Quantification of microbial productivity via multi-angle light scattering and supervised learning

Biotechnol Bioeng. 1998 Jul 20;59(2):131-43. doi: 10.1002/(sici)1097-0290(19980720)59:2<131::aid-bit1>3.0.co;2-i.

Abstract

This article describes the use of chemometric methods for prediction of biological parameters of cell suspensions on the basis of their light scattering profiles. Laser light is directed into a vial or flow cell containing media from the suspension. The intensity of the scattered light is recorded at 18 angles. Supervised learning methods are then used to calibrate a model relating the parameter of interest to the intensity values. Using such models opens up the possibility of estimating the biological properties of fermentor broths extremely rapidly (typically every 4 sec), and, using the flow cell, without user interaction. Our work has demonstrated the usefulness of this approach for estimation of yeast cell counts over a wide range of values (10(5)-10(9) cells mL-1), although it was less successful in predicting cell viability in such suspensions.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Biotechnology / methods*
  • Calibration
  • Culture Media
  • Fermentation
  • Lasers
  • Light
  • Models, Theoretical
  • Neural Networks, Computer
  • Saccharomyces cerevisiae / growth & development*
  • Scattering, Radiation

Substances

  • Culture Media