Genetic investigations into the use of sensory evaluation: the case of boar taint discrimination in Pietrain sired crossbreds.
2025
Markey, Alice | Groβe-Brinkhaus, Christine | Mörlein, Daniel | Mörlein, Johanna | Wilmot, Hélène | Tholen, Ernst | Gengler, Nicolas
anglais. peer reviewed
Afficher plus [+] Moins [-]anglais. Using genetic selection for raising intact boars, which improves growth and feed efficiency, is a promising alternative to castration for mitigating boar taint. Selective breeding has the potential to help to identify and select genetic lines with a reduced risk of boar taint. Common phenotypes are laboratory measurements of skatole (SKA) and androstenone (ANON) i.e., the major compounds responsible for boar taint, in backfat. However, an alternative exists: sensory evaluation by human assessors. The objectives of this study were (1) to estimate the genetic relationships among sensory scores (SENS) obtained by different assessors, (2) to correlate these scores with SKA and ANON, (3) to establish the independence of SENS from the causal traits, here SKA and ANON, by recursive modeling, holding those constant, and (4) to combine different assessors to allow an efficient selection against boar taint. Data included up to 1,016 records of SKA, ANON, and SENS (0-5) from 10 trained assessors on the backfat of intact males reared at least until puberty at three performance testing stations testing the products of Pietrain × commercial crossbred sows. Genetic parameters were estimated using restricted estimate maximum likelihood. Traits SKA and ANON were log10 transformed (SKAt and ANONt) and SENS traits were Snell transformed SENS (SENSt). Heritability estimates were 0.52 for SKAt and 0.53 for ANONt, those for SENSt ranged from 0.07 to 0.30. Moderate to high genetic correlations between some SENSt and SKAt (up to 0.87) and ANONt (up to 0.61) were found. Heritabilities and correlations indicated that some SENSt could be used to select against boar taint. Studying the independence of SENSt from SKAt and ANONt based on a posteriori recursive model revealed a large range of reductions of genetic variance: up to 71.08%. However, some SENSt remained moderately heritable (0.04-0.19) indicating independent genetic variance from SKAt and ANONt. This reflects that some heritable compounds potentially not related to SKA or ANON are perceived. Finally, the combination of assessors allowed, here shown with three assessors, to obtain a high heritability of 0.40, associated to high genetic and phenotypic correlations. Moreover, these results demonstrate the potential of using the sensory scores of several trained assessors for selection against boar taint.
Afficher plus [+] Moins [-]anglais. Meat quality can be impacted by different practices during the whole life of pigs. Raising intact boars is interesting to potentially improve the growth rate and feed efficiency of boars. Moreover, castration is a major welfare and health issue. However, in intact boars so called boar taint, a fecal and urinary smell, can occur which can repel consumers. This odor is mainly caused by skatole (SKA) and androstenone (ANON) accumulation in fat tissues. To reduce their impact, genetic selection against these heritable compounds can be applied. However, their analytical measurements are costly, time-consuming, and, in consequence, in low numbers. Alternative routine data collection based on sensory evaluation scores (SENS) has been proposed. These SENS were attributed to heated fat samples by 10 trained assessors to detect SKA and ANON together. Genetic relationships indicated that some SENS could potentially be used for genetic selection against SKA and ANON. Investigations on the origin of attributed SENS demonstrated that some (unknown) compounds probably correlated to SKA and ANON are perceived, too. Finally, SENS from different assessors were combined to select more efficiently against boar taint.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par University of Liège
Découvrez la collection de ce fournisseur de données dans AGRIS