Journal articles on the topic 'Interval PLS (iPLS)'
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1
Jiao, Long, Shan Bing, Xiaofeng Zhang, and Hua Li. "Interval partial least squares and moving window partial least squares in determining the enantiomeric composition of tryptophan by using UV-Vis spectroscopy." Journal of the Serbian Chemical Society 81, no. 2 (2016): 209–18. http://dx.doi.org/10.2298/jsc150227065j.
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The application of interval partial least squares (IPLS) and moving window partial least squares (MWPLS) to the enantiomeric analysis of tryptophan (Trp) was investigated. A UV-Vis spectroscopy method for determining the enantiomeric composition of Trp was developed. The calibration model was built by using partial least squares (PLS), IPLS and MWPLS respectively. Leave-one-out cross validation and external test validation were used to assess the prediction performance of the established models. The validation result demonstrates the established full-spectrum PLS model is impractical for quantifying the relationship between the spectral data and enantiomeric composition of L-Trp. On the contrary, the developed IPLS and MWPLS model are both practicable for modeling this relationship. For the IPLS model, the root mean square relative error (RMSRE) of external test validation and leave-one-out cross validation is 4.03 and 6.50 respectively. For the MWPLS model, the RMSRE of external test validation and leave-one-out cross validation is 2.93 and 4.73 respectively. Obviously, the prediction accuracy of the MWPLS model is higher than that of the IPLS model. It is demonstrated UV-Vis spectroscopy combined with MWPLS is a commendable method for determining the enantiomeric composition of Trp. MWPLS is superior to IPLS for selecting spectral region in UV-Vis spectroscopy analysis.
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Ju, Wei, Changhua Lu, Yujun Zhang, Weiwei Jiang, Jizhou Wang, Yi Bing Lu, and Feng Hong. "Characteristic wavelength selection of volatile organic compounds infrared spectra based on improved interval partial least squares." Journal of Innovative Optical Health Sciences 12, no. 02 (March 2019): 1950005. http://dx.doi.org/10.1142/s1793545819500056.
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As important components of air pollutant, volatile organic compounds (VOCs) can cause great harm to environment and human body. The concentration change of VOCs should be focused on in real-time environment monitoring system. In order to solve the problem of wavelength redundancy in full spectrum partial least squares (PLS) modeling for VOCs concentration analysis, a new method based on improved interval PLS (iPLS) integrated with Monte-Carlo sampling, called iPLS-MC method, was proposed to select optimal characteristic wavelengths of VOCs spectra. This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling. The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum. Different wavelength selection methods were built, respectively, on Fourier transform infrared (FTIR) spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory. When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times, the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10, which occupies only 0.22% of the full spectrum wavelengths. While the RMSECV and correlation coefficient (Rc) for ethylene are 0.2977 and 0.9999[Formula: see text]ppm, and those for ethanol gas are 0.2977 ppm and 0.9999. The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively, and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.
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Gao, Hong Zhi, Qi Peng Lu, and Fu Rong Huang. "Reagentless near-Infrared Determination of Cholesterol in Undiluted Human Serum Using Interval Partial Least Square." Advanced Materials Research 345 (September 2011): 128–33. http://dx.doi.org/10.4028/www.scientific.net/amr.345.128.
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In order to determination of cholesterol in human serum with no reagent using near-infrared (NIR) spectroscopy. Interval partial least square (iPLS) was proposed as an effective variable selection approach for multivariate calibration. For this purpose, an independent sample set was employed to evaluate the prediction ability of the resulting model. The spectrum was split into different interval. Then, the informative region of cholesterol (1688-1760nm), in which the PLS model has a low RMSEP with 0.241mmol/L and a high R with 0.975, is selected with 23 intervals. The results indicate that, the informative region of cholesterol can be obtained by iPLS and applied to design the simpler reagentless NIR instruments for inexpensive cholesterol measurement in future.
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Paschoal, Juliana, Fernando D. Barboza, and Ronei J. Poppi. "Analysis of Contaminants in Lubricant Oil by near Infrared Spectroscopy and Interval Partial Least-Squares." Journal of Near Infrared Spectroscopy 11, no. 3 (June 2003): 211–18. http://dx.doi.org/10.1255/jnirs.367.
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The feasibility of using near infrared (NIR) transmission spectroscopy for rapid and conclusive determination of contaminants in lubricant oil was investigated. The NIR spectrum in the region from 1300 to 1700 nm was used to predict gasoline and ethylene glycol concentrations present in lubricant oil. A graphically-oriented local multivariate calibration modelling procedure called interval partial least-squares (iPLS) was applied to find variable intervals that featured the lowest prediction error. When compared with the full spectrum PLS model, better results were obtained through the iPLS program. High correlation coefficients and low root mean square errors of cross-validation ( RMSECV) were obtained for gasoline ( R = 0.98, RMSECV = 0.38%, range = 0.2–8.0% w/w) and ethylene glycol determinations ( R = 0.97, RMSECV = 0.04%, range = 0.06 to 0.7% w/w), indicating that the proposed methodology can be used for contaminant determinations in lubricant oil.
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Yuan, Leiming, Xueping Fu, Xiaofeng Yang, Xiaojing Chen, Guangzao Huang, Xi Chen, Wen Shi, and Limin Li. "Non-Destructive Measurement of Egg’s Haugh Unit by Vis-NIR with iPLS-Lasso Selection." Foods 12, no. 1 (January 1, 2023): 184. http://dx.doi.org/10.3390/foods12010184.
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Egg freshness is of great importance to daily nutrition and food consumption. In this work, visible near-infrared (vis-NIR) spectroscopy combined with the sparsity of interval partial least square regression (iPLS) were carried out to measure the egg’s freshness by semi-transmittance spectral acquisition. A fiber spectrometer with a spectral range of 550-985 nm was embedded in the developed spectral scanner, which was designed with rich light irradiation mode from another two reflective surfaces. The semi-transmittance spectra were collected from the waist of eggs and monitored every two days. Haugh unit (HU) is a key indicator of egg’s freshness, and ranged 56–91 in 14 days after delivery. The profile of spectra was analyzed the relation to the changes of egg’s freshness. A series of iPLS models were constructed on the basis of spectral intervals at different divisions of the spectral region to predict the egg’s HU, and then the least absolute shrinkage and selection operator (Lasso) was used to sparse the number of iPLS member models acting as a role of model selection and fusion regression. By optimization of the number of spectral intervals in the range of 1 to 40, the 26th fusion model obtained the best performance with the minimum root mean of squared error of prediction (RMSEP) of 5.161, and performed the best among the general PLS model and other intervals-combined PLS models. This study provided a new, rapid, and reliable method for the non-destructive and in-site determination of egg’s freshness.
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Xiaobo, Zou, Li Yanxiao, and Zhao Jiewen. "Using Genetic Algorithm Interval Partial Least Squares Selection of the Optimal near Infrared Wavelength Regions for Determination of the Soluble Solids Content of “Fuji” Apple." Journal of Near Infrared Spectroscopy 15, no. 3 (June 2007): 153–59. http://dx.doi.org/10.1255/jnirs.732.
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A near infrared (NIR) spectroscopy acquisition device was developed in this study using an apple as the test sample. With this device, the apple was rolled while collecting the NIR spectra. The feasibility of using efficient selection of wavelength regions in Fourier transform NIR for a rapid and conclusive determination of the inner qualities of fruit such as soluble solids content (SSC) of apples was investigated. Graphically-oriented local multivariate calibration modelling procedures called genetic algorithm interval partial least-squares (GA-iPLS) were applied to select efficient spectral regions that provide the lowest prediction error, in comparison to the full-spectrum model. The optimal SSC predictions were obtained from a seven-factor model using five intervals among 40 intervals selected by GA-iPLS. In the determination, a root mean square error of prediction of 0.42 °Brix for SSC of apples was obtained. The result demonstrated that the new method is a very useful and effective method for developing high precision PLS models based on optimal wavelength regions.
