Qualitative and quantitative chemometric modelling of nanostructured carbon samples based on infrared spectroscopy

Rapid population growth necessitates a continuous increase in industrial productivity, with a concomitant environmental burden. During the past few years, nanostructured carbon materials have proved their effectiveness in reducing the usage of several hazardous substances, owing to their distinct characteristics. These properties depend on the type of feedstock and the parameters of pyrolysis. We have developed multivariate prediction models to determine several physical properties of nanostructured carbon samples, which are usually calculated from the adsorption isotherms. Adsorption measurement is a time-consuming and exhaustive process. Therefore, our goal was to provide a fast and environmentally friendly alternative to current methods to determine micropore volume, specific surface area and total pore volume, based on FTIR spectroscopy coupled with chemometric methods. Moreover, we have created classification models, which are capable of predicting from IR spectra what feedstock materials were used. Our support vector machine–based classification model had the best accuracy values, above 0.86. Our classification and regression models have excellent performance and were properly validated, thus they are good alternatives for determining the important qualitative and quantitative features of carbon samples robustly and fast.