Effective Raman spectra identification with tree-based methods

Machine learning for recognizing minerals from multispectral data

“…Typically, Raman spectra are preprocessed before these classifiers are applied (Carey et al, 2015a,b;Sevetlidis and Pavlidis, 2019) in order to help in the elimination of noise (unwanted signals) and to enhance discriminating features. As was done in Carey et al (2015b), we split the dataset into a "training set", constructed by selecting three spectra per mineral species at random, and assigning the remaining spectra into the testing set.…”

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

“…Of the scattered light, the majority will scatter elastically, in a process known as Rayleigh Scattering. However, a proportionally small amount will scatter inelastically, producing an emission "Raman spectrum" of light, 6 -8 orders of magnitude weaker than the source [17,18]. One of the major obstacles to obtaining well-defined spectra is a condition known as fluorescence background [12,18,[20][21][22][23][24][25][26].…”

“…However, a proportionally small amount will scatter inelastically, producing an emission "Raman spectrum" of light, 6 -8 orders of magnitude weaker than the source [17,18]. One of the major obstacles to obtaining well-defined spectra is a condition known as fluorescence background [12,18,[20][21][22][23][24][25][26]. This has the effect of "over-saturating" the spectra, such that the identifying molecular peaks are no longer visible.…”

“…A variety of hardware and software-based methods have been explored for quenching fluorescence background and enhancing weak Raman signals. These range from modifying the aperture time of the Raman instrumentation, so that there isn't enough time for the fluorescence to develop in the signal [12,18,[20][21][22][23][24][25][26], to applying baseline detection algorithm to estimate a clean signal [28]. All these techniques interfere with the data itself by either modifying conditions for acquisition or altering data directly, which impacts interpretation.…”

Espectroscopia Raman e redes neurais artificiais na identificação de minerais dos grupos do pirocloro e da microlita

“…realizar a classificação de minerais. [89][90][91] Estes trabalhos focam em mais de quatro espécies de minerais, utilizam outras técnicas de pré-processamento de dados e outros métodos de aprendizado de máquina (além de Perceptron de múltiplas camadas, árvores de decisão,…”