Univariate and Multivariate Analysis of Tannin-Impregnated Wood Species Using Vibrational Spectroscopy

2014 ◽  
Vol 68 (4) ◽  
pp. 488-494 ◽  
Author(s):  
Thomas Schnabel ◽  
Maurizio Musso ◽  
Gianluca Tondi
Keyword(s):  
Multivariate Analysis ◽  
Vibrational Spectroscopy ◽  
Wood Species ◽  
Impregnated Wood

Prediction of diesel fuel properties by vibrational spectroscopy using multivariate analysis

2012 ◽  
Vol 67 (12) ◽  
pp. 939-949 ◽  
Author(s):  
Slavica Marinović ◽  
Marko Krištović ◽  
Branka Špehar ◽  
Vinko Rukavina ◽  
Ante Jukić
Keyword(s):  
Multivariate Analysis ◽  
Vibrational Spectroscopy ◽  
Diesel Fuel ◽  
Fuel Properties

Interactions of water confined in a metal–organic framework as studied by a combined approach of Raman, FTIR, and IR electroabsorption spectroscopies and multivariate curve resolution analysis

2020 ◽  
Vol 22 (32) ◽  
pp. 17798-17806
Author(s):  
Takayuki Hiraoka ◽  
Shinsuke Shigeto
Keyword(s):  
Multivariate Analysis ◽  
Vibrational Spectroscopy ◽  
Metal Organic Framework ◽  
Multivariate Curve Resolution ◽  
Combined Approach ◽  
Curve Resolution ◽  
Resolution Analysis ◽  
Metal Organic ◽  
Organic Framework

Distinct interactions of water in heterogeneous confinement of a pillared-layer type MOF elucidated by a joint vibrational spectroscopy-multivariate analysis investigation.

Infrared Chemical Micro-Imaging Assisted by Interactive Self-Modeling Multivariate Analysis

1994 ◽  
Vol 48 (3) ◽  
pp. 320-326 ◽  
Author(s):  
Jean Guilment ◽  
Sharon Markel ◽  
Willem Windig
Keyword(s):  
Multivariate Analysis ◽  
Spectral Data ◽  
Vibrational Spectroscopy ◽  
Spatial Resolution ◽  
Chemical Imaging ◽  
Pure Component ◽  
Mixture Analysis

In the analytical environment, spectral data resulting from analysis of samples often represent mixtures of several components. Extraction of information about pure components from that kind of mixture is a major problem, especially when reference spectra are not available. Self-modeling multivariate mixture analysis has been developed for this type of problem. In this paper, two examples will be used to show the potential of the technique for vibrational spectroscopy. Infrared microspectroscopic chemical imaging has been employed to improve spatial resolution for distinguishing differences between adjacent, nonidentical materials. The resolution of a 2- to 3- μm-thick inner layer, from a four-layer polymer laminate, has been achieved. The same approach has been utilized to extract pure component spectra out of a KBr pellet of a mixture of three compounds.

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