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Association between nutritional values of hays fed to horses and sensory properties as perceived by human sight, touch and smell

Published online by Cambridge University Press:  05 February 2019

S. Julliand ,
C. Dacremont ,
C. Omphalius ,
C. Villot  and
V. Julliand
S. Julliand*
Affiliation:
Lab To Field, 26 Boulevard Docteur Petitjean, 21000 Dijon, France
C. Dacremont
Affiliation:
Centre des Sciences du Goût et de l’Alimentation, UMR 6265/UMR A1324 University of Burgundy – CNRS – INRA, 9E Boulevard Jeanne d’Arc, 21000 Dijon, France
C. Omphalius
Affiliation:
AgroSupDijon, 26 Boulevard Docteur Petitjean, 21000 Dijon, France
C. Villot
Affiliation:
AgroSupDijon, 26 Boulevard Docteur Petitjean, 21000 Dijon, France
V. Julliand
Affiliation:
UMR A 102-02 PAM-PMB, University of Burgundy/AgroSup Dijon, 1 Esplanade Erasme, 21000 Dijon, France
*
†E-mail: [email protected]
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Abstract

Although hay is the foundation of most equine diets, horse owners rarely ask for biochemical analysis and the routine practice is to choose hay based on its ‘perceived‘ nutritional value. The present study aimed at exploring the relationship between sensory properties as perceived by sight, touch and smell, and the nutritional value of hay measured by biochemical analysis using a ‘free sorting task’ method. Fifty-four non-expert participants were asked individually to: (1) observe 21 hays samples, (2) group together hays that they perceived as similar for each of the three modalities (hay appearance, odour or texture) and (3) characterize each formed group with a maximum of five descriptive terms. For each modality, results were recorded in a contingency matrix (hays × terms) where only terms cited at the minimum five times for at least one sample, were kept for data analysis. A correspondence analysis (CA) was performed on the contingency matrix to plot both samples and descriptive terms on a χ2 metric map. Then, a Hierarchical Ascending Classification (HAC) was performed on the coordinates of samples in the CA space. Clusters were identified by truncating the HAC tree-diagrams. The attributes that defined the best resulting clusters were identified by computing their probability of characterizing a cluster. Correlations were computed between each biochemical parameter on one hand, and the first two dimensions of the CA map on the other. Finally, correlations between the values of each hay on the first dimension of the three CA maps (appearance, odour and texture) were computed. Hedonic descriptive terms were primarily used for describing odour and texture modalities. For describing hay appearance, participants spontaneously used visual cues referring to colour or aspect. Based on the tree-diagrams resulting from the HAC, 3, 5 and 2 groups were clustered, respectively for appearance, odour and texture description. Digestible energy was correlated to the first dimension on the three CA maps, whereas CP was correlated to the first dimension of the CA appearance map only. While NDF value was correlated to the first and second dimensions on the CA odour map only, ADF content was correlated to the first dimension on the three CA maps. Non-fibre carbohydrates were correlated to the first dimension of the CA appearance map only. The similarity-based approach which is part of the standard toolbox of food sensory evaluation by untrained consumers was well adapted to animal feeds evaluation by non-experts.

Keywords

biochemical compositionenergy contentforagesensory evaluationequine
Type
Research Article
Information
animal , Volume 13 , Issue 9 , September 2019 , pp. 1834 - 1842
Copyright
© The Animal Consortium 2019 

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Footnotes

a

Present address: Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada.

