scholarly journals Development and Validation of a Simple, Green Infrared Spectroscopic Method for Quantitation of Sildenafil Citrate in Siloflam Tablets of Unknown Manufacturing Formula

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Van Trung Bui ◽  
Cao Son Doan ◽  
Thi Thanh Vuong Tong ◽  
Dinh Chi Le
Keyword(s):  
Spectroscopic Method ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sildenafil Citrate ◽  
Calibration Model ◽  
Mass Content ◽  
Exact Mass ◽  
Infrared Spectroscopic ◽  
Development And Validation

A simple, easy-to-implement, and green infrared spectroscopic method was developed and validated for the quantitative determination of sildenafil citrate in tablets of unknown manufacturing formula. Homogenized tablet powder with known mass content (%, m/m) of sildenafil citrate was mixed with paracetamol to form standard mixtures with different percentages of sildenafil citrate on the total quantity of sildenafil citrate and paracetamol (designated as R). Unknown tablet samples were finely ground and mixed with paracetamol to form test mixtures having R values about 50%. Infrared spectra of standard mixtures, measured in attenuated total reflectance mode, in the wavenumber zone from 1800 cm−1 to 1300 cm−1 were selected and processed by partial least square regression to form the calibration model for quantitation of sildenafil citrate in unknown samples. Spectral responses of test mixtures and the calibration model were used to determine the exact mass content (%, m/m) of sildenafil citrate in the powder of unknown tablet samples. The method was fully validated in terms of linearity, precision, and accuracy according to the requirements of current guidelines and was proved as reliable and suitable for the intended application.

Field Nitrogen Dioxide and Ozone Monitoring Using Electrochemical Sensors with Partial Least Squares Regression

2021 ◽  
Vol 5 (1) ◽  
pp. 61
Author(s):  
Rachid Laref ◽  
Etienne Losson ◽  
Alexandre Sava ◽  
Maryam Siadat
Keyword(s):  
Nitrogen Dioxide ◽  
Electrochemical Sensors ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Monitoring Networks ◽  
Concentration Estimation ◽  
The Impact

Low-cost gas sensors detect pollutants gas at the parts-per-billion level and may be installed in small devices to densify air quality monitoring networks for the spread analysis of pollutants around an emissive source. However, these sensors suffer from several issues such as the impact of environmental factors and cross-interfering gases. For instance, the ozone (O3) electrochemical sensor senses nitrogen dioxide (NO2) and O3 simultaneously without discrimination. Alphasense proposes the use of a pair of sensors; the first one, NO2-B43F, is equipped with a filter dedicated to measure NO2. The second one, OX-B431, is sensitive to both NO2 and O3. Thus, O3 concentration can be obtained by subtracting the concentration of NO2 from the sum of the two concentrations. This technique is not practical and requires calibrating each sensor individually, leading to biased concentration estimation. In this paper, we propose Partial Least Square regression (PLS) to build a calibration model including both sensors’ responses and also temperature and humidity variations. The results obtained from data collected in the field for two months show that PLS regression provides better gas concentration estimation in terms of accuracy than calibrating each sensor individually.

scholarly journals Spectrophotometric determination of phenol in the presence of congeners by multivariated calibration

2001 ◽  
Vol 73 (4) ◽  
pp. 519-524 ◽  
Author(s):  
KELY VIVIANE DE SOUZA ◽  
PATRICIO PERALTA-ZAMORA
Keyword(s):  
Spectrophotometric Determination ◽  
Multivariate Calibration ◽  
Close Correlation ◽  
Degradation Process ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Transient Species

The generation of poly-hydroxilated transient species during the photochemical treatment of phenol usually impedes the spectrophotmetric monitoring of its degradation process. Frequently, the appearance of compounds such as pyrocatechol, hydroquinone and benzoquinone produces serious spectral interference, which hinder the use of the classical univariate calibration process. In this work, the use of multivariate calibration is proposed to permit the spectrophotometric determination of phenol in the presence of these intermediates. Using 20 synthetic mixtures containing phenol and the interferents, a calibration model was developed by using a partial least square regression process (PLSR) and processing the absorbance signal between 180 and 300 nm. The model was validated by using 3 synthetic mixtures. In this operation, typical errors lower than 3% were observed. Close correlation between the results obtained by liquid chromatography and the proposed method was also observed.