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Chen, Tao, Zhi Li, Fang Rong Hu, and Wei Mo. "Quantitative Analysis of Mixtures Using Terahertz Time-Domain Spectroscopy and Different PLS Algorithms." Advanced Materials Research 804 (September 2013): 23–28. http://dx.doi.org/10.4028/www.scientific.net/amr.804.23.
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This paper attempted the feasibility to determine component concentrations in multicomponent mixtures with terahertz time-domain spectroscopy (THz-TDS) combined with different partial least-squares regression (PLS) algorithms. First, THz absorbance spectra for 75 ternary mixtures of anhydrous theophylline, lactose monohydrate and magnesium stearate were investigated using THz-TDS in the frequency range from 0.1 to 3.0 THz, then four different PLS methods, including interval PLS (iPLS), backward interval PLS (biPLS), synergy interval PLS (siPLS) and moving window PLS (mwPLS), were employed to perform quantitative analysis of anhydrous theophylline concentrations in ternary mixtures. The performance of mwPLS model is the best in contrast to other PLS models and full spectrum PLS. The optimal model was achieved with higher correlation coefficient for calibration (RC) of 0.9842, higher correlation coefficient for prediction (RP) of 0.9851, lower root mean square error of cross-validation (RMSECV) of 3.8241, and lower root mean square error of prediction (RMSEP) of 4.1540. Experimental results demonstrate that THz spectroscopy combined with PLS algorithms could be successfully applied as an effective nondestructive tool for the quantitative analysis of component concentrations in multicomponent mixtures, and mwPLS is an ideal method for reducing the complexity and improving the performance of the model.
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Öjelund, Henrik, Henrik Madsen, and Poul Thyregod. "Calibration with Empirically Weighted Mean Subset." Applied Spectroscopy 56, no. 7 (July 2002): 887–96. http://dx.doi.org/10.1366/000370202760171563.
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In this article a new calibration method called empirically weighted mean subset (EMS) is presented. The method is illustrated using spectral data. Using several near-infrared (NIR) benchmark data sets, EMS is compared to partial least-squares regression (PLS) and interval partial least-squares regression (iPLS). It is found that EMS improves on the prediction performance over PLS in terms of the mean squared errors and is more robust than iPLS. Furthermore, by investigating the estimated coefficient vector of EMS, knowledge about the important spectral regions can be gained. The EMS solution is obtained by calculating the weighted mean of all coefficient vectors for subsets of the same size. The weighting is proportional to SS−ωγ, where SSγ is the residual sum of squares from a linear regression with subset γ and ω is a weighting parameter estimated using cross-validation. This construction of the weighting implies that even if some coefficients will become numerically small, none will become exactly zero. An efficient algorithm has been implemented in MATLAB to calculate the EMS solution and the source code has been made available on the Internet.
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Zhang, Lu, Long Xue, Mu Hua Liu, and Jing Li. "Nondestructive Detection of Soluble Solids Content of Nanfeng Mandarin Orange Using VIS-NIR Spectroscopy." Advanced Materials Research 361-363 (October 2011): 1634–37. http://dx.doi.org/10.4028/www.scientific.net/amr.361-363.1634.
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This study demonstrated how VIS-NIR spectroscopy can be used in the quantitative, noninvasive probing of soluble solids content (SSC) of mandarin orange. Total 197 mandarin oranges were divided into calibration set (133 samples) and prediction set (64 samples). Multiple scatter correction (MSC) was used to preprocess the collected visible and near infrared (Vis-NIR) spectra (350-1800nm) of mandarin orange. Partial least square (PLS), interval partial least square (IPLS) and synergy interval partial least square (SIPLS) methods were applied for constructing predictive models of SSC. Experimental results showed that the optimal SIPLS model obtained with 10 PLS components and the optimal combinations of intervals were number 5,7,8,9. The correlation coefficient (r) between the predicted and actual SSC was 0.9265 and 0.8577 for calibration and prediction set, respectively. The root mean square error of calibration (RMSEC) and prediction (RMSEP) set was 0.4890 and 0.7113, respectively. In conclusion, the combination of Vis-NIR spectroscopy and SIPLS methods can be used to provide a technique of noninvasive, convenient and rapid analysis for SSC in fruit.
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Wang, Fei, Wei Jiang, Can Li, Hui Zhang, Lei Nie, Lian Li, Pei Wang, and Hengchang Zang. "Application of near infrared spectroscopy in monitoring the moisture content in freeze-drying process of human coagulation factor VIII." Journal of Innovative Optical Health Sciences 08, no. 06 (October 27, 2015): 1550034. http://dx.doi.org/10.1142/s1793545815500340.
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As an important process analysis tool, near infrared spectroscopy (NIRS) has been widely used in process monitoring. In the present work, the feasibility of NIRS for monitoring the moisture content of human coagulation factor VIII (FVIII) in freeze-drying process was investigated. A partial least squares regression (PLS-R) model for moisture content determination was built with 88 samples. Different pre-processing methods were explored, and the best method found was standard normal variate (SNV) transformation combined with 1st derivation with Savitzky–Golay (SG) 15 point smoothing. Then, four different variable selection methods, including uninformative variable elimination (UVE), interval partial least squares regression (iPLS), competitive adaptive reweighted sampling (CARS) and manual method, were compared for eliminating irrelevant variables, and iPLS was chosen as the best variable selection method. The correlation coefficient (R), correlation coefficient of calibration set (R cal ), correlation coefficient of validation set (R val ), root mean square errors of cross-validation (RMSECV) and root mean square errors of prediction (RMSEP) of PLS model were 0.9284, 0.9463, 0.8890, 0.4986% and 0.4514%, respectively. The results showed that the model for moisture content determination has a wide range, good linearity, accuracy and precision. The developed approach was demonstrated to be a potential for monitoring the moisture content of FVIII in freeze-drying process.
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Xia, Zhengyan, Chu Zhang, Haiyong Weng, Pengcheng Nie, and Yong He. "Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology." International Journal of Analytical Chemistry 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/6018769.
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Hyperspectral imaging (HSI) technology has increasingly been applied as an analytical tool in fields of agricultural, food, and Traditional Chinese Medicine over the past few years. The HSI spectrum of a sample is typically achieved by a spectroradiometer at hundreds of wavelengths. In recent years, considerable effort has been made towards identifying wavelengths (variables) that contribute useful information. Wavelengths selection is a critical step in data analysis for Raman, NIRS, or HSI spectroscopy. In this study, the performances of 10 different wavelength selection methods for the discrimination of Ophiopogon japonicus of different origin were compared. The wavelength selection algorithms tested include successive projections algorithm (SPA), loading weights (LW), regression coefficients (RC), uninformative variable elimination (UVE), UVE-SPA, competitive adaptive reweighted sampling (CARS), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), and genetic algorithms (GA-PLS). One linear technique (partial least squares-discriminant analysis) was established for the evaluation of identification. And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. The results indicate that wavelengths selection methods are tools to identify more concise and effective spectral data and play important roles in the multivariate analysis, which can be used for subsequent modeling analysis.
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Zhang, Min, Jiaming Guo, Chengying Ma, Guangjun Qiu, Junjie Ren, Fanguo Zeng, and Enli Lü. "An Effective Prediction Approach for Moisture Content of Tea Leaves Based on Discrete Wavelet Transforms and Bootstrap Soft Shrinkage Algorithm." Applied Sciences 10, no. 14 (July 14, 2020): 4839. http://dx.doi.org/10.3390/app10144839.
