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Graphical abstract Highlights • Hyper spectral imaging was used to get many spectra in a short time. • The identification of eight paper types was done by using machine learning. • Even papers with very similar spectra could be distinguished with high accuracy. • For 68,400 spectra per sample, the accuracy was 87.8%. Abstract Paper is non-uniform; thus, it is difficult to ascertain whether the slight differences in near-infrared absorption spectra are caused by the difference in paper type or the paper's non-uniformity. We used near-infrared hyperspectral imaging to measure the spectra of many different points quickly for eight different paper samples. Next, we analysed the data using machine learning and constructed a model to ascertain whether the differences in the spectra are due to the difference in paper type or the paper's non-uniformity. Even papers with very similar spectral shapes were distinguished with high accuracy with this method. As the number of spectra used to construct the classification model increased, the accuracy of the classification model increased. With 6840 spectra per sample, the accuracy exceeded 85%, and with 68,400 spectra per sample, the accuracy for identification was 87.8%. [ABSTRACT FROM AUTHOR]

The accuracy of the fundamental electric quadrupolar constitutive relation used in the paper by Yaghjian and Silveirinha (2016, https://doi.org/10.1002/2016RS006066) is determined and used to improve upon the derivation of one of the boundary conditions for electric quadrupolar media. In addition, three typographical errors in the aforementioned paper are noted. Key Points: The accuracy of expressions in the previous paper is determinedThe derivation of one of the boundary conditions in the previous paper is improvedTypographical errors in the previous paper are noted [ABSTRACT FROM AUTHOR]