6533b7dafe1ef96bd126e0d1
RESEARCH PRODUCT
Statistical analysis of sensory profiling data. Graphs for presenting results (PCA and ANOVA)
Claire Chabanetsubject
2. Zero hunger0303 health sciencesNutrition and Dietetics030309 nutrition & dieteticsFlavour04 agricultural and veterinary sciences[SDV.IDA] Life Sciences [q-bio]/Food engineering040401 food scienceSensory analysis03 medical and health sciences0404 agricultural biotechnology[SDV.IDA]Life Sciences [q-bio]/Food engineeringPrincipal component analysisMixed linear modelStatisticsStatistical analysisAnalysis of varianceComputingMilieux_MISCELLANEOUSFood ScienceMathematicsdescription
Abstract A principal component analysis is performed to analyse the matrix of median values, with 16 varieties in rows, and all descriptors in columns. Only texture descriptors contribute to the definition of the main axes. Six varieties are identified that score high on mealiness and low on moisture, the 10 other varieties are ordered according to a mashable axis which involves texture descriptors. Next we exclude texture descriptors since the main characteristics are already found, and mealy varieties since they are not suitable for steamed potatoes. A mixed linear model (ANOVA) with random subject effects is then used for each descriptor. Flavour and taste differences are found among the 10 varieties involved. Sweet, chesnut, pastry, cereal, earthy, herbace, rawpotat, artichok and celery descriptors differ between varieties ( p ⩽5%).
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2000-01-01 | Food Quality and Preference |