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The traditional method used to determine the moisture content of tea leaves is time consuming and destructive. To address this problem, an effective and non-destructive prediction method based on near-infrared spectroscopy (NIRS) is proposed in this paper. This new method combines discrete wavelet transforms (DWT) with the bootstrap soft shrinkage algorithm (BOSS). To eliminate uninformative or interfering variables, DWT is applied to remove the noise in the spectral data by decomposing the origin spectrum into six layers. BOSS is used to select informative variables by reducing the dimensions of the sub-layers’ reconstruction spectrum. After selecting the effective variables using DWT and BOSS, a prediction model based on partial least squares (PLS) is built. To validate effectiveness and stability of the prediction model, full-spectrum PLS, genetic algorithm PLS (GA-PLS), and interval PLS (iPLS) were compared with the proposed method. The experiment results illustrate that the proposed prediction model outperforms the other classical models considered in this study and shows promise for the prediction of the moisture content in Yinghong No. 9 tea leaves.
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Meng, Xianghe, Qin Ye, Xiaohua Nie, Qiuyue Pan, and Lianzhou Jiang. "Assessment of Interval PLS (iPLS) Calibration for the Determination of Peroxide Value in Edible Oils." Journal of the American Oil Chemists' Society 92, no. 10 (August 26, 2015): 1405–12. http://dx.doi.org/10.1007/s11746-015-2712-6.
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Sarrafi, Amir H. M., Elahe Konoz, and Maryam Ghiyasvand. "Simultaneous Detemination of Atorvastatin Calcium and Amlodipine Besylate by Spectrophotometry and Multivariate Calibration Methods in Pharmaceutical Formulations." E-Journal of Chemistry 8, no. 4 (2011): 1670–79. http://dx.doi.org/10.1155/2011/292346.
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Resolution of binary mixture of atorvastatin (ATV) and amlodipine (AML) with minimum sample pretreatment and without analyte separation has been successfully achieved using a rapid method based on partial least square analysis of UV–spectral data. Multivariate calibration modeling procedures, traditional partial least squares (PLS-2), interval partial least squares (iPLS) and synergy partial least squares (siPLS), were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. The simultaneous determination of both analytes was possible by PLS processing of sample absorbance between 220-425 nm. The correlation coefficients (R) and root mean squared error of cross validation (RMSECV) for ATV and AML in synthetic mixture were 0.9991, 0.9958 and 0.4538, 0.2411 in best siPLS models respectively. The optimized method has been used for determination of ATV and AML in amostatin commercial tablets. The proposed method are simple, fast, inexpensive and do not need any separation or preparation methods.
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Pei, Yanling, Zhisheng Wu, Xinyuan Shi, Xiaoning Pan, Yanfang Peng, and Yanjiang Qiao. "NIR assignment of isopsoralen by 2D-COS technology and model application in Yunkang Oral Liquid." Journal of Innovative Optical Health Sciences 08, no. 06 (October 27, 2015): 1550023. http://dx.doi.org/10.1142/s1793545815500236.
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Near infrared (NIR) assignment of Isopsoralen was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. Yunkang Oral Liquid was applied to study Isopsoralen, the characteristic bands by spectral assignment as well as the bands by interval partial least squares (iPLS) and synergy interval partial least squares (siPLS) were used to establish partial least squares (PLS) model. The coefficient of determination in calibration [Formula: see text] were 0.9987, 0.9970 and 0.9982. The coefficient of determination in cross validation [Formula: see text] were 0.9985, 0.9921 and 0.9982. The coefficient of determination in prediction [Formula: see text] were 0.9987, 0.9955 and 0.9988. The root mean square error of calibration (RMSEC) were 0.27, 0.40 and 0.31 ppm. The root mean square error of cross validation (RMSECV) were 0.30, 0.67 and 0.32 ppm. The root mean square error of prediction (RMSEP) were 0.23, 0.43 and 0.22 ppm. The residual predictive deviation (RPD) were 31.00, 16.58 and 32.41. It turned out that the characteristic bands by spectral assignment had the same results with the chemometrics methods in PLS model. It provided guidance for NIR spectral assignment of chemical compositions in Chinese Materia Medica (CMM).
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dos Santos, Glaucio Leboso Alemparte Abrantes, Marcos Renan Besen, Renato Herrig Furlanetto, Luís Guilherme Teixeira Crusiol, Marlon Rodrigues, Amanda Silveira Reis, Karym Mayara de Oliveira, et al. "Spectral Method for Liming Recommendation in Oxisol Based on the Prediction of Chemical Characteristics Using Interval Partial Least Squares Regression." Remote Sensing 14, no. 9 (April 20, 2022): 1972. http://dx.doi.org/10.3390/rs14091972.
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Thousands of chemical analyses are carried out annually with the aim of recommending soil correction; however, these analyses are expensive, destructive, time-consuming, and can be harmful to the environment. As an alternative to conventional analysis methods, diffuse reflectance spectroscopy has been proposed as an option for evaluating the chemical characteristics of soil. The selection of variables has also emerged as an alternative to improve the performance of PLSR (partial least squares regression), as it decreases the root mean square error (RMSE) and increases the accuracy of the models. However, few studies have used a previous selection of variables for the construction of PLSR models to estimate the chemical characteristics of soil. In this context, the hypothesis in this study was that it is possible to calculate the liming recommendation in Oxisol based on the chemical characteristics estimated by PLSR, with a previous selection of variables using iPLS (Interval PLS). The objective was to calculate the need for liming based on chemical characteristics estimated via iPLS selection and PLSR modeling of specific wavelengths of soil reflectance. The experimental area was treated with different application rates of limestone, with and without incorporation, and phosphogypsum was applied in additional treatments. Soil assessments were carried out 5, 12, 24, and 36 months after the application of the treatments, using six layers: 0.00–0.05, 0.05–0.10, 0.10–0.20, 0.20–0.30, 0.30–0.40 and 0.40–0.60 m. Samples were subjected to conventional laboratory analyses, and spectral readings (400–2500 nm) were obtained with a spectroradiometer. The spectral curves were subjected to the iPLS variable selection method to generate PLSR models of the chemical characteristics used to calculate the liming recommendation. The chemical characteristics of the soil, such as Ca2+, sum of bases (SB), effective cation exchange capacity (CTCe), cation exchange capacity (CTC), and base saturation (BS), could be estimated, with values of R2 ranging from 0.83 to 0.92 in the calibration and validation steps, and from 0.84 to 0.90 for the prediction step (in the fourth assessment). The liming recommendation calculated based on the chemical characteristics predicted from the PLSR models showed a strong correlation (r > 0.86) with the liming recommendation calculated by conventional laboratory techniques. The fourth soil assessment yielded the best correlation coefficient (r = 0.95).
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Silva, Fabiana E. B. da, Érico M. M. Flores, Graciele Parisotto, Edson I. Müller, and Marco F. Ferrão. "Green method by diffuse reflectance infrared spectroscopy and spectral region selection for the quantification of sulphamethoxazole and trimethoprim in pharmaceutical formulations." Anais da Academia Brasileira de Ciências 88, no. 1 (March 4, 2016): 1–15. http://dx.doi.org/10.1590/0001-3765201620150057.
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An alternative method for the quantification of sulphametoxazole (SMZ) and trimethoprim (TMP) using diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) and partial least square regression (PLS) was developed. Interval Partial Least Square (iPLS) and Synergy Partial Least Square (siPLS) were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. Fifteen commercial tablet formulations and forty-nine synthetic samples were used. The ranges of concentration considered were 400 to 900 mg g-1SMZ and 80 to 240 mg g-1 TMP. Spectral data were recorded between 600 and 4000 cm-1 with a 4 cm-1 resolution by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS). The proposed procedure was compared to high performance liquid chromatography (HPLC). The results obtained from the root mean square error of prediction (RMSEP), during the validation of the models for samples of sulphamethoxazole (SMZ) and trimethoprim (TMP) using siPLS, demonstrate that this approach is a valid technique for use in quantitative analysis of pharmaceutical formulations. The selected interval algorithm allowed building regression models with minor errors when compared to the full spectrum PLS model. A RMSEP of 13.03 mg g-1for SMZ and 4.88 mg g-1 for TMP was obtained after the selection the best spectral regions by siPLS.
