John A. Bower

The influence of socio-economic background on perceptions of vegetables among Scottish primary school children

Abstract Forty-nine children aged between 8 and 10 years old from primary schools in two socially disparate areas of Edinburgh participated in this study. Repertory-grid method obtained information from the children regarding their perceptions of eight vegetables commonly consumed in the UK (baked beans, carrots, tomatoes, cauliflower, turnip (swede), cabbage, sweetcorn and peas). Between 6 and 23 personal constructs were elicited from each child (mean=15). Individual score-sheets contained each childs unique set of constructs represented by linear scales. Children scored all eight vegetables for every construct, and for preference. Data were analysed using Generalised Procrustes Analysis. The first five dimensions of the product space (explaining a total of 88% of the variance) separated the vegetables into distinct groups according to physical and sensory properties, requisite preparation, and appropriateness for eating with particular meals. Socio-economic background was found to influence childrens perceptions of the vegetables. Internal preference mapping examined the variance in the childrens hedonic responses. Three clusters were identified, with baked beans being particularly liked. Friedmans two-way ANOVA confirmed that the childrens preferences for the vegetables were not equal ( p p

Statistics for food science V: ANOVA and multiple comparisons (Part B)

Describes multiple comparison procedures applied to three or more sample groups after ANOVA. Illustrates ANOVA applied to experiments with completely randomised design and randomised block design.

Statistics for food science V: comparison of many groups (part A)

Describes statistical methods applied to three or more sample groups. Discusses analysis of variance in parametric forms and the requirement for experimental design control before its application.

Statistics for food science III: sensory evaluation data. Part A – sensory data types and significance testing

Introduces statistical methods employed in analysing sensory data. Describes significance testing and simple procedures for determination of population characteristics in sensory data and highlights sources of error and replication in the sensory context. Discusses factors important in selection of an appropriate statistical test for sensory data.

Statistics for food science ‐ V part C: non‐parametric ANOVA

Describes non‐parametric methods applied to comparisons of three or more sample groups.Illustrates non‐parametric ANOVA applied to a randomized block design for a consumer sensory experiment.

Statistics for food science III: sensory evaluation data. Part B ‐ discrimination tests

Describes statistical methods applied to sensory discrimination tests. Illustrates binomial and chi‐square statistical analysis and discusses similarity testing, power and replication in discrimination testing.

Statistics for food science IV: two‐sample tests

Describes statistical methods applied to two‐sample significance tests. Discusses independent, related and paired samples, Wilcoxon, sign, Mann‐Whitney U and t‐tests and illustrates their application.

Sensory characteristics of sugar-free and sugar-based boiled sweets

Sugar-free boiled sweets and a similar sugar-based product were produced on a laboratory-scale and compared using sensory and consumer panels. A combination of a maltitol-based bulk sweetener with an intense sweetener (saccharin) was used as a replacement for the sucrose and glucose syrup components in a conventional formulation. Results indicate that the production of such products required little variation of conventional processing procedures. Differences in sensory characteristics and consumer preference between the two types of boiled sweets were not generally statistically significant. Results suggest that it is possible to produce a sugar-fre boiled sweet with comparable sensory qualities and consumer acceptance to conventional, sugar-based confectionery, if so desired.

Statistics for food science II: chemical analysis data

Introduces some simple statistics employed in analysing chemical analysis data. Describes measures of precision and accuracy and how the use of confidence intervals and repeatability can guide validation of data.

Statistics for food science I

Begins an introduction to statistics for the food scientist. Aimed at the non‐mathematician, discusses the application of the statistical approach and introduces statistical measures for data presentation and summary.