8586, but need to be experts for the products
to be tested. They need to know production
procedures as well as possible errors in production.
This knowledge allows testers to identify
sensory defects and even determine authenticity
and variety of these products.
This is for example used for the identification
of different types of honey. Honey is produced
worldwide and bees use different plants as nectar
source. This results in different tastes of different
honeys. Monofloral honeys, like Sunflower honey,
Acacia honey or Chestnut honey are identified
by smell and taste, in combination with a microscopic
pollen analysis. In the literature the characteristics
of monofloral honeys are described. In
addition, the honey odour and aroma wheel for
honey has been published by IHC (see figure 3).
This ensures a universal vocabulary; everybody
uses the same words to describe honey and can
thus exchange and evaluate knowledge.
The literature already describes the characteristics
of monofloral honeys. Sensory properties
are always one of the main criteria as this is
what the customer perceives. But despite sensory
properties also physico-/chemical characteristics
are very important for the classification of
monofloral honey. For illustration about the different
sensory properties some main monofloral
honeys are described on the right 6, 7.
Sensory is a very important tool in Quality
Assurance. The possibilities of use vary from product
development and optimisation, consumer
research and acceptance to ensuring authenticity
and the absence of defects. But sensory also plays
a huge role in identification of varieties and even
geographical origin for some products.
References:
1 Jacob / Oehlenschläger / Schneider
Häder, Grundlagenvokabular Sensorik,
DLG-Verlag GmbH, Frankfurt (2012)
2 Fliedner / Wilhelmi, Grundlagen und Prüfverfahren
der Lebensmittelsensorik, Behr´s Verlag
(1995)
3 M. Nießen / S. Thölking, Sensorische
Prüfverfahren Anpassung für mittelständische
Betriebe, Behr´s Verlag (2007)
4 M.L. Piana / L. Persano Oddo / A.
Bentabol / E. Bruneau / S. Bogdanov
/ C. Guyot Declerck, Sensory analysis
applied to honey: state of the art
5 Goldstein et al. (2010)
6 Stefan Bogdanov, Kaspar Ruoff and Livia Persano
Oddo, Physico-chemical methods for the
characterisation of unifloral honeys: a review,
Apidologie, 35 Suppl. 1 (2004) S4-S17
7 Leitsätze für Honig, In der Fassung der
Bekanntmachung vom 30. 5. 2011, BAnz.
Nr. 111a S. 1, 5
Acacia honey (Robinia seudoacacia)
Pollen in % At least 20
Electrical conductivity in mS/cm Max. 0.20
Ratio Fructose / Glucose At least 1.55
Visual assessment Very light colour, water bright to light yellow, liquid
Olfactory assessment Floral, fresh fruit, warm
Tasting assessment Floral, fresh fruit, warm
Brassica honey (Brassica spp.)
Pollen in % At least 80
Electrical conductivity in mS/cm Max. 0.22
Ratio Fructose / Glucose Max. 1.00
Visual assessment light colour, crystallized
Olfactory assessment Spoiled and vegetal
Tasting assessment Floral, fresh fruit (fruity), warm, spoiled and vegetal
Sunflower honey (Helianthus annuus L.)
Pollen in % At least 50
Electrical conductivity in mS/cm 0.20 - 0.40
Ratio Fructose / Glucose Max. 1.10
Visual assessment Medium, bright yellow, crystallized
Olfactory assessment Floral, fresh fruit (fruity), warm, vegetal
Tasting assessment Floral, fresh fruit (fruity), warm, vegetal
Castanea honey (Castanea sativa Miller)
Pollen in % At least 90
Electrical conductivity in mS/cm 0.80 - 2.00
Ratio Fructose / Glucose At least 1.45
Visual assessment Dark to very dark, reddish, liquid to viscous
Olfactory assessment Woody, chemical, warm
Tasting assessment Woody, chemical, warm, spoiled
Eucalyptus honey (Eucalyptus spp.)
Pollen in % At least 85
Electrical conductivity in mS/cm 0.30 - 1.00
Ratio Fructose / Glucose At least 1.05
Visual assessment Medium to dark, greyish, liquid to crystallized
Olfactory assessment Woody, warm, spoiled
Tasting assessment Woody, warm, spoiled
22 Sensory Analysis