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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Mees, C., Kauffmann, J-M., Fernández Pierna, J. A. & De Braekeleer, K. (2022). Benchtop NIR data standardization on handheld spectrometers to identify paracetamol tablets. Journal of Chemometrics, 36: (3389), 1-17.
[2]	Eustaquio A, Graham P, Jee RD, Moffatt AC, Trafford AD. Quantification of paracetamol in intact tablets using near-infrared transmittance spectroscopy. Analyst. 1998 Nov; 123 (11): 2303-6. DOI: 10.1039/a804528c. PMID: 10396807.
[3]	Trafford AD, Jee RD, Moffat AC, Graham P. A rapid quantitative assay of intact paracetamol tablets by reflectance near-infrared spectroscopy. Analyst. 1999 Feb; 124 (2): 163-7. DOI: 10.1039/a806629i. PMID: 10563053.
[4]	Muntean, D. M., Alecu, C., & Tomuta, I. (2017). Simultaneous quantification of paracetamol and caffeine in powder blends for tableting by NIR-chemometry. Journal of spectroscopy, 2017.
[5]	A. Eustaquio, M. Blanco, R. D. Jee, A. C. Moffat. Determination of paracetamol in intact tablets by use of near-infrared transmittance spectroscopy, Analytica Chimica Acta, Volume 383, Issue 3, 1999, Pages 283-290, ISSN 0003-2670.
[6]	Said, M. M., Gibbons, S., Moffat, A. C., Zloh, M., Near-infrared spectroscopy (NIRS) and chemometric analysis of Malaysian and UK paracetamol tablets: A spectral database study, International Journal of Pharmaceutics 415 (2011) 102-109.
[7]	A. Savitzky and M. J. E. Golay, “Smoothing and differentiation of data by simplified least squares procedures”, Anal. Chem. 36, 1627 (1964). DOI: https://doi.org/10.1021/ac60214a047.
[8]	Geladi P, MacDougall D, Martens H. Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat. Applied Spectroscopy. 1985; 39 (3): 491-500. DOI: 10.1366/0003702854248656.
[9]	Abraham. Savitzky and M. J. E. Golay. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964, 36, 8, 1627–1639. DOI: 10.1021/ac60214a047.
[10]	Barnes RJ, Dhanoa MS, Lister SJ. Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra. Applied Spectroscopy. 1989; 43 (5): 772-777. DOI: 10.1366/0003702894202201.
[11]	Wold, S., Kettaneh-Wold, N., Skagerberg, B.: Nonlinear PLS modeling. Chemometrics Intell. Lab. Syst. 7, 53–65 (1989). DOI: 10.1016/0169-7439(89)80111-X.
[12]	V. Centner, O. E. de Noord and D. L. Massart, "Detection of nonlinearity in multivariate calibration", Analytica Chimica Acta, vol. 376, pp. 153-168, 1998. DOI: 10.1016/S0003-2670(98)00543-1.
[13]	Ildiko E. Frank, A nonlinear PLS model, Chem. Intel. Lab. Syst. 8 (1990) 109-119. DOI: 10.1016/0169-7439(90)80128-S.
[14]	Martens H. Understanding the root cause (s) of nonlinearities in near-infrared spectroscopy. NIR news. 2021; 32 (1-2): 20-26. DOI: 10.1177/09603360211003758.
[15]	J. Pieran et al., A Backward Variable Selection method for PLS regression (BVSPLS), Analytica Chimica Acta 642, 89-93 (2009).
[16]	F. Westad and H. Martens, "Variable Selection in near Infrared Spectroscopy Based on Significance Testing in Partial Least Squares Regression," J. Near Infrared Spectrosc. 8, 117-124 (2000).
[17]	Brenchley JM, Hörchner U, Kalivas JH. Wavelength Selection Characterization for NIR Spectra. Applied Spectroscopy. 1997; 51 (5): 689-699. DOI: 10.1366/0003702971940837.
[18]	Swapan Kumar Haldar, Chapter 9 - Statistical and Geostatistical Applications in Geology, Editor (s): Swapan Kumar Haldar, Mineral Exploration (Second Edition), Elsevier, 2018, Pages 167-194. DOI: 10.1016/B978-0-12-814022-2.00009-5.
