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

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@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

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