• DocumentCode
    1991496
  • Title

    Application of Near Infrared Spectroscopy in Quality Control of Paecilomyces Tenuipes

  • Author

    Song, Jia ; Du, Lin-na ; Yang, Shuang ; Lu, Jia-hui ; Meng, Qing-fan ; Teng, Li-rong

  • Author_Institution
    Coll. of Life Sci., Jilin Univ., Changchun, China
  • fYear
    2012
  • fDate
    27-30 May 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Near infrared reflectance spectroscopy was used to quantify the cordycepic acid and adenosine content in Paecilomyces tenuipes mycelia. Quantification of cordycepic acid and adenosine content was achieved by the conventional method. Models were built using near infrared reflectance for determinating the contents of cordycepic acid and adenosine. Monte Carlo partial least square (MCPLS) and Moving window partial least square (MWPLS) was applied to optimize the models. Finally, the optimum models for determination of cordycepic acid and adenosine contents in Paecilomyces tenuipes powder samples. The correlation between actual and predictive values of calibration samples (Rc) were 0.8851 and 0.9185, the root mean square error of prediction set (RMSEP) were 16.6171 and 0.5949, the root mean square error of calibration set RMSEC were 15.5724 and 0.5844, respectively. These results demonstrate that the robustness, fit and predictive capability of these models were satisfied. This method should be popular in determination the key parameters during fermentation processes.
  • Keywords
    Monte Carlo methods; biotechnology; fermentation; infrared spectra; least squares approximations; mean square error methods; microorganisms; quality control; MCPLS; MWPLS; Monte Carlo partial least square; Paecilomyces tenuipes mycelia; RMSEC; RMSEP; adenosine content; cordycepic acid; fermentation process; moving window partial least square; near infrared reflectance; near infrared spectroscopy; quality control; root mean square error-of-calibration set; root mean square error-of-prediction set; Analytical models; Calibration; Powders; Predictive models; Reflectivity; Spectroscopy; Statistical analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering and Technology (S-CET), 2012 Spring Congress on
  • Conference_Location
    Xian
  • Print_ISBN
    978-1-4577-1965-3
  • Type

    conf

  • DOI
    10.1109/SCET.2012.6342075
  • Filename
    6342075