Recognition of pharmaceuticals with compact mini-Raman-spectrometer and automized pattern recognition algorithms

Hendrik Jähme; Giuseppe Di Florio; Valeria Conti Nibali; Cemal Esen; Andreas Ostendorf; Markus Grafen; Erich Henke; Jens Soetebier; Carsten Brenner; Martina Havenith; Martin R. Hofmann

doi:10.1117/12.2228070

4 May 2016 Recognition of pharmaceuticals with compact mini-Raman-spectrometer and automized pattern recognition algorithms

Hendrik Jähme, Giuseppe Di Florio, Valeria Conti Nibali, Cemal Esen, Andreas Ostendorf, Markus Grafen, Erich Henke, Jens Soetebier, Carsten Brenner, Martina Havenith, Martin R. Hofmann

Author Affiliations +

Proceedings Volume 9899, Optical Sensing and Detection IV; 98992M (2016) https://doi.org/10.1117/12.2228070
Event: SPIE Photonics Europe, 2016, Brussels, Belgium

Abstract

Robust classiﬁcation of pharmaceuticals in an industrial process is an important step for validation of the ﬁnal product. Especially for pharmaceuticals with similar visual appearance a quality control is only possible if a reliable algorithm based on easily obtainable spectroscopic data is available. We used Principal Component Analysis (PCA) and Support Vector Machines (SVM) on Raman spectroscopy data from a compact Raman system to classify several look-alike pharmaceuticals. This paper describes the data gathering and analysis process to robustly discriminate 19 diﬀerent pharmaceuticals with similar visual appearance. With the described process we successfully identiﬁed all given pharmaceuticals which had a signiﬁcant amount of active ingredients. Thus automatic validation of these pharmaceuticals in a process can be used to prevent wrong administration of look-alike drugs in an industrial setting, e.g. patient individual blistering.

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Hendrik Jähme, Giuseppe Di Florio, Valeria Conti Nibali, Cemal Esen, Andreas Ostendorf, Markus Grafen, Erich Henke, Jens Soetebier, Carsten Brenner, Martina Havenith, and Martin R. Hofmann "Recognition of pharmaceuticals with compact mini-Raman-spectrometer and automized pattern recognition algorithms", Proc. SPIE 9899, Optical Sensing and Detection IV, 98992M (4 May 2016); https://doi.org/10.1117/12.2228070

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