Pharmaceutical tablet classification using a portable spectrometer with combinations of visible and near-infrared spectra

We demonstrate a system for identifying tablets using a portable visible-near-infrared (VIS-NIR) spectrometer and a convolutional neural network (CNN), which is one of the machine learning algorithms. According to spectroscopy techniques, the spectrum of each tablet has unique reflectance features. To classify tablets using their spectra, we have implemented three comparative experiments on the wavelength range and successfully classified 14 kinds of tablets. The results of the three experiments are 97.86% (using the VIS spectra), 96.90% (using the NIR spectra), and 98.81% (using the VIS-NIR spectra). This shows high accuracy without reference to the wavelength range.