Non-linear modelling of 13C NMR chemical shift data using artificial neural networks and partial least squares method

13C NMR Chemical Shift Prediction of the sp3 Carbon Atoms in the α Position Relative to the Double Bond in Acyclic Alkenes

“…1 The use of ANNs in spectra interpretation and structure elucidation is 2-fold, i.e., either for classification (recognition of structural characteristics from IR [2][3][4][5][6][7][8][9][10][11] or MS spectra, [12][13][14][15] joint IR-13 C-NMR spectra 1 or IR-MS spectra 16 ) or for a quantitative prediction of a certain atomic property (the chemical shift in 13 C NMR spectra). [17][18][19][20][21][22][23][24][25][26][27][28] In a previous paper 28 we have estimated the 13 C NMR chemical shift of sp 2 carbon atoms in acyclic alkenes with MultiLinear Regression (MLR) and MultiLayer Feedforward (MLF) ANN models, using as structural descriptor of the environment of the resonating carbon a Topo-Stereochemical Code (TSC) with 12 components allowing for a unique description of the topo-stereochemical location of the carbon atoms around the double bond. The study investigated the 13 C NMR chemical shift of 130 acyclic alkenes with 244 structurally unique sp 2 carbon atoms.…”

Nondestructive Speciation of Solid Mixtures by Multivariate Calibration of X-Ray Absorption Near-Edge Structure Using Artificial Neural Networks and Partial Least-Squares

“…2,3 In our previous study, 4 partial least-squares (PLS) was employed to resolve the XANES spectra of mixtures and to quantify the species therein. In this study, artificial neural networks (ANN) are employed and compared with PLS, because ANN have the advantage of capability of nonlinear modeling, 5,6 and have never been applied to the interpretation of XANES. Even though these methods were applied to Fe XANES in the present work because of the ease to cross-check by 57 Fe Mössbauer spectroscopy, the application of the fundamental methods presented here is able to be extended to other elements.…”