Automated identification of genuine cut diamonds (brilliants) by luminescence spectrum

The problem of recognition of natural and synthetic diamonds (diamonds) is relevant today. A technique for computer processing of the luminescence spectra of diamonds using the Origin mathematical package is proposed. The processing technique is presented on specific examples. The spectra were measured using a RAOS-3 spectrometer-fluorimeter. A laser with a wavelength of 532 nm was used to excite diamond luminescence. A method is proposed for identifying diamonds of unknown origin by the number of bands of elementary components in the luminescence spectrum when decomposed into Gaussian curves. Luminescence spectra in faceted diamonds (brilliants) are widely used to study their physical properties. Synthetic faceted diamonds are significantly inferior to natural ones in terms of luminescence intensity. The light signal of photoluminescence in the former, in some cases, is comparable with the noise level of the measuring device. As a result, the instantaneous value of the useful signal can take both positive and negative values over the entire wavelength range of the spectrum. Therefore, the detection of a useful signal against the background of interference is of great importance. Along with this, to identify a diamond, it is necessary to solve the problem of decomposing the spectrum into elementary components in the form of Gaussian curves. Since it has been established that the spectra of natural diamonds consist of two peaks, while synthetic diamonds contain from three to eight peaks, which indicates a loose structure of the diamond crystal lattice. The efficiency of solving a number of these problems can be significantly improved by using software applications with special functionality. To demonstrate the features and advantages of the automated technique, the Origin mathematical package was taken, which, in particular, makes it possible to improve the quality of the results of processing a low luminescence spectrum and to find the number of peaks for Gaussian curves with sufficient accuracy.