Enhanced Quality Control In Pharmaceutical Applications By Combining Raman Spectroscopy And Machine Learning Techniques

Keywords

Acetaminophen; Machine learning; Polymorph detection; Principal components analysis; Raman spectroscopy

Abstract

In this work, we applied machine learning techniques to Raman spectra for the characterization and classification of manufactured pharmaceutical products. Our measurements were taken with commercial equipment, for accurate assessment of variations with respect to one calibrated control sample. Unlike the typical use of Raman spectroscopy in pharmaceutical applications, in our approach the principal components of the Raman spectrum are used concurrently as attributes in machine learning algorithms. This permits an efficient comparison and classification of the spectra measured from the samples under study. This also allows for accurate quality control as all relevant spectral components are considered simultaneously. We demonstrate our approach with respect to the specific case of acetaminophen, which is one of the most widely used analgesics in the market. In the experiments, commercial samples from thirteen different laboratories were analyzed and compared against a control sample. The raw data were analyzed based on an arithmetic difference between the nominal active substance and the measured values in each commercial sample. The principal component analysis was applied to the data for quantitative verification (i.e., without considering the actual concentration of the active substance) of the difference in the calibrated sample. Our results show that by following this approach adulterations in pharmaceutical compositions can be clearly identified and accurately quantified.

Publication Date

6-1-2018

Publication Title

International Journal of Thermophysics

Volume

39

Issue

6

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s10765-018-2391-2

Socpus ID

85046635251 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85046635251

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