Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy

Aminata Sow; Issiaka Traore; Tidiane Diallo; Abdramane Ba

doi:doi:10.11648/j.sjc.20221006.12

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Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy

Aminata Sow, Issiaka Traore, Tidiane Diallo, Abdramane Ba

Published in Science Journal of Chemistry (Volume 10, Issue 6)

Received: 26 October 2022 Accepted: 11 November 2022 Published: 30 November 2022

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Abstract

The approach of Near-Infrared Spectroscopy (NIRS) together with Chemometric techniques are used in order to analyze sixty (60) tablets of paracetamol of different batch numbers in the local markets in Bamako. The primary objective is to model these samples by doing multivariate regression computation. Prior to this, various statistical pretreatment methods such as second derivative (SD) correction, first derivative correction (FD), multiple scattering correction (MSC), smoothing the spectra (smooth), the standard variate normalization (SNV) correction and some combinations are performed. The Partial least square (PLS) regression on the SNV data preprocessing resulted in the detection of two outliers. Additionally, the presence of nonlinear effects is conducted. Its presence compels one to consider nonlinear regression such as the artificial neural network ANN or relevance vector machine RVM. A supporting fact of the use of these types of regressions is that ANN regression applied to the spectra under consideration significantly improves the RMSEP and the relative standard error of prediction RSEP. To further analyze the samples, the selection of wavelengths based on the p-value approach proved its usefulness in this investigation. The best calibration of the PLS multivariate regression model is obtained with the MSC combined with FD correction, and its statistical values for the fourteen wavelengths, having the smallest individual p-value, are R²= 85.26%, RMSEP= 2.38×10^-4 and finally RSEP=1.45%.

Published in	Science Journal of Chemistry (Volume 10, Issue 6)
DOI	10.11648/j.sjc.20221006.12
Page(s)	202-210
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Paracetamol, Near-Infrared Spectroscopy, Chemometric Techniques, Partial Least Squares Regression, Data Preprocessing, Variable Selection, Nonlinearity

References

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APA Style

Aminata Sow, Issiaka Traore, Tidiane Diallo, Abdramane Ba. (2022). Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy. Science Journal of Chemistry, 10(6), 202-210. https://doi.org/10.11648/j.sjc.20221006.12

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ACS Style

Aminata Sow; Issiaka Traore; Tidiane Diallo; Abdramane Ba. Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy. Sci. J. Chem. 2022, 10(6), 202-210. doi: 10.11648/j.sjc.20221006.12

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AMA Style

Aminata Sow, Issiaka Traore, Tidiane Diallo, Abdramane Ba. Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy. Sci J Chem. 2022;10(6):202-210. doi: 10.11648/j.sjc.20221006.12

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@article{10.11648/j.sjc.20221006.12,
  author = {Aminata Sow and Issiaka Traore and Tidiane Diallo and Abdramane Ba},
  title = {Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy},
  journal = {Science Journal of Chemistry},
  volume = {10},
  number = {6},
  pages = {202-210},
  doi = {10.11648/j.sjc.20221006.12},
  url = {https://doi.org/10.11648/j.sjc.20221006.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjc.20221006.12},
  abstract = {The approach of Near-Infrared Spectroscopy (NIRS) together with Chemometric techniques are used in order to analyze sixty (60) tablets of paracetamol of different batch numbers in the local markets in Bamako. The primary objective is to model these samples by doing multivariate regression computation. Prior to this, various statistical pretreatment methods such as second derivative (SD) correction, first derivative correction (FD), multiple scattering correction (MSC), smoothing the spectra (smooth), the standard variate normalization (SNV) correction and some combinations are performed. The Partial least square (PLS) regression on the SNV data preprocessing resulted in the detection of two outliers. Additionally, the presence of nonlinear effects is conducted. Its presence compels one to consider nonlinear regression such as the artificial neural network ANN or relevance vector machine RVM. A supporting fact of the use of these types of regressions is that ANN regression applied to the spectra under consideration significantly improves the RMSEP and the relative standard error of prediction RSEP. To further analyze the samples, the selection of wavelengths based on the p-value approach proved its usefulness in this investigation. The best calibration of the PLS multivariate regression model is obtained with the MSC combined with FD correction, and its statistical values for the fourteen wavelengths, having the smallest individual p-value, are R2= 85.26%, RMSEP= 2.38×10-4 and finally RSEP=1.45%.},
 year = {2022}
}

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TY  - JOUR
T1  - Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy
AU  - Aminata Sow
AU  - Issiaka Traore
AU  - Tidiane Diallo
AU  - Abdramane Ba
Y1  - 2022/11/30
PY  - 2022
N1  - https://doi.org/10.11648/j.sjc.20221006.12
DO  - 10.11648/j.sjc.20221006.12
T2  - Science Journal of Chemistry
JF  - Science Journal of Chemistry
JO  - Science Journal of Chemistry
SP  - 202
EP  - 210
PB  - Science Publishing Group
SN  - 2330-099X
UR  - https://doi.org/10.11648/j.sjc.20221006.12
AB  - The approach of Near-Infrared Spectroscopy (NIRS) together with Chemometric techniques are used in order to analyze sixty (60) tablets of paracetamol of different batch numbers in the local markets in Bamako. The primary objective is to model these samples by doing multivariate regression computation. Prior to this, various statistical pretreatment methods such as second derivative (SD) correction, first derivative correction (FD), multiple scattering correction (MSC), smoothing the spectra (smooth), the standard variate normalization (SNV) correction and some combinations are performed. The Partial least square (PLS) regression on the SNV data preprocessing resulted in the detection of two outliers. Additionally, the presence of nonlinear effects is conducted. Its presence compels one to consider nonlinear regression such as the artificial neural network ANN or relevance vector machine RVM. A supporting fact of the use of these types of regressions is that ANN regression applied to the spectra under consideration significantly improves the RMSEP and the relative standard error of prediction RSEP. To further analyze the samples, the selection of wavelengths based on the p-value approach proved its usefulness in this investigation. The best calibration of the PLS multivariate regression model is obtained with the MSC combined with FD correction, and its statistical values for the fourteen wavelengths, having the smallest individual p-value, are R2= 85.26%, RMSEP= 2.38×10-4 and finally RSEP=1.45%.
VL  - 10
IS  - 6
ER  -

