The absorption spectrum of 4-hydroxy-7-nitrobenzofurazan (NBDOH) and its conjugate anion has been recorded in 23 solvents. The data has been analysed according to the Taft and Kamlet treatment and compared with that of some parent compounds: 4-methoxy-, 4-propylamino- and 4-diethylamine-7-nitrobenzofurazan. The phenolate anion NBDO^– has been shown to exhibit the solvatochromic behaviour characteristic of this NBD series in aprotic media, although in protic media hydrogen bonding had a drastic effect on the UV–VIS spectrum. Such a sensitivity to erotogenic solvents was not encountered for other phenoxide anions such as picrate. This difference of behaviour is discussed on the basis of electrostatic potentials obtained by MNDO calculations. Evidence is given of strong negative charges located on the C(5) and C(7) atoms of NBDO^–, indicating that strong hydrogen bonding may take place with solvent molecules. However, this stabilization of the anion by hydrogen bonding does not seem to influence the dissociation equilibrium, the acidity of NBDOH being perfectly in line with that of other nitrophenols whatever the solvent.

Most mining companies have registered important amounts of drill core composite spectra using different acquisition equipment and by following diverse protocols. These companies have used classic spectrography based on the detection of absorption features to perform semi-quantitative mineralogy. This methodology requires ideal laboratory conditions in order to obtain normalized spectra to compare. However, the inherent variability of spectral features—due to environmental conditions and geological context, among others—is unavoidable and needs to be managed. This work presents a novel methodology for geometallurgical sample characterization consisting of a heterogeneous, multi-pixel processing pipeline which addresses the effects of ambient conditions and geological context variability to estimate critical geological and geometallurgical variables. It relies on the assumptions that the acquisition of hyperspectral images is an inherently stochastic process and that ore sample information is deployed in the whole spectrum. The proposed framework is basically composed of: (a) a new hyperspectral image segmentation algorithm, (b) a preserving-information dimensionality reduction scheme and (c) a stochastic hierarchical regression model. A set of experiments considering white reference spectral characterization and geometallurgical variable estimation is presented to show promising results for the proposed approach.