scholarly journals High Peak Density Artifacts in Fourier Transform Mass Spectra and their Effects on Data Analysis

2017 ◽  
Author(s):  
Joshua M. Mitchell ◽  
Robert M. Flight ◽  
Qing Jun Wang ◽  
Woo-Young Kang ◽  
Richard M Higashi ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Mass Spectra ◽  
Metabolic Networks ◽  
High Peak ◽  
Main Peak ◽  
Accurate Identification ◽  
Peak Density ◽  
Intense Peak ◽  
Correct Assignment ◽  
The Given

AbstractFourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolites. Observed spectral features (peaks) that are not isotopologues do not directly correspond to known compounds and cannot be placed into existing metabolic networks. Spectral artifacts account for many of these unidentified peaks, and misassignments made to these artifact peaks can create large interpretative errors. Without accurate identification of artifactual features and correct assignment of real features, discerning their roles within living systems is effectively impossible.We have observed three types of artifacts unique to FT-MS that often result in regions of abnormally high peak density (HPD), which we collectively refer to as HPD artifacts: i) fuzzy sites representing small regions of m/z space with a ‘fuzzy’ appearance due to the extremely high number of peaks present; ii) ringing due to a very intense peak producing side bands of decreasing intensity that are symmetrically distributed around the main peak; and iii) partial ringing where only a subset of the side bands are observed for an intense peak. Fuzzy sites and partial ringing appear to be novel artifacts previously unreported in the literature and we hypothesize that all three artifact types derive from Fourier transformation-based issues. In some spectra, these artifacts account for roughly a third of the peaks present in the given spectrum. We have developed a set of tools to detect these artifacts and approaches to mitigate their effects on downstream analyses.

scholarly journals New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis

Metabolomics ◽  
2018 ◽  
Vol 14 (10) ◽  
Author(s):  
Joshua M. Mitchell ◽  
Robert M. Flight ◽  
Qing Jun Wang ◽  
Richard M. Higashi ◽  
Teresa W.-M. Fan ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Data Analysis ◽  
High Throughput ◽  
Mass Spectra ◽  
High Peak ◽  
Peak Density ◽  
New Methods ◽  
Metabolomic Data

scholarly journals Accurate identification of mass peaks for tandem mass spectra using MCMC model

2015 ◽  
Vol 20 (5) ◽  
pp. 453-459
Author(s):  
Hui Li ◽  
Chunmei Liu ◽  
Mugizi Robert Rwebangira ◽  
Legand Burge
Keyword(s):  
Mass Spectra ◽  
Tandem Mass ◽  
Accurate Identification ◽  
Tandem Mass Spectra

scholarly journals Comparison and Identification for Rhizomes and Leaves of Paris yunnanensis Based on Fourier Transform Mid-Infrared Spectroscopy Combined with Chemometrics

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3343 ◽  
Author(s):  
Yi-Fei Pei ◽  
Qing-Zhi Zhang ◽  
Zhi-Tian Zuo ◽  
Yuan-Zhong Wang
Keyword(s):  
Fourier Transform ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Chemical Information ◽  
Accurate Identification ◽  
Mid Infrared ◽  
Model Classification ◽  
Paris Polyphylla ◽  
Mir Spectra ◽  
The Individual

Paris polyphylla, as a traditional herb with long history, has been widely used to treat diseases in multiple nationalities of China. Nevertheless, the quality of P. yunnanensis fluctuates among from different geographical origins, so that a fast and accurate classification method was necessary for establishment. In our study, the geographical origin identification of 462 P. yunnanensis rhizome and leaf samples from Kunming, Yuxi, Chuxiong, Dali, Lijiang, and Honghe were analyzed by Fourier transform mid infrared (FT-MIR) spectra, combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. The obvious cluster tendency of rhizomes and leaves FT-MIR spectra was displayed by principal component analysis (PCA). The distribution of the variable importance for the projection (VIP) was more uniform than the important variables obtained by RF, while PLS-DA models obtained higher classification abilities. Hence, a PLS-DA model was more suitably used to classify the different geographical origins of P. yunnanensis than the RF model. Additionally, the clustering results of different geographical origins obtained by HCA dendrograms also proved the chemical information difference between rhizomes and leaves. The identification performances of PLS-DA and the RF models of leaves FT-MIR matrixes were better than those of rhizomes datasets. In addition, the model classification abilities of combination datasets were higher than the individual matrixes of rhizomes and leaves spectra. Our study provides a reference to the rational utilization of resources, as well as a fast and accurate identification research for P. yunnanensis samples.