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Author Information

Aminata Sow

Departmenf of Physics, Faculty of Science and Technique, Laboratory of Optics Spectroscopy and Atmospheric Science, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
Issiaka Traore

Departmenf of Physics, Faculty of Science and Technique, Laboratory of Optics Spectroscopy and Atmospheric Science, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
Tidiane Diallo

Department of Medicine Sciences, Faculty of de Pharmacy, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
Abdramane Ba

Departmenf of Physics, Faculty of Science and Technique, Laboratory of Optics Spectroscopy and Atmospheric Science, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali

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APA Style

Aminata Sow, Issiaka Traore, Tidiane Diallo, Abdramane Ba. (2022). Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy. Science Journal of Chemistry, 10(6), 202-210. https://doi.org/10.11648/j.sjc.20221006.12

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ACS Style

Aminata Sow; Issiaka Traore; Tidiane Diallo; Abdramane Ba. Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy. Sci. J. Chem. 2022, 10(6), 202-210. doi: 10.11648/j.sjc.20221006.12

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AMA Style

Aminata Sow, Issiaka Traore, Tidiane Diallo, Abdramane Ba. Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy. Sci J Chem. 2022;10(6):202-210. doi: 10.11648/j.sjc.20221006.12

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@article{10.11648/j.sjc.20221006.12,
  author = {Aminata Sow and Issiaka Traore and Tidiane Diallo and Abdramane Ba},
  title = {Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy},
  journal = {Science Journal of Chemistry},
  volume = {10},
  number = {6},
  pages = {202-210},
  doi = {10.11648/j.sjc.20221006.12},
  url = {https://doi.org/10.11648/j.sjc.20221006.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjc.20221006.12},
  abstract = {The approach of Near-Infrared Spectroscopy (NIRS) together with Chemometric techniques are used in order to analyze sixty (60) tablets of paracetamol of different batch numbers in the local markets in Bamako. The primary objective is to model these samples by doing multivariate regression computation. Prior to this, various statistical pretreatment methods such as second derivative (SD) correction, first derivative correction (FD), multiple scattering correction (MSC), smoothing the spectra (smooth), the standard variate normalization (SNV) correction and some combinations are performed. The Partial least square (PLS) regression on the SNV data preprocessing resulted in the detection of two outliers. Additionally, the presence of nonlinear effects is conducted. Its presence compels one to consider nonlinear regression such as the artificial neural network ANN or relevance vector machine RVM. A supporting fact of the use of these types of regressions is that ANN regression applied to the spectra under consideration significantly improves the RMSEP and the relative standard error of prediction RSEP. To further analyze the samples, the selection of wavelengths based on the p-value approach proved its usefulness in this investigation. The best calibration of the PLS multivariate regression model is obtained with the MSC combined with FD correction, and its statistical values for the fourteen wavelengths, having the smallest individual p-value, are R2= 85.26%, RMSEP= 2.38×10-4 and finally RSEP=1.45%.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Analysis of Local Samples of Paracetamol at Bamako by Reflectance Near-Infrared Spectroscopy
AU  - Aminata Sow
AU  - Issiaka Traore
AU  - Tidiane Diallo
AU  - Abdramane Ba
Y1  - 2022/11/30
PY  - 2022
N1  - https://doi.org/10.11648/j.sjc.20221006.12
DO  - 10.11648/j.sjc.20221006.12
T2  - Science Journal of Chemistry
JF  - Science Journal of Chemistry
JO  - Science Journal of Chemistry
SP  - 202
EP  - 210
PB  - Science Publishing Group
SN  - 2330-099X
UR  - https://doi.org/10.11648/j.sjc.20221006.12
AB  - The approach of Near-Infrared Spectroscopy (NIRS) together with Chemometric techniques are used in order to analyze sixty (60) tablets of paracetamol of different batch numbers in the local markets in Bamako. The primary objective is to model these samples by doing multivariate regression computation. Prior to this, various statistical pretreatment methods such as second derivative (SD) correction, first derivative correction (FD), multiple scattering correction (MSC), smoothing the spectra (smooth), the standard variate normalization (SNV) correction and some combinations are performed. The Partial least square (PLS) regression on the SNV data preprocessing resulted in the detection of two outliers. Additionally, the presence of nonlinear effects is conducted. Its presence compels one to consider nonlinear regression such as the artificial neural network ANN or relevance vector machine RVM. A supporting fact of the use of these types of regressions is that ANN regression applied to the spectra under consideration significantly improves the RMSEP and the relative standard error of prediction RSEP. To further analyze the samples, the selection of wavelengths based on the p-value approach proved its usefulness in this investigation. The best calibration of the PLS multivariate regression model is obtained with the MSC combined with FD correction, and its statistical values for the fourteen wavelengths, having the smallest individual p-value, are R2= 85.26%, RMSEP= 2.38×10-4 and finally RSEP=1.45%.
VL  - 10
IS  - 6
ER  -

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