Monitoring Substrate and Products in a Bioprocess with FTIR Spectroscopy Coupled to Artificial Neural Networks Enhanced with a Genetic-Algorithm-Based Method For Wavelength Selection

VANINA FRANCO,; C PERIN, JUAN; VÍCTOR MANTOIVANI,; GOICOECHEA, HÉCTOR C

TALANTA

Elsevier

Lugar: Amsterdam; Año: 2006 vol. 68 p. 1005 - 1012

0039-9140

An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (PLS-1 and ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determination was calculated by analysing spiked real fermentation samples (recoveries c.a. 115 %).