scholarly journals Mass Spectral Filtering by Mass-Remainder Analysis (MARA) at High Resolution and Its Application to Metabolite Profiling of Flavonoids

2021 ◽  
Vol 22 (2) ◽  
pp. 864
Author(s):  
Tibor Nagy ◽  
Gergő Róth ◽  
Ákos Kuki ◽  
Miklós Zsuga ◽  
Sándor Kéki
Keyword(s):  
High Speed ◽  
Principal Component ◽  
Spectrometric Analysis ◽  
Spectral Filtering ◽  
Potential Health ◽  
Mass Spectral ◽  
Novel Method ◽  
Tandem Mass Spectrometric ◽  
Mining Procedure ◽  
Natural Herb

Flavonoids represent an important class of secondary metabolites because of their potential health benefits and functions in plants. We propose a novel method for the comprehensive flavonoid filtering and screening based on direct infusion mass spectrometry (DIMS) analysis. The recently invented data mining procedure, the multi-step mass-remainder analysis (M-MARA) technique is applied for the effective mass spectral filtering of the peak rich spectra of natural herb extracts. In addition, our flavonoid-filtering algorithm facilitates the determination of the elemental composition. M-MARA flavonoid-filtering uses simple mathematical and logical operations and thus, it can easily be implemented in a regular spreadsheet software. A huge benefit of our method is the high speed and the low demand for computing power and memory that enables the real time application even for tandem mass spectrometric analysis. Our novel method was applied for the electrospray ionization (ESI) DIMS spectra of various herb extract, and the filtered mass spectral data were subjected to chemometrics analysis using principal component analysis (PCA).

Scanning x-ray fluorescence microscopy and principal component analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1752-1753
Author(s):  
Brian Cross
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
High Speed ◽  
Image Acquisition ◽  
Principal Component ◽  
Fluorescence Microscope ◽  
Acquisition Rate ◽  
X Ray ◽  
Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

Generic monitoring tools for water characteristics assessment

2003 ◽  
Vol 3 (1-2) ◽  
pp. 351-357
Author(s):  
S. Le Bonté ◽  
M.-N. Pons ◽  
O. Potier ◽  
S. Chanel ◽  
M. Baklouti
Keyword(s):  
Municipal Wastewater ◽  
Principal Component ◽  
Toxic Substances ◽  
Capacity Curves ◽  
Water Characteristics ◽  
Monitoring Tools ◽  
Visible Spectra ◽  
On Line ◽  
Mining Procedure

An adaptive principal component analysis applied to sets of data provided by global analytical methods (UV-visible spectra, buffer capacity curves, respirometric tests) is proposed as a generic procedure for on-line and fast characterization of wastewater. The data-mining procedure is able to deal with a large amount of information, takes into account the normal variations of wastewater composition related to human activity, and enables a rapid detection of abnormal situations such as the presence of toxic substances by comparison of the actual wastewater state with a continuously updated reference. The procedure has been validated on municipal wastewater.

scholarly journals Adsorption Mechanism of Patulin from Apple Juice by Inactivated Lactic Acid Bacteria Isolated from Kefir Grains

Toxins ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 434
Author(s):  
Pascaline Bahati ◽  
Xuejun Zeng ◽  
Ferdinand Uzizerimana ◽  
Ariunsaikhan Tsoggerel ◽  
Muhammad Awais ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
High Performance ◽  
Apple Juice ◽  
Adsorption Mechanism ◽  
Health Hazard ◽  
Principal Component ◽  
Poor Quality ◽  
Potential Health ◽  
Potential Health Hazard ◽  
Kefir Grains

In the food industry, microbiological safety is a major concern. Mycotoxin patulin represents a potential health hazard, as it is heat-resistant and may develop at any stage during the food chain, especially in apple-based products, leading to severe effects on human health, poor quality products, and profit reductions. The target of the study was to identify and characterize an excellent adsorbent to remove patulin from apple juice efficiently and to assess its adsorption mechanism. To prevent juice fermentation and/or contamination, autoclaving was involved to inactivate bacteria before the adsorption process. The HPLC (high-performance liquid chromatography) outcome proved that all isolated strains from kefir grains could reduce patulin from apple juice. A high removal of 93% was found for juice having a 4.6 pH, 15° Brix, and patulin concentration of 100 μg/L by Lactobacillus kefiranofacien, named JKSP109, which was morphologically the smoothest and biggest of all isolates in terms of cell wall volume and surface area characterized by SEM (Scanning electron microscopy) and TEM (transmission electron microscopy). C=O, OH, C–H, and N–O were the main functional groups engaged in patulin adsorption indicated by FTIR (Fourier transform–infrared). E-nose (electronic nose) was performed to evaluate the aroma quality of the juices. PCA (Principal component analysis) results showed that no significant changes occurred between control and treated juice.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

scholarly journals Additively Manufactured Parts Made of a Polymer Material Used for the Experimental Verification of a Component of a High-Speed Machine with an Optimised Geometry—Preliminary Research

