Biomedical Engineering Reference
In-Depth Information
43. Ödman P, Johansen CL, Olsson L, Gernaey KV, Lantz AE (2010) Sensor combination and
chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch
cultivations. Appl Microbiol Biotechnol 86:1745-1759
44. Pate ME, Turner MK, Thornhill NF, Titchener-Hooker NJ (2004) Principal component
analysis of nonlinear chromatography. Biotechnol Prog 20:215-222
45. Rhee JI, Kang TH, Lee KI, Sohn OJ, Kim SY, Chung SW (2006) Application of principal
component analysis and self-organizing map to the analysis of 2D fluorescence spectra and
the monitoring of fermentation processes. Biotechnol Bioprocess Eng 11(5):432-441
46. Roger JM, Chauchard F, Williams P (2008) Removing the block effects in calibration by
means of dynamic orthogonal projection. Application to the year effect correction for wheat
protein prediction. J Near Infrared Spectrosc 16(3):311-315
47. Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A, Jent N (2007) A review of near
infrared spectroscopy and chemometrics in pharmaceutical technologies. J Pharmaceut
Biomed Anal 44(3):683-700
48. Shaffer RE, Rose-Pehrsson SL, McGill A (1999) A comparison study of chemical sensor
array pattern recognition algorithms. Anal Chim Acta 384:305-317
49. Shen D, Kiehl TR, Khattak SF, Li ZJ, He A, Kayne PS, Patel V, Neuhaus IM, Sharfstein ST
(2010) Transcriptomic responses to sodium chloride-induced osmotic stress: a study of
industrial fed-batch CHO cell cultures. Biotechnol Prog 26(4):1104-1115
50. Schölkopf B, Smola A, Müller KR (1998) Nonlinear component analysis as a kernel
eigenvalue problem. Neural Comput 10:1299-1319
51. Tarazona S, Prado-López S, Dopazo J, Ferre A, Conesa A (2012) Variable selection for
multifactorial genomic data. Chemometrics Intell Lab Syst 110(1):113-122
52. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear
dimensionality reduction. Science 290:2319-2323
53. Tewari J, Vivechana D, Kamal M (2011) On-line monitoring of residual solvent during the
pharmaceutical
drying
process
using
non-contact
infrared
sensor:
a
process
analytical
technology (PAT) approach. Sens Actuators B Chem 144(1):104-111
54. Varmuza K (2009) Introduction to multivariate statistical analysis in chemometrics. Taylor &
Francis, CRC, New York
55. von Stosch M, Oliveira R, Peres J, Feyo de Azevedo S (2011) A novel identification method
for hybrid (N) PLS dynamical systems with application to bioprocesses. Expert Syst Appl
38(9):10862-10874
56. Walczak B, Massart DL (2000) Local modelling with radial basis function networks.
Chemometrics Intell Lab Syst 50:179-198
57. Warnes MR, Glassey J, Montague GA, Kara B (1998) Application of radial basis function
and feedforward artificial neural networks to the Escherichia coli fermentation process.
Neurocomputing 20:67-82
58. Weiss GH, Romagnoli JA, Islam KA (1996) Data reconciliation—an industrial case study.
Comput Chem Eng 20:1441-1449
59. Widrow B, Lehr MA (1990) 30 years of adaptive neural networks: perceptron, Madaline, and
backpropagation. In: Proceeding of the IEEE, vol 78(9). p 1415
60. Wilkes JG, Rushing L, Nayak R, Buzatu DA, Sutherland JB (2005) Rapid phenotypic
characterization of Salmonella enterica strains by pyrolysis metastable atom bombardment
mass
spectrometry
with
multivariate
statistical
and
artificial
neural
network
pattern
recognition. J Microbiol Methods 61(3):321-334
61. Wold S, Geladi P, Esbensen K, Ohman J (1987) Multi-way principal components and PLS-
analysis. J Chemom 1:41-56
62. Wold S, Trygg J, Berglund A, Antti H (2001) Some recent developments in PLS modelling.
Chemom Intell Lab Syst 58:131-150
63. Yin H (2008) The self-organizing maps: background, theories, extensions and applications.
In:
Fulcher
J,
Jain
LC
(eds)
Computational
intelligence:
a
compendium.
Springer,
Heidelberg, pp 715-762
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