlation coefficient and the root mean squared error of calibration and validation were 0.99, 0.25% and 0.98, 0.34%, and the measurement process was accomplished in less than 2 min compared to over 60 min for traditional thermo gravimetric analyzsis. The paired t-test results revealed that there was no significant difference between the two methods.
{"title":"Determination of ash content in silicon dioxide filled epoxy-phenolic prepreg using near infrared spectroscopy","authors":"Yi-Hui Wu, Dan Li","doi":"10.1177/09670335221110011","DOIUrl":"https://doi.org/10.1177/09670335221110011","url":null,"abstract":"This paper presents an application of near infrared spectroscopy associated with partial least squares regression calibration, for ash content analysis of prepreg in the manufacturing progress of copper clad laminate for printed circuited boards. The performance of the model was assessed by cross validation and external validation. The correlation coefficient and the root mean squared error of calibration and validation were 0.99, 0.25% and 0.98, 0.34%, and the measurement process was accomplished in less than 2 min compared to over 60 min for traditional thermo gravimetric analyzsis. The paired t-test results revealed that there was no significant difference between the two methods.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"227 - 233"},"PeriodicalIF":1.8,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48840265","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 : 2022-07-18DOI: 10.1177/09670335221097004
G. Altieri, Mahdi Rashvand, O. Mammadov, Attilio Matera, Francesco Genovese, G. C. Di Renzo
Ranchers are continuously searching for suitable tools to rapidly and inexpensively assess the characteristics of donkey milk and because spectroscopic models are useful to assess the composition of many foods, an attempt to further improve the prediction performance of donkey milk protein, lactose and dry-matter content has been made using three widely used spectroscopic models by adding some interaction terms, namely product, ratio, sum and difference of absorbances for each couple of wavelengths. Principal component regression using product terms showed an improvement in prediction error achieving 1.8%, 1.7% and 0.9% for protein, lactose and dry-matter content respectively. Furthermore, the added ratio terms showed a very great improvement in the predictive overall performance achieving 0.3%, 0.4% and 0.2%. A coefficient has been found relating the widely used RPD, a standard index of prediction performance, to the new proposed “range of confident prediction error percent” being a more understandable parameter to assess the goodness of the prediction model.
{"title":"Use of wavelength interaction terms to improve near infrared spectroscopy models of donkey milk properties","authors":"G. Altieri, Mahdi Rashvand, O. Mammadov, Attilio Matera, Francesco Genovese, G. C. Di Renzo","doi":"10.1177/09670335221097004","DOIUrl":"https://doi.org/10.1177/09670335221097004","url":null,"abstract":"Ranchers are continuously searching for suitable tools to rapidly and inexpensively assess the characteristics of donkey milk and because spectroscopic models are useful to assess the composition of many foods, an attempt to further improve the prediction performance of donkey milk protein, lactose and dry-matter content has been made using three widely used spectroscopic models by adding some interaction terms, namely product, ratio, sum and difference of absorbances for each couple of wavelengths. Principal component regression using product terms showed an improvement in prediction error achieving 1.8%, 1.7% and 0.9% for protein, lactose and dry-matter content respectively. Furthermore, the added ratio terms showed a very great improvement in the predictive overall performance achieving 0.3%, 0.4% and 0.2%. A coefficient has been found relating the widely used RPD, a standard index of prediction performance, to the new proposed “range of confident prediction error percent” being a more understandable parameter to assess the goodness of the prediction model.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"219 - 226"},"PeriodicalIF":1.8,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45647602","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 : 2022-05-29DOI: 10.1177/09670335221098527
Raziyeh Pourdarbani, S. Sabzi, M. Rohban, G. García-Mateos, J. Molina-Martínez, J. Paliwal, J. I. Arribas
Chemical fertilizers are widely applied in agriculture to achieve high yield, enhance produce quality and build resistance to diseases; in our case the plant being tomato (Solanum lycopersicum L. var. Royal). However, the acidity, size and taste of tomato fruits could change with excess nitrogen (N) application. The present study aims at the early detection of nitrogen-rich tomato leaves using hyperspectral imaging techniques in the visible and near infrared (Vis-NIR) spectrum, in order to improve plant nutrition composition at an early growth stage. A 30% over-dose of nitrogen was applied to half of the tomato pots. Five leaves were randomly collected from each pot for 3 days (classes D0, D1, D2 and D3), and images were captured with a hyperspectral camera. A metaheuristic approach of artificial neural networks and the firefly algorithm (ANN-FA) was used to determine the most discriminative wavelengths. Afterwards, a combination of ANN and particle swarm optimization (ANN-PSO) was used to classify tomato leaves into the four classes. The training/classification process was repeated 200 times, and results indicated that the proposed approach was able to detect the excess of nitrogen even at the first day (D1), with a precision of 92.9%. Considering all the classes, the average correct classification rate was 92.6%, while the best execution achieved 95.5% accuracy. Thus, the method showed a high performance for practical uses.
