Classification and Quantitation of 1H NMR Spectra of Alditols Binary Mixtures Using Artificial Neural Networks

• Published in: Analytical chemistry (Washington) Vol. 70; no. 7; pp. 1249 - 1254
• Main Authors: Amendolia, Salvator Roberto, Doppiu, Angelino, Ganadu, Maria Luisa, Lubinu, Giuseppe
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 01-04-1998
• Subjects: Analytical chemistry; Chemistry; Exact sciences and technology; Galactitol - chemistry; Magnetic Resonance Spectroscopy - methods; Neural networks; Neural Networks (Computer); NMR; Nuclear magnetic resonance; Reproducibility of Results; Spectrometric and optical methods; Sugar Alcohols - analysis; Sugar Alcohols - chemistry; Alditol; Chemical analysis; Multicomponent analysis; NMR spectrometry; Pattern recognition; Neural network; Computer aided analysis
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac970868g

A pattern recognition method based on artificial neural networks (ANNs) to analyze and quantify the components of six alditol binary mixtures is presented. This method is suitable to classify the spectra of the 15 mixtures obtained from the six alditols and to produce quantitative estimates of the component concentrations. The system is user-friendly and is helpful in solving the problem of greatly overlapping signals, often encountered in NMR spectroscopy of carbohydrates. A “classification” ANN uses 200 intensity values of the 1H NMR spectrum in the range 3.5−4 ppm. When the correct mixture is identified, the quantification is solved by assigning a specific ANN to each mixture. These ANNs use the same 200 values of the spectrum and output the values of the two concentrations. The error in the ANN responses is studied, and a method is developed to estimate the accuracy in determining the concentrations. The networks' abilities to recognize previously unseen mixtures are tested. When the classification ANN (trained on the 15 binary mixtures) is exposed to complex (i.e., more than binary) mixtures of the six known alditols, it successfully identifies the components if their minimum concentration is 10%. Given the precision of the results and the small number of errors reported, we believe that the method can be used in all fields in which the recognition and quantification of components are necessary.