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

• Published in: Animal (Cambridge, England) England), 2019-09, Vol.13 (9), p.1834-1842
• Main Authors: Julliand, S., Dacremont, C., Omphalius, C., Villot, C., Julliand, V.
• Format: Article
• Language: English
• Subjects: Biochemical analysis; biochemical composition; Biochemistry; Carbohydrates; Clusters; Computation; Contingency; Correlation; Data analysis; energy content; equine; Feeds; Food; Food engineering; forage; Hay; Horses; Life Sciences; Nutrition; Nutritive value; Odor; Perceptions; Proteins; Research Article; Sensory evaluation; Sensory properties; Smell; Texture; Touch; Visual perception; Visual stimuli
• ISSN: 1751-7311; 1751-732X
• DOI: 10.1017/S1751731118003725

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