{"title":"Metaheuristic algorithms in visible and near infrared spectra to detect excess nitrogen content in tomato plants","authors":"Raziyeh Pourdarbani, S. Sabzi, M. Rohban, G. García-Mateos, J. Molina-Martínez, J. Paliwal, J. I. Arribas","doi":"10.1177/09670335221098527","DOIUrl":"https://doi.org/10.1177/09670335221098527","url":null,"abstract":"Chemical fertilizers are widely applied in agriculture to achieve high yield, enhance produce quality and build resistance to diseases; in our case the plant being tomato (Solanum lycopersicum L. var. Royal). However, the acidity, size and taste of tomato fruits could change with excess nitrogen (N) application. The present study aims at the early detection of nitrogen-rich tomato leaves using hyperspectral imaging techniques in the visible and near infrared (Vis-NIR) spectrum, in order to improve plant nutrition composition at an early growth stage. A 30% over-dose of nitrogen was applied to half of the tomato pots. Five leaves were randomly collected from each pot for 3 days (classes D0, D1, D2 and D3), and images were captured with a hyperspectral camera. A metaheuristic approach of artificial neural networks and the firefly algorithm (ANN-FA) was used to determine the most discriminative wavelengths. Afterwards, a combination of ANN and particle swarm optimization (ANN-PSO) was used to classify tomato leaves into the four classes. The training/classification process was repeated 200 times, and results indicated that the proposed approach was able to detect the excess of nitrogen even at the first day (D1), with a precision of 92.9%. Considering all the classes, the average correct classification rate was 92.6%, while the best execution achieved 95.5% accuracy. Thus, the method showed a high performance for practical uses.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"197 - 207"},"PeriodicalIF":1.8,"publicationDate":"2022-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42961842","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 : 2022-05-22DOI: 10.1177/09670335221097005
T. TenBrink, Sandra K. Neidetcher, Morgan B. Arrington, I. Benson, C. Conrath, T. Helser
Fourier-transform near infrared (FT-NIR) spectroscopy of ovarian tissue was used to predict maturity status of fish species with variable reproductive strategies collected at limited time periods of their spawning cycle. Reference data were derived from histologically prepared tissue samples from four species: Pacific cod (Gadus macrocephalus), walleye pollock (Gadus chalcogrammus), Greenland turbot (Reinhardtius hippoglossoides), and northern rockfish (Sebastes polyspinis). Each data set was classified into reproductively immature (non-spawning) and reproductively mature (spawning-capable) categories. Principal component analysis of spectral data showed separation between ovarian tissues of spawning-capable and non-spawning females. Multivariate classification using partial least squares discriminant analysis indicated good discrimination based on spawning status with high predictive accuracy. Greenland turbot and northern rockfish showed clear distinction between ovaries of spawning-capable and non-spawning females and a model validation with 100% and 96.6% classification accuracy, respectively. Pacific cod and walleye pollock had more complicated reproductive patterns at time of collection and classification rates were still 96.6% and 92.1%. This study demonstrated the potential application of FT-NIR spectroscopy to predict spawning status from ovarian tissue even for species with complicated spawning patterns and for collections outside of the preferred spawning period. Future work may include the use of this technology to classify distinct oocyte development stages.
