Document Detail


Statistical models for analyzing repeated quality measurements of horticultural products. Model evaluations and practical example.
MedLine Citation:
PMID:  12941535     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In the field of postharvest quality assessment of horticultural products, research on the development of non-destructive quality sensors, replacing destructive and often time consuming sensors, has spurred in the last decennium offering the possibility of taking repeated quality measures on the same product. Repeated measures analysis is gaining importance during recent years and several software packages offer a broad class of routines. A dataset dealing with the postharvest quality evolution of different tomato cultivars serves as practical example for the comparison and discussion of four different statistical model types. Starting from an analysis at each time point and an ordinary least squares regression model as standard and widely used methods, this contribution aims at comparing these two methods to a repeated measures analysis and a longitudinal mixed model. It is shown that the flexibility of such a mixed model, both towards the repeated measures design of the experiments as towards the large product variability inherent to these horticultural products, is an important advantage over classical techniques. This research shows that different conclusions could be drawn depending on which technique is used due to the basic assumptions of each model and which are not always fulfilled. The results further demonstrate the flexibility of the mixed model concept. Using a mixed model for repeated measures, the different sources of variability, being inter-tomato variability, intra-tomato variability and measurement error were characterized being of great benefit to the researcher.
Authors:
Bart De Ketelaere; Jeroen Lammertyn; Geert Molenberghs; Bart Nicolaï; Josse De Baerdemaeker
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Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Mathematical biosciences     Volume:  185     ISSN:  0025-5564     ISO Abbreviation:  Math Biosci     Publication Date:  2003 Oct 
Date Detail:
Created Date:  2003-08-27     Completed Date:  2003-10-09     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0103146     Medline TA:  Math Biosci     Country:  United States    
Other Details:
Languages:  eng     Pagination:  169-89     Citation Subset:  IM    
Affiliation:
K.U. Leuven, Department of Agro-Engineering and Economics, Laboratory for Agricultural Machinery and Processing, Kasteelpark Arenberg 30, 3001 Leuven, Belgium. bart.deketelaere@agr.kuleuven.ac.be
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MeSH Terms
Descriptor/Qualifier:
Computer Simulation
Crops, Agricultural / standards*
Data Interpretation, Statistical*
Lycopersicon esculentum / standards
Models, Statistical*
Quality Control

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