scholarly journals Online Measurement of Urea Concentration in Spent Dialysate during Hemodialysis

2004 ◽  
Vol 50 (1) ◽  
pp. 175-181 ◽  
Author(s):  
Jonathon T Olesberg ◽  
Mark A Arnold ◽  
Michael J Flanigan
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Squares Regression ◽  
Mean Square ◽  
Least Squares Regression ◽  
Net Analyte Signal ◽  
Analyte Signal

Abstract Background: We describe online optical measurements of urea in the effluent dialysate line during regular hemodialysis treatment of several patients. Monitoring urea removal can provide valuable information about dialysis efficiency. Methods: Spectral measurements were performed with a Fourier-transform infrared spectrometer equipped with a flow-through cell. Spectra were recorded across the 5000–4000 cm−1 (2.0–2.5 μm) wavelength range at 1-min intervals. Savitzky–Golay filtering was used to remove baseline variations attributable to the temperature dependence of the water absorption spectrum. Urea concentrations were extracted from the filtered spectra by use of partial least-squares regression and the net analyte signal of urea. Results: Urea concentrations predicted by partial least-squares regression matched concentrations obtained from standard chemical assays with a root mean square error of 0.30 mmol/L (0.84 mg/dL urea nitrogen) over an observed concentration range of 0–11 mmol/L. The root mean square error obtained with the net analyte signal of urea was 0.43 mmol/L with a calibration based only on a set of pure-component spectra. The error decreased to 0.23 mmol/L when a slope and offset correction were used. Conclusions: Urea concentrations can be continuously monitored during hemodialysis by near-infrared spectroscopy. Calibrations based on the net analyte signal of urea are particularly appealing because they do not require a training step, as do statistical multivariate calibration procedures such as partial least-squares regression.

scholarly journals Online NIR Analysis and Prediction Model for Synthesis Process of Ethyl 2-Chloropropionate

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Wei Zhang ◽  
Hang Song ◽  
Jing Lu ◽  
Wen Liu ◽  
Lirong Nie ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Squares Regression ◽  
Synthesis Process ◽  
Calibration Model ◽  
Mean Square ◽  
Least Squares Regression

Online near-infrared spectroscopy was used as a process analysis technique in the synthesis of 2-chloropropionate for the first time. Then, the partial least squares regression (PLSR) quantitative model of the product solution concentration was established and optimized. Correlation coefficient (R2) of partial least squares regression (PLSR) calibration model was 0.9944, and the root mean square error of correction (RMSEC) was 0.018105 mol/L. These values of PLSR and RMSEC could prove that the quantitative calibration model had good performance. Moreover, the root mean square error of prediction (RMSEP) of validation set was 0.036429 mol/L. The results were very similar to those of offline gas chromatographic analysis, which could prove the method was valid.

scholarly journals Utilization of Picolinaldehyde-4-phenyl-3-thiosemicarbazone in Sodium Dodecylsulfate Micelles for the Spectrophotometric Determination of Iron, Vanadium, and Cobalt Following Partial Least-Squares Regression Analysis

2009 ◽  
Vol 92 (1) ◽  
pp. 248-256
Author(s):  
Aamna Balouch ◽  
Najma Memon ◽  
Muhammad I Bhanger ◽  
Muhammad Y Khuhawar
Keyword(s):  
Root Mean Square Error ◽  
Metal Ions ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sodium Dodecylsulfate ◽  
Mean Square ◽  
Least Squares Regression

Abstract Partial least-squares regression was applied for the simultaneous determination of iron, vanadium, and cobalt after complexation with picolinaldehyde-4-phenyl-3-thiosemicarbazone (PAPT) in the presence of anionic sodium dodecylsulfate (SDS) micelles. These 3 complexed metal ions exhibited overlapping spectra in the 390510 nm region with a maximum absorbance at 415 nm at pH 3.0 and enhanced absorbance in the presence of SDS. The data for the simultaneous determination of these metal ions were analyzed using a simple partial least-squares (SIMPLS) algorithm. Formation constants (log Kf) were found to be 4.65, 3.29, and 4.85 for PAPT complexes of Fe, V, and Co, respectively, and the detection limits for Fe, V, and Co were 0.013, 0.002, and 0.010 g/mL, respectively. Common anions and cations did not interfere with the proposed method. The method was validated by calculating root mean square error of cross-validation, root mean square error of calibration, and root mean square error of prediction and was applied to determine these 3 metal ions in real crude oil samples.