{"title":"Fourier transform near infrared spectroscopy as a tool to predict spawning status in Alaskan fishes with variable reproductive strategies","authors":"T. TenBrink, Sandra K. Neidetcher, Morgan B. Arrington, I. Benson, C. Conrath, T. Helser","doi":"10.1177/09670335221097005","DOIUrl":"https://doi.org/10.1177/09670335221097005","url":null,"abstract":"Fourier-transform near infrared (FT-NIR) spectroscopy of ovarian tissue was used to predict maturity status of fish species with variable reproductive strategies collected at limited time periods of their spawning cycle. Reference data were derived from histologically prepared tissue samples from four species: Pacific cod (Gadus macrocephalus), walleye pollock (Gadus chalcogrammus), Greenland turbot (Reinhardtius hippoglossoides), and northern rockfish (Sebastes polyspinis). Each data set was classified into reproductively immature (non-spawning) and reproductively mature (spawning-capable) categories. Principal component analysis of spectral data showed separation between ovarian tissues of spawning-capable and non-spawning females. Multivariate classification using partial least squares discriminant analysis indicated good discrimination based on spawning status with high predictive accuracy. Greenland turbot and northern rockfish showed clear distinction between ovaries of spawning-capable and non-spawning females and a model validation with 100% and 96.6% classification accuracy, respectively. Pacific cod and walleye pollock had more complicated reproductive patterns at time of collection and classification rates were still 96.6% and 92.1%. This study demonstrated the potential application of FT-NIR spectroscopy to predict spawning status from ovarian tissue even for species with complicated spawning patterns and for collections outside of the preferred spawning period. Future work may include the use of this technology to classify distinct oocyte development stages.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"179 - 188"},"PeriodicalIF":1.8,"publicationDate":"2022-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48508591","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 : 2022-05-12DOI: 10.1177/09670335221097236
Na Zhao, Zhisheng Wu, Chunying Wu, Shuyu Wang, Xueyan Zhan
Variable selection can improve the robustness and prediction accuracy of partial least squares (PLS) regression models and decrease the calculation time by selecting the optimal subset of variables in multivariate calibration. In this study, the performance of two variable selection methods for wavelength interval and individual wavelength coupled with partial least squares regression are investigated by employing the experimental data of asiaticoside (AS) and madecassoside (MS) contents in centella total glucosides (CTG) and a public dataset of corn. The studied variable selection methods include interval partial least squares regression (iPLS), backward interval partial least squares (biPLS), synergy interval partial least squares regression (siPLS), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE) and variable importance in projection (VIP). The results show that the implementation of variable selection methods improved the performance of the model compared with full-spectrum modeling. All variable selection methods improved the prediction of AS or MS contents in CTG. When latent variables for PLS models are less than 10 in the practical application, the RPD value of AS models by iPLS method is 7.5, and the RPD value of MS models by biPLS method is 2.9. The results of wavelength interval selection are better than individual wavelength selection, especially for iPLS and biPLS. The same results were obtained with the public data for moisture in corn, and the RPD value of biPLS model of moisture is 1.6. Therefore, the wavelength interval selection methods, such as iPLS or biPLS, are appropriate for improving the PLS model’s accuracy and robustness to determine the target components’ contents in solid samples. Graphical Abstract
{"title":"Performance evaluation of variable selection methods coupled with partial least squares regression to determine the target component in solid samples","authors":"Na Zhao, Zhisheng Wu, Chunying Wu, Shuyu Wang, Xueyan Zhan","doi":"10.1177/09670335221097236","DOIUrl":"https://doi.org/10.1177/09670335221097236","url":null,"abstract":"Variable selection can improve the robustness and prediction accuracy of partial least squares (PLS) regression models and decrease the calculation time by selecting the optimal subset of variables in multivariate calibration. In this study, the performance of two variable selection methods for wavelength interval and individual wavelength coupled with partial least squares regression are investigated by employing the experimental data of asiaticoside (AS) and madecassoside (MS) contents in centella total glucosides (CTG) and a public dataset of corn. The studied variable selection methods include interval partial least squares regression (iPLS), backward interval partial least squares (biPLS), synergy interval partial least squares regression (siPLS), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE) and variable importance in projection (VIP). The results show that the implementation of variable selection methods improved the performance of the model compared with full-spectrum modeling. All variable selection methods improved the prediction of AS or MS contents in CTG. When latent variables for PLS models are less than 10 in the practical application, the RPD value of AS models by iPLS method is 7.5, and the RPD value of MS models by biPLS method is 2.9. The results of wavelength interval selection are better than individual wavelength selection, especially for iPLS and biPLS. The same results were obtained with the public data for moisture in corn, and the RPD value of biPLS model of moisture is 1.6. Therefore, the wavelength interval selection methods, such as iPLS or biPLS, are appropriate for improving the PLS model’s accuracy and robustness to determine the target components’ contents in solid samples. Graphical Abstract","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"171 - 178"},"PeriodicalIF":1.8,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42611250","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 : 2022-04-29DOI: 10.1177/09670335221083070
A. Oluk, Hatice Yucel, Feyza D Bilgin, U. Serbester
Dallisgrass (Paspalum dilatatum Poir.) is an economically important and widely cultivated forage crop for livestock feeding in the tropical, subtropical, and warm temperate regions because of good adaptation to unsuitable pasture conditions. In this study, 216 dallisgrass samples were used to develop near infrared reflectance calibrations to estimate five forage quality parameters: dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and ash. Second derivative pretreatment was applied for calibration of DM, CP and NDF while a first derivative pretreatment was used for ADF and ash. The coefficients of determination in the internal validation set (r 2 ) were 0.78 for DM, 0.80 for CP, 0.95 for NDF 0.75 for ADF, and 0.71 for ash. The relative predictive determinant ratios for calibration indicate that the NDF equations were acceptable for quantitative prediction of dallisgrass quality, whereas the DM, CP, ADF, and ash equations were useful for screening purposes. The near infrared prediction models developed in this study can be used for screening in the forage breeding researches to be carried out for five quality parameters in the future.
