To improve the understanding of what constitutes bread freshness, relationships between consumers’
perceptions of freshness and sensory character were determined for different bread types. Descriptive
sensory analysis was carried out on 20 bread types, using a panel of twelve trained assessors and a
defined vocabulary of 28 terms. Representative consumers (n = 115) rated the perceived freshness of
ten different bread types using a labelled scale that was labeled with ‘‘not at all fresh” to ‘‘greatest freshness
imaginable”. Principal component analysis (PCA) and Hierarchical cluster analysis identified three
consumer segments that were homogeneous in their freshness perceptions. Partial least squares regression
(PLSR) was used to investigate the relationships between consumers’ freshness perceptions for each
segment and descriptive sensory data. Cluster analysis greatly enhanced the understanding of the consumer
test results by indicating that expectations of bread freshness varied among consumers. Positive
drivers of bread freshness for consumers in cluster one were ‘‘porous” appearance, and ‘‘floury” odour,
while positive drivers for cluster two consumers were ‘‘malty” odour, and ‘‘sweet”, ‘‘buttery”, ‘‘oily” flavour.
Cluster three consumers were positively driven by ‘‘porous” appearance, ‘‘floury”, ‘‘toasted” odour
and ‘‘sweet” aftertaste. Using PLSR models, consumer freshness perceptions for the ten remaining breads
not evaluated by consumers, but assessed by descriptive sensory analysis, were predicted for each consumer
segment
To improve the understanding of what constitutes bread freshness, relationships between consumers’
perceptions of freshness and sensory character were determined for different bread types. Descriptive
sensory analysis was carried out on 20 bread types, using a panel of twelve trained assessors and a
defined vocabulary of 28 terms. Representative consumers (n = 115) rated the perceived freshness of
ten different bread types using a labelled scale that was labeled with ‘‘not at all fresh” to ‘‘greatest freshness
imaginable”. Principal component analysis (PCA) and Hierarchical cluster analysis identified three
consumer segments that were homogeneous in their freshness perceptions. Partial least squares regression
(PLSR) was used to investigate the relationships between consumers’ freshness perceptions for each
segment and descriptive sensory data. Cluster analysis greatly enhanced the understanding of the consumer
test results by indicating that expectations of bread freshness varied among consumers. Positive
drivers of bread freshness for consumers in cluster one were ‘‘porous” appearance, and ‘‘floury” odour,
while positive drivers for cluster two consumers were ‘‘malty” odour, and ‘‘sweet”, ‘‘buttery”, ‘‘oily” flavour.
Cluster three consumers were positively driven by ‘‘porous” appearance, ‘‘floury”, ‘‘toasted” odour
and ‘‘sweet” aftertaste. Using PLSR models, consumer freshness perceptions for the ten remaining breads
not evaluated by consumers, but assessed by descriptive sensory analysis, were predicted for each consumer
segment
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