scholarly journals A useful quantitative model for determination of enantiomeric composition of racemate praziquantel by ultraviolet spectroscopy combined with partial least squares and its application to praziquantel tablets

2018 ◽  
Vol 11 (03) ◽  
pp. 1850011 ◽  
Author(s):  
Man Zhao ◽  
Ran Meng ◽  
Yifang Lu ◽  
Lingyun Hu ◽  
Na Sun ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Reference Value ◽  
Quantitative Model ◽  
Enantiomeric Composition ◽  
Mean Square ◽  
Validation Set

A simple and novel method has been proposed to determine the enantiomeric composition of racemate praziquantel (PZQ) by using the analysis of ultraviolet (UV) spectroscopy combined with partial least squares (PLS). This method does not rely on the use of expensive carbohydrates such as cyclodextrins, but on the use of inexpensive sucrose, which is equally effective as carbohydrate. PZQ has two enantiomers. Through measuring the slight difference in the UV spectral absorption of PZQ due to different interactions between its two enantiomers and sucrose, the enantiomeric composition was determined by a quantitative model based on PLS analysis. The model showed that the correlation coefficients of calibration set and validation set were 0.9971 and 0.9972, respectively. The root mean square error of calibration (RMSEC) and the root mean square error of prediction (RMSEP) were 0.0167 and 0.0129, respectively. Then, the independent data of PZQ tablets were also used to test how well the quantitative model of PLS predicted the enantiomeric composition. The ratio of S-PZQ in tablet was 0.492, determined by high-performance liquid chromatography as the reference value. Six solutions of the tablet samples were prepared, and the ratios of S-PZQ in tablet samples in the validation set were predicted by the PLS model. Their relative errors with the reference value were not more than 4%. Therefore, the established model could be accurate and employed to predict the enantiomeric compositions of PZQ tablets.

Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine

2017 ◽  
Vol 71 (11) ◽  
pp. 2427-2436 ◽  
Author(s):  
Mi Lei ◽  
Long Chen ◽  
Bisheng Huang ◽  
Keli Chen
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Mean Square ◽  
Characteristic Spectrum

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard–Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995–10664, 7991–6661, and 4326–3999 cm−1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42–33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.

scholarly journals NIR assignment of isopsoralen by 2D-COS technology and model application in Yunkang Oral Liquid

2015 ◽  
Vol 08 (06) ◽  
pp. 1550023 ◽  
Author(s):  
Yanling Pei ◽  
Zhisheng Wu ◽  
Xinyuan Shi ◽  
Xiaoning Pan ◽  
Yanfang Peng ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Oral Liquid ◽  
Spectral Assignment

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).

Near-infrared imaging for quantitative analysis of active component in counterfeit dimethomorph using partial least squares regression

2012 ◽  
Vol 66 (11) ◽  
Author(s):  
Yue Huang ◽  
Shun-Geng Min ◽  
Jin-Li Cao ◽  
Sheng-Feng Ye ◽  
Jia Duan
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Active Component ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Mean Square ◽  
Commercial Formulation

AbstractNear-infrared (NIR) imaging systems simultaneously record spectral and spatial information. Near-infrared imaging was applied to the identification of (E,Z)-4-(3-(4-chlorophenyl)-3-(3,4-dimethoxyphenyl)acryloyl)morpholine (dimethomorph) in both mixed samples and commercial formulation in this study. The distributions of technical dimethomorph and additive in the heterogeneous counterfeit product were obtained by the relationship imaging (RI) mode. Furthermore, a series of samples which consisted of different contents of uniformly distributed dimethomorph were prepared and three data cubes were generated for each content. The spectra extracted from these images were imported to establish the partial least squares model. The model’s evaluating indicators were: coefficient of determination (R 2) 99.42 %, root mean square error of calibration (RMSEC) 0.02612, root mean square error of cross-validation (RMSECV) 0.01693, RMSECVmean 0.03577, relative standard error of prediction (RSEP) 0.01999, and residual predictive deviation (RPD) 15.14. Relative error of prediction of the commercial formulation was 0.077, indicating the predicted value correlated with the real content. The chemical value reconstruction image of dimethomorph formulation products was calculated by a MATLAB program. NIR microscopy imaging here manifests its potential in identifying the active component in the counterfeit pesticide and quantifying the active component in its scanned image.