[19]	Keke Song, Yuhua Qin, Bingyang Xu, Nuoqing Zhang, Junjie Yang, Study on outlier detection method of the near infrared spectroscopy analysis by probability metric, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Volume 280, 2022, 121473, ISSN 1386-1425, https://doi.org/10.1016/j.saa.2022.121473.
[20]	Zheng-Feng LI, Guang-Jin XU, Jia-Jun WANG, Guo-Rong DU, Wen-Sheng CAI, Xue-Guang SHAO, Outlier Detection for Multivariate Calibration in Near Infrared Spectroscopic Analysis by Model Diagnostics, Chinese Journal of Analytical Chemistry, Volume 44, Issue 2, 2016, Pages 305-309, ISSN 1872-2040, https://doi.org/10.1016/S1872-2040(16)60907-6.
[21]	P. Valderrama, J. W. B. Braga, R. J. Poppi, Variable selection, outlier detection, and figures of merit estimation in a partial least-squares regression multivariate calibration model. A case study for the determination of quality parameters in the alcohol industry by near-infrared spectroscopy, J. Agric. Food Chem. 55 (2007) 8331–8338.
[22]	Dhanoa MS, Lister SJ, Sanderson R, Barnes RJ. The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra. Journal of Near Infrared Spectroscopy. 1994; 2 (1): 43-47. DOI: 10.1255/jnirs.30.
[23]	Becht, A., Schollmayer, C., Monakhova, Y. et al. Tracing the origin of paracetamol tablets by near-infrared, mid-infrared, and nuclear magnetic resonance spectroscopy using principal component analysis and linear discriminant analysis. Anal Bioanal Chem 413, 3107–3118 (2021). https://doi.org/10.1007/s00216-021-03249-z
[24]	Alboukadel Kassambara, Machine Learning Essentials: Practical Guide in R, Edition 1, ISBN-10: 1986406857, page 51, 8: 12.
[25]	Åsmund Rinnan, Frans van den Berg, Søren Balling Engelsen, Review of the most common pre-processing techniques for near-infrared spectra, TrAC Trends in Analytical Chemistry, Volume 28, Issue 10, 2009, Pages 1201-1222, ISSN 0165-9936. DOI: 10.1016/j.trac.2009.07.007.
[26]	Bjørn-Helge Mevik and Henrik René Cederkvist. Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR). Journal of Chemometrics, 18 (9): 422–429, 2004.
[27]	Heise, H. M. Donald A. Burns, Emil W. Ciurczak (Eds.): Handbook of near-infrared analysis, 3rd ed. Anal Bioanal Chem 393, 1387–1389 (2009). DOI: 1007/s00216-008-2580-0.
[28]	Grabska J, Beć KB, Ozaki Y, Huck CW. Anharmonic DFT Study of Near-Infrared Spectra of Caffeine: Vibrational Analysis of the Second Overtones and Ternary Combinations. Molecules. 2021; 26 (17): 5212. DOI: 10.3390/molecules26175212.
[29]	Ying Dou, Ying Sun, Yuqiu Ren, Yulin Ren, Artificial neural network for simultaneous determination of two components of compound paracetamol and diphenhydramine hydrochloride powder on NIR spectroscopy, Analytica Chimica Acta, Volume 528, Issue 1, 2005, Pages 55-61, ISSN 0003-2670 DOI: 10.1016/j.aca.2004.10.050.
[30]	Aminata Sow, Issiaka Traore, Tidiane Diallo, Mohamed Traore, Abdramane Ba, Comparison of Gaussian process regression, partial least squares, random forest and support vector machines for a near infrared calibration of paracetamol samples, Results in Chemistry, Volume 4, 2022, 100508, ISSN 2211-7156, https://doi.org/10.1016/j.rechem.2022.100508.
[31]	Said, M. M., Gibbons, S., Moffat, A., & Zloh, M. (2019). Use of near infrared spectroscopy and spectral databases to assess the quality of pharmaceutical products and aid characterization of unknown components. Journal of Near Infrared Spectroscopy, 27 (5), 379-390. https://doi.org/10.1177/0967033519866009.
[32]	Kalivas, John H., "Two Data Sets of Near Infrared Spectra,"Chemometrics and Intelligent Laboratory Systems, v. 37 (1997) pp. 255-259.

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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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