QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand

• Published in: Molecules (Basel, Switzerland) Vol. 29; no. 15; p. 3562
• Main Authors: Martínez-Trespalacios, José A., Polo-Herrera, Daniel E., Félix-Massa, Tamara Y., Hernandez-Rivera, Samuel P., Hernandez-Fernandez, Joaquín, Colpas-Castillo, Fredy, Castro-Suarez, John R.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.08.2024
• Subjects: Acetaminophen; Acetaminophen - analysis; Acetaminophen - chemistry; Algorithms; Analgesics; Artificial intelligence; chemometrics; counterfeit drugs; Counterfeiting; COVID-19; Data mining; Disease transmission; Drug overdose; Fourier transforms; Infrared spectroscopy; Ingredients; Lasers; Light; Machine Learning; Masks; Medical supplies; mid-infrared; Neural networks; Neural Networks, Computer; Pharmaceutical industry; Pharmaceuticals; Prescription drugs; Principal Component Analysis; Spectrophotometry, Infrared - methods; Support Vector Machine; Support vector machines; Tablets - chemistry; Toxicity; vibrational spectroscopy; France; United States > US; Asia; India; vibrational spectroscopy; chemometrics; machine learning; mid-infrared; counterfeit drugs

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 acetamino...