Calibration Methods for NIRS Instruments: A Theoretical Evaluation and Comparisons by Data Splitting and Simulations

1994 ◽  
Vol 48 (3) ◽  
pp. 327-332 ◽  
Author(s):  
Trygve Almøy ◽  
Espen Haugland
Keyword(s):  
Prediction Error ◽  
Principal Component Regression ◽  
Principal Component ◽  
Calibration Method ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Theoretical Evaluation ◽  
Number Of Components ◽  
Calibration Methods ◽  
Small Eigenvalues

The properties of the recently proposed calibration method called restricted principal component regression (RPCR) were evaluated and compared with partial least-squares regression (PLSR) and two types of principal component regression (PCR1, selected according to the size of the eigenvalues, and PCR2, selected according to the t-value). RPCR can be considered a compromise between PCR and PLSR, since the first component of RPCR is equivalent to the first component of PLSR, while the rest can be regarded as principal components on a space orthogonal to the first. The methods showed almost the same properties when the irrelevant components had small eigenvalues. The prediction error of RPCR selected according to the size of the eigenvalues was intermediate to those of PCR1 and PLSR when the number of components was low, while RPCR and PCR1 nearly coincided when the number of components exceeded the number of relevant ones. The prediction error minimum was about the same for RPCR, PCR1, and PLSR, but the minimum of PLSR was obtained when a lower number of components were included in the calibration model.

Chemometrics-Assisted UV Spectrophotometric Method for Determination of Metformin Hydrochloride and Glyburide in Pharmaceutical Tablets

2014 ◽  
Vol 1060 ◽  
pp. 164-167
Author(s):  
Lawan Sratthaphut ◽  
Kanong Ruttanakorn
Keyword(s):  
Spectrophotometric Method ◽  
Principal Component Regression ◽  
Principal Component ◽  
Uv Spectra ◽  
Metformin Hydrochloride ◽  
Least Squares Regression ◽  
Pharmaceutical Tablets ◽  
Calibration Models ◽  
Calibration Methods ◽  
Leave One Out

This study aims to estimate simultaneously metformin hydrochloride (MET) and glyburide (GLY) in a multicomponent tablets dosage form by spectrophotometric method using chemometric approaches such as principal component regression (PCR) and partial least-squares regression (PLS). Because of highly overlapped in UV spectra and difference proportions of two active ingredients, the conventional univariate calibration methods was not allowed without previous separation. The linearity ranges used to construct the calibration matrix were selected in the ranges from 40.00 to 200.00 mg L-1for MET and from 1.00 to 10.00 mg L-1for GLY. The absorbances were measured in the wavelength range of 200-400 nm, using ethanol as solvent. The resulting UV spectra were subjected to PCR and PLS algorithms and the optimum numbers of principal components (PCs) were selected according to prediction residual error sum of squares (PRESS) values of leave-one out cross-validation. The number of PCs for MET and GLY were found to be 5, 3 by PCR and 5, 3 by PLS, respectively. A set of synthetic mixtures was employed to verify the models and the performance of the models were shown in the values of the root mean square error in prediction (RMSEP). RMSEP values of MET and GLY were 1.806, 0.256 for PCR and 1.802, 0.185 for PLS, respectively. The suitable calibration models were applied to the analysis of these compounds in pharmaceutical formulation.

scholarly journals The Determination of Parkinson’s Drugs in Human Urine by Applying Chemometric Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Guzide Pekcan Ertokus
Keyword(s):  
Urine Sample ◽  
Human Urine ◽  
Principal Component Regression ◽  
Principal Component ◽  
Healthy Person ◽  
Drug Analysis ◽  
Uv Spectrophotometry ◽  
Least Squares Regression ◽  
Chemometric Methods