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Rethfeldt, Nina, Pia Brinkmann, Daniel Riebe, Toralf Beitz, Nicole Köllner, Uwe Altenberger, and Hans-Gerd Löhmannsröben. "Detection of Rare Earth Elements in Minerals and Soils by Laser-Induced Breakdown Spectroscopy (LIBS) Using Interval PLS." Minerals 11, no. 12 (December 7, 2021): 1379. http://dx.doi.org/10.3390/min11121379.
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The numerous applications of rare earth elements (REE) has lead to a growing global demand and to the search for new REE deposits. One promising technique for exploration of these deposits is laser-induced breakdown spectroscopy (LIBS). Among a number of advantages of the technique is the possibility to perform on-site measurements without sample preparation. Since the exploration of a deposit is based on the analysis of various geological compartments of the surrounding area, REE-bearing rock and soil samples were analyzed in this work. The field samples are from three European REE deposits in Sweden and Norway. The focus is on the REE cerium, lanthanum, neodymium and yttrium. Two different approaches of data analysis were used for the evaluation. The first approach is univariate regression (UVR). While this approach was successful for the analysis of synthetic REE samples, the quantitative analysis of field samples from different sites was influenced by matrix effects. Principal component analysis (PCA) can be used to determine the origin of the samples from the three deposits. The second approach is based on multivariate regression methods, in particular interval PLS (iPLS) regression. In comparison to UVR, this method is better suited for the determination of REE contents in heterogeneous field samples.
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Tunny, Salma Sultana, Hanim Z. Amanah, Mohammad Akbar Faqeerzada, Collins Wakholi, Moon S. Kim, Insuck Baek, and Byoung-Kwan Cho. "Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables." Sensors 22, no. 5 (February 24, 2022): 1775. http://dx.doi.org/10.3390/s22051775.
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Ensuring the quality of fresh-cut vegetables is the greatest challenge for the food industry and is equally as important to consumers (and their health). Several investigations have proven the necessity of advanced technology for detecting foreign materials (FMs) in fresh-cut vegetables. In this study, the possibility of using near infrared spectral analysis as a potential technique was investigated to identify various types of FMs in seven common fresh-cut vegetables by selecting important wavebands. Various waveband selection methods, such as the weighted regression coefficient (WRC), variable importance in projection (VIP), sequential feature selection (SFS), successive projection algorithm (SPA), and interval PLS (iPLS), were used to investigate the optimal multispectral wavebands to classify the FMs and vegetables. The application of selected wavebands was further tested using NIR imaging, and the results showed good potentiality by identifying 99 out of 107 FMs. The results indicate the high applicability of the multispectral NIR imaging technique to detect FMs in fresh-cut vegetables for industrial application.
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Li, Lian, Baoyang Ding, Qi Yang, Shang Chen, Huaying Ren, Jinfeng Wang, Hengchang Zang, Fengshan Wang, and Lixuan Zang. "The relevance study of effective information between near infrared spectroscopy and chondroitin sulfate in ethanol precipitation process." Journal of Innovative Optical Health Sciences 07, no. 06 (October 21, 2014): 1450022. http://dx.doi.org/10.1142/s1793545814500229.
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Near infrared spectroscopy (NIRS) is based on molecular overtone and combination vibrations. It is difficult to assign specific features under complicated system. So it is necessary to find the relevance between NIRS and target compound. For this purpose, the chondroitin sulfate (CS) ethanol precipitation process was selected as the research model, and 90 samples of 5 different batches were collected and the content of CS was determined by modified carbazole method. The relevance between NIRS and CS was studied throughout optical pathlength, pretreatment methods and variables selection methods. In conclusion, the first derivative with Savitzky–Golay (SG) smoothing was selected as the best pretreatment, and the best spectral region was selected using interval partial least squares (iPLS) method under 1 mm optical cell. A multivariate calibration model was established using PLS algorithm for determining the content of CS, and the root mean square error of prediction (RMSEP) is 3.934 g⋅L-1. This method will have great potential in process analytical technology in the future.
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Włodarska, Katarzyna, Igor Khmelinskii, and Ewa Sikorska. "Evaluation of Quality Parameters of Apple Juices Using Near-Infrared Spectroscopy and Chemometrics." Journal of Spectroscopy 2018 (June 28, 2018): 1–8. http://dx.doi.org/10.1155/2018/5191283.
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Near-infrared (NIR) spectra were recorded for commercial apple juices. Analysis of these spectra using partial least squares (PLS) regression revealed quantitative relations between the spectra and quality- and taste-related properties of juices: soluble solids content (SSC), titratable acidity (TA), and the ratio of soluble solids content to titratable acidity (SSC/TA). Various spectral preprocessing methods were used for model optimization. The optimal spectral variables were chosen using the jack-knife-based method and different variants of the interval PLS (iPLS) method. The models were cross-validated and evaluated based on the determination coefficients (R2), root-mean-square error of cross-validation (RMSECV), and relative error (RE). The best model for the prediction of SSC (R2 = 0.881, RMSECV = 0.277 °Brix, and RE = 2.37%) was obtained for the first-derivative preprocessed spectra and jack-knife variable selection. The optimal model for TA (R2 = 0.761, RMSECV = 0.239 g/L, and RE = 4.55%) was obtained for smoothed spectra in the range of 6224–5350 cm−1. The best model for the SSC/TA (R2 = 0.843, RMSECV = 0.113, and RE = 5.04%) was obtained for the spectra without preprocessing in the range of 6224–5350 cm−1. The present results show the potential of the NIR spectroscopy for screening the important quality parameters of apple juices.
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Fu, Xianshu, Xiaoping Yu, Zihong Ye, and Haifeng Cui. "Analysis of Antioxidant Activity of Chinese Brown Rice by Fourier-Transformed Near Infrared Spectroscopy and Chemometrics." Journal of Chemistry 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/379327.
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This paper develops a rapid method using near infrared (NIR) spectroscopy for analyzing the antioxidant activity of brown rice as total phenol content (TPC) and radical scavenging activity by DPPH (2,2-diphenyl-2-picrylhydrazyl) expressed as gallic acid equivalent (GAE). Brown rice (n=121) collected from five producing areas was analyzed for TPC and DPPH by reference methods. The NIR reflectance spectra were measured with compact powders of samples and no treatment was used. Full-spectrum partial least squares (FS-PLS) and interval PLS (iPLS) were used as the regression methods to relate the antioxidant activity values to the NIR data. The spectral range of 4800–5600 cm−1plus 6000–6400 cm−1has the best correlation with TPC, while the range of 4400–5200 cm−1plus 6000–6400 cm−1is the most suitable for predicting DPPH. With standard normal variate (SNV) transformation and the selected wavelength ranges, the root mean squared error of prediction (RMSEP) is 0.062 mg GAE g−1for TPC and 0.141 mg GAE g−1for DPPH radical, respectively. The multiple correlation coefficients of predictions for TPC and DPPH are 0.962 and 0.974, respectively. The developed NIR method might have a potential application to quality control of brown rice in the domestic market.
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Moraes, Francisca, Rosangela Costa, Camilo Morais, Fábio Medeiros, Tássia Fernandes, Roberta Hoskin, and Kássio Lima. "Estimation of Ascorbic Acid in Intact Acerola (Malpighia emarginata DC) Fruit by NIRS and Chemometric Analysis." Horticulturae 5, no. 1 (January 24, 2019): 12. http://dx.doi.org/10.3390/horticulturae5010012.
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Acerola fruit is one of the richest natural sources of ascorbic acid ever known. As a consequence, acerola fruit and its products are demanded worldwide for the production of health supplements and the development of functional products. However, the analytical determination of ascorbic acid is time-consuming and costly. In this study, we show a non-destructive, reliable, and fast method to measure the ascorbic acid content in intact acerola, using near-infrared spectroscopy (NIRS) associated with multivariate calibration methods. Models using variable selection by means of interval partial least squares (iPLS) and a genetic algorithm (GA) were tested. The best model for ascorbic acid content, based on the prediction performance, was the GA-PLS method with second derivative spectral pretreatment, with a root mean square error of cross-validation equal to 22.9 mg/100 g, root mean square error of prediction equal to 46.3 mg/100 g, ratio of prediction to deviation equal to 8.0, determination coefficient for calibration equal to 0.98 and determination coefficient for prediction equal to 0.96. The current methodology, using NIR spectroscopy and chemometrics, is a promising and rapid tool to determine the ascorbic acid content of intact acerola fruit.