scholarly journals Reagent-Free Identification of Clinical Yeasts by Use of Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy

2019 ◽  
Vol 57 (5) ◽  
Author(s):  
Lisa M. T. Lam ◽  
Philippe J. Dufresne ◽  
Jean Longtin ◽  
Jacqueline Sedman ◽  
Ashraf A. Ismail
Keyword(s):  
Fourier Transform ◽  
Ftir Spectroscopy ◽  
Fourier Transform Infrared ◽  
Attenuated Total Reflectance ◽  
Reference Database ◽  
Accurate Identification ◽  
Total Reflectance ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Tof Ms

ABSTRACT Invasive fungal infections by opportunistic yeasts have increased concomitantly with the growth of an immunocompromised patient population. Misidentification of yeasts can lead to inappropriate antifungal treatment and complications. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy is a promising method for rapid and accurate identification of microorganisms. ATR-FTIR spectroscopy is a standalone, inexpensive, reagent-free technique that provides results within minutes after initial culture. In this study, a comprehensive spectral reference database of 65 clinically relevant yeast species was constructed and tested prospectively on spectra recorded (from colonies taken from culture plates) for 318 routine yeasts isolated from various body fluids and specimens received from 38 microbiology laboratories over a 4-month period in our clinical laboratory. ATR-FTIR spectroscopy attained comparable identification performance with matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS). In a preliminary validation of the ATR-FTIR method, correct identification rates of 100% and 95.6% at the genus and species levels, respectively, were achieved, with 3.5% unidentified and 0.9% misidentified. By expanding the number of spectra in the spectral reference database for species for which isolates could not be identified or had been misidentified, we were able to improve identification at the species level to 99.7%. Thus, ATR-FTIR spectroscopy provides a new standalone method that can rival MALDI-TOF MS for the accurate identification of a broad range of medically important yeasts. The simplicity of the ATR-FTIR spectroscopy workflow favors its use in clinical laboratories for timely and low-cost identification of life-threatening yeast strains for appropriate treatment.

scholarly journals 5-(6-Hydroxy-6-methyl-5-oxoheptan-2-yl)-2-methyl Phenyl Acetate

Molbank ◽  
10.3390/m1041 ◽  
2018 ◽  
Vol 2019 (1) ◽  
pp. M1041
Author(s):  
Maya Rahayu ◽  
Susi Kusumaningrum ◽  
Hayun Hayun
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Fourier Transform ◽  
Mass Spectra ◽  
Multiple Bond ◽  
Phenyl Acetate ◽  
Two Dimensional ◽  
Heteronuclear Multiple Bond Correlation ◽  
1H Nuclear Magnetic Resonance ◽  
Resolution Mass ◽  
Methyl Phenyl

We synthesized a novel compound, 5-(6-hydroxy-6-methyl-5-oxoheptan-2-yl)-2-methylphenyl acetate, in a good yield by oxidation of 1-O-acetyl-xanthorrizol using potassium permanganate in acidic condition. The structure was elucidated by Fourier Transform Infrared (FTIR), 1H-Nuclear Magnetic Resonance (NMR) and 13C-NMR, two-dimensional (2D)-HSQC, Distortionless Enhancement by Polarization Transfer (DEPT), 2D-Heteronuclear Multiple Bond Correlation (HMBC), and High-Resolution Mass Spectra (HRMS) spectral data.

