- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Ultrafast Elemental Mapping of Platinum Group Elements and Mineral Identification in Platinum-Palladium Ore Using Laser Induced Breakdown Spectroscopy
摘要： This paper demonstrates the capability of performing an ultrafast chemical mapping of drill cores collected from a platinum/palladium mine using laser‐induced breakdown spectroscopy (LIBS). A scan of 40 mm × 30 mm was performed, using a commercial LIBS analyzer, onto the flat surface of a drill core with a scanning speed of 1000 Hz, and a spatial resolution of 50 μm, in about 8 min. Maps of the scanned areas for seven chemical elements (platinum, palladium, nickel, copper, iron, silicon, and magnesium), as well as a single map including the seven elements altogether, were then generated using the proprietary software integrated into the LIBS analyzer. Based on the latter image, seven minerals were identified using the principal component analysis (PCA) and correlations with the elemental maps.
关键词： laser induced breakdown spectroscopy (LIBS),mineral identification,platinum‐group elements (PGE),principal component analysis (PCA),scanning speed at 1000 Hz
更新于2025-09-16 10:30:52
-
Effective Raman spectra identification with tree-based methods
摘要： Treatment of spectral information is an essential tool for the examination of various cultural heritage materials. Raman spectroscopy has become an everyday practice for compound identification due to its non-intrusive nature, but often it can be a complex operation. Spectral identification and analysis on artists’ materials is being done with the aid of already existing spectral databases and spectrum matching algorithms. We demonstrate that with a machine learning method called Extremely Randomised Trees, we can learn a model in a supervised learning fashion, able to accurately match an entire-spectrum range into its respective mineral. Our approach was tested and was found to outperform the state-of-the-art methods on the corrected RRUFF dataset, while maintaining low computational complexity and inherently supporting parallelisation.
关键词： Randomised trees,Random forest,Mineral identification,Raman spectroscopy,Machine learning,Classification,Raman spectra identification
更新于2025-09-10 09:29:36
-
Mineral identification in LWIR hyperspectral imagery applying sparse-based clustering
摘要： An assessment of mineral identification applying hyperspectral infrared imagery in laboratory conditions is presented here and strives to identify nine different minerals (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, quartz). A hyperspectral camera in Long-Wave Infrared (LWIR, 7.7–11.8 μm) with a LW-macro lens, an infragold plate, and a heating source are instruments used in the experiment. For automated identification, a Sparse Principal Component Analysis (Sparse PCA)-based K-means clustering is employed to categorise all pixel-spectra in different groups. Then the best representatives of each cluster (using spectral averaging) are chosen to compare these spectra to ASTER spectral library of JPL/NASA through spectral comparison techniques. Spectral angle mapper (SAM) and Normalized Cross Correlation (NCC) are two of such techniques, which are used herein to measure the spectral difference. In order to evaluate robustness of clustering results among the minerals spectra, we have added three levels of Gaussian and salt&pepper noise, 0%; 2%, and 4%, to input spectra which dropped the accuracy percentage from more than 84.73%, for 0% added noise, to 44.57%, for 2% average of both additive noise, and 22.21%, for 4% additive noise. The results conclusively indicate a promising performance but noise sensitive behaviour of the proposed approach.
关键词： mineral identification,Hyperspectral imagery,sparse principle components analysis,clustering
更新于2025-09-04 15:30:14