The Effects of Cranberry Polyphenol Extract (CPE) Supplementation on Astringency and Flavor Perception as a Function of PROP Taster Status and Other Individual Factors

Neeta Y. Yousaf and Beverly J. Tepper ()
Additional contact information
Neeta Y. Yousaf: Department of Food Science & Center for Sensory Sciences & Innovation, Rutgers University, New Brunswick, NJ 08901, USA
Beverly J. Tepper: Department of Food Science & Center for Sensory Sciences & Innovation, Rutgers University, New Brunswick, NJ 08901, USA

IJERPH, 2022, vol. 19, issue 19, 1-23

Abstract: This study investigated whether PROP (6-n-propylthiouracil) taster status and other individual factors (gender, ethnicity, BMI, and age) are markers of variation in perceptions of astringency and other flavor attributes. Participants ( n = 125) evaluated cranberry juice cocktail samples (CJC) supplemented with cranberry-derived polyphenol extract (CPE, added at 0, 0.3, 0.5 and 0.75 g/L), as well as control samples, unsweetened cranberry juice (CJ) and an aqueous solution of 0.75 g/L CPE. Subjects evaluated samples for key sensory attributes and overall liking using a 15 cm line scale. The data were analyzed using ANCOVA and machine learning tools (regression trees and random forest modeling) to examine if the latter approach would extract more meaningful insights about the roles of personal factors in sensory perceptions of cranberry-derived stimuli. ANCOVA revealed robust stimulus effects, but no effect of PROP taster status on astringency perception was observed. Several effects of PROP×gender, ethnicity, and other factors were observed on other sensory attributes and liking. ANCOVA and machine learning tools yielded similar findings, but regression trees provided a more visualized framework. These data show that PROP taster status has a limited role in astringency perception in complex samples and that other personal factors deserve greater focus in future research on astringency perception.

Keywords: astringency; PROP phenotype; cranberry; polyphenols; machine learning; taste (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/19/11995/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/19/11995/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:19:p:11995-:d:922186

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().