Phase-based spectrum analysis method for identifying weak harmonics

2018 ◽  
Vol 24 (23) ◽  
pp. 5585-5596 ◽  
Author(s):  
Jingsong Xie ◽  
Wei Cheng ◽  
Yanyang Zi ◽  
Mingquan Zhang
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Characteristic Frequency ◽  
Discrete Wavelet ◽  
Accurate Identification ◽  
Processing Methods ◽  
Characteristic Frequencies ◽  
Frequency Detection ◽  
Signal Processing Methods

Fault characteristic frequency extraction is an important means for the fault diagnosis of rotating machineries. Traditional signal processing methods commonly use the amplitude information of signals to detect damages. However, when the amplitudes of characteristic frequencies are weak, the recognition effects of traditional methods may be unsatisfactory. Therefore, this paper proposes the phase-based enhanced phase waterfall plot (EPWP) method and frequency equal ratio line (FERL) method for identifying weak harmonics. Taking a cracked rotor as an example, the characteristic frequency detection performances of the EPWP and FERL methods are compared with that of the traditional signal processing methods namely fast Fourier transform, short-time Fourier transform, discrete wavelet transform, continuous wavelet transform, ensemble empirical mode decomposition, and Hilbert–Huang transform. Research results demonstrate that the effects of EPWP and FERL for the recognitions of weak harmonics which are contained in steady signals and transient signals are better than that of the traditional signal processing methods. The accurate identification of weak characteristic frequencies in the vibration signals can provide an important reference for damage detections and improve the diagnostic accuracy.

scholarly journals Error analysis of tomographic reconstructions in the absence of projection data

2015 ◽  
Vol 373 (2043) ◽  
pp. 20140394 ◽  
Author(s):  
Snehlata Shakya ◽  
Prabhat Munshi
Keyword(s):  
Experimental Data ◽  
Fourier Transform ◽  
Error Estimates ◽  
Projection Data ◽  
Micro Ct ◽  
Ct Scanner ◽  
X Ray ◽  
Level Data ◽  
The Given ◽  
Comparative Error

Error estimates for tomographic reconstructions (using Fourier transform-based algorithm) are available for cases where projection data are available. These data are used for reconstructions with different filter functions and the reliability of these reconstructions can be checked as per guidelines of those error estimates. There are cases where projection data are large (in gigabytes or terabytes) so storage of these data becomes an issue. It leads to storing of only the reconstructed images. Error estimation in such cases is presented here. Second-level projection data are calculated from the given reconstructed images (‘first-level’ images). These ‘second-level’ data are now used to generate ‘second-level’ reconstructed images. Different filter functions are employed to check the fidelity of these ‘second-level’ images. This inference is extended to first-level images in view of the characteristics of the convolution operator. This approach is validated with experimental data obtained by the X-ray micro-CT scanner installed at IIT Kanpur. Five specimens (of same material) have been scanned. Data are available in this case thus we have performed a comparative error estimate analysis for the ‘first-level’ reconstructions (data obtained from CT machine) and second-level reconstructions (data generated from first-level reconstructions). We observe that both approaches show similar outcome. It indicates that error estimates can also be applied to images when data are not available.

Speaker Identification in Total Variability Space

2013 ◽  
Vol 401-403 ◽  
pp. 1489-1492
Author(s):  
Qiang Li ◽  
Ming Bing Zhao ◽  
Yong Feng
Keyword(s):  
Speaker Identification ◽  
Gaussian Mixture ◽  
Dimensional Subspace ◽  
Identification System ◽  
Accurate Identification ◽  
Identification Rate ◽  
Total Variability ◽  
Rate Improvement ◽  
Low Dimensional ◽  
The Given

Gaussian Mixture Model-Universal Background Model based approaches have been popular used for speaker identification task. But in real complex environment the identification system performs too much worse than in laboratory, and the main reason is the mismatch of the training and testing channel and also the variability of the speaker himself. In this paper we introduce i-vector to the speaker identification system. In i-vector approach, a low dimensional subspace called total variability space is used to estimate both speaker and channel variability. Baum-Welch statistics are first computed over the given UBM to estimate the total variability. From the experiment results, we obtain 2.44% relative accurate identification rate improvement when using total variability space to compensate the mismatch of the variabilities from both the speaker and channel.

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