{"title":"Estimation of forage quality by near infrared reflectance spectroscopy in dallisgrass, Paspalum dilatatum, poir","authors":"A. Oluk, Hatice Yucel, Feyza D Bilgin, U. Serbester","doi":"10.1177/09670335221083070","DOIUrl":"https://doi.org/10.1177/09670335221083070","url":null,"abstract":"Dallisgrass (Paspalum dilatatum Poir.) is an economically important and widely cultivated forage crop for livestock feeding in the tropical, subtropical, and warm temperate regions because of good adaptation to unsuitable pasture conditions. In this study, 216 dallisgrass samples were used to develop near infrared reflectance calibrations to estimate five forage quality parameters: dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and ash. Second derivative pretreatment was applied for calibration of DM, CP and NDF while a first derivative pretreatment was used for ADF and ash. The coefficients of determination in the internal validation set (r 2 ) were 0.78 for DM, 0.80 for CP, 0.95 for NDF 0.75 for ADF, and 0.71 for ash. The relative predictive determinant ratios for calibration indicate that the NDF equations were acceptable for quantitative prediction of dallisgrass quality, whereas the DM, CP, ADF, and ash equations were useful for screening purposes. The near infrared prediction models developed in this study can be used for screening in the forage breeding researches to be carried out for five quality parameters in the future.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"189 - 196"},"PeriodicalIF":1.8,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44745090","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 : 2022-04-25DOI: 10.1177/09670335221078354
Fang Wang, Bin Jia, Jun Dai, Xiang-wen Song, Xiaoli Li, Haidi Gao, Hui Yan, Bangxing Han
Because of the similar appearance and properties of different quality grades of the product, super Dendrobium huoshanense could be easily adulterated with first-grade D. huoshanense and second-grade D. huoshanense products, thereby affecting its clinical application and causing market distortion. In this study, a combination of hand-held near infrared spectroscopy and chemometrics was used to classify different grades of D. huoshanense. The standard normal variate was employed to preprocess the original near infrared spectra, following which linear analysis models (principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), and a non-linear support vector machine (SVM) model, were utilized to establish the identification models. The results showed that PCA analysis could not identify the three grades of D. huoshanense, and the LDA analysis could distinguish the second-grade from the other two grades. The PLSDA model resulted in prediction accuracies for the calibration cross-validation, and test sets of 91.83%, 83.58%, and 84.29%, respectively. Unfortunately, the super and first-grade D. huoshanense were not identified by the linear analysis model. Further analysis was performed with a non-linear model, where SVM was used to analyze all grades of D. huoshanense. The recognition rate of thel training set and validation set were 88% and 84%, respectively. All in all, the use of a hand-held near infrared spectrometer combined with chemometrics could identify the quality grade of D. huoshanense samples on-site in real-time, and provide a simple, fast, and reliable method for the quality control of the traditional Chinese medicine herb of D. huoshanense.