Polymers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 137
Author(s):  
Artur Andrearczyk ◽  
Bartlomiej Konieczny ◽  
Jerzy Sokołowski
Keyword(s):  
Polymer Material ◽  
High Speed ◽  
Numerical Models ◽  
Flow Analysis ◽  
Experimental Tests ◽  
Strength Analysis ◽  
Compressor Wheel ◽  
Operational Characteristics ◽  
3D Printed ◽  
Novel Method

This paper describes a novel method for the experimental validation of numerically optimised turbomachinery components. In the field of additive manufacturing, numerical models still need to be improved, especially with the experimental data. The paper presents the operational characteristics of a compressor wheel, measured during experimental research. The validation process included conducting a computational flow analysis and experimental tests of two compressor wheels: The aluminium wheel and the 3D printed wheel (made of a polymer material). The chosen manufacturing technology and the results obtained made it possible to determine the speed range in which the operation of the tested machine is stable. In addition, dynamic destructive tests were performed on the polymer disc and their results were compared with the results of the strength analysis. The tests were carried out at high rotational speeds (up to 120,000 rpm). The results of the research described above have proven the utility of this technology in the research and development of high-speed turbomachines operating at speeds up to 90,000 rpm. The research results obtained show that the technology used is suitable for multi-variant optimization of the tested machine part. This work has also contributed to the further development of numerical models.

scholarly journals Brain Levels of Prostaglandins, Endocannabinoids, and Related Lipids Are Affected by Mating Strategies

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Jordyn M. Stuart ◽  
Jason J. Paris ◽  
Cheryl Frye ◽  
Heather B. Bradshaw
Keyword(s):  
Sexual Differentiation ◽  
Home Cage ◽  
Mating Strategies ◽  
Spectrometric Analysis ◽  
Tandem Mass ◽  
Reproductive Behaviors ◽  
Tandem Mass Spectrometric Analysis ◽  
Tandem Mass Spectrometric ◽  
Endogenous Cannabinoids ◽  
The Brain

Background. Endogenous cannabinoids (eCBs) are involved in the development and regulation of reproductive behaviors. Likewise, prostaglandins (PGs) drive sexual differentiation and initiation of ovulation. Here, we use lipidomics strategies to test the hypotheses that mating immediately activates the biosynthesis and/or metabolism of eCBs and PGs and that specific mating strategies differentially regulate these lipids in the brain.Methods. Lipid extractions and tandem mass spectrometric analysis were performed on brains from proestrous rats that had experienced one of two mating strategies (paced or standard mating) and two nonmated groups (chamber exposed and home cage controls). Levels of PGs (PGE2 and PGF2alpha), eCBs (AEA and 2-AG,N-arachidonoyl glycine), and 4 related lipids (4N-acylethanolamides) were measured in olfactory bulb, hypothalamus, hippocampus, thalamus, striatum, midbrain, cerebellum, and brainstem.Results. Overall, levels of these lipids were significantly lower among paced compared to standard mated rats with the most dramatic decreases observed in brainstem, hippocampus, midbrain, and striatum. However, chamber exposed rats had significantly higher levels of these lipids compared to home cage controls and paced mated wherein the hippocampus showed the largest increases.Conclusions. These data demonstrate that mating strategies and exposure to mating arenas influence lipid signaling in the brain.

Application of Arithmetic Operations Method in Single Pair-Pole Magnetic Encoder

2011 ◽  
Vol 128-129 ◽  
pp. 85-91
Author(s):  
Yi Fan Zeng ◽  
Rui Li
Keyword(s):  
High Speed ◽  
Angle Measurement ◽  
Single Pair ◽  
Nonlinear Correction ◽  
Arithmetic Operations ◽  
Rotational Angle ◽  
Voltage Signal ◽  
Fpga Chip ◽  
Novel Method ◽  
Hall Sensors

This paper proposes a novel method called arithmetic operations to analyze and process the generated voltage-signal from the single pair-pole magnetic encoder. Dual orthogonal voltage-signals are generated by two vertical hall sensors which are placed in the bottom of a columned magnet. When signals pass A/D converter, the quadrant determination, arithmetic operations and nonlinear correction in FPGA chip are performed before the values of rotational angle are displayed on the LED. This paper also designs and implements the single pair-pole magnetic encoder which has advantages such as high-speed, high-resolution and high-accuracy in the area of angle measurement.

scholarly journals PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Kanokmon Rujirakul ◽  
Chakchai So-In ◽  
Banchar Arnonkijpanich
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Expectation Maximization ◽  
High Speed ◽  
Expectation Maximization Algorithm ◽  
Parallel Architecture ◽  
Principal Component ◽  
Eigenvalue Decomposition ◽  
Computational Time ◽  
Recognition Systems

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

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