Linked Life Data

18268779

Source:http://linkedlifedata.com/resource/pubmed/id/18268779

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2008-2-12
pubmed:abstractText
We describe a model-based instrument design combined with a statistical classification approach for the development and realization of high speed cell classification systems based on light scatter. In our work, angular light scatter from cells of four bacterial species of interest, Bacillus subtilis, Escherichia coli, Listeria innocua, and Enterococcus faecalis, was modeled using the discrete dipole approximation. We then optimized a scattering detector array design subject to some hardware constraints, configured the instrument, and gathered experimental data from the relevant bacterial cells. Using these models and experiments, it is shown that optimization using a nominal bacteria model (i.e., using a representative size and refractive index) is insufficient for classification of most bacteria in realistic applications. Hence the computational predictions were constituted in the form of scattering-data-vector distributions that accounted for expected variability in the physical properties between individual bacteria within the four species. After the detectors were optimized using the numerical results, they were used to measure scatter from both the known control samples and unknown bacterial cells. A multivariate statistical method based on a support vector machine (SVM) was used to classify the bacteria species based on light scatter signatures. In our final instrument, we realized correct classification of B. subtilis in the presence of E. coli,L. innocua, and E. faecalis using SVM at 99.1%, 99.6%, and 98.5%, respectively, in the optimal detector array configuration. For comparison, the corresponding values for another set of angles were only 69.9%, 71.7%, and 70.2% using SVM, and more importantly, this improved performance is consistent with classification predictions.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0003-6935
pubmed:author
pubmed:issnType
Print
pubmed:day
10
pubmed:volume
47
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
678-86
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
High speed classification of individual bacterial cells using a model-based light scatter system and multivariate statistics.
pubmed:affiliation
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA. mvenkata@purdue.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't