Principal Component Analysis and Target Testing of Substituent Effect Using Carbonyl Stretching Frequency and 13C NMR Chemical Shift Data Matrices

1993 ◽  
Vol 58 (2) ◽  
pp. 385-394 ◽  
Author(s):  
Ghazwan F. Fadhil
Keyword(s):  
Principal Component Analysis ◽  
Substituent Effect ◽  
Chemical Shifts ◽  
Linear Regression Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multiple Linear Regression Analysis ◽  
Probe Site ◽  
C Nmr ◽  
Data Matrices

Principal component analysis technique has been applied to analyse the substituent effect on carbonyl stretching frequency and 13C NMR chemical shifts. The general formula for the investigated molecules is X-G-Y, where X represents the set of substituent (OMe, Me, F, Cl, Br, CN and NO2), Y is the probe site and G is benzene ring. According to the indicator function two significant components are responsible for the substituent effect. The validity of several substituent parameters have been investigated by target testing technique. Invariabily substituent parameters derived by iterative multiple linear regression analysis viz. σR (Reynolds), σF (Reynolds) and σR (NMR) have lower SPOIL values when compared with other substituent parameters. Model designing f IR and 13C NMR data matrices separately have shown that models which incorporate σR (Reynolds) and σF (Reynolds) or σR (NMR) and a substituent field parameters have the lowest root mean square error RMSE. Substituent effect on several properties are better correlated with Reynolds' σR and σF than with other commonly used substituent parameter(s). The orthogonality of substituent parameters used in the model can be achieved by including the methyl group in the substituent set.

scholarly journals Towards reproducibility of traditional fermented sausages: Texture profile analyses and modelling

2020 ◽  
Vol 26 (1) ◽  
pp. 79-87
Author(s):  
Marija Jokanovic ◽  
Bojana Ikonic ◽  
Predrag Ikonic ◽  
Vladimir Tomovic ◽  
Tatjana Peulic ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multiple Linear Regression Analysis ◽  
Sensory Characteristics ◽  
Small Scale ◽  
Regression Equations ◽  
Textural Characteristics ◽  
Fermented Sausages

The aim of this study was to investigate textural characteristics of three traditional dry fermented sausages (Sremski kulen, Lemeski kulen and Petrovsk? klob?sa) manufactured in different small-scale facilities in northern Serbia, and to correlate them with physicochemical and sensory characteristics. The sample sausages were supplied by different local traditional producers. The textural characteristics were correlated with physicochemical and sensory characteristics using multiple linear regression analysis and principal component analysis. Differences in physicochemical characteristics reflected even more notable differences in texture characteristics. Regarding regression equations, obtained results showed that moisture content was significant for hardness, springiness and cohesiveness. Hardness was also influenced by fat content, while chewiness was influenced by protein content. Principal component analysis separated samples of Petrovsk? klob?sa, as the group with the most reproducible analysed characteristics. Obtained results of statistical analyses should provide knowledge for possible improvements of the traditional production, in a way that these sausages could be produced in different facilities with consistent textural characteristics.

scholarly journals Use of 13C-NMR Chemical Shifts; Application of Principal Component Analysis for Categorizing Structurally Similar Methoxyflavones and Correlation Analysis between Chemical Shifts and Cytotoxicity

2021 ◽  
Vol 69 (2) ◽  
pp. 199-202
Author(s):  
Ryuichiro Suzuki ◽  
Yoshihiro Uesawa ◽  
Yoshihito Okada ◽  
Takumi Horikawa ◽  
Yui Okabe ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Chemical Shifts ◽  
Principal Component ◽  
Component Analysis ◽  
Nmr Chemical Shifts ◽  
C Nmr

Principal component analysis in studies of substituent-induced chemical shifts of 1,4-disubstituted benzenes

2001 ◽  
Vol 39 (6) ◽  
pp. 316-322 ◽  
Author(s):  
Eduardo L. Canto ◽  
Ljubica Tasic ◽  
Roy E. Bruns ◽  
Roberto Rittner
Keyword(s):  
Principal Component Analysis ◽  
Chemical Shifts ◽  
Principal Component ◽  
Component Analysis

scholarly journals Chemical Composition and Variability of Leaf and Berry oils from Corsican Juniperus macrocarpa

2018 ◽  
Vol 13 (2) ◽  
pp. 1934578X1801300
Author(s):  
Joséphine Ottavioli ◽  
Ange Bighelli ◽  
Joseph Casanova ◽  
Félix Tomi
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Chemical Composition ◽  
Principal Component ◽  
Component Analysis ◽  
Germacrene D ◽  
Leaf Oil ◽  
Main Components ◽  
Main Component ◽  
C Nmr

