Identification of Structural Features from Mass Spectrometry Using a Neural Network Approach:  Application to Trimethylsilyl Derivatives Used for Medical Diagnosis

Abstract

An artificial neural network (ANN) has been trained to recognize the presence or absence of specific structural features (SF) in trimethylsilyl derivatives of organic acids from their mass spectra. The input vector is constructed without knowledge of the molecular ion, which is generally not observed in the spectra of these compounds. The results are used in conjunction with a classical search in a spectral library to identify organic acids in biological fluids for rapid acidemias diagnosis.

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

“…The growing interest in the application of Artificial Neural Networks (ANNs) in the field of computer-assisted spectral interpretation is a result of their demonstrated superiority over the traditional models . The use of ANNs in spectra interpretation and structure elucidation is 2-fold, i.e., either for classification (recognition of structural characteristics from IR − or MS spectra, − joint IR- 13 C-NMR spectra 1 or IR-MS spectra) or for a quantitative prediction of a certain atomic property (the chemical shift in 13 C NMR spectra). − …”

“…The growing interest in the application of Artificial Neural Networks (ANNs) in the field of computer-assisted spectral interpretation is a result of their demonstrated superiority over the traditional models. 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.…”

Application of a Pruning Algorithm To Optimize Artificial Neural Networks for Pharmaceutical Fingerprinting

“…Analysis of the input parameters selected by pruning methods is useful for interpreting the predicted results, for setting the boundaries of the fingerprinting region, and for introducing new parameters containing more information for making classifications more efficiently. The current approach can be extended for detection of localized fingerprinting regions and interpretation of results is neural network analysis of mass spectra 19 and infrared spectra 20,21 or for QSAR (quantitative structure-activity relationship) studies using, for example, "spectrumlike" representations of chemical structures. 22 The general idea of pruning can be also used for interpretation of more complex ANNs, such as the neural device proposed by Bashkin et al 23 There are certain advantages gained by decreasing the width of the fingerprint region.…”

Building an Honest Tree for Mass Spectra Classification Based on Prior Logarithm Normal Distribution

“…Such relationship is essentially very complicated. Many efficient classifiers have been manipulated to fulfill the above task, such as linear discriminant analysis (LDA) (Varmuza and Werther 1996), classification and regression trees (CART) (Breiman et al 1984), K-nearest neighbor (KNN) (Alsberg et al 1997) and neural network (Eghbaldar et al 1996) and so on. Among these, the decision-tree method, although simple but powerful, has been wildly used in data mining.…”