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Meenu, Maninder, Yaqian Zhang, Uma Kamboj, Shifeng Zhao, Lixia Cao, Ping He, and Baojun Xu. "Rapid Determination of β-Glucan Content of Hulled and Naked Oats Using near Infrared Spectroscopy Combined with Chemometrics." Foods 11, no. 1 (December 24, 2021): 43. http://dx.doi.org/10.3390/foods11010043.
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The quantification of β-glucan in oats is of immense importance for plant breeders and food scientists to develop plant varieties and food products with a high quantity of β-glucan. However, the chemical analysis of β-glucan is time consuming, destructive, and laborious. In this study, near-infrared (NIR) spectroscopy in conjunction with Chemometrics was employed for rapid and non-destructive prediction of β-glucan content in oats. The interval Partial Least Square (iPLS) along with correlation matrix plots were employed to analyze the NIR spectrum from 700–1300 nm, 1300–1900 nm, and 1900–2500 nm for the selection of important wavelengths for the prediction of β-glucan. The NIR spectral data were pre-treated using Savitzky Golay smoothening and normalization before employing partial least square regression (PLSR) analysis. The PLSR models were established based on the selection of wavelengths from PLS loading plots that present a high correlation with β-glucan content. It was observed that wavelength region 700–1300 nm is sufficient for the satisfactory prediction of β-glucan of hulled and naked oats with R2c of 0.789 and 0.677, respectively, and RMSE < 0.229.
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Dong, Jing, Junwu Tang, Guojun Wu, and Ruizhuo Li. "A Turbidity-Compensation Method for Nitrate Measurement Based on Ultraviolet Difference Spectroscopy." Molecules 28, no. 1 (December 28, 2022): 250. http://dx.doi.org/10.3390/molecules28010250.
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To solve the problem that turbidity in water has a significant effect on the spectra of nitrate and reduces the accuracy of nitrate detection, a turbidity-compensation method for nitrate measurement based on ultraviolet difference spectra is proposed. The effect of turbidity on the absorption spectra of nitrate was studied by using the difference spectra of the mixed solution and a nitrate solution. The results showed that the same turbidity had different effects on the absorbance of different concentrations of nitrate. The change in absorbance due to turbidity decreased with an increase in the nitrate concentration at wavelengths from 200 nm to 230 nm, although this change was constant when the wavelength was greater than 230 nm. On the basis of this characteristic, we combined the residual sum of squares (RSS) and interval partial least squares (iPLS) to select wavelengths of 230–240 nm as the optimal modeling interval. Furthermore, the turbidity-compensation model was established by the linear fitting of the difference spectra of various levels of turbidity. The absorption spectra of the nitrate were extracted by subtracting the turbidity-compensation curve from the original spectra of the water samples, and the nitrate concentration was calculated by using a partial least squares (PLS)-based nitrate-prediction model. The experimental results showed that the average relative error of the nitrate predictions was reduced by 50.33% to 1.33% by the proposed turbidity-compensation method. This indicated that this method can better correct the deviation in nitrate’s absorbance caused by turbidity and improve the accuracy of nitrate predictions.
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Germans, Sara, Guus L. Van Heck, Douglas R. Langbehn, and Paul P. G. Hodiamont. "The Iowa Personality Disorder Screen." European Journal of Psychological Assessment 26, no. 1 (January 2010): 11–18. http://dx.doi.org/10.1027/1015-5759/a000003.
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The internal consistency, test-retest reliability, and predictive validity of the Iowa Personality Disorder Screen (IPDS) as a screening instrument for personality disorders (PDs) were studied in 195 Dutch psychiatric outpatients, using the SCID-II as the gold standard. All patients completed a self-administered version of the IPDS. Internal consistency was moderate (0.64), and the test-retest reliability was good (0.87). According to the SCID-II, 97 patients (50%) had at least one personality disorder (PD). The IPDS correctly classified 81.0 percent of all participants in the category PD present/absent. The sensitivity and specificity were 77% and 88%, respectively. Positive and negative predictive values were 83% and 79%. Test-retest reliability after a 2-week interval was 0.87. These results are comparable with those reported in earlier studies with respect to the interview version of the IPDS and more promising than previously reported results obtained with a self-report version of the IPDS. Therefore, it is concluded that a self-report version of the IPDS may be useful as a screening measure for determining the presence/absence of PD in a population of psychiatric outpatients.
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He, Quan. "Phospholipase A 2 Metabolites Mediate Endothelin Stimulation of the Human Brain Natriuretic Peptide Promoter." Hypertension 36, suppl_1 (October 2000): 721. http://dx.doi.org/10.1161/hyp.36.suppl_1.721-b.
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P155 Brain natriuretic peptide (BNP) gene expression accompanies cardiac hypertrophy and heart failure. The vasoconstrictor endothelin-1 (ET)may be involved in the development of these diseases. ET has also been shown to activate phospholipase A 2 (PLA 2 ). Thus we studied whether ET and PLA 2 metabolites regulate BNP gene expression. The hBNP promoter (-1818 to + 100) coupled to a luciferase reporter gene was transferred into neonatal ventricular myocytes (NVM),and luciferase activity was measured as an index of promoter activity. ET (10 -7 M)induced BNP mRNA in NVM as assessed by Northern blot. It also stimulated the hBNP promoter 4-fold vs control, an effect completely inhibited by actinomycin D. To test the involvement of different PLA 2 isoforms, transfected cells were treated with the Ca ++ -independent PLA 2 (iPLA 2 )inhibitor bromoenol lactone (BEL), the cytosolic PLA 2 inhibitor methyl arachidonyl fluorophosphonate, or the secretory PLA 2 inhibitor ONO-RS-082 prior to stimulation with ET. Only the iPLA 2 inhibitor BEL prevented ET-stimulated hBNP promoter activity. The PLA 2 metabolite lysophosphatidic acid (LPA) also activated the hBNP promoter (2.2-fold; n = 3), but lysophosphatidylcholine did not. To test whether arachidonic acid metabolites are involved in ET’s effect, cells were pretreated with either a lipoxygenase (LO), cyclooxygenase, or p450 monooxygenase inhibitor. Only the LO inhibitor baicalein prevented ET stimulation of the hBNP promoter. Finally, we studied the involvement of cis elements in ET-stimulated hBNP promoter activity. Deletion of BNP promoter sequences from -1818 to -408 and from -408 to -40 reduced ET’s effect by 54% and 78%, respectively. Moreover, ET-stimulated luciferase activity was reduced by 53% when the GATA element (at position -85 relative to the start site of transcription) was mutated. These data suggest that: 1) ET activates the hBNP promoter through a transcriptional mechanism; 2) LPA, perhaps generated by a BEL-sensitive iPLA 2 , is involved in ET’s effect; 3) a LO pathway may also mediate ET signaling; and 4) ET regulation of the hBNP promoter targets both distal and proximal cis elements, including GATA.
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Liao, Jun, Yihua Huang, Jiadi Gan, Lanlan Pang, Wael A. S. Ali, Yunpeng Yang, Likun Chen, Li Zhang, and Wenfeng Fang. "Epidermal growth factor receptor-Mutated Non-small-cell Lung Cancer with Intracranial Progressions and Stable Extracranial Diseases Benefit from Osimertinib Regardless of T790M Mutational Status." Cancer Control 29 (January 2022): 107327482210813. http://dx.doi.org/10.1177/10732748221081360.
