title: Quantification of microbial productivity via multi-angle light scattering and supervised learning.
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.
authors: Institute of Biological Sciences, University of Wales, ABERYSTWYTH, Ceredigion SY23 3DD, Wales, United Kingdom. auj/diy/jjt95/dbk/jjr@aber.ac.uk
authors: A
authors: A
authors: Jones
authors: D
authors: D
authors: Young
authors: J
authors: J
authors: Taylor
authors: D B
authors: DB
authors: Kell
authors: J J
authors: JJ
authors: Rowland
mesh_terms: D001709:Biotechnology / Q000379:methods*; D002138:Calibration; D003470:Culture Media; D005285:Fermentation; D007834:Lasers; D008027:Light; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012441:Saccharomyces cerevisiae / Q000254:growth & development*; D012542:Scattering, Radiation
chemical_list: D003470:Culture Media