Qualitative Prediction of Yeast Growth Process Based on Near Infrared Spectroscopy / 分析化学

ABSTRACT

To improve the yield of industrial fermentation, a method based on near infrared spectroscopy was presented to predict the growth of yeast.The spectral data of fermentation sample were measured by Fourier-transform near-infrared (FT-NIR) spectrometer in the process of yeast culture.Each spectrum was acquired over the range of 10000-4000 cm1.Meanwhile, the optical density (OD) of fermentation sample was determined with photoelectric turbidity method.After that, a method based on competitive adaptive reweighted sampling (CARS) was used to select characteristic wavelength variables of NIR data, and then extreme learning machine (ELM) algorithm was employed to develop the categorization model about the four growth processes of yeast.Experimental result showed that, only 30 characteristic wavelength variables of NIR data were selected by CRAS algorithms, and the prediction accuracies of training set and test set of the CARS-ELM model were 98.68% and 97.37%, respectively.The research showed that the near infrared spectrum analysis technology was feasible to predict the growth process of yeast.