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Objectives Osimertinib has exhibited promising central nervous system (CNS) efficacy in Epidermal growth factor receptor (EGFR)-mutated advanced non-small-cell lung cancer (NSCLC) patients. In real-world clinical practice, patients would turn to plasma genotyping or take osimertinib blindly after CNS progression on previous tyrosine kinase inhibitors (TKIs). However, the efficacy of osimertinib in those patients according to their T790M mutational status has not been explored. Materials and methods Twenty-five patients who received osimertinib due to intracranial progressions with stable extracranial diseases after early-generation EGFR-TKI treatment were collected from 1032 EGFR-mutated NSCLC. Plasma samples were analyzed for EGFR mutations using next-generation sequencing (NGS) or polymerase chain reaction (PCR). Results Among the 25 patients, 17 patients took plasma genotyping before osimertinib treatment with 8 patients EGFR T790M mutation-positive and the rest started osimertinib blindly. The median progression-free survival (PFS) was 8.0 months (95% confidence interval [CI]: 6.12-9.94) and median intracranial PFS (iPFS) was 14.4 months (95% CI: 7.27-21.59) for the total population. No statistical difference was found in PFS and iPFS among patients with different EGFR T790M mutational statuses. Intracranial disease control rate (DCR) was 100.0% for 14 patients with evaluable intracranial lesions despite different T790M mutational statuses. DCR for extracranial lesions and overall lesions were 100.0%, 66.7%, and 87.5% for patients with T790M, no T790M, and unknown T790M mutational status, respectively. Conclusion For EGFR-mutated NSCLC patients with only intracranial progressions after previous TKI treatments, osimertinib is a promising treatment option regardless of T790M mutational status from plasma genotyping.
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Kushiyev, Rahman, Celal Tunçer, İsmail Oğuz Özdemir, İsmail Erper, Ruslan Kalendar, Mehtap Alkan, and Göksel Özer. "Molecular Characterization of Native Entomopathogenic Fungi from Ambrosia Beetles in Hazelnut Orchards of Turkey and Evaluation of Their In Vitro Efficacy." Insects 13, no. 9 (September 11, 2022): 824. http://dx.doi.org/10.3390/insects13090824.
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Ambrosia beetles, Anisandrus dispar Fabricius, Xylosandrus germanus Blandford, and Xyleborinus saxesenii Ratzeburg (Coleoptera: Curculionidae: Scolytinae) are among the most significant hazelnut pests in Turkey. The control of these pests is difficult and expensive due to their biology. The present study aimed to isolate entomopathogenic fungi (EPF) from A. dispar, X. germanus, and X. saxesenii individuals that were obtained from the main hazelnut production areas of Turkey, characterize the EPF isolates using internal transcribed spacer (ITS)-DNA sequencing and iPBS profiling, and determine the efficacy of the isolates against A. dispar, X. germanus, and X. saxesenii under laboratory conditions. Phylogenetic analyses based on ITS revealed that the 47 native isolates were Beauveria bassiana (11), B. pseudobassiana (8), Cordyceps fumosorosea (6), Cordyceps farinosa (1), Akanthomyces lecanii (13), Purpureocillium lilacinum (3), Clonostachys rosea (2) and Metarhizium anisopliae (3). For the first time, the primer binding site (PBS) marker system, based on retrotransposons, was used to discriminate successfully among the EPF species. Some isolates of B. bassiana, B. pseudobassiana, C. fumosorosea, A. lecanii, and M. anisopliae caused 100% mortality of the beetle species within 7 to 9 days. The findings of this study indicated that some isolated entomopathogenic fungi provide an essential basis for the development of bioproducts, as well as a promising alternative method for controlling these ambrosia beetles.
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Erper, Ismail, Goksel Ozer, Ruslan Kalendar, Sirin Avci, Elif Yildirim, Mehtap Alkan, and Muharrem Turkkan. "Genetic Diversity and Pathogenicity of Rhizoctonia spp. Isolates Associated with Red Cabbage in Samsun (Turkey)." Journal of Fungi 7, no. 3 (March 21, 2021): 234. http://dx.doi.org/10.3390/jof7030234.
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A total of 132 Rhizoctonia isolates were recovered from red cabbage plants with root rot and wirestem symptoms in the province of Samsun (Turkey) between 2018 and 2019. Based on the sequence analysis of the internal transcribed spacer (ITS) region located between the 18S and 28S ribosomal RNA genes and including nuclear staining, these 124 isolates were assigned to multinucleate Rhizoctonia solani, and eight were binucleate Rhizoctonia. The most prevalent anastomosis group (AG) was AG 4 (84%), which was subdivided into AG 4 HG-I (81%) and AG 4 HG-III (3%), followed by AG 5 (10%) and AG-A (6%), respectively. The unweighted pair group method phylogenetic tree resulting from the data of 68 isolates with the inter-PBS amplification DNA profiling method based on interspersed retrotransposon element sequences confirmed the differentiation of AGs with a higher resolution. In the greenhouse experiment with representative isolates (n = 24) from AGs on red cabbage (cv. Rondale), the disease severity index was between 3.33 and 4.0 for multinucleate AG isolates and ranged from 2.5 to 3.17 for AG-A isolates. In the pathogenicity assay of six red cabbage cultivars, one isolate for each AG was tested using a similar method, and all cultivars were susceptible to AG 4 HG-I and AG 4 HG-III isolates. Redriver and Remale were moderately susceptible, while Rescue, Travero, Integro, and Rondale were susceptible to the AG 5 isolate. The results indicate that the most prevalent and aggressive AGs of Rhizoctonia are devastating pathogens to red cabbage, which means that rotation with nonhost-crops for these AGs may be the most effective control strategy. This is the first comprehensive study of Rhizoctonia isolates in red cabbage using a molecular approach to assess genetic diversity using iPBS-amplified DNA profiling.
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Valcárcel, David, Guillermo Sanz, Margarita Ortega, Benet Nomdedeu, Elisa Luño, María Diez-Campelo, M. Teresa Ardanaz, et al. "Identification of Poor Risk Patients in Low and Intermediate-1 (Int-1) IPSS MDS with the New Ipssr Index and Comparison with Other Prognostic Indexes. A Study by the Spanish Group of MDS (GESMD)." Blood 120, no. 21 (November 16, 2012): 702. http://dx.doi.org/10.1182/blood.v120.21.702.702.
