Principal component analysis of normalized full spectrum mass spectrometry data in multiMS-toolbox: An effective tool to identify important factors for classification of different metabolic patterns and bacterial strains

2018 ◽  
Vol 32 (11) ◽  
pp. 871-881 ◽  
Author(s):  
Pavel Cejnar ◽  
Stepanka Kuckova ◽  
Ales Prochazka ◽  
Ludmila Karamonova ◽  
Barbora Svobodova
Keyword(s):  
Mass Spectrometry ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Mass Spectrometry Data ◽  
Bacterial Strains ◽  
Full Spectrum

scholarly journals Principal Component Analysis from Mass Spectrometry Data Combined to a Sensory Evaluation as a Suitable Method for Assessing Bitterness of Enzymatic Hydrolysates Produced from Micellar Casein Proteins

Foods ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1354
Author(s):  
Dahlia Daher ◽  
Barbara Deracinois ◽  
Alain Baniel ◽  
Elodie Wattez ◽  
Justine Dantin ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Principal Component Analysis ◽  
High Performance ◽  
High Resolution Mass Spectrometry ◽  
Liquid Fraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mass Spectrometry Data ◽  
Sensory Panel ◽  
Enzymatic Hydrolysates

Enzymatic hydrolysis of food proteins generally changes the techno-functional, nutritional, and organoleptic properties of hydrolyzed proteins. As a result, protein hydrolysates have an important interest in the food industries. However, they tend to be characterized by a bitter taste and some off-flavors, which limit their use in the food industry. These tastes and aromas come from peptides, amino acids, and volatile compounds generated during hydrolysis. In this article, sixteen more or less bitter enzymatic hydrolysates produced from a milk protein liquid fraction enriched in micellar caseins using commercially available, food-grade proteases were subjected to a sensory analysis using a trained and validated sensory panel combined to a peptidomics approach based on the peptide characterization by reverse-phase high-performance liquid chromatography, high-resolution mass spectrometry, and bioinformatics software. The comparison between the sensory characteristics and the principal components of the principal component analysis (PCA) of mass spectrometry data reveals that peptidomics constitutes a convenient, valuable, fast, and economic intermediate method to evaluating the bitterness of enzymatic hydrolysates, as a trained sensory panel can do it.

Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data

PROTEOMICS ◽  
2010 ◽  
Vol 10 (14) ◽  
pp. 2564-2572 ◽  
Author(s):  
Elise Mostacci ◽  
Caroline Truntzer ◽  
Hervé Cardot ◽  
Patrick Ducoroy
Keyword(s):  
Mass Spectrometry ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Mass Spectrometry Data

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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