Point of Interest Mid-Infrared Spectroscopy for Inline Pharmaceutical Packaging Quality Control

Good manufacturing practice for medicinal products is laid down in several guidelines and Directives of the European Commission. Those regulations imply, among other aspects, that medicinal product manufacturers have to ensure that the final products are fit for their intended use and do not place patients at risk due to the inadequate safety, quality, or efficacy. For the case of manufacturing of pharmaceutical blisters, the attainment of this quality objective often leads to the resourcing of qualified personnel for final visual verification of the blister pack content. The need for inline content verification of pharmaceutical blisters asks therefore for sensors that provide fast, noncontact, and accurate chemical information of each individual blister content. Here, we report on a quantum cascade laser (QCL)-based blister-verification sensor. The verification principle is substance chemical identification by means of backscattering mid-infrared (IR) spectroscopy. The light source is a palm-size wavelength-tunable mid-IR QCL with ∼ 1-kHz tuning speed. The blister content verification uses machine vision to obtain the required position information for each individual content and fast spatial scanning facilitated by a two-axis galvanometer scanner. Diffuse reflectance mid-IR spectra are acquired at each location, and their classification is conducted instantaneously. Different classifier approaches are evaluated and discussed including machine learning and standard cross correlation to Fourier-transform-IR (FTIR) data. Altogether, this sensor is capable of scanning a standard 12-pill blister pack in ∼ 0.3 s, whereas this scanning time is essentially related to the desired classification accuracy, but not to the spectral resolution, which is fixed. Using machine learning classification, 100% identification accuracy is demonstrated for 13 different medication types (i.e., with different chemical nature), whereas only 97.4% identification accuracy is achieved by standard cross correlation to FTIR data. The used pills have all similar size, shape, and color, so that classification by visual inspection is barely possible.