Substituent effects on1H and13C NMR chemical shifts in ?-monosubstituted ethyl acetates: principal component analysis and1H chemical shift calculations

2002 ◽  
Vol 40 (7) ◽  
pp. 449-454 ◽  
Author(s):  
Ljubica Tasic ◽  
Raymond J. Abraham ◽  
Roberto Rittner
Keyword(s):  
Principal Component Analysis ◽  
Chemical Shift ◽  
Chemical Shifts ◽  
Substituent Effects ◽  
Principal Component ◽  
Component Analysis ◽  
Nmr Chemical Shifts ◽  
Chemical Shift Calculations

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

13C NMR substituent effects in aliphatic systems: Principal component analysis and correlations with electronegativities

1992 ◽  
Vol 30 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Romuald I. Zalewski ◽  
Hans-Jörg Schneider ◽  
Ulrich Buchheit
Keyword(s):  
Principal Component Analysis ◽  
13C Nmr ◽  
Substituent Effects ◽  
Principal Component ◽  
Component Analysis

29Si NMR chemical shifts in pertrimethylsilylated 1,6-anhydro-β-D-glucopyranose derivatives. Empirical assignment from Hammett type dependence of the chemical shifts

1983 ◽  
Vol 48 (9) ◽  
pp. 2503-2508 ◽  
Author(s):  
Jan Schraml ◽  
Jaroslav Včelák ◽  
Miloslav Černý ◽  
Václav Chvalovský
Keyword(s):  
Chemical Shift ◽  
Carbon Atom ◽  
Silicon Atom ◽  
Chemical Shifts ◽  
Substituent Effects ◽  
Polar Effect ◽  
29Si Nmr ◽  
Nmr Chemical Shifts ◽  
Trimethylsilyl Groups

29Si chemical shift of Si-3 silicon atom of the trimethylsiloxy group attached to C(3) carbon atom in 1,6-anhydro-β-D-glucopyranose derivatives correlates linearly with the sum of Taft polar constants σσ of substituents R2 and R4 on C(2) and C(4) carbon atoms. Quality of this correlation allows assignment of Si-3 line in the spectra of derivatives containing two or three trimethylsilyl groups in the molecule. The shielding order δ(Si-4) < δ(Si-3) found in 1,6-anhydro-2,3,4-O-tris(trimethylsilyl)-β-D-glucopyranose is the same as recently found in other pyranose derivatives with the same configuration of substituents but the order is reversed by strong polar effect of the substituent in 1,6-anhydro-2-O-p-toluenesulphonyl-3,4-O-bis(trimethylsilyl)-β-D-glucopyranose. This finding warns against indiscriminate use of empirical assignment rules based on simple shielding order without considering possible substituent effects.

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.

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