Food Quality and Preference
Session 1 Session 2 Session 3 a Session 4 Session 5 Session 6 a Three months
3.2.3 Tasting Methodology
Three sensory methods, CP, GN and PN were used to evaluate the six brandies. The CP result was considered the control or benchmark in this study. All of the evaluations were conducted in white tasting booths with controlled air conditioning and lighting to ensure unbiased responses. The data for
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the CP were captured using Compusense Five Release 5.2 (www.compusense.com) while data for both GN and PN were captured on paper ballots.
3.2.3.1 Conventional descriptive profiling
The nine brandies evaluated in this experiment, plus the blind duplicates of Brandy 1 and Brandy 6, were evaluated according to the methodology described in Lawless and Heymann (1998). A sensory lexicon of defined attributes, which was used to describe the samples, was compiled during three sessions (Table 3.3). Appearance was considered to be representative of the consumer product experience and was evaluated despite the possibility of biasing the aroma and palate perceptions. Reference standards were developed to represent each of the attributes. Colour charts were not used as the different hues were quite obvious to the panel and could be measured analytically. The aroma references were presented to the panel at the start of each training session. The panel was trained further to recognise and rate the intensity of attributes in the samples over a period of eight sessions. The attributes were measured on 100 mm unstructured line scales that were anchored with the terms “none” on the left end and “intense” on the right end of the scale. Panel performance was monitored using PanelCHECK 1.40 (www.panelcheck.com). The final data were captured over three replicate sessions using a Williams Latin Square design (as generated in Compusense Five version 5.20) where all the panellists evaluated all the samples while compensating for first order carry-over effects. Each replicate was captured over two days in a partial presentation. On the first day, the panellists evaluated the first six samples in their specific order of presentation according to the Williams design and on the second day they continued with the next five samples. Thus the data were captured over a total of six sessions. The reason for the partial presentation was to reduce the number of samples tasted per session, thereby reducing sensory fatigue, while retaining a balanced complete block design. The samples were evaluated in a monadic sequential order. In total the CP took 17 sessions (eight weeks) to complete.
Table 3.3 Sensory lexicon developed to evaluate the sensory profiles of South African brandies
Attribute Description Amber n/a Yellow/gold n/a Green n/a Dried peach/apricot
Aroma and flavour typical of sun dried fruit with a slightly sour note
Citrus Volatile, peely, oily aroma and flavour associated with orange peel or naartjies
Apple/pear Clean, sweet, fresh aroma and flavour associated with green apples or cloudy apple juice
Raisin/Prune Aroma and flavour of dried fruit skins, stalky, dusty, dry and sour, reminiscent of fruit cake
Honey Natural, waxy, stuffy aroma and flavour associated with plain honey. Slightly sour
Sherry Dry, sweet, oxidative character associated with sweet wines Tobacco/tea/
straw
Dried grassy character with tea-like, straw, dusty, cigar box notes
Oaky Sharp, dry resinous aroma and flavour associated with oak barrels and old books
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Caramel Sweet associated aroma and flavour with a caramelized, sugary note. Sticky aroma and flavour reminiscent of toffees
Vanilla Light, sweet, fragrant aroma and flavour associated with cake batter and custard. Clean note
Chocolate Dark chocolate aroma and flavour with a distinct cocoa note Molasses Dark savoury note with smoky, syrupy, Marmite-like tones
Sweet spice General spicy aroma and flavour associated with bun spices with a cinnamon top note
Soapy Aroma and flavour associated with unscented soap. Sharp, chemical, bitter- associated, industrial
Sweet A taste and aftertaste on tongue stimulated by sugars Sour Basic taste on the tongue associated with acids
Bitter A sharp taste and aftertaste experienced at the back of the throat, e.g. caffeine, aloe and tonic water
Alcohol burn The burning/warming sensation caused by high levels of alcohol that lingers in the entire mouth cavity and lips
Drying The sensation of de-lubrication in the oral cavity Viscosity/
density
The sensation of the "thickness"/viscosity or weight of the product in the oral cavity
Smoothness The sensation that the brandy passes easily through the mouth cavity
3.2.3.2 Global Napping
In each GN session performed in this study, the brandies were evaluated according to the Napping® procedure in combination with ultra flash profiling as described by Perrin et al. (2008). The panellists were instructed to arrange the samples on a blank A3 sheet of paper, according to similarity based on their overall sensory perception. After arranging the samples, the panellists had to write down descriptors for each brandy to explain the differences between the samples. The samples were evaluated in three sessions. The first two sessions (Rep 1 and Rep 2) were evaluated two days apart to test for immediate repeatability. A third session, Rep 3, was presented two weeks later to test for intermediate repeatability. The samples were also presented once to a second, independent, panel to test for reproducibility (Rep R).
3.2.3.3 Partial Napping
For PN, the panel repeated the above mentioned Napping® procedure three times per session, each time focussing on a different sensory modality, as described by Dehlholm et al. (2012). Each modality was evaluated on a separate A3 tasting sheet. The samples were first evaluated according to appearance, then according to aroma and then according to all in-mouth sensations: flavour, mouthfeel and basic taste.
As with the GN data, the PN data were captured over three replicate sessions (Rep 1-3) and also with a second independent panel (Rep R) as described in section 2.3.2. The PN data were collected independently from the GN data.