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Abstract Abstract 702 Despite that low and intermediate-1 (int-1) IPSS groups are commonly considered as low risk diseases with a median overall survival exceeding 60 months, some of these patients will evolve as higher risk myelodysplastic syndrome (MDS). Recently several new prognosis indexes (PI) have been proposed: The new IPSSr, WPSSr, MD Anderson for lower risk patients (MDA) Index, and the Spanish Group of MDS (GESMD) proposal that considers as high risk those patients with int-1 IPSS and at least one of the following: platelets <30×109/L, granulocytes <0.5×109/L, poor or very poor-risk karyotype or the presence of bone marrow (BM) fibrosis. The aim of the study was to compare the four PI and to analyze which of them was the best to identify patients with the poorest risk (defined as those with a median overall survival (OS) lower than 30 months) and to segregate different risk groups in a population of lower risk MDS patients. Indexes were compared using the Akaike analysis methodology. A total of 2410 patients from the Spanish registry of MDS with low or int-1 IPSS were included. Median age was 74 years (42.6% female). The IPSS value was of: 0, 0.5 and 1 in 1314, 761 and 335 patients, respectively. The four poor risk variables defined by the GESMD confirmed its adverse predictive value for OS: granulocytes <0.5×109/L (n=101, P<0.001), platelets <30×109/L (n=94, P<0.001), poor or very poor risk karyotype (n=35, P=0.007), and BM fibrosis (n=109 of 698 evaluable patients, P<0.001). The presence of at least one of these was associated with adverse prognosis in the int-1 group but not in the low IPSS risk group, thus only the former was considered as high risk. The distribution of patients across the four PI is detailed in the Table. These new PI identified between 16.9% and 46% of patients having a median OS of around 30 months within the int-1 patients (wide line in the table), but none of the PIs could identify such a poor prognosis patients in the low IPSS group. The PI that identified the highest number of patients with shorter OS was the new IPSSr, while MDA IP was the most discriminative in the Akaike analysis. In conclusion, IPSS is not discriminative enough in the int-1 group. In contrast, the application of the new PI can be employed to better identify poor prognosis patients within the int-1 group who could benefit from a high-risk approach. Table. Overall survival and AML evolution according to the different prognostic index. PROGNOSIS INDEX (AIC for the whole population/and for the Int-1) populations) PROGNOSIS GROUP IPSS LOW (N=1314) OS: 87.78 m (95% CI:74.5-101.0) AML EVOLUTION (3 years): 9.1% (95% CI: 6.9-11.3%) IPSS INT-1 (N= 1096) OS: 44.2 m (95% CI:39.1-49.3) AML EVOLUTION (3 years): 26.9% (95% CI: 23-30.8%) N (%) Overall Survival Median (95% CI) months AML evolution (3 years) % (95% CI) N (%) Overall Survival Median (95% CI) months AML evolution (3 years) % (95% CI) GESMD (12566.6/6425.9) LOW 1314 (100) 860 (78.5) 48.1 (40.9-55.3)* 25.1 (20.7-29.5)** HIGH 0 (0) 236 (21.5) 32.7 (39.1-49.3)* 34.3 (24.5-44.1)** MD. Anderson (12381.4/6357.2) LOW 508 (39.3) 130.3 (104.6-157.0)* 9% (5.4-12.6)! 109 (9.9) 115.2 (83.8-146.6)* 15.7 (6.5-24.9)* INT 781 (59.4) 69.7 (62.4-77.1)* 8.9% (6.9-11.9)! 653 (59.6) 51.3 (44.2-58.3)* 23.3 (18.3-28.3)* HIGH 25 (1.9) 58.4* (25.4-91.5)* ——–——–— 334 (30.5) 24.1 (19.3-28.9)* 39.9 (31.3-48.5)* IPSS-R (12409.9/6369.6) VERY LOW 690 (52.5) 118.8 (105.7-131.7)* 6.4% (4.2-8.6)*** 79 (7.2) 113.7 (39.9-187.4)* 17.8 (4.6-31)* LOW 602 (45.8) 65.9 (57.6-74.2)* 11.6% (7.8-15.4)*** 505 (46.1) 60.3 (53.3-67.2)* 18.2 (13.4-23)* INT 22 (1.7) 58.9 (25.2-92.7)* 26% (2-50)*** 416 (38) 30.5 (26.1-34.8)* 38.6 (30-47.2)* HIGH 0 (0) 95 (8.7) 21.2 (16.5-25.9)* 48.5 (32.5-64.5)* VERY HIGH 0 (0) 1 (0.1) WPSS-R (12477.4/6414.7) VERY LOW 517 (39.3) 115.2 (103.0-127.4)* 6.5 (3.7-9.3)$ 76 (6.9) 56.5 (38.2-74.9)* 22.8 (10.6-35)* LOW 524 (39.9) 78.5 (66.7-90.3)* 12.1 (7.7-15.5)$ 289 (26.4) 61.3 (48.3-74.2)* 19.2 (12.4-25.6)* INT 61 (4.6) 46.0 (30.8-61.1)* 13.7 (3.1-24.3)$ 386 (5.2) 42.5 (32.8-52.2)* 27.8 (20.8-34.8)* HIGH 3 (0.2) 185 (16.9) 24.11 (19.4-28.8)* 49.3 (35.7-62.9)* VERY HIGH 0 (0) 4 (0.4) NOT EVAL 209 (15.9) 87.8 (74.6-101.3)* 7.2 (3-11.4)$ 156 (14.2) 48 (30.8-65.2) 18.3 (9.1-27.5)* AIC: Akaike Information Criteria. Int: Intermediate, Not Eval: Not evaluable, CI: Confidence interval. * P<0.001; ** P=0.02; *** P=0.04; ! !P=0.7 $P=0.1 Figure. Actuarial curves of overall survival according to the different PI. Figure. Actuarial curves of overall survival according to the different PI. Disclosures: No relevant conflicts of interest to declare.
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Zhao, Na, Zhisheng Wu, Chunying Wu, Shuyu Wang, and Xueyan Zhan. "Performance evaluation of variable selection methods coupled with partial least squares regression to determine the target component in solid samples." Journal of Near Infrared Spectroscopy, May 12, 2022, 096703352210972. http://dx.doi.org/10.1177/09670335221097236.
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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.
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Matese, Alessandro, Salvatore Filippo Di Gennaro, Giorgia Orlandi, Matteo Gatti, and Stefano Poni. "Assessing Grapevine Biophysical Parameters From Unmanned Aerial Vehicles Hyperspectral Imagery." Frontiers in Plant Science 13 (June 2, 2022). http://dx.doi.org/10.3389/fpls.2022.898722.
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Over the last 50 years, many approaches for extracting plant key parameters from remotely sensed data have been developed, especially in the last decade with the spread of unmanned aerial vehicles (UAVs) in agriculture. Multispectral sensors are very useful for the elaboration of common vegetation indices (VIs), however, the spectral accuracy and range may not be enough. In this scenario, hyperspectral (HS) technologies are gaining particular attention thanks to the highest spectral resolution, which allows deep characterization of vegetative/soil response. Literature presents few papers encompassing UAV-based HS applications in vineyard, a challenging conditions respect to other crops due to high presence of bare soil, grass cover, shadows and high heterogeneity canopy structure with different leaf inclination. The purpose of this paper is to present the first contribution combining traditional and multivariate HS data elaboration techniques, supported by strong ground truthing of vine ecophysiological, vegetative and productive variables. Firstly the research describes the UAV image acquisition and processing workflow to generate a 50 bands HS orthomosaic of a study vineyard. Subsequently, the spectral data extracted from 60 sample vines were elaborated both investigating the relationship between traditional narrowband VIs and grapevine traits. Then, multivariate calibration models were built using a double approach based on Partial Least Square (PLS) regression and interval-PLS (iPLS), to evaluate the correlation performance between the biophysical parameters and HS imagery using the whole spectral range and a selection of more relevant bands applying a variable selection algorithm, respectively. All techniques (VIs, PLS and iPLS) provided satisfactory correlation performances for the ecophysiological (R2 = 0.65), productive (R2 = 0.48), and qualitative (R2 = 0.63) grape parameters. The novelty of this work is represented by the first assessment of a UAV HS dataset with the expression of the entire vine ecosystem, from the physiological and vegetative state to grapes production and quality, using narrowband VIs and multivariate PLS regressions. A correct non-destructive estimation of key parameters in vineyard, above all physiological parameters which must be measured in a short time as they are extremely influenced by the variability of environmental conditions during the day, represents a powerful tool to support the winegrower in vineyard management.
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Li, Ze Ying, Xin Kang Li, Yuan Lin, Na Feng, Xiang-Zhi Zhang, Qing-Lin Li, and Bao Qiong Li. "A comparative study of three chemometrics methods combined with excitation–emission matrix fluorescence for quantification of the bioactive compounds aesculin and aesculetin in Cortex Fraxini." Frontiers in Chemistry 10 (September 7, 2022). http://dx.doi.org/10.3389/fchem.2022.984010.