The spectrophotometric-chemometric analysis of levodopa and carbidopa that are used for Parkinson’s disease was analyzed without any prior reservation. Parkinson’s drugs in the urine sample of a healthy person (never used drugs in his life) were analyzed at the same time spectrophotometrically. The chemometric methods used were partial least squares regression (PLS) and principal component regression (PCR). PLS and PCR were successfully applied as chemometric determination of levodopa and carbidopa in human urine samples. A concentration set including binary mixtures of levodopa and carbidopa in 15 different combinations was randomly prepared in acetate buffer (pH 3.5).). UV spectrophotometry is a relatively inexpensive, reliable, and less time-consuming method. Minitab program was used for absorbance and concentration values. The normalization values for each active substance were good (r2>0.9997). Additionally, experimental data were validated statistically. The results of the analyses of the results revealed high recoveries and low standard deviations. Hence, the results encouraged us to apply the method to drug analysis. The proposed methods are highly sensitive and precise, and therefore they were implemented for the determination of the active substances in the urine sample of a healthy person in triumph.

scholarly journals Prediction of Total Phenolic Content in Extracts of Prunella Species from HPLC Profiles by Multivariate Calibration

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Saliha Sahin ◽  
Esra Isik ◽  
Cevdet Demir
Keyword(s):  
High Performance ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Total Phenol ◽  
Total Phenolic ◽  
Total Phenol Content ◽  
Phenol Content ◽  
Calibration Methods ◽  
Hplc Profiles

The multivariate calibration methods—principal component regression (PCR) and partial least squares (PLSs)—were employed for the prediction of total phenol contents of four Prunella species. High performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total phenol content of the Prunella samples. Several preprocessing techniques such as smoothing, normalization, and column centering were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping (COW). The importance of the preprocessing was investigated by calculating the root mean square error (RMSE) for the calibration set of the total phenol content of Prunella samples. The models developed based on the preprocessed data were able to predict the total phenol content with a precision comparable to that of the reference of the Folin-Ciocalteu method. PLS model seems preferable, because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total phenol content. Multivariate calibration methods were constructed to model the total phenol content of the Prunella samples from the HPLC profiles and indicate peaks responsible for the total phenol content successfully.

scholarly journals Classification and Authentication of Paprika by UHPLC-HRMS Fingerprinting and Multivariate Calibration Methods (PCA and PLS-DA)

Foods ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 486 ◽  
Author(s):  
Sergio Barbosa ◽  
Javier Saurina ◽  
Lluís Puignou ◽  
Oscar Núñez
Keyword(s):  
Multivariate Calibration ◽  
Principal Component ◽  
Small Region ◽  
Reversed Phase ◽  
Negative Ion ◽  
Mass Analyzer ◽  
Least Squares Regression ◽  
Calibration Methods ◽  
Chemical Descriptors ◽  
Negative Ion Mode

In this study, the feasibility of non-targeted UHPLC-HRMS fingerprints as chemical descriptors to address the classification and authentication of paprika samples was evaluated. Non-targeted UHPLC-HRMS fingerprints were obtained after a simple sample extraction method and C18 reversed-phase separation. Fingerprinting data based on signal intensities as a function of m/z values and retention times were registered in negative ion mode using a q-Orbitrap high-resolution mass analyzer, and the obtained non-targeted UHPLC-HRMS fingerprints subjected to unsupervised principal component analysis (PCA) and supervised partial least squares regression-discriminant analysis (PLS-DA) to study sample discrimination and classification. A total of 105 paprika samples produced in three different regions, La Vera PDO and Murcia PDO, in Spain, and the Czech Republic, and all of them composed of samples of at least two different taste varieties, were analyzed. Non-targeted UHPLC-HRMS fingerprints demonstrated to be excellent sample chemical descriptors to achieve the authentication of paprika production regions with 100% sample classification rates by PLS-DA. Besides, the obtained fingerprints were also able to perfectly discriminate among the different paprika taste varieties in all the studied cases, even in the case of the different La Vera PDO paprika tastes (sweet, bittersweet, and spicy) which are produced in a very small region.

Use of residual maximum likelihood to evaluate accuracy of two NIRS calibration procedures, relative to Kjeldahl, for determining nitrogen concentration of soybeans

1991 ◽  
Vol 71 (2) ◽  
pp. 385-392 ◽  
Author(s):  
G. B. Schaalje ◽  
H. -H. Mündel
Keyword(s):  
Maximum Likelihood ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Field Trials ◽  
Near Infrared Reflectance ◽  
Residual Maximum Likelihood ◽  
Maximum Likelihood Procedure ◽  
Calibration Methods