References

Agabriel, J 2007. Alimentation des bovins, ovins et caprins: Besoins des animaux, valeurs des aliments: Tables Inra 2010. Edition remaniée. Quae Editions, Versailles, France.Google Scholar
Andueza, D, Rodrigues, AM, Picard, F, Rossignol, N, Baumont, R, Cecato, U and Farruggia, A 2016. Relationships between botanical composition, yield and forage quality of permanent grasslands over the first growth cycle. Grass and Forage Science 71, 366–378.CrossRefGoogle Scholar
Association of Official Analytical Chemists (AOAC) 2012. Official methods of analysis of AOAC international, 19th edition. AOAC, Gaithersburg, MD, USA.Google Scholar
Bruinenberg, MH, Valk, H, Korevaar, H and Struik, PC 2002. Factors affecting digestibility of temperate forages from seminatural grasslands: a review. Grass and Forage Science 57, 292–301.CrossRefGoogle Scholar
Cariou, V and Qannari, EM 2018. Statistical treatment of free sorting data by means of correspondence and cluster analyses. Food Quality and Preference 68, 1–11.CrossRefGoogle Scholar
Chollet, S, Lelièvre, M, Abdi, H and Valentin, D 2011. Sort and beer: everything you wanted to know about the sorting task but did not dare to ask. Food Quality and Preference 22, 507–520.Google Scholar
Dairy One Forage Lab 2018. Feed composition library. Retrieved on 22 May 2018 from http://dairyone.com/analytical-services/feed-and-forage/feed-composition-library/interactive-feed-composition-library/.Google Scholar
Faye, P, Brémaud, D, Teillet, E, Courcoux, P, Giboreau, A and Nicod, H 2006. An alternative to external preference mapping based on consumer perceptive mapping. Food Quality and Preference 17, 604–614.CrossRefGoogle Scholar
Geor, RJ and Harris, PA 2013. Obesity. In Equine applied and clinical nutrition (ed. RJ Geor, PA Harris and M Coenen), pp. 487–502. Saunders, Elsevier, Amsterdam, The Netherlands.CrossRefGoogle Scholar
Harris, PA, Ellis, AD, Fradinho, MJ, Jansson, A, Julliand, V, Luthersson, N, Santos, AS and Vervuert, I 2017. Review: Feeding conserved forage to horses: recent advances and recommendations. Animal 11, 958–967.CrossRefGoogle ScholarPubMed
Julliand, V and Grimm, P 2017. The impact of diet on the hindgut microbiome. Journal of Equine Veterinary Science 52, 23–28.CrossRefGoogle Scholar
Lebart, L, Morineau, A and Piron, M 1995. Statistique exploratoire multidimensionnelle, volume 3. Dunod, Paris, France.Google Scholar
Lyon, DH, Francombe, MA, Hasdell, TA and Lawson, K 1992. What is sensory analysis used for?. In Guidelines for sensory analysis in food product development and quality control (ed. DH Lyon), pp. 9–17. Springer, Boston, MA, USA.CrossRefGoogle Scholar
McGregor Argo, C 2013. Feeding thin and starved horses. In Equine applied and clinical nutrition (ed. RJ Geor, PA Harris and M Coenen), pp 503–511. Saunders, Elsevier, Amsterdam, The Netherlands.CrossRefGoogle Scholar
Morrison, IM 1980. Changes in the lignin and hemicellulose concentrations of ten varieties of temperate grasses with increasing maturity. Grass and Forage Science 35, 287–293.CrossRefGoogle Scholar
National Research Council (NRC) 2007. Nutrient requirements of horses, 6th revised edition. National Academies Press, Washington, DC, USA.Google Scholar
Sester, C, Dacremont, C, Deroy, O and Valentin, D 2013. Investigating consumers’ representations of beers through a free association task: a comparison between packaging and blind conditions. Food Quality and Preference 28, 475–483.CrossRefGoogle Scholar
Shankar, MU, Levitan, CA and Spence, C 2010. Grape expectations: the role of cognitive influences in color–flavor interactions. Consciousness and Cognition 19, 380–390.CrossRefGoogle ScholarPubMed
Spence, C 2015. On the psychological impact of food colour. Flavour 4, 21.CrossRefGoogle Scholar
Valentin, D, Chollet, S, Lelièvre, M and Abdi, H 2012. Quick and dirty but still pretty good: a review of new descriptive methods in food science. International Journal of Food Science and Technology 47, 1563–1578.CrossRefGoogle Scholar
Valentin, D, Chollet, S, Nestrud, M and Abdi, H 2018. Projecting mapping and sorting tasks. In Descriptive analysis in sensory evaluation (ed. SE Kemp, J Hort, T and Hollowood), pp. 535–559. John Wiley & Sons, Chichester, UK.CrossRefGoogle Scholar
Van Soest, PJ 1965. Symposium on factors influencing the voluntary intake of herbage by ruminants: voluntary intake in relation to chemical composition and digestibility. Journal of Animal Science 24, 834–843.CrossRefGoogle Scholar
Van Soest, PV, Robertson, JB and Lewis, BA 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science 74, 3583–3597.CrossRefGoogle ScholarPubMed
Varela, P and Ares, G 2012. Sensory profiling, the blurred line between sensory and consumer science. A review of novel methods for product characterization. Food Research International 48, 893–908.CrossRefGoogle Scholar
Verhagen, JV and Engelen, L 2006. The neurocognitive bases of human multimodal food perception: sensory integration. Neuroscience and Biobehavioral Reviews 30, 613–650.CrossRefGoogle ScholarPubMed
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