Single-cultivar juices may be a valuable way to introduce different versions of a product to the market and obtain price discrimination. To communicate a product’s value, complex characteristics incorporated by each cultivar must be identified. New sensory methods rely on the assessor’s ability to recall attributes; however, the use of objective vocabularies may improve the sensory profiling. This work aimed to profile monovarietal apple juices by using projective mapping (PM) combined with ultra-flash profiling (UFP) supported by a sensory wheel built with a textmining tool. Samples were also analyzed for physicochemical parameters to provide more information to the assessment. The assessor coordinates from PM were used in multiple factor analysis with confidence ellipses to assess differences among samples. A goodness-of-fit test was applied to select the most meaningful descriptors generated through the UFP test by calculating the expected frequency of choosing a descriptor from the sensory wheel and comparing it with the observed values. The methodology provided a more accurate sensory profile compared to previous research on fresh apples and juices. Elstar, Jonagold, and Pinova were considered as sweet juices, and Gravensteiner was described as sour and astringent, with green-apple notes. Rubinette was described as having a strong taste and cloudy aspect.

A New Sensory Approach Combined with a TextMining Tool to Create a Sensory Lexicon and Profile of Monovarietal Apple Juices

Thais Mendes da Silva;Daniela Torello Marinoni;Cristiana Peano;Nicole Roberta Giuggioli
Last
2019

Abstract

Single-cultivar juices may be a valuable way to introduce different versions of a product to the market and obtain price discrimination. To communicate a product’s value, complex characteristics incorporated by each cultivar must be identified. New sensory methods rely on the assessor’s ability to recall attributes; however, the use of objective vocabularies may improve the sensory profiling. This work aimed to profile monovarietal apple juices by using projective mapping (PM) combined with ultra-flash profiling (UFP) supported by a sensory wheel built with a textmining tool. Samples were also analyzed for physicochemical parameters to provide more information to the assessment. The assessor coordinates from PM were used in multiple factor analysis with confidence ellipses to assess differences among samples. A goodness-of-fit test was applied to select the most meaningful descriptors generated through the UFP test by calculating the expected frequency of choosing a descriptor from the sensory wheel and comparing it with the observed values. The methodology provided a more accurate sensory profile compared to previous research on fresh apples and juices. Elstar, Jonagold, and Pinova were considered as sweet juices, and Gravensteiner was described as sour and astringent, with green-apple notes. Rubinette was described as having a strong taste and cloudy aspect.
608
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https://www.mdpi.com/2304-8158/8/12/608/pdf
sensory; fruit; projective mapping; quality; apple juice; text mining; sensory wheel
Thais Mendes da Silva , Daniela Torello Marinoni, Cristiana Peano , Nicole Roberta Giuggioli
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1717127
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