Improving Biopharmaceutical Manufacturing Yield Using Neural Network Classification
Files in This Item:
|VaccineYieldNeuralNet_FaheyCarroll_final.pdf||649.14 kB||Adobe PDF||Download|
|Title:||Improving Biopharmaceutical Manufacturing Yield Using Neural Network Classification||Authors:||Fahey, Will
|Permanent link:||http://hdl.handle.net/10197/8746||Date:||Jan-2016||Abstract:||Traditionally, the Six Sigma framework has underpinned quality improvement and assurance in biopharmaceutical manufacturing process management. This paper proposes a neural network (NN) approach to vaccine yield classification and compares it to an existing multiple linear regression approach. As part of the Six Sigma process, this paper shows how a data mining framework can be used to extract further value and insight from the data gathered during the manufacturing process, and how insights into yield classification can be used in the quality improvement process.||Type of material:||Journal Article||Publisher:||BioProcessing Journal||Keywords:||Biopharmaceutical manufacturing;Neural network;Classification;Business analyitics||DOI:||10.12665/J144.Carroll||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Business Research Collection|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.