PMID- 30166180
OWN - NLM
STAT- MEDLINE
DCOM- 20181102
LR  - 20181102
IS  - 1095-9998 (Electronic)
IS  - 0740-0020 (Linking)
VI  - 76
DP  - 2018 Dec
TI  - Comparing design of experiments and optimal experimental design techniques for 
      modelling the microbial growth rate under static environmental conditions.
PG  - 504-512
LID - S0740-0020(18)30004-2 [pii]
LID - 10.1016/j.fm.2018.05.010 [doi]
AB  - Building secondary models that describe the growth rate as a function of multiple 
      environmental conditions is often very labour intensive and costly. As such, the 
      current research aims to assist in decreasing the required experimental effort by 
      studying the efficacy of both design of experiments (DOE) and optimal 
      experimental designs (OED) techniques. This is the first research in predictive 
      microbiology (i) to make a comparison of these techniques based on the (relative) 
      model prediction uncertainty of the obtained models and (ii) to compare OED 
      criteria for the design of experiments with static (instead of dynamic) 
      environmental conditions. A comparison of the DOE techniques demonstrated that 
      the inscribed central composite design and full factorial design were most 
      suitable. Five conventional and two tailor made OED criteria were tested. The 
      commonly used D-criterion performed best out of the conventional designs and 
      almost equally well as the best of the dedicated criteria. Moreover, the 
      modelling results of the D-criterion were less dependent on the experimental 
      variability and differences in the microbial response than the two selected DOE 
      techniques. Finally, it was proven that solving the optimisation of the 
      D-criterion can be made more efficient by considering the sensitivities of the 
      growth rate relative to its value as Jacobian matrix instead of the sensitivities 
      of the cell density measurements.
CI  - Copyright (c) 2018. Published by Elsevier Ltd.
FAU - Akkermans, Simen
AU  - Akkermans S
AD  - BioTeC, Chemical and Biochemical Process Technology and Control, Department of 
      Chemical Engineering, KU Leuven, Ghent, Belgium; OPTEC, Optimization in 
      Engineering Center-of-Excellence, KU Leuven, Belgium; CPMF(2), Flemish Cluster 
      Predictive Microbiology in Foods, Belgium1. Electronic address: 
      simen.akkermans@kuleuven.be.
FAU - Nimmegeers, Philippe
AU  - Nimmegeers P
AD  - BioTeC, Chemical and Biochemical Process Technology and Control, Department of 
      Chemical Engineering, KU Leuven, Ghent, Belgium; OPTEC, Optimization in 
      Engineering Center-of-Excellence, KU Leuven, Belgium; CPMF(2), Flemish Cluster 
      Predictive Microbiology in Foods, Belgium1.
FAU - Van Impe, Jan F
AU  - Van Impe JF
AD  - BioTeC, Chemical and Biochemical Process Technology and Control, Department of 
      Chemical Engineering, KU Leuven, Ghent, Belgium; OPTEC, Optimization in 
      Engineering Center-of-Excellence, KU Leuven, Belgium; CPMF(2), Flemish Cluster 
      Predictive Microbiology in Foods, Belgium1. Electronic address: 
      jan.vanimpe@kuleuven.be.
LA  - eng
PT  - Comparative Study
PT  - Journal Article
DEP - 20180607
PL  - England
TA  - Food Microbiol
JT  - Food microbiology
JID - 8601127
SB  - IM
MH  - Bacteria/chemistry/*growth & development
MH  - Kinetics
MH  - Models, Biological
MH  - *Research Design
OTO - NOTNLM
OT  - D-criterion
OT  - Experimental design
OT  - Model prediction uncertainty
OT  - Predictive microbiology
OT  - Secondary model
EDAT- 2018/09/01 06:00
MHDA- 2018/11/06 06:00
CRDT- 2018/09/01 06:00
PHST- 2018/01/02 00:00 [received]
PHST- 2018/05/24 00:00 [revised]
PHST- 2018/05/24 00:00 [accepted]
PHST- 2018/09/01 06:00 [entrez]
PHST- 2018/09/01 06:00 [pubmed]
PHST- 2018/11/06 06:00 [medline]
AID - S0740-0020(18)30004-2 [pii]
AID - 10.1016/j.fm.2018.05.010 [doi]
PST - ppublish
SO  - Food Microbiol. 2018 Dec;76:504-512. doi: 10.1016/j.fm.2018.05.010. Epub 2018 Jun 
      7.