{"title":"Qualitative classification of Dendrobium huoshanense (Feng dou) using fast non-destructive hand-held near infrared spectroscopy","authors":"Fang Wang, Bin Jia, Jun Dai, Xiang-wen Song, Xiaoli Li, Haidi Gao, Hui Yan, Bangxing Han","doi":"10.1177/09670335221078354","DOIUrl":"https://doi.org/10.1177/09670335221078354","url":null,"abstract":"Because of the similar appearance and properties of different quality grades of the product, super Dendrobium huoshanense could be easily adulterated with first-grade D. huoshanense and second-grade D. huoshanense products, thereby affecting its clinical application and causing market distortion. In this study, a combination of hand-held near infrared spectroscopy and chemometrics was used to classify different grades of D. huoshanense. The standard normal variate was employed to preprocess the original near infrared spectra, following which linear analysis models (principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), and a non-linear support vector machine (SVM) model, were utilized to establish the identification models. The results showed that PCA analysis could not identify the three grades of D. huoshanense, and the LDA analysis could distinguish the second-grade from the other two grades. The PLSDA model resulted in prediction accuracies for the calibration cross-validation, and test sets of 91.83%, 83.58%, and 84.29%, respectively. Unfortunately, the super and first-grade D. huoshanense were not identified by the linear analysis model. Further analysis was performed with a non-linear model, where SVM was used to analyze all grades of D. huoshanense. The recognition rate of thel training set and validation set were 88% and 84%, respectively. All in all, the use of a hand-held near infrared spectrometer combined with chemometrics could identify the quality grade of D. huoshanense samples on-site in real-time, and provide a simple, fast, and reliable method for the quality control of the traditional Chinese medicine herb of D. huoshanense.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"147 - 153"},"PeriodicalIF":1.8,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49660004","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 : 2022-04-25DOI: 10.1177/09670335221085616
Joel B. Johnson
Non-destructively identifying the centre composition of panned chocolate goods may be useful in quality assurance settings. However, no studies to date have investigated this topic. In this study, near infrared spectra (1000–2500 nm) were collected from chocolate-coated peanuts and chocolate-coated sultanas (n = 170 of each) in order to investigate the prospect of non-invasively detecting the composition of the centre. Principal component analysis confirmed that the spectra of these samples were distinct from one another. The partial least squares discriminant analysis (PLS-DA) model showed a high level of separation between chocolate-coated peanuts and sultanas in the training set (R2 = 0.95; RPD = 4.4). Discrimination between peanut and sultana samples from an independent test set was also possible, although with slightly less distinct separation between the sample types. A soft independent modelling by class analogy model was also able to differentiate between the two sample types, albeit with higher levels of misclassification compared to PLS-DA. Incorporating samples from different manufacturers may be useful for improving the broader applicability of the model.
{"title":"Discrimination of centre composition in panned chocolate goods using near infrared spectroscopy","authors":"Joel B. Johnson","doi":"10.1177/09670335221085616","DOIUrl":"https://doi.org/10.1177/09670335221085616","url":null,"abstract":"Non-destructively identifying the centre composition of panned chocolate goods may be useful in quality assurance settings. However, no studies to date have investigated this topic. In this study, near infrared spectra (1000–2500 nm) were collected from chocolate-coated peanuts and chocolate-coated sultanas (n = 170 of each) in order to investigate the prospect of non-invasively detecting the composition of the centre. Principal component analysis confirmed that the spectra of these samples were distinct from one another. The partial least squares discriminant analysis (PLS-DA) model showed a high level of separation between chocolate-coated peanuts and sultanas in the training set (R2 = 0.95; RPD = 4.4). Discrimination between peanut and sultana samples from an independent test set was also possible, although with slightly less distinct separation between the sample types. A soft independent modelling by class analogy model was also able to differentiate between the two sample types, albeit with higher levels of misclassification compared to PLS-DA. Incorporating samples from different manufacturers may be useful for improving the broader applicability of the model.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"130 - 137"},"PeriodicalIF":1.8,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47382783","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 : 2022-04-22DOI: 10.1177/09670335221082220
Mian Wang, Yan Sun, Chaoshu Duan, W. Cai, Xueguang Shao
Confined water has an important effect on the structural stability and biological activity of biomolecules. Reverse micelles (RM) are a good system for investigating the structure of water in confined environment. In this work, the structure of water in RMs with different water content (w0) was studied using near infrared spectra measured at different temperature. Independent component analysis was used to extract the spectral features changing with the w0 and temperature. Three independent components representing the spectral features of trapped water, bound water, and core water were obtained. Furthermore, through the variation of the trapped water and bound water with temperature, an increase of the former and a reduction of the latter were found, revealing that the two water structures play an important role for the mobility of the RM’s shell.
{"title":"Investigating the water structures in reverse micelles by temperature-dependent near infrared spectroscopy combined with independent component analysis","authors":"Mian Wang, Yan Sun, Chaoshu Duan, W. Cai, Xueguang Shao","doi":"10.1177/09670335221082220","DOIUrl":"https://doi.org/10.1177/09670335221082220","url":null,"abstract":"Confined water has an important effect on the structural stability and biological activity of biomolecules. Reverse micelles (RM) are a good system for investigating the structure of water in confined environment. In this work, the structure of water in RMs with different water content (w0) was studied using near infrared spectra measured at different temperature. Independent component analysis was used to extract the spectral features changing with the w0 and temperature. Three independent components representing the spectral features of trapped water, bound water, and core water were obtained. Furthermore, through the variation of the trapped water and bound water with temperature, an increase of the former and a reduction of the latter were found, revealing that the two water structures play an important role for the mobility of the RM’s shell.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"154 - 159"},"PeriodicalIF":1.8,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42732955","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：