Consumer Sensory Characterization and Acceptance of Jelly, Nougat, and Praline Candies Using CATA and JAR Approaches

ABSTRACT The aim of this study was to evaluate the sensory profiles, consumer acceptance, and attribute appropriateness of three confectionery products—Jelly, Nougat, and Praline—based on consumer perception. A total of 150 untrained consumers participated in the sensory evaluation. Overall liking was assessed using a 9-point hedonic scale, in combination with Check-All-That-Apply (CATA) and Just-About-Right (JAR) methods. The results showed that consumers were able to clearly differentiate the sensory profiles of the three candy types. Jelly received the highest overall liking score (7.01 ± 1.09), followed by Nougat (6.54 ± 1.59) and Praline (6.38 ± 1.58), with significant differences observed among candies (p< 0.05). CATA and multivariate analyses revealed that Jelly was characterized by soft texture, smooth mouthfeel, fruity flavour, and rapid melting behaviour, whereas Nougat and Praline were associated with firmer textures and richer flavour attributes. JAR analysis indicated higher alignment of Jelly attributes with ideal intensity levels, with JAR percentages ranging from 62.0% to 72.5%, compared to 44.6–51.4% for Nougat and 41.3–48.6% for Praline. Penalty analysis showed that although larger mean drops in overall liking were observed for non-JAR perceptions in Nougat and Praline (MD up to −1.38), these penalties were largely non-significant. Overall, the integrated application of hedonic evaluation, CATA, JAR, and multivariate analyses provided a comprehensive understanding of consumer perception, drivers of liking, and attribute appropriateness across different confectionery matrices.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-24
DOI
https://doi.org/10.5281/zenodo.18593040
Primary Topic
Sensory Analysis and Statistical Methods
Type
article
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article

Consumer Sensory Characterization and Acceptance of Jelly, Nougat, and Praline Candies Using CATA and JAR Approaches

Neelam Chaturvedi, Shivi Tyagi
Zenodo (CERN European Organization for Nuclear Research)
Sensory Analysis and Statistical Methods
article

Consumer Sensory Characterization and Acceptance of Jelly, Nougat, and Praline Candies Using CATA and JAR Approaches

Neelam Chaturvedi, Shivi Tyagi
article en

Abstract

ABSTRACT The aim of this study was to evaluate the sensory profiles, consumer acceptance, and attribute appropriateness of three confectionery products—Jelly, Nougat, and Praline—based on consumer perception. A total of 150 untrained consumers participated in the sensory evaluation. Overall liking was assessed using a 9-point hedonic scale, in combination with Check-All-That-Apply (CATA) and Just-About-Right (JAR) methods. The results showed that consumers were able to clearly differentiate the sensory profiles of the three candy types. Jelly received the highest overall liking score (7.01 ± 1.09), followed by Nougat (6.54 ± 1.59) and Praline (6.38 ± 1.58), with significant differences observed among candies (p< 0.05). CATA and multivariate analyses revealed that Jelly was characterized by soft texture, smooth mouthfeel, fruity flavour, and rapid melting behaviour, whereas Nougat and Praline were associated with firmer textures and richer flavour attributes. JAR analysis indicated higher alignment of Jelly attributes with ideal intensity levels, with JAR percentages ranging from 62.0% to 72.5%, compared to 44.6–51.4% for Nougat and 41.3–48.6% for Praline. Penalty analysis showed that although larger mean drops in overall liking were observed for non-JAR perceptions in Nougat and Praline (MD up to −1.38), these penalties were largely non-significant. Overall, the integrated application of hedonic evaluation, CATA, JAR, and multivariate analyses provided a comprehensive understanding of consumer perception, drivers of liking, and attribute appropriateness across different confectionery matrices.

Zenodo (CERN European Organization for Nuclear Research)
Banasthali University (IN)
Openalex Percentile: Top 28%
Sensory Analysis and Statistical Methods
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