scholarly journals Predicting ambient aerosol thermal–optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sites

2016 ◽  
Vol 9 (2) ◽  
pp. 441-454 ◽  
Author(s):  
Matteo Reggente ◽  
Ann M. Dillner ◽  
Satoshi Takahama
Keyword(s):  
Mahalanobis Distance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Optical Methods ◽  
Calibration Model ◽  
Optical Reflectance ◽  
Wide Range ◽  
Ft Ir ◽  
Absorbance Spectra

Abstract. Organic carbon (OC) and elemental carbon (EC) are major components of atmospheric particulate matter (PM), which has been associated with increased morbidity and mortality, climate change, and reduced visibility. Typically OC and EC concentrations are measured using thermal–optical methods such as thermal–optical reflectance (TOR) from samples collected on quartz filters. In this work, we estimate TOR OC and EC using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE Teflon) filters using partial least square regression (PLSR) calibrated to TOR OC and EC measurements for a wide range of samples. The proposed method can be integrated with analysis of routinely collected PTFE filter samples that, in addition to OC and EC concentrations, can concurrently provide information regarding the functional group composition of the organic aerosol. We have used the FT-IR absorbance spectra and TOR OC and EC concentrations collected in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network (USA). We used 526 samples collected in 2011 at seven sites to calibrate the models, and more than 2000 samples collected in 2013 at 17 sites to test the models. Samples from six sites are present both in the calibration and test sets. The calibrations produce accurate predictions both for samples collected at the same six sites present in the calibration set (R2 = 0.97 and R2 = 0.95 for OC and EC respectively), and for samples from 9 of the 11 sites not included in the calibration set (R2 = 0.96 and R2 = 0.91 for OC and EC respectively). Samples collected at the other two sites require a different calibration model to achieve accurate predictions. We also propose a method to anticipate the prediction error; we calculate the squared Mahalanobis distance in the feature space (scores determined by PLSR) between new spectra and spectra in the calibration set. The squared Mahalanobis distance provides a crude method for assessing the magnitude of mean error when applying a calibration model to a new set of samples.

scholarly journals A Rapid Method to Measure Serum Retinol Concentrations in Japanese Black Cattle Using Multidimensional Fluorescence

2020 ◽  
Author(s):  
Yoshio Tamura ◽  
Hiroki Inoue ◽  
Satoshi Takemoto ◽  
Kazuo Hirano ◽  
Kazutoshi Miyaura
Keyword(s):  
Mean Squared Error ◽  
Fluorescence Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Retinol Binding Protein ◽  
Calibration Model ◽  
Serum Retinol ◽  
Japanese Black Cattle ◽  
Multidimensional Fluorescence

Abstract Vitamin A levels in fattening Japanese Black cattle affect meat quality; therefore, it is important to monitor serum retinol concentrations. To simplify and accelerate the evaluation of serum retinol concentrations in cattle, we developed a new predictive method using excitation-emission matrix (EEM) fluorescence spectrophotometry. For analytical comparison, the concentration of serum retinol was also measured using the conventional HPLC method. We examined excitation (Ex) and emission (Em) wavelengths of cattle serum, which were 250–450 and 250–600 nm, respectively. Parallel factor analysis separated four components from EEM data, one of which was related to retinol. Next, a partial least square regression model was created using the obtained EEMs as explanatory variables and accrual measurement values as objective variables. The determination coefficient value (R2), root mean squared error of prediction (RMSEP), and the ratio of performance to deviation (RPD) of the model were determined. A comparison with reference values found that R2, RMSEP, and RPD of the calibration model were 0.95, 6.4 IU/dl, and 4.2, respectively. This implies that EEM can estimate the serum retinol concentration with high accuracy. Additionally, the fluorescent peaks that contributed to the calibration, which were extracted from the regression coefficient and variable importance in projection plots, were Ex/Em = 320/390 and 330/520 nm. Thus, we assume that this method observes not only free retinol, but also retinol-binding protein. In conclusion, multidimensional fluorescence analysis can accurately and quickly determine serum retinol concentrations in fattening cattle.