Application of Chemometrics for Spectral Resolving and Determination of Three Analgesics in Water Samples

2020 ◽  
Vol 103 (1) ◽  
pp. 257-264 ◽  
Author(s):  
Ali M Yehia ◽  
Heba T Elbalkiny ◽  
Safa’a M Riad ◽  
Yasser S Elsaharty
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Water Samples ◽  
Principal Component ◽  
Multivariate Curve Resolution ◽  
Mean Square ◽  
Qualitative And Quantitative

Abstract Background: Chemometrics is a discipline that allows the spectral resolution of drugs in a complicated matrix (e.g., environmental water samples) as an alternative to chromatographic methods. Objective: Three analgesics were traced in wastewater samples with simple and cost-effective multivariate approaches using spectrophotometric data. Methods and Results: Four chemometric approaches were applied for the simultaneous determination of diclofenac, paracetamol, and ibuprofen. Partial least squares (PLS), principal component regression (PCR), artificial neural networks (ANN), and multivariate curve resolution (MCR)–alternating least squares (ALS) were selected. The presented methods were compared and validated for their qualitative and quantitative analyses. Moreover, statistical comparison between the results obtained by the proposed methods and the official methods showed no significant differences. Conclusions: The proposed multivariate calibrations were accurate and specific for quantitative analysis of the studied components. MCR-ALS is the only method that has the capacity for both the quantitative and qualitative analysis of the studied drugs. Highlights: Four chemometric approaches were used for analysis of severally overlapped ternary mixture of three analgesics. The analytical performance of PCR, PLS, MCR-ALS, and ANN was compared and validated in terms of root mean square error of calibration (RMSEC), SE of prediction, and recoveries. ANN gave the highest predicted concentrations with the lowest RMSEC and root mean square error of prediction. MCR-ALS has the capacity for both qualitative and quantitative measurement. The methods have been effectively applied for real samples and compared to official methods.

scholarly journals Carotenoid determination in tomato juice using near infrared spectroscopy

2015 ◽  
Vol 29 (3) ◽  
pp. 275-282 ◽  
Author(s):  
Konrád Deák ◽  
Tamás Szigedi ◽  
Zoltán Pék ◽  
Piotr Baranowski ◽  
Lajos Helyes
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Cross Validation ◽  
Diode Array ◽  
Mean Square ◽  
Least Squares Regression ◽  
Carotene Content

AbstractA rapid non-destructive method for profiling tomato carotenoids was developed using NIR spectrometry. One hundred and twenty tomato samples were produced at the Experimental Farm of Szent István University in Gödöllő (Hungary). The sample preparation was based on homogenization. The mixed samples were scanned with a diode array Perten DA7200 NIR Analyzer (950-1650 nm) and analyzed by high performance liquid chromatography. The calibration was based on partial least squares regression with cross-validation. The performance of the final model was evaluated according to root mean square error of cross-validation. The results indicate that the main carotenoid components were accurately predicted. The correlation between the NIR measurement and the β-carotene content of tomatoes was adequately high [R2CV = 0.89; root mean square error of cross-validation = 0.174 μg g−1]. The NIR method was also performed for the determination of the all-trans lycopene content (R2CV = 0.75; root mean square error of cross-validation = 6.88 μg g−1). It can be concluded that the diode array NIR spectrometer has the potential to be used for the determination of the main carotenoids of tomatoes.

scholarly journals Quantitative Analysis ofPanax ginsengby FT-NIR Spectroscopy

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xin-fang Xu ◽  
Li-xing Nie ◽  
Li-li Pan ◽  
Bian Hao ◽  
Shao-xiong Yuan ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Mean Square ◽  
Least Squares Regression ◽  
Regression Methods

Near-infrared spectroscopy (NIRS), a rapid and efficient tool, was used to determine the total amount of nine ginsenosides inPanax ginseng. In the study, the regression models were established using multivariate regression methods with the results from conventional chemical analytical methods as reference values. The multivariate regression methods, partial least squares regression (PLSR) and principal component regression (PCR), were discussed and the PLSR was more suitable. Multiplicative scatter correction (MSC), second derivative, and Savitzky-Golay smoothing were utilized together for the spectral preprocessing. When evaluating the final model, factors such as correlation coefficient (R2) and the root mean square error of prediction (RMSEP) were considered. The final optimal results of PLSR model showed that root mean square error of prediction (RMSEP) and correlation coefficients (R2) in the calibration set were 0.159 and 0.963, respectively. The results demonstrated that the NIRS as a new method can be applied to the quality control ofGinseng Radix et Rhizoma.

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