Let us consider the spectrum by Kumar et al., 2019, , and fit the processed data by means of q-Gaussians. This is the same what we can do as for data available in literature, usually given just in the processed form, without any information about the applied processing method of the raw data. The result of the q-Gaussian fitting is shown in the Fig. , for a part of the spectrum. The misfit is evidencing that a further investigation could be interesting for the largest peak, that is the second peak from the left. But before, we compare the q-Gaussian fitting with fits obtained with Lorentzian and Gaussian functions. In the Figures and we have compared the best fits made with q-Gaussians, Lorentzian functions and Gaussians. The sum of the squares of the deviations has its lowest value for the q-Gaussians. In the case of the Lepidocrocite spectrum, the resulting q-Gaussian line shapes are closer to Lorentzian than to Gaussian functions. For the previous comparisons, we have used the processed data. As shown by the image in the Fig. , there are some differences between processed and raw data. In the case we consider the raw data, the results are given in the following plots. The results (Fig. ) are consistent with those previously given. We have seen that the q-Gaussians are providing a better fitting result, in comparison to Lorentzian and Gaussian functions, for processed and raw data. Considering the first two peaks of the spectrum, let us compare the q-Gaussian fit with that we can obtain with pseudo-Voigt functions. Pseudo-Voigt functions are linear combination of Lorentzian and Gaussian functions . The best fit with the q-Gaussians has a lower sum of the squares of deviations, and this is just due to the shape of the functions. Besides being lower the misfit, an advantage of using the q-Gaussians is in the possibility to give just one FWHM (Full Width at Half Maximum) of the peak (see Appendix A), and not the two values, which are corresponding to the two components of the pseudo-Voigt function.