| NIR technology is an non-preprocessing, non-destructive, non-pollution analysis technology. It can simultaneously determine multi-ingredients and it is a fast, low cost analysis technology. It is great interested in chemical analysis regions. Since the NIR spectrum is molecular vibrational spectrum which is weak and much complex. It is difficult to comprehensive parse. This problem can be solved since the compute technology and chemometrics are greatly developed. This technology can be applied to deal with the numerous NIR spectrum data and extract the effective messages. NIR technology was applied in agriculture, chemical, medicine, pharmaceuticals, food and life science and so on. There are several literatures on the application of NIR spectroscopy on fermentation processes especially on at line/on line monitoring key parameters. There are few of them in china. There are several reasons obstruct apply NIR technology to fermentation. Firstly there is much water in the broth, which seriously interfere the messages. Secondly, the morphology of the bacterial, the viscosity and the pH of the broth, the components of broth are changing during the fermentation processes. It is difficult to extract the effective messages in these spectra. There are several reports on successfully applying the NIR technology on monitoring key parameters during fermentation processes. However, most of them were only limited in the same batch, the defined media, the same bacterial. The generalization, the stability and the predictive ability of the calibration models is not good enough. In this paper, the application of NIR analysis technology in examining the quality of the fermentation production, real time monitoring the key parameters during fungi fermentation and bacterial fermentation.In this paper, NIR analysis method was applied to examining the quality of the Cordyceps militaris mycelia. Adenosine, protein, intracellular polysaccharide and Cordyceps acid can be simultaneously determined using this method, while only one integrate in the production can be determined using traditional methods. The calibration models for determining the contents of adenosine, protein, intracellular polysaccharide and cordyceps acid in mycelia have been developed using NIR spectra. The samples for modeling were collected by mutants'fermentation under various fermentation conditions. These samples were representative. MCPLS was employed to examine the outliers, which can enhance the stability of model. Several preprocessing methods including Savitzky-Golay smoothing method, FFT, SNV, first order derivative method, second order derivative method and WT were applied to preprocess the NIR spectra. The effect of preprocessing windows'size was investigated. PLS and RBFNN were employed to develop the models respectively. MWPLS was applied to select the wavelengths for developing the PLS model. The number of latent variables of PLS model was selected depending on Da. MWRBFNN was employed to select the wavelengths for developing RBFNN model. The number of hidden nodes and the spread constant of the RBFNN models were selected depending on Da. A comparing study between the optimized PLS models and RBFNN models has been done and the optimum models for determining the contents of adenosine, protein, intracellular polysaccharide and Cordyceps acid in Cordyceps militaris mycelia was obtained. Using these models for determining the contents of adenosine, protein, intracellular polysaccharide and Cordyceps acid in the samples, Rc were 0.9436,0.9884,0.9079 and 0.8848 respectively, which indicated that the fit of models was satisfied. The RMSEP of there models were 0.6225,0.0179,0.0113 and 0.0102 respectively, which indicated that the predictive abilities of these model were satisfied. The generalization of these models was fine and they can be applied to screen the high production mutants and optimize the fermentation conditions.NIR analysis technology was applied to real time monitoring the biomass, intracellular polysaccharide, extracellular polysaccharide, Cordyceps acid, adenosine and glucose during Cordyceps militaris fermentation. The samples for calibration modeling were collected from 39 batches of Cordyceps militaris fermentation at various fermentation conditions. These samples were representative. To enhance the stability of models, MCPLS was employed to examine the outliers. Several preprocessing methods including Savitzky-Golay smoothing method, FFT, SNV, first order derivative method, second order derivative method were applied to preprocess the NIR spectra. The effect of preprocessing windows'size was investigated. PLS was employed to develop the models. MWPLS was applied to select the wavelengths for developing the PLS model. The number of latent variables of PLS model was selected depending on Da. The optimum PLS models for determining biomass, intracellular polysaccharide, extracellular polysaccharide, Cordyceps acid, adenosine and glucose during Cordyceps militaris fermentation were obtained. The Rc of the models for determining biomass and glucose were 0.9114 and 0.9185 respectively and the RMSEC of them were 1.5230 and 0.0171. These results demonstrated that the fit and the predictive ability of these models were satisfied. The Rc and RMSEP of the model for determining extracellular polysaccharide concentration were 0.6875 and 0.6016, it was greatly interference with water and other factors. The Rc of these models for determining the intracellular polysaccharide, adenosine and cordyceps acid contents in the mycelia were 0.7632, 0.7252 and 0.7786, and their RMSEP were 0.3193,0.3341 and 11.4215 respectively. These results demonstrated that it was feasible to apply NIR analysis technology in real time monitoring the components in mycelia.NIR analysis technology was applied to real time monitoring nisin titer, glucose, pH and biomass during Lactococcus lactis subsp. lactis fermentation. The samples for calibration modeling were collected from 15 batches of Lactococcus lactis subsp. lactis fermentation in 3 different 5 l fermentors at various fermentation conditions. These samples were representative. To enhance the stability of models, MCPLS was employed to examine the outliers. Several preprocessing methods including Savitzky-Golay smoothing method, FFT, SNV, first order derivative method, second order derivative method were applied to preprocess the NIR spectra. The effect of preprocessing windows'size was investigated. PLS and RBFNN were employed to develop the models respectively. MWPLS was applied to select the wavelengths for developing the PLS model. The number of latent variables of PLS model was selected depending on Da. MWRBFNN was employed to select the wavelengths for developing RBFNN model. The number of hidden nodes and the spread constant of the RBFNN models were selected depending on Da. A comparing study between the optimized PLS models and RBFNN models has been done and the optimum models for monitoring nisin titer, glucose, pH and biomass during Lactococcus lactis subsp. lactis fermentation was obtained. The Rc of these models were 0.8649,0.9491,0.9390 and 0.9914 respectively, which indicated that the fit of models was satisfied. The RMSEP of there models were 2865.05,1.3076,0.2471 and 0.1414 respectively, which indicated that the predictive abilities of these model were satisfied. These methods should be popular in at line monitoring the key parameters during bacterial fermentation processes.
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