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

Created by W.Langdon from gp-bibliography.bib Revision:1.4472

  author =       "Alun Jones and Daniella Young and Janet Taylor and 
                 Douglas B. Kell and Jem J Rowland",
  title =        "Quantification of microbial productivity via
                 multi-angle light scattering and supervised learning",
  journal =      "Biotechnology and Bioengineering",
  year =         "1998",
  volume =       "59",
  number =       "2",
  pages =        "131--143",
  month =        "20 " # jul,
  publisher =    "John Wiley and Sons",
  keywords =     "genetic algorithms, genetic programming, chemometrics,
                 light scattering. microbial productivity",
  ISSN =         "0006-3592",
  DOI =          "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.",
  notes =        "PMID: 10099324",

Genetic Programming entries for Alun Jones Daniella Young Janet Taylor Douglas B Kell Jem J Rowland