The chemical composition of five leaf oil samples and eighteen berry oil samples from Corsican Juniperus macrocarpa have been investigated by GC(RI), GC-MS and 13C NMR. The composition of berry oils was dominated by monoterpene hydrocarbons with α-pinene (56.4-78.9%) as main component followed by myrcene (2.2-11.9%). Germacrene D (4.5-103%) was the major sesquiterpene. The contents of the main components of leaf oils varied drastically from sample to sample: α-pinene (28.7-76.4%), δ3-carene (up to 17.3%), β-phellandrene (up to 12.3%), manoyl oxide (up to 8.1%). The occurrence of the unusual ( Z)-pentadec-6-en-2-one (0.1-1.2%) should be pointed out. Statistical analysis (Principal Component Analysis and k- means partition) suggested a unique group with atypical samples.

scholarly journals Convex combination of data matrices: PCA perturbation bounds for multi-objective optimal design of mechanical metafilters

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Giorgio Gnecco ◽  
Andrea Bacigalupo
Keyword(s):  
Principal Component Analysis ◽  
Optimization Problems ◽  
Convex Combination ◽  
Principal Component ◽  
Component Analysis ◽  
Data Matrix ◽  
Matrix Perturbation ◽  
Perturbation Bounds ◽  
Multi Objective ◽  
Data Matrices

<p style='text-indent:20px;'>In the present study, matrix perturbation bounds on the eigenvalues and on the invariant subspaces found by principal component analysis is investigated, for the case in which the data matrix on which principal component analysis is performed is a convex combination of two data matrices. The application of the theoretical analysis to multi-objective optimization problems – e.g., those arising in the design of mechanical metamaterial filters – is also discussed, together with possible extensions.</p>

scholarly journals Quantitative Analysis of a Weak Correlation between Complicated Data on the Basis of Principal Component Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tao Pang ◽  
Haitao Zhang ◽  
Liliang Wen ◽  
Jun Tang ◽  
Bing Zhou ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
New Method ◽  
Data Matrix ◽  
Chemical Compositions ◽  
Weak Correlation ◽  
The Difference ◽  
Correlation Information ◽  
Data Matrices

The mining of weak correlation information between two data matrices with high complexity is a very challenging task. A new method named principal component analysis-based multiconfidence ellipse analysis (PCA/MCEA) was proposed in this study, which first applied a confidence ellipse to describe the difference and correlation of such information among different categories of objects/samples on the basis of PCA operation of a single targeted data. This helps to find the number of objects contained in the overlapping and nonoverlapping areas of ellipses obtained from PCA runs. Then, a quantitative evaluation index of correlation between data matrices was defined by comparing the PCA results of more than one data matrix. The similarity and difference between data matrices was further quantified through comprehensively analyzing the outcomes. Complicated data of tobacco agriculture were used as an example to illustrate the strategy of the proposed method, which includes rich features of climate, altitude, and chemical compositions of tobacco leaves. The number of objects of these data reached 171,516 with 14, 4, and 5 descriptors of climate, altitude, and chemicals, respectively. On the basis of the new method, the complex but weak relationship between these independent and dependent variables were interestingly studied. Three widely used but conventional methods were applied for comparison in this work. The results showed the power of the new method to discover the weak correlation between complicated data.

scholarly journals Structural Polymorphism of Chitin and Chitosan in Fungal Cell Walls From Solid-State NMR and Principal Component Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Liyanage D. Fernando ◽  
Malitha C. Dickwella Widanage ◽  
Jackson Penfield ◽  
Andrew S. Lipton ◽  
Nancy Washton ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cell Wall ◽  
Cell Walls ◽  
Chemical Shifts ◽  
Principal Component ◽  
Component Analysis ◽  
Antifungal Drugs ◽  
Fungal Cell ◽  
Structural Resistance ◽  
Chitin And Chitosan

Chitin is a major carbohydrate component of the fungal cell wall and a promising target for novel antifungal agents. However, it is technically challenging to characterize the structure of this polymer in native cell walls. Here, we recorded and compared 13C chemical shifts of chitin using isotopically enriched cells of six Aspergillus, Rhizopus, and Candida strains, with data interpretation assisted by principal component analysis (PCA) and linear discriminant analysis (LDA) methods. The structure of chitin is found to be intrinsically heterogeneous, with peak multiplicity detected in each sample and distinct fingerprints observed across fungal species. Fungal chitin exhibits partial similarity to the model structures of α- and γ-allomorphs; therefore, chitin structure is not significantly affected by interactions with other cell wall components. Addition of antifungal drugs and salts did not significantly perturb the chemical shifts, revealing the structural resistance of chitin to external stress. In addition, the structure of the deacetylated form, chitosan, was found to resemble a relaxed two-fold helix conformation. This study provides high-resolution information on the structure of chitin and chitosan in their cellular contexts. The method is applicable to the analysis of other complex carbohydrates and polymer composites.

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