Quantification of microbial productivity via multi-angle light scattering and supervised learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Jones:1998:qmpmalssl,
-
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%3C131::AID-BIT1%3E3.0.CO%3B2-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
Citations