- Email: [email protected]
Sensory local uniqueness of Danish honeys
Food Research International 44 (2011) 2766–2774
Contents lists available at ScienceDirect
Food Research International j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / f o o d r e s
Sensory local uniqueness of Danish honeys Sandra Stolzenbach ⁎, Derek V. Byrne, Wender L.P. Bredie Department of Food Science, Faculty of Life Sciences, University of Copenhagen, Rolighedsvej 30, DK-1958, Frederiksberg C, Denmark
a r t i c l e
i n f o
Article history: Received 25 January 2011 Accepted 2 June 2011 Keywords: Local foods Danish honey Sensory descriptive analysis Uniqueness Location
a b s t r a c t Sensory characteristics of 21 locally produced Danish honeys representing a span in seasonal variation and location across Denmark were studied. Seasonal variation influenced the sensory characteristics predominantly as a consequence of changes in the flora of the honeybees' habitat. However, honeys from different locations displayed specific sensory characteristics within same season. Thus, the Danish honeys had distinct and unique flavours related to origin of location — sensory local uniqueness. In addition, honeys from similar locations could also differ in sensory profiles. The sensory variation within the honeys due to the flora in the local habitat was mirrored by pollen analysis showing diverse pollen profiles. This finding assists the beekeeper in formulating precise product descriptions for communication and creation of recognition of the products sensory uniqueness in their promotion strategy of the honeys towards the consumers. © 2011 Elsevier Ltd. All rights reserved.
1. Introduction The globalisation of the food supply chain that has happened over the last decades has resulted in a diminished knowledge of the origin of foods and the raw materials they are made from. As a consequence of this, a new trend in local foods has appeared with increasing interest in food origin. This is confirmed by Guerrero et al. (2009) who found that food origin was important for consumers in all countries studied including Belgium, France, Norway, Poland, Spain and Italy. According to the Committee of the Regions (1996), products with regional identity are demanded by consumers in all Member States of the Community. Additionally, a growing demand for authentic, exclusive, differentiated and value-added food products has been observed in most industrialised countries. An example in Scandinavia is the increasing attention to Nordic foods. In Denmark, initiatives have been taken by the Ministry of Food, Agriculture and Fisheries — The Danish Food Industry Agency — with the project “Taste Denmark” focussing on improved food quality, taste and food experiences. Also, an association called “the Taste of Denmark” has been established to improve the cooperation between the many local food producer networks in Denmark. As impetus to this Nordic focusing, a declaration of intents by Nordic chefs in which the keywords are simplicity, purity, freshness and ethical standards (Hellström et al., 2004) has been developed. Furthermore, the New Nordic Food programme build on this manifesto supporting business innovation,
⁎ Corresponding author. Tel.: + 45 3533 1018; fax: + 45 3533 3509. E-mail address: [email protected] (S. Stolzenbach). 0963-9969/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodres.2011.06.006
product development of local and regional food producers and the Nordic food tourism sector has been established. However, few studies have been addressing the sensory local uniqueness of food products from the Nordic region. In the present paper, by the phrase sensory