Neural Network Analysis of Spectroscopic Data of Lycopene and β-Carotene Content in Food Samples Compared to HPLC-UV-Vis

2010 ◽  
Vol 58 (1) ◽  
pp. 72-75 ◽  
Author(s):  
Montaña Cámara ◽  
José S. Torrecilla ◽  
Jorge O. Caceres ◽  
M. Cortes Sánchez Mata ◽  
Virginia Fernández-Ruiz
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Spectroscopic Data ◽  
Food Samples ◽  
Neural Network Analysis ◽  
Carotene Content ◽  
Β Carotene

Artificial intelligence analysis of FTIR and CD spectroscopic data for predicting and quantifying the length and content of protein secondary structures

2021 ◽  
pp. 1-7
Author(s):  
P.I. Haris ◽  
J.A. Hering
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Analysis ◽  
Secondary Structure ◽  
Ftir Spectroscopy ◽  
Spectral Data ◽  
Spectroscopic Data ◽  
Protein Secondary Structure ◽  
Neural Network Analysis ◽  
Structure Sensitive

Besides NMR and X-ray crystallography, FTIR and CD spectroscopy are widely considered to be useful for determining protein secondary structure. These techniques can be used to obtain data in few minutes, using small quantities of proteins, which make them amenable for proteomics research. Here we explore the possibility of using artificial intelligence techniques to simultaneously analyse both FTIR and CD spectroscopic data for an identical set of proteins. Neural network analysis was carried out on normalised regions of FTIR (1700-1600 cm−1) and CD (180-259 nm) spectral data both with and without boxcar averaging in order to quantify the average length and percentages of secondary structures. A hybrid genetic algorithm/neural network approach, that automatically selects structure-sensitive wavelength/frequency, was used for the quantification of the protein secondary structure. Using this algorithm we also successfully identified the region of the CD spectrum that contains the most structure-sensitive information. This was located between 214-251 nm, suggesting that this region alone may be sufficient to rapidly determine the secondary structure content from CD spectral data. Overall, CD spectroscopic analysis produced better results compared to FTIR spectroscopy when selected wavelengths were used, although FTIR was better when the entire region between 1700-1600 cm−1 (FTIR), and 180-259 nm (CD), was subjected to neural network analysis. Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) with fuzzy subtractive clustering for the analysis of the spectral data led to a slightly better prediction of the average helix/sheet length for FTIR spectroscopy compared to CD. Our findings reveal the potential of using artificial intelligence techniques for not only extracting structural information but also for better understanding of the relationship between complex spectral data and biologically important information.

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

NEURAL NETWORK ANALYSIS OF INTERACTION OF POSTURAL CONTROL MACHANISMS UNDER ARTUFICIAL FEEDBACK

2016 ◽  
Vol 34 (2) ◽  
pp. 025-036
Author(s):  
Oleg G. Gorshkov ◽  
◽  
Irina B. Starchenko ◽  
Andrey S. Sliva ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Postural Control ◽  
Neural Network Analysis

scholarly journals Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danijela Šantić ◽  
Kasia Piwosz ◽  
Frano Matić ◽  
Ana Vrdoljak Tomaš ◽  
Jasna Arapov ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Bacterial Community ◽  
Microbial Diversity ◽  
Community Composition ◽  
Adriatic Sea ◽  
Bacterial Community Composition ◽  
Environmental Data ◽  
Large Contribution ◽  
Neural Network Analysis

AbstractBacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.

scholarly journals Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials

2021 ◽  
Author(s):  
Daniil A. Boiko ◽  
Evgeniy O. Pentsak ◽  
Vera A. Cherepanova ◽  
Evgeniy G. Gordeev ◽  
Valentine P. Ananikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Carbon Material ◽  
Deep Neural Network ◽  
Carbon Materials ◽  
Material Surface ◽  
Neural Network Analysis ◽  
Pd Nanoparticle ◽  
Sem Imaging

Defectiveness of carbon material surface is a key issue for many applications. Pd-nanoparticle SEM imaging was used to highlight “hidden” defects and analyzed by neural networks to solve order/disorder classification and defect segmentation tasks.

scholarly journals Quantitative Dynamics Effects of Belt and Road Economies Trade Using Structural Gravity and Neural Networks

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110326
Author(s):  
Koffi Dumor ◽  
Li Yao ◽  
Jean-Paul Ainam ◽  
Edem Koffi Amouzou ◽  
Williams Ayivi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Bilateral Trade ◽  
Belt And Road Initiative ◽  
Neural Network Analysis ◽  
Analysis Techniques ◽  
Belt And Road ◽  
The Impact ◽  
Trade Partners

Recent research suggests that China’s Belt and Road Initiative (BRI) would improve the bilateral trade between China and its partners. This article uses detailed bilateral export data from 1990 to 2017 to investigate the impact of China’s BRI on its trade partners using neural network analysis techniques and structural gravity model estimations. Our main findings suggest that the BRI countries would raise exports by a modest 5.053%. This indicates that export and network upgrades should be considered from economic and policy perspectives. The results also show that neural networks is more robust compared with structural gravity framework.

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