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

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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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

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545818500116 ◽

2018 ◽

Vol 11 (03) ◽

pp. 1850011 ◽

Cited By ~ 1

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.

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

Applied Spectroscopy ◽

10.1177/0003702817727016 ◽

2017 ◽

Vol 71 (11) ◽

pp. 2427-2436 ◽

Cited By ~ 5

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.

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

International Journal of Analytical Chemistry ◽

10.1155/2015/145315 ◽

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.

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Online Measurement of Urea Concentration in Spent Dialysate during Hemodialysis

Clinical Chemistry ◽

10.1373/clinchem.2003.025569 ◽

2004 ◽

Vol 50 (1) ◽

pp. 175-181 ◽

Cited By ~ 32

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.

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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

Journal of AOAC International ◽

10.1093/jaoac/92.1.248 ◽

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.

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

Chemical Papers ◽

10.2478/s11696-012-0212-9 ◽

2012 ◽

Vol 66 (11) ◽

Cited By ~ 2

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.

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Quantitative Analysis of Forsythiae fructus by Near Infrared Spectroscopy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.807-809.1967 ◽

2013 ◽

Vol 807-809 ◽

pp. 1967-1971

Author(s):

Yan Bai ◽

Xiao Yan Duan ◽

Hai Yan Gong ◽

Cai Xia Xie ◽

Zhi Hong Chen ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Cross Validation ◽

Ethanol Extract ◽

Mean Square ◽

Validation Set

In this paper, the content of forsythoside A and ethanol-extract were rapidly determinated by near-infrared reflectance spectroscopy (NIRS). 85 samples of Forsythiae Fructus harvested in Luoyang from July to September in 2012 were divided into a calibration set (75 samples) and a validation set (10 samples). In combination with the partical least square (PLS), the quantitative calibration models of forsythoside A and ethanol-extract were established. The correlation coefficient of cross-validation (R2) was 0.98247 and 0.97214 for forsythoside A and ethanol-extract, the root-mean-square error of calibration (RMSEC) was 0.184 and 0.570, the root-mean-square error of cross-validation (RMSECV) was 0.81736 and 0.36656. The validation set were used to evaluate the performance of the models, the root-mean-square error of prediction (RMSEP) was 0.221 and 0.518. The results indicated that it was feasible to determine the content of forsythoside A and ethanol-extract in Forsythiae Fructus by near-infrared spectroscopy.

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Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

Indonesian Journal of Chemometrics and Pharmaceutical Analysis ◽

10.22146/ijcpa.487 ◽

2020 ◽

pp. 23

Author(s):

Anggita Rosiana Putri ◽

Abdul Rohman ◽

Sugeng Riyanto ◽

Widiastuti Setyaningsih

Keyword(s):

Root Mean Square Error ◽

Fish Oil ◽

Ftir Spectroscopy ◽

Mean Square Error ◽

Root Mean Square ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

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Estimating Center of Mass Kinematics During Perturbed Human Standing Using Accelerometers

Journal of Applied Biomechanics ◽

10.1123/jab.2020-0222 ◽

2021 ◽

pp. 1-10

Author(s):

Sandra K. Hnat ◽

Musa L. Audu ◽

Ronald J. Triolo ◽

Roger D. Quinn

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Squared Error ◽

Balance Control ◽

Center Of Mass ◽

Coefficient Of Determination ◽

Mean Square ◽

Cord Injury ◽

Movement Type

Estimating center of mass (COM) through sensor measurements is done to maintain walking and standing stability with exoskeletons. The authors present a method for estimating COM kinematics through an artificial neural network, which was trained by minimizing the mean squared error between COM displacements measured by a gold-standard motion capture system and recorded acceleration signals from body-mounted accelerometers. A total of 5 able-bodied participants were destabilized during standing through: (1) unexpected perturbations caused by 4 linear actuators pulling on the waist and (2) volitionally moving weighted jars on a shelf. Each movement type was averaged across all participants. The algorithm’s performance was quantified by the root mean square error and coefficient of determination (R2) calculated from both the entire trial and during each perturbation type. Throughout the trials and movement types, the average coefficient of determination was 0.83, with 89% of the movements with R2 > .70, while the average root mean square error ranged between 7.3% and 22.0%, corresponding to 0.5- and 0.94-cm error in both the coronal and sagittal planes. COM can be estimated in real time for balance control of exoskeletons for individuals with a spinal cord injury, and the procedure can be generalized for other gait studies.

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Multivariate analysis of three chemometric algorithms on rapid prediction of some important quality parameters of crude shea butter using Fourier transform-near infrared spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033519830061 ◽

2019 ◽

Vol 27 (3) ◽

pp. 220-231

Author(s):

Emmanuel Amomba Seweh ◽

Zou Xiaobo ◽

Feng Tao ◽

Shi Jiachen ◽

Haroon Elrasheid Tahir ◽

...

Keyword(s):

Genetic Algorithm ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Cross Validation ◽

Partial Least Square ◽

Least Square ◽

Mean Square ◽

Saponification Value ◽

Peroxide Values

A comparative study of three chemometric algorithms combined with NIR spectroscopy with the aim of determining the best performing algorithm for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results showed that genetic-algorithm partial least square models were superior in predicting iodine value and saponification value while partial least squares was excellent in predicting free fatty acids content and peroxide values. The nine-factor genetic-algorithm partial least square iodine value calibration model for predicting iodine value yielded excellent ( R2 cal = 0.97), ( R2 val = 0.97), low (root mean square error of cross-validation = 0.26), low (root mean square error of Prediction = 0.23), and (ratio of performance to deviation = 6.41); for saponification value, the nine-factor genetic-algorithm partial least square saponification value calibration model had excellent R2 cal (0.97), R2 val (0.99); low root mean square error of cross-validation (0.73), low root mean square error of Prediction (0.53), and (ratio of performance to deviation = 8.27); while for free fatty acids, the 11-factor partial least square free fatty acids produced very high R2 cal (0.97) and R2 val (0.97) with very low root mean square error of cross-validation (0.03), low root mean square error of Prediction (0.04) and (ratio of performance to deviation = 5.30) and finally for peroxide values, the 11-factor partial least square peroxide values calibration model obtained excellent R2 cal (0.96) and R2val (0.98) with low root mean square error of cross-validation (0.05), low root mean square error of Prediction (0.04), and (ratio of performance to deviation = 5.86). The built models were accurate and robust and can be reliably applied in developing a handheld quality detection device for screening, quality control checks, and prediction of shea butter quality on-site.

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