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Open AccessArticle
QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand
by
José A. Martínez-Trespalacios
José A. Martínez-Trespalacios 1
,
Daniel E. Polo-Herrera
Daniel E. Polo-Herrera 2,
Tamara Y. Félix-Massa
Tamara Y. Félix-Massa 3,
Samuel P. Hernandez-Rivera
Samuel P. Hernandez-Rivera 3
,
Joaquín Hernandez-Fernandez
Joaquín Hernandez-Fernandez 1,2,4
,
Fredy Colpas-Castillo
Fredy Colpas-Castillo 2 and
John R. Castro-Suarez
John R. Castro-Suarez 5,*
1
Mechanical Engineering Program, School of Engineering, Universidad Tecnológica de Bolívar, Parque Industrial y Tecnológico Carlos Vélez Pombo, Cartagena 130001, Colombia
2
Chemistry Program, Department of Natural and Exact Sciences, San Pablo Campus, University of Cartagena, Cartagena 130015, Colombia
3
Center for Chemical Sensors and Chemical Imaging and Surface Analysis Center, Department of Chemistry, University of Puerto Rico, Mayaguez, PR 00681, USA
4
Department of Natural and Exact Science, Universidad de la Costa, Barranquilla 080002, Colombia
5
Área Básicas Exactas, Universidad del Sinú, Seccional Cartagena, Cartagena 130015, Colombia
*
Author to whom correspondence should be addressed.
Molecules 2024, 29(15), 3562; https://doi.org/10.3390/molecules29153562 (registering DOI)
Submission received: 15 June 2024
/
Revised: 18 July 2024
/
Accepted: 23 July 2024
/
Published: 28 July 2024
Abstract
The development of new methods of identification of active pharmaceutical ingredients (API) is a subject of paramount importance for research centers, the pharmaceutical industry, and law enforcement agencies. Here, a system for identifying and classifying pharmaceutical tablets containing acetaminophen (AAP) by brand has been developed. In total, 15 tablets of 11 brands for a total of 165 samples were analyzed. Mid-infrared vibrational spectroscopy with multivariate analysis was employed. Quantum cascade lasers (QCLs) were used as mid-infrared sources. IR spectra in the spectral range 980–1600 cm−1 were recorded. Five different classification methods were used. First, a spectral search through correlation indices. Second, machine learning algorithms such as principal component analysis (PCA), support vector classification (SVC), decision tree classifier (DTC), and artificial neural network (ANN) were employed to classify tablets by brands. SNV and first derivative were used as preprocessing to improve the spectral information. Precision, recall, specificity, F1-score, and accuracy were used as criteria to evaluate the best SVC, DEE, and ANN classification models obtained. The IR spectra of the tablets show characteristic vibrational signals of AAP and other APIs present. Spectral classification by spectral search and PCA showed limitations in differentiating between brands, particularly for tablets containing AAP as the only API. Machine learning models, specifically SVC, achieved high accuracy in classifying AAP tablets according to their brand, even for brands containing only AAP.
Share and Cite
MDPI and ACS Style
Martínez-Trespalacios, J.A.; Polo-Herrera, D.E.; Félix-Massa, T.Y.; Hernandez-Rivera, S.P.; Hernandez-Fernandez, J.; Colpas-Castillo, F.; Castro-Suarez, J.R.
QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand. Molecules 2024, 29, 3562.
https://doi.org/10.3390/molecules29153562
AMA Style
Martínez-Trespalacios JA, Polo-Herrera DE, Félix-Massa TY, Hernandez-Rivera SP, Hernandez-Fernandez J, Colpas-Castillo F, Castro-Suarez JR.
QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand. Molecules. 2024; 29(15):3562.
https://doi.org/10.3390/molecules29153562
Chicago/Turabian Style
Martínez-Trespalacios, José A., Daniel E. Polo-Herrera, Tamara Y. Félix-Massa, Samuel P. Hernandez-Rivera, Joaquín Hernandez-Fernandez, Fredy Colpas-Castillo, and John R. Castro-Suarez.
2024. "QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand" Molecules 29, no. 15: 3562.
https://doi.org/10.3390/molecules29153562
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