Sensory guided selection criteria for breeding consumer-preferred sweetpotatoes in Uganda

Abstract

Prioritizing sensory attributes and consumer evaluation early in breeding trials to screen for end-user preferred traits could improve adoption rates of released genotypes. In this study, a lexicon and protocol for descriptive sensory analysis (DSA) was established for sweetpotato and used to validate an instrumental texture method for which critical values for consumer preference were set. The study comprised several phases: lexicon development during a 4-day workshop; 3-day intensive panel training; follow-up virtual training, evaluation of 12 advanced genotypes and 101 additional samples from two trials in 2021 by DSA and instrumental texture analysis using TPA double compression; and DSA, instrumental texture analysis and consumer acceptability tests on 7 genotypes in on-farm trials. The established sweetpotato lexicon comprising 27 sensory attributes enabled characterization and differentiation of genotypes by sensory profiles. Significant correlation was found between sensory firmness by hand and mouth with TPA peak positive force (r = 0.695 and r = 0.648, respectively) and positive area (r = 0.748, r = 0.715, respectively). D20, NAROSPOT 1, NASPOT 8, and Umbrella were the most liked genotypes in on-farm trials (overall liking = 7). An average peak positive force of 3700 gf was proposed as a minimum texture value for screening sweetpotato genotypes, since it corresponded with at least 46 % of consumers perceiving sweetpotatoes as just-about-right in firmness and a minimum overall liking of 6 on average. Combining DSA with instrumental texture analysis facilitates efficient screening of genotypes in sweetpotato breeding programs.

Keywords: Descriptive sensory analysis; Ipomoea batatas; Penalty analysis; Sweet potato; Sweetpotato breeding; Sweetpotato lexicon.

Fig. 1

Principal component analysis (PCA) showing the correlation between sensory attributes of 6 sweetpotato genotypes (2 evaluated in duplicate) as evaluated by a trained sensory panel in office setting.

Fig. 2

Principal component analysis maps showing the relationship between sensory attributes of 12 genotypes in advanced trial (with 3 evaluated in duplicate) as evaluated by a trained descriptive sensory analysis panel with A showing second principal component, F2 versus first principal component, F1 and B showing third principal component, F3 versus first principal component, F1.

Fig. 3

Principal component analysis map showing the relationship between sensory texture attributes of 12 genotypes in advanced trial (with 3 evaluated in duplicate) as evaluated by a trained descriptive sensory analysis panel.

Fig. 4

Plots of (A) sensory firmness versus predicted sensory firmness from the developed multiple linear regression model using material from DDBIO population and (B) sensory firmness versus predicted sensory firmness from the developed linear regression model using MDP population.

Fig. 5

Principal Component Analysis showing the relationship between sensory attributes of 7 genotypes (2 served in duplicate) used in on-farm trails.

Fig. 6

Frequency plot showing proportion of respondents who perceived sweetpotato samples in on-farm trials to be ‘too soft’ versus peak force 1 and positive area under curve.

Fig. 7

Frequency plot showing proportion of respondents who perceived sweetpotato samples in on-farm trials as being 'too hard' versus instrumental texture parameters of peak positive force 1 (gf) and positive area 1 (gf·s).

Fig. 8

Frequency plot showing proportion of respondents who perceived the firmness of sweetpotato samples from on-farm trials in Hoima to be ‘just-about-right’ versus instrumental texture parameters of peak positive force 1 (gf) or positive area 1 (gf·s).

Fig. 9

Plot showing average overall liking for sweetpotato samples rated by consumers in on-farm trials in Hoima verses instrumental texture parameter of peak positive force 1 (gf) and positive area (gf·s).