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Texture of Hot-Air-Dried Persimmon (Diospyros kaki) Chips: Instrumental, Sensory, and Consumer Input for Product Development.


ABSTRACT: Persimmon (Diospyros kaki) is an underutilized tree fruit. Previous studies have shown the feasibility of making a hot-air-dried, chip-style product from persimmon. However, the texture of this type of product has not been explored or connected to consumer preference. Thus, for dried samples representing 37 cultivars, this study aimed to (1) predict trained sensory panel texture attributes from instrumental measurements, (2) predict consumer liking from instrumental measurements and sensory texture attributes, and (3) elucidate whether astringency type affects dried product texture. Partial least-squares regression models of fair-to-good quality predicted all measured sensory texture attributes (except Tooth Packing) from instrumental measurements. Modeling also identified that consumer preference is for a moist, smooth texture. Lastly, while astringency type has significant (p < 0.05) effects on several individual texture attributes, astringency type should not be used a priori to screen-in or -out persimmon cultivars for processing into a hot-air-dried product.

SUBMITTER: R Milczarek R 

PROVIDER: S-EPMC7601633 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Texture of Hot-Air-Dried Persimmon (<i>Diospyros kaki</i>) Chips: Instrumental, Sensory, and Consumer Input for Product Development.

R Milczarek Rebecca R   D Woods Rachelle R   I LaFond Sean S   L Smith Jenny J   Sedej Ivana I   W Olsen Carl C   M Vilches Ana A   P Breksa Andrew A   E Preece John J  

Foods (Basel, Switzerland) 20201010 10


Persimmon (<i>Diospyros kaki</i>) is an underutilized tree fruit. Previous studies have shown the feasibility of making a hot-air-dried, chip-style product from persimmon. However, the texture of this type of product has not been explored or connected to consumer preference. Thus, for dried samples representing 37 cultivars, this study aimed to (1) predict trained sensory panel texture attributes from instrumental measurements, (2) predict consumer liking from instrumental measurements and senso  ...[more]

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