Citation: WANG Wei, JIANG Hui, LIU Guo-Hai, MEI Cong-Li, JI Yi. Qualitative Prediction of Yeast Growth Process Based on Near Infrared Spectroscopy[J]. Chinese Journal of Analytical Chemistry, ;2017, 45(8): 1137-1142. doi: 10.11895/j.issn.0253-3820.170349 shu

Qualitative Prediction of Yeast Growth Process Based on Near Infrared Spectroscopy

School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Received Date: 18 February 2017
Revised Date: 7 June 2017

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 cm^-1. 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.
- Near infrared spectroscopy,
- Growth of yeast,
- Competitive adaptive reweighted sampling,
- Extreme learning machine
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沈阳化工大学材料科学与工程学院沈阳 110142