scholarly journals Intersession reproducibility of mass spectrometry profiles and its effect on accuracy of multivariate classification models

2007 ◽  
Vol 23 (22) ◽  
pp. 3065-3072 ◽  
Author(s):  
Richard Pelikan ◽  
William L. Bigbee ◽  
David Malehorn ◽  
James Lyons-Weiler ◽  
Milos Hauskrecht
Keyword(s):  
Mass Spectrometry ◽  
Classification Models ◽  
Multivariate Classification

scholarly journals Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data

2019 ◽  
Vol 18 (9) ◽  
pp. 3282-3294 ◽  
Author(s):  
Thao Vu ◽  
Parker Siemek ◽  
Fatema Bhinderwala ◽  
Yuhang Xu ◽  
Robert Powers
Keyword(s):  
Classification Models ◽  
Multivariate Classification ◽  
Metabolomics Data ◽  
Nmr Metabolomics

Impact of data reduction on multivariate classification models built on spectral data from bio-samples

2015 ◽  
Vol 30 (5) ◽  
pp. 1117-1127 ◽  
Author(s):  
Anders Larsson ◽  
Henrik Andersson ◽  
Lars Landström
Keyword(s):  
Spectral Data ◽  
Data Reduction ◽  
Image Data ◽  
Classification Models ◽  
Multivariate Classification

Multivariate classification models, both from 1D spectra and 2D image data (also with simulated shifts), were evaluated and compared.

Transfer of multivariate classification models applied to digital images and fluorescence spectroscopy data

2017 ◽  
Vol 133 ◽  
pp. 669-675 ◽  
Author(s):  
Karla Danielle Tavares Melo Milanez ◽  
Thiago César Araújo Nóbrega ◽  
Danielle Silva Nascimento ◽  
Matías Insausti ◽  
Márcio José Coelho Pontes
Keyword(s):  
Fluorescence Spectroscopy ◽  
Digital Images ◽  
Classification Models ◽  
Spectroscopy Data ◽  
Multivariate Classification

scholarly journals Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcelo V. S. Alves ◽  
Lanaia I. L. Maciel ◽  
Ruver R. F. Ramalho ◽  
Leomir A. S. Lima ◽  
Boniek G. Vaz ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Principal Component ◽  
Depression And Anxiety ◽  
Multivariate Classification ◽  
Successive Projections Algorithm ◽  
Linear Discriminant ◽  
Paper Spray Ionization ◽  
Chemical Work ◽  
Successive Projections

AbstractFibromyalgia is a rheumatological disorder that causes chronic pain and other symptomatic conditions such as depression and anxiety. Despite its relevance, the disease still presents a complex diagnosis where the doctor needs to have a correct clinical interpretation of the symptoms. In this context, it is valid to study tools that assist in the screening of this disease, using chemical work techniques such as mass spectroscopy. In this study, an analytical method is proposed to detect individuals with fibromyalgia (n = 20, 10 control samples and 10 samples with fibromyalgia) from blood plasma samples analyzed by mass spectrometry with paper spray ionization and subsequent multivariate classification of the spectral data (unsupervised and supervised), in addition to the treatment of selected variables with possible associations with metabolomics. Exploratory analysis with principal component analysis (PCA) and supervised analysis with successive projections algorithm with linear discriminant analysis (SPA-LDA) showed satisfactory results with 100% accuracy for sample prediction in both groups. This demonstrates that this combination of techniques can be used as a simple, reliable and fast tool in the development of clinical diagnosis of Fibromyalgia.

scholarly journals Multivariate classification of the geographic origin of Chinese cabbage using an electronic nose-mass spectrometry

2017 ◽  
Vol 26 (3) ◽  
pp. 603-609 ◽  
Author(s):  
Wang-Hee Lee ◽  
Seunghyun Choi ◽  
Il-Nam Oh ◽  
Joon-Yong Shim ◽  
Kwang-Sik Lee ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Electronic Nose ◽  
Chinese Cabbage ◽  
Geographic Origin ◽  
Multivariate Classification
Sign in / Sign up

Export Citation Format

Share Document