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Cortex Fraxini is an important traditional Chinese herbal medicine with various medical functions. Aesculin and aesculetin are the main effective components of Cortex Fraxini. The fluorescence signals of the two compounds have a high degree of overlap with each other, making quantitative analysis difficult with conventional analytical methods. In the present study, different chemometrics methods, including lasso regression (LAR), interval partial least squares (iPLS), and multidimensional partial least squares (N-PLS) methods, were employed and combined with excitation–emission matrix (EEM) fluorescence for the purpose of accurate quantification of aesculin and aesculetin in Cortex Fraxini samples. The most satisfactory results were obtained by using the N-PLS method based on the EEM spectra without scatterings, with correlation coefficient of calibration and prediction values higher than 0.9972 and 0.9962, respectively, root mean squared errors for calibration and prediction values lower than 0.0304 and 0.1165, respectively, and recovery values in the range of 83.32%–104.62%. The obtained credible models indicated that the N-PLS method combined with EEM spectra has the advantages of being green, low cost, and accurate and it is a good strategy for the determination of active compounds in complex samples. To further confirm the accuracy of the obtained results, the same samples were analyzed by the recognized ultra-performance liquid chromatography method.
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López-Maestresalas, Ainara, Carlos Lopez-Molina, Gil Alfonso Oliva-Lobo, Carmen Jarén, Jose Ignacio Ruiz de Galarreta, Carlos Miguel Peraza-Alemán, and Silvia Arazuri. "Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude." Frontiers in Nutrition 9 (October 17, 2022). http://dx.doi.org/10.3389/fnut.2022.999877.
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The potato (Solanum tuberosum L.) is the world’s fifth most important staple food with high socioeconomic relevance. Several potato cultivars obtained by selection and crossbreeding are currently on the market. This diversity causes tubers to exhibit different behaviors depending on the processing to which they are subjected. Therefore, it is interesting to identify cultivars with specific characteristics that best suit consumer preferences. In this work, we present a method to classify potatoes according to their cooking or frying as crisps aptitude using NIR hyperspectral imaging (HIS) combined with a Partial Least Squares Discriminant Analysis (PLS-DA). Two classification approaches were used in this study. First, a classification model using the mean spectra of a dataset composed of 80 tubers belonging to 10 different cultivars. Then, a pixel-wise classification using all the pixels of each sample of a small subset of samples comprised of 30 tubers. Hyperspectral images were acquired using fresh-cut potato slices as sample material placed on a mobile platform of a hyperspectral system in the NIR range from 900 to 1,700 nm. After image processing, PLS-DA models were built using different pre-processing combinations. Excellent accuracy rates were obtained for the models developed using the mean spectra of all samples with 90% of tubers correctly classified in the external dataset. Pixel-wise classification models achieved lower accuracy rates between 66.62 and 71.97% in the external validation datasets. Moreover, a forward interval PLS (iPLS) method was used to build pixel-wise PLS-DA models reaching accuracies above 80 and 71% in cross-validation and external validation datasets, respectively. Best classification result was obtained using a subset of 100 wavelengths (20 intervals) with 71.86% of pixels correctly classified in the validation dataset. Classification maps were generated showing that false negative pixels were mainly located at the edges of the fresh-cut slices while false positive were principally distributed at the central pith, which has singular characteristics.
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Babilas, Philipp. "Use of intense pulsed light sources in dermatology: Update 2012." Photonics & Lasers in Medicine 1, no. 1 (January 1, 2012). http://dx.doi.org/10.1515/plm-2011-0006.
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AbstractIntense pulsed light sources (IPLs) consist of flash lamps with bandpass filters and emit incoherent polychromatic pulsed light of a high intensity and determined wavelength spectrum, fluence, and pulse duration. The combination of prescribed wavelengths, fluencies, pulse durations, and pulse intervals facilitates the treatment of a wide spectrum of skin conditions. Hereby, IPLs follow the basic principle of a more or less selective thermal damage of the target. This review discusses the current literature on IPLs with regard to the treatment of unwanted hair growth, vascular lesions, pigmented lesions, and as a light source for photodynamic therapy and skin rejuvenation. It also summarizes the physics of IPLs and provides guidance for the practical use of IPLs.
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Sakuma, Rika, Miku Kobayashi, Rui Kobashi, Mako Onishi, Mitsuyo Maeda, Yosky Kataoka, and Susumu Imaoka. "Brain pericytes acquire stemness via the Nrf2-dependent antioxidant system." Stem Cells, March 30, 2022. http://dx.doi.org/10.1093/stmcls/sxac024.
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Abstract Pericytes (PCs) are a mural support cell population elongated at intervals along the walls of capillaries. Recent studies reported that pericytes are multipotent cells that are activated in response to tissue injury and contribute to the regenerative process. Using a C.B-17 mouse model of ischemic stroke, it has been proposed that normal brain pericytes (nPCs) are converted to ischemic pericytes (iPCs), some of which function as multipotent stem cells. Furthermore, oxygen-glucose deprivation (OGD) promoted mesenchymal-epithelial transition in nPCs; however, nestin was not induced under OGD conditions. Therefore, further studies are needed to elucidate the PC reprogramming phenomenon. We herein isolated nPCs from the cortex of C.B-17 mice, and compared the traits of iPCs and nPCs. The results obtained showed that nPCs and iPCs shared common pericytic markers. Furthermore, intercellular levels of reactive oxygen species and the nuclear accumulation of nuclear factor erythroid-2-related factor 2 (Nrf2), a key player in antioxidant defenses, were higher in iPCs than in nPCs. OGD/Reoxygenation and a treatment with tBHQ, a Nrf2 inducer, increased nestin levels in nPCs. Moreover, epithelial marker levels, including nestin, Sox2, and CDH1 (E-cadherin) mRNAs, were elevated in Nrf2-overexpressing PCs, which formed neurosphere-like cell clusters that differentiated into Tuj1-positive neurons. The present results demonstrate that oxidative stress and Nrf2 are required for the generation of stem cells after stroke, and will contribute to the development of novel therapeutic strategies for ischemic stroke.
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Xing, Puyuan, Xuezhi Hao, Xin Zhang, and Junling Li. "Efficacy and safety of brigatinib in ALK-positive non-small cell lung cancer treatment: A systematic review and meta-analysis." Frontiers in Oncology 12 (November 3, 2022). http://dx.doi.org/10.3389/fonc.2022.920709.
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BackgroundBrigatinib is a central nervous system-active second-generation anaplastic lymphoma kinase (ALK) inhibitor that targets a broad range of ALK rearrangements in patients with non-small cell lung cancer (NSCLC). The current study aimed to analyze the pooled effects and adverse events of brigatinib in patients with ALK-positive NSCLC.MethodsThe pooled estimates and 95% confidence intervals (CI) were calculated with DerSimonian-Laird method and the random effect model.ResultsThe pooled objective response rate (ORR) and disease control rate (DCR) of brigatinib were 64% (95% CI 45%-83%) and 88% (95% CI 80%-96%), respectively. The pooled mPFS was 10.52 months (95% CI 7.66-13.37). In the subgroup analyses by treatment line, the highest mPFS was reached in first-line treatment (24.00 months, 95% CI 18.40-43.20), followed by post-crizotinib second-line treatment (mPFS=16.26 months, 95% CI 12.87-19.65), and second-line with any prior ALK tyrosine kinase inhibitors (mPFS=12.96 months, 95% CI 11.14-14.78). Among patients with any baseline brain metastases, the pooled intracranial ORR (iORR) was estimated as 54% (95% CI 35%-73%) for any treatment line, and 60% (95% CI 39%-81%) for first-line treatment. Intracranial PFS (iPFS) reached 19.26 months (95% CI 14.82-23.70) in patients with any baseline brain metastases. Creatine phosphokinase (CPK) increased (44%, 95% CI 26%-63%), diarrhea (37%, 95% CI 27%-48%), and nausea (28%, 95% CI 17%-39%) of any grade were the most common adverse events.ConclusionBrigatinib is effective in the treatment of patients with ALK-positive NSCLC, particularly showing robust intracranial PFS. Brigatinib used as first-line treatment yielded superior PFS compared with brigatinib used as other treatment lines. These results suggested a benefit of using brigatinib earlier in the patient’s management. All adverse events are manageable, with CPK increased and gastrointestinal reactions found to be the most common types.Systematic Review Registrationhttps://inplasy.com/inplasy-2022-3-0142/, identifier (INPLASY202230141).