Identification of Clay Minerals by Infrared Spectroscopy and Discriminant Analysis

Abstract

Identification of clay minerals based on chemometric analysis of measured infrared (IR) spectra was suggested. IR spectra were collected using the diffuse reflection technique. Discriminant analysis and principal component analysis were used as chemometric methods. Four statistical models were created for separation and identification of clay minerals. More than 50 samples of various clay mineral standards from different localities were used for the creation of statistical models. The results of this study confirm that the discriminant analysis of IR spectra of clay minerals could provide a powerful tool for identification of clay minerals. Differentiation of muscovite from illite and identification of mixed structures of illitesmectite were achieved.

Investigation of polymer interaction with nanoscale modifier and development of its identification algorithm

“…Its emergence and widespread use in statistical research not only made it possible to accelerate and facilitate the identification procedure significantly, but also created the potential to use more complex mathematical methods together with promising methods (NMR, Mass spectrometry, Fourier-IR spectroscopy). Depending on the identification task complexity, researchers suggest the use of one method of Fourier spectroscopy or thermogravimetry with subsequent computer data processing [16][17][18]. There is a number of disadvantages in the algorithm operation: 1) there are no neglects and tolerances in multicomponent analysis;…”

Chemometric Study on the Forensic Discrimination of Soil Types Using Their Infrared Spectral Characteristics

“…It can also be used as a method of data reduction prior to the use of supervised learning tools such as linear discriminant analysis (LDA). This approach is commonly used with PCA 17,18 and offers the advantages of significant data reduction, allowing the use of tools such as LDA (not possible with the initial dataset) and providing orthogonal variables, which removes problems due to variable co-linearity observed in spectral data. Table II summarizes the variance explained by the first ten NIPALS factors for the 400-4000 cm À1 data set with soil sample replicate spectra and with average spectra.…”

“…PC1 shows a number of the characteristic bands described earlier, including the SiO-H hydroxyl bands at 3700 and 3650 cm À1 seen with clay minerals. 17 The region 1850-2400 cm À1 is dominated by noise in the Fourier transform infrared (FT-IR) spectra, which appears in all the loadings spectra. Although this is generally a region of little interest in mid-IR spectroscopy, the PC1 loadings spectrum shows a feature in this region centered around 1900 cm À1 .…”

“…17 These bands are not always seen 16 in soil and extracted soil organic material but were considered as important features for differentiation of soil from mine sites 14 and featured in the PC1 loadings spectra used for classification of clay minerals. 17 Here they are more prominent in the river bank soil and absent in the flowerbed soil. The bands in the region 1600-1700 cm À1 are normally attributed to several functional groups including aromatic C=C and C=O.…”

HyLogger™ near-infrared spectral analysis: a non-destructive mineral analysis of Aboriginal Australian objects

“…While IR techniques, especially mid-IR analysis, have been used for some applications in pigment analysis, the near-IR range has not been developed for cultural heritage applications. [1][2][3][4][5][6][7][8] Some recent studies highlight the application of various infrared spectroscopies on cultural heritage materials from paintings to mineral studies, although most use FTIR techniques. 7,[9][10][11][12][13] Many of these studies in the area of cultural heritage have made advances in the use of IR, especially in the application of IR spectroscopic imaging, rather than mineral analysis.…”