A Fast Classification Scheme in Raman Spectroscopy for the Identification of Mineral Mixtures Using a Large Database With Correlated Predictors

Abstract

Robust classification methods are vital to the successful implementation of many material characterization techniques, particularly where large databases exist. In this paper, we demonstrate an extremely fast classification method for the identification of mineral mixtures in Raman spectroscopy using the large RRUFF database. However, this method is equally applicable to other techniques meeting the large database criteria, these including laser-induced breakdown, X-ray diffraction, and mass spectroscopy methods. Classification of these multivariate datasets can be challenging due in part to the various obscuring features inherently present within the underlying dataset and in part to the volume and variety of information known a priori. Some of the more specific challenges include the observation of mixtures with overlapping spectral features, the use of large databases (i.e., the number of predictors far outweighs the number of observations), the use of databases that contain groups of correlated spectra, and the ever present, clouding contaminants of noise, undesired background, and spectrometer artifacts. Although many existing classification algorithms attempt to address these problems individually, not many address them as a whole. Here, we apply a multistage approach, which leverages well-established constrained regression techniques, to overcome these challenges. Our modifications to conventional algorithm implementations are shown to increase speed and performance of the classification process. Unlike many other techniques, our method is able to rapidly classify mixtures while simultaneously preserving sparsity. It is easily implemented, has very few tuning parameters, does not require extensive parameter training, and does not require data dimensionality reduction prior to classification.Index Terms-Classification, elastic net (EN), planetary mineralogy, Raman spectroscopy, regression.

The role of intensity and instrument sensitivity in Raman mineral identification

“…CrystalSleuth accomplishes background removal using piecewise linear interpolation between smoothed off‐peak segments and uses the cosine similarity metric for spectrum matching. While the background removal method can have a significant effect on match success and spectrum matching methods more applicable to mineral spectra are being developed, the CrystalSleuth algorithms were used for this study for consistency with other Raman users in the geosciences community.…”

“…1994) or it can be based upon whole‐spectrum matching, for example, CrystalSleuth, the spectrum processing/matching software distributed by the RRUFF Project. The success of this process depends upon the degree to which characteristic peaks rise above the background noise level [signal‐to‐noise (S/N) ratio] and low intensity minor peaks are often critical for discrimination between related mineral species …”

GRaVN: A Convolutional Network Approach to Generalised Characterisation of Raman Spectra for Space Exploration

“…Raman spectroscopy exploits reactive shifts in the frequency of monochromatic light upon interaction with [molecular species in] target materials. Inelastic scattering occurs when monochromatic laser light of wavelength λ 0 and energy E 0 = hc/λ 0 is incident on a surface, where h is Plancks constant and c is the velocity of light [17]. In this process, the light that interacts with the matter will either be scattered or absorbed.…”

Miniaturized time-resolved Raman spectrometer for planetary science based on a fast single photon avalanche diode detector array

“…Finally, the determination of mineral phases present from the measured spectra is accomplished with the use of our custom classification software [36] designed to handle large databases with robustness against inconsistencies in database spectra related to, for example, variability in instrumentation and sample purity. In this work we use the RRUFF (RRUFFÔ Project: http://rruff.info/) database combined with our own database of collected spectra to determine the mineral phases present in mixtures such as naturally occurring rock samples.…”