论文信息 - A comparison of methods for analyzing multivariate sensory data in designed experiments - A case study of salt reduction in liver paste

A comparison of methods for analyzing multivariate sensory data in designed experiments - A case study of salt reduction in liver paste

This paper presents a comparison of different methods for analyzing designed experiments. The methods used are based on PCA, PLS and ANOVA, used either separately or in combination. Special emphasis will be on how to obtain information about medium and less important factors in the presence of very dominating ones. It will be shown that this could be done by splitting the dataset in two. Our propositions will be illustrated on a data set obtained for studying the effect of salt reduction in liver paste.

[1] Rasmus Bro,et al. PARAFASCA: ASCA combined with PARAFAC for the analysis of metabolic fingerprinting data , 2008 .

[2] Age K. Smilde,et al. ANOVA–principal component analysis and ANOVA–simultaneous component analysis: a comparison , 2011 .

[3] Tormod Næs,et al. Interpreting sensory data by combining principal component analysis and analysis of variance , 2009 .

[4] Tormod Næs,et al. A case study of the use of experimental design and multivariate analysis in product improvement , 1996 .

[5] Tormod Næs,et al. Interpretation, validation and segmentation of preference mapping models , 2014 .

[6] Tormod Næs,et al. Multivariate optimization by visual inspection , 2007 .

[7] R. H. Myers,et al. Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[8] Peter de B. Harrington,et al. Analysis of variance–principal component analysis: A soft tool for proteomic discovery , 2005 .

[9] Tormod Næs,et al. Statistics for Sensory and Consumer Science , 2010 .

[10] Julian Morris,et al. Process performance monitoring in the presence of confounding variation , 2008 .

[11] J. J. Jansen,et al. ASCA: analysis of multivariate data obtained from an experimental design , 2005 .

[12] Magni Martens,et al. Multivariate Analysis of Quality : An Introduction , 2001 .