scholarly journals Raman Spectral Analysis for Quality Determination of Grignard Reagent

2020 ◽  
Vol 10 (10) ◽  
pp. 3545
Author(s):  
Rahul Joshi ◽  
Ritu Joshi ◽  
Changyeun Mo ◽  
Mohammad Akbar Faqeerzada ◽  
Hanim Z. Amanah ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Grignard Reagent ◽  
Rapid Determination ◽  
Spectroscopic Method ◽  
Spectroscopic Technique ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Toluene Concentration

Grignard reagent is one of the most popular materials in chemical and pharmaceutical reaction processes, and requires high quality with minimal adulteration. In this study, Raman spectroscopic technique was investigated for the rapid determination of toluene content, which is one of the common adulterants in Grignard reagent. Raman spectroscopy is the most suitable spectroscopic method to mitigate moisture and CO2 interference in the molecules of Grignard reagent. Raman spectra for the mixtures of toluene and Grignard reagent with different concentrations were analyzed with a partial least square regression (PLSR) method. The combination of spectral wavebands in the prediction model was optimized with a variables selection method of variable importance in projection (VIP). The results obtained from the VIP-based PLSR model showed the reliable performance of Raman spectroscopy for predicting the toluene concentration present in Grignard reagent with a correlation coefficient value of 0.97 and a standard error of prediction (SEP) of 0.71%. The results showed that Raman spectroscopy combined with multivariate analysis could be an effective analytical tool for rapid determination of the quality of Grignard reagent.

scholarly journals The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia
Keyword(s):  
Visible Region ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Simple Method ◽  
Calibration Models ◽  
Visible Spectra ◽  
Palm Civet ◽  
Uv Visible

Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world’s priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest RPDcal value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both RPDp and RER values.

scholarly journals RAPID AUTHENTICATION OF TURMERIC POWDER ADULTERATED WITH CURCUMA ZEDOARIA AND CURCUMA XANTHORRHIZA USING FTIR-ATR SPECTROSCOPY AND CHEMOMETRICS

2019 ◽  
pp. 216-221
Author(s):  
ELLSYA ANGELINE ◽  
RATNA ASMAH SUSIDARTI ◽  
ABDUL ROHMAN
Keyword(s):  
Curcuma Longa ◽  
External Validation ◽  
Attenuated Total Reflection ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Curcuma Zedoaria ◽  
Curcuma Xanthorrhiza ◽  
Atr Spectroscopy

Objective: The objective of this study is to develop a rapid, simple, non-destructive and inexpensive analytical method using Fourier Transform Infrared (FTIR) spectroscopy with Attenuated Total Reflection (ATR) as a sampling technique, combined with chemometrics for authentication of turmeric powder adulterated with Curcuma zedoaria and Curcuma xanthorrhiza. Methods: Turmeric powder is placed above the diamond crystal in ATR compartment. Spectra are scanned in the absorbance mode from 4000 to 600 cm-1. The obtained spectra is further analyzed by Principal Component Analysis (PCA), Partial Least Square Discriminant Analysis (PLS-DA), and Partial Least Square Regression (PLS-R). Results: PCA score plot shows that Curcuma longa, Curcuma zedoaria, and Curcuma xanthorrhiza can be discriminated well. PLS-DA can be used to build the model for classification between pure turmeric powder and adulterated powder with the values of Q2, R2X, and R2Y of 0.9558, 0.9813, and 0.9746, respectively. The good calibration model for quantification of each adulterant is obtained by PLS-R with R2 value more than 0.99 and lower RMSEC value. Both models have been validated by internal and external validation which result in the high R2 value and low RMSEP value which indicates that both models are accurate and precise. Conclusion: The combination of FTIR-ATR spectroscopy and chemometrics can be used to authenticate turmeric powder adulterated with Curcuma zedoaria and Curcuma xanthorrhiza.