The accuracy of estimates of plant properties based on near-infrared reflectance spectroscopy (NIRS) varies with many factors including the biological material in question and the method used to calibrate the NIRS instrument. This study investigated the accuracy, relative to Kjeldahl analysis, of NIRS analysis based on two calibration methods in estimating nitrogen concentration of four stages and/or parts of soybean (Glycine max (L.) Merr.) plants. Samples of whole top growth at anthesis, whole top growth at maturity, whole top growth at maturity excluding seeds, and seeds were obtained from two field trials and one phytotron experiment. Two Kjeldahl determinations of nitrogen concentration were obtained for each sample, as well as reflectance values at each of 19 infrared wavelengths, using a Technicon InfraAlyser 400R. Different subsets of the sample data were used for calibration and assessment of accuracy. The instrument was calibrated using stepwise multiple linear regression (SMLR) and principal component regression (PCR). The residual maximum likelihood procedure was useful in showing that NIRS estimates based on either SMLR or PCR were at least as accurate as Kjeldahl estimates for all stages and/or parts except whole top growth at maturity excluding seeds. Key words: Calibration, principal component regression, stepwise regression

scholarly journals Multivariate Method Based on Raman Spectroscopy for Quantification of Dipyrone in Oral Solutions

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Luciana Lopes Guimarães ◽  
Letícia Parada Moreira ◽  
Bárbara Faria Lourenço ◽  
Walber Toma ◽  
Renato Amaro Zângaro ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Quality Control ◽  
Principal Component Regression ◽  
Principal Component ◽  
Quantitative Model ◽  
Calibration Model ◽  
Analytical Methodology ◽  
Figures Of Merit ◽  
Model Based ◽  
Raman Probe

This work employed a quantitative model based on Raman spectroscopy and principal component regression (RS/PCR) to quantify the active ingredient dipyrone (metamizole) in commercially available formulations as an analytical methodology for quality control in the pharmaceutical industry. Raman spectra were collected using a dispersive Raman spectrometer (830 nm, 250 mW excitation, and 20 s exposure time) coupled to a Raman probe. Solutions of dipyrone diluted in water in the range of 80 to 120% of the concentration of commercial formulations (500 mg/mL) were used to develop a calibration model based on PCR to obtain the figures of merit for class I validation from the Brazilian Sanitary Surveillance Agency (ANVISA, RE no. 899/2003). This spectral model was then used to predict the concentration of dipyrone in commercial formulations from distinct brands with 500 mg/mL. A prediction error of 6.5 mg/mL (1.3%) was found for this PCR model using the diluted samples. Commercial formulations had predicted concentrations with a difference below 5.0% compared to the label concentration, indicating the applicability of Raman spectroscopy for quality control in the final product.

A Calibration Tutorial for Spectral Data. Part 1: Data Pretreatment and Principal Component Regression Using Matlab

1996 ◽  
Vol 4 (1) ◽  
pp. 225-242 ◽  
Author(s):  
Paul Geladi ◽  
Harald Martens
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Regression Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Statistical Parameters ◽  
Calibration Samples

Regression and calibration play an important role in analytical chemistry. All analytical instrumentation is dependent on a calibration that uses some regression model for a set of calibration samples. The ordinary least squares (OLS) method of building a multivariate linear regression (MLR) model has strict limitations. Therefore, biased or regularised regression models have been introduced. Some selected ones are ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLS or PLSR). Also, artificial neural networks (ANN) based on back-propagation can be used as regression models. In order to understand regression models more is needed than just a set of statistical parameters. A deeper understanding of the underlying chemistry and physics is always equally important. For spectral data this means that a basic understanding of spectra and their errors is useful and that spectral representation should be included in judging the usefulness of the data treatment. A “constructed” spectrometric example is introduced. It consists of real spectrometric measurements in the range 408–1176 nm for 26 calibration samples and 10 test samples. The main response variable is litmus concentration, but other constituents such as bromocresolgreen and ZnO are added as interferents and also the pH is changed. The example is introduced as a tutorial. All calculations are shown in detail in Matlab. This makes it easy for the reader to follow and understand the calculations. It also makes the calculations completely traceable. The raw data are available as a file. In Part 1, the emphasis is on pretreatment of the data and on visualisation in different stages of the calculations. Part 1 ends with principal component regression calculations. Partial least squares calculations and some ANN results are presented in Part 2.

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