A5042653183- Spectroscopy and Chemometric Analyses
- Analytical Chemistry and Chromatography
- Spectroscopy Techniques in Biomedical and Chemical Research
- Advanced Statistical Methods and Models
- Computational Drug Discovery Methods
- Blind Source Separation Techniques
- Tensor decomposition and applications
- Remote-Sensing Image Classification
- Water Quality Monitoring and Analysis
- Advanced Chemical Sensor Technologies
- Fault Detection and Control Systems
- Mass Spectrometry Techniques and Applications
- Educational Technology and Assessment
- Metabolomics and Mass Spectrometry Studies
- Sensory Analysis and Statistical Methods
- Advanced biosensing and bioanalysis techniques
- Statistical and numerical algorithms
- Rough Sets and Fuzzy Logic
- Leaf Properties and Growth Measurement
- Monoclonal and Polyclonal Antibodies Research
- Protein purification and stability
- Analytical Chemistry and Sensors
- Remote Sensing in Agriculture
- Microplastics and Plastic Pollution
- Advanced Neuroimaging Techniques and Applications
Royal Netherlands Institute for Sea Research
2024
University of Sistan and Baluchestan
2018-2024
Radboud University Nijmegen
2022-2024
Radboud Institute for Molecular Life Sciences
2023
Radboud University Medical Center
2023
Institute for Advanced Studies in Basic Sciences
2010-2020
University of Szeged
2014-2019
University of Copenhagen
2010
We propose a methodology to select essential spectral pixels (ESPs) of chemical images. These are on the outer envelope principal component scores data and can be identified by convex-hull computation. As ESPs carry all linearly mixed information, large hyperspectral images dramatically reduced before multivariate curve resolution (MCR) analysis. investigated different spectroscopies, sizes, complexities show that analysis full sets hundreds thousands only require few tenths them.
Abstract Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, has diverse applications food quality control, pharmaceutical processes, waste sorting. However, due large size HSI datasets, it can be challenging analyze store them within a reasonable digital infrastructure, especially sorting where speed data storage resources are limited. Additionally, as with most spectroscopic data, there is...
The analysis of mixtures is a routine task in the analytical chemistry area as well other research fields. objective to identify, quantify, and interpret chemical components mixtures. Various bilinear factor decomposition methods, including MCR-ALS, NMFand BNFA, have been proposed solve this problem. However, there little knowledge about their comparative performance terms different factors, such solution reliability, calculation speed, convergence, flexibility constraint implementation,...
Abstract Multivariate curve resolution techniques try to estimate physically and/or chemically meaningful profiles underlying a set of chemical or related measurements. However, the estimation is not generally unique and it often complicated by intensity rotational ambiguities. Constraints as further information entities can be imposed reduce extent Not only long list constraints has been introduced but also some them applied in different ways. Either investigating constraint effects on...
Abstract Self‐modeling curve resolution (SMCR) techniques are widely applied for resolving chemical data to the pure‐component spectra and composition profiles. In most circumstances, there is a range of mathematical solutions problem. The generated by SMCR obey constraints coming from priori physicochemical information about system under investigation. However, several studies demonstrate that unique solution can be obtained implementing some such as trilinearity, equality, zero...
Several constraints are designed to further restrict bilinear decompositions a unique solution. Constraints physico-chemical restrictions on the curve resolution task. Sparsity, as constraint, was introduced create solutions with zero elements. As neither number of zeros nor places not initially available, sparsity constraint should be implemented caution. Regarding two important issues can addressed. The first issue is effect possible decompositions, i.e., set sparse solutions. second type...
Distinguishing isomeric saccharides poses a major challenge for analytical workflows based on (liquid chromatography) mass spectrometry (LC–MS). In recent years, many studies have proposed infrared ion spectroscopy as possible solution the orthogonal, spectroscopic characterization of mass-selected ions can often distinguish species that remain unresolved using conventional MS. However, high conformational flexibility and extensive hydrogen bonding in cause their room-temperature fingerprint...
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Hyperspectral Raman imaging not only offers spectroscopic fingerprints but also reveals morphological information such as spatial distributions in an analytical sample. However, the spectrum-per-pixel nature of hyperspectral (HSI) results a vast amount data. Furthermore, HSI often requires pre- and post-processing steps to extract valuable chemical information. To derive pure spectral signatures concentration abundance maps active compounds, both endmember extraction (EX) Multivariate Curve...
In this article, the idea of essential information-based compression is extended to trilinear datasets. This basically boils down identifying and labelling rows (ERs), columns (ECs) tubes (ETs) such three-dimensional datasets that allow by themselves reconstruct in a linear way entire space original measurements. ERs, ECs ETs can be determined exploiting convex geometry computational approaches as hull or polytope estimations used generate reduced version data at hand. These compressed their...
ABSTRACT In this article, the idea of essential information‐based compression is extended to trilinear datasets. This basically boils down identifying and labelling rows (ERs), columns (ECs) tubes (ETs) such three‐dimensional datasets that allow by themselves reconstruct in a linear way entire space original measurements. ERs, ECs ETs can be determined exploiting convex geometry computational approaches as hull or polytope estimations used generate reduced version data at hand. These...
This study is an implementation of a robust jackknife-based descriptor selection procedure assisted with Gram-Schmidt orthogonalization. Selwood data including 31 molecules and 53 descriptors was considered in this study. Both multiple linear regression (MLR) partial least squares (PLS) methods were applied during the jackknife procedures, desired results obtained when using PLS on both autoscaled orthogonalized sets. Having used technique, all orthogonalized, their number reduced to 30. A...
Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of on predictive power and stability CoMFA models.Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors 57 ATAD2 bromodomain modelled by CoMFA. First all, for all three sets, models with descriptors created then applying each method a new model was developed so set, 9 built. Obtained results show noisy uninformative variables affect results. Based models, 5 FFD, SRD-FFD, IVE-PLS,...