RESUMO
Identifying pharmaceutical ingredients is a routine procedure required during industrial manufacturing. Here we show that a recently developed Raman compressive detection strategy can be employed to classify various widely used pharmaceutical materials using a hybrid supervised/unsupervised strategy in which only two ingredients are used for training and yet six other ingredients can also be distinguished. More specifically, our liquid crystal spatial light modulator (LC-SLM) based compressive detection instrument is trained using only the active ingredient, tadalafil, and the excipient, lactose, but is tested using these and various other excipients; microcrystalline cellulose, magnesium stearate, titanium (IV) oxide, talc, sodium lauryl sulfate and hydroxypropyl cellulose. Partial least squares discriminant analysis (PLS-DA) is used to generate the compressive detection filters necessary for fast chemical classification. Although the filters used in this study are trained on only lactose and tadalafil, we show that all the pharmaceutical ingredients mentioned above can be differentiated and classified using PLS-DA compressive detection filters with an accumulation time of 10ms per filter.