Analysis of UV spectral bands using multidimensional scaling

Abstract

This study describes the change of the ultravi-olet spectral bands starting from 0.1 to 5.0 nm slit width in the spectral range of 200-400 nm. The analysis of the spectral bands is carried out by using the multidimensional scaling (MDS) approach to reach the latent spectral back-ground. This approach indicates that 0.1 nm slit width gives higher-order noise together with better spectral details. Thus, 5.0 nm slit width possesses the higher peak amplitude and lower-order noise together with poor spectral details. In the above-mentioned conditions, the main problem is to find the relationship between the spectral band properties and the slit width. For this aim, the MDS tool is to used recognize the hidden information of the ultraviolet spectra of sildenafil cit-rate by using a Shimadzu UV-VIS 2550, which is in theworld the best double monochromator instrument. In this study, the proposed mathematical approach gives the rich findings for the efficient use of the spectrophotometer in the qualitative and quantitative studies.

Exploring Appropriate Preprocessing Techniques for Hyperspectral Soil Organic Matter Content Estimation in Black Soil Area

“…If the relation between the spectrum of the sample points and the dependent variable is nonlinear, MDS and LLE can achieve better performance. MDS was applied to the hidden information extraction of ultraviolet absorption spectrum [32], and LLE was used to detect egg freshness and low-grade porphyry copper deposit using Vis-NIR spectrum [33,34]. Therefore, it is significant to explore the potential of MDS and LLE used in soil Vis-NIR spectroscopy, particularly in SOM content estimation.…”

Energy-based damage localization under ambient vibration and non-stationary signals by ensemble empirical mode decomposition and Mahalanobis-squared distance

“…An important note is that the conventional and common signal processing techniques in time and frequencies domains may not be sufficiently informative for extracting reliable features from non-stationary signals (Feng et al, 2013). To address this limitation, one can apply effective techniques for signal processing and feature extraction (Dinç and Baleanu, 2010; Dinç et al, 2013; Machado et al, 2015; Nigmatullin et al, 2012; Nigmatullin et al, 2013; Nigmatullin et al, 2014; Nigmatullin and Gubaidullin, 2018). Adaptive time–frequency signal decomposition methods are taken into account as one of the influential and efficient approaches to feature extraction (Feng et al, 2013; Lei et al, 2013).…”