scholarly journals Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 335
Author(s):  
Ning Ai ◽  
Yibo Jiang ◽  
Sainab Omar ◽  
Jiawei Wang ◽  
Luyue Xia ◽  
...  
Keyword(s):  
Near Infrared ◽  
Lignin Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sargassum Horneri ◽  
Calibration Model ◽  
Selection Methods ◽  
Calibration Models ◽  
Variables Selection

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.

scholarly journals Predicting ambient aerosol thermal-optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sites

2015 ◽  
Vol 8 (11) ◽  
pp. 12433-12474 ◽  
Author(s):  
M. Reggente ◽  
A. M. Dillner ◽  
S. Takahama
Keyword(s):  
Mahalanobis Distance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Optical Methods ◽  
Calibration Model ◽  
Optical Reflectance ◽  
Wide Range ◽  
Ft Ir ◽  
Absorbance Spectra

Abstract. Organic carbon (OC) and elemental carbon (EC) are major components of atmospheric particulate matter (PM), which has been associated with increased morbidity and mortality, climate change and reduced visibility. Typically OC and EC concentrations are measured using thermal optical methods such as thermal optical reflectance (TOR) from samples collected on quartz filters. In this work, we estimate TOR OC and EC using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE or Teflon) filters using partial least square regression (PLSR) calibrated to TOR OC and EC measurements for a wide range of samples. The proposed method can be integrated with analysis of routinely collected PTFE filter samples that, in addition to OC and EC concentrations, can concurrently provide information regarding the composition of the organic aerosol. We have used the FT-IR absorbance spectra and TOR OC and EC concentrations collected in the Interagency Monitoring of PROtected Visual Environment (IMPROVE) network (USA). We used 526 samples collected in 2011 at seven sites to calibrate the models, and more than 2000 samples collected in 2013 at 17 sites to test the models. Samples from six sites are present both in the calibration and test sets. The calibrations produce accurate predictions both for samples collected at the same six sites present in the calibration set (R2=0.97 and R2=0.95 for OC and EC respectively), and for samples from nine of the 11 sites not included in the calibration set (R2=0.96 and R2=0.91 for OC and EC respectively). Samples collected at the other two sites require a different calibration model to achieve accurate predictions. We also propose a method to anticipate the prediction error: we calculate the squared Mahalanobis distance in the feature space (scores determined by PLSR) between new spectra and spectra in the calibration set. The squared Mahalanobis distance provides a crude method for assessing the magnitude of mean error when applying a calibration model to a new set of samples.

Quantitative Evaluation of Glycyrrhizic Acid That Affects the Product Quality of Kakkonto Extract, a Traditional Herbal Medicine, by a Chemometric near Infrared Spectroscopic Method

2009 ◽  
Vol 17 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Yoshifumi Mohri ◽  
Yukoh Sakata ◽  
Makoto Otsuka
Keyword(s):  
Acid Content ◽  
Glycyrrhizic Acid ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Wave Number ◽  
Coefficient Of Determination ◽  
Calibration Model

The purpose of this study was to construct a calibration model for the prediction of glycyrrhizic acid content in Kakkonto extracts using near infrared (NIR) spectroscopy. The NIR spectra of the Kakkonto extracts were obtained using a Fourier transform NIR spectrometer in transmission mode and chemometric analysis was performed using partial least-square (PLS) regression. The calibration model was constructed by the selection of wave number regions and by the first derivative pre-treatment of NIR spectra. The glycyrrhizic acid content could be predicted using a calibration model comprising three principal components (PCs) obtained by the PLS method. The calibration model was theoretically analysed by investigating the standard error of prediction values, the loading vectors of each PC and the regression vector. The relationship between the actual and predicted glycyrrhizic acid contents in the Kakkonto extract exhibited a straight line with a coefficient of determination of 0.966 (calibration) and 0.945 (validation), respectively. The predicted glycyrrhizic acid content in the Kakkonto extract was within the 95% predictive intervals.

Sign in / Sign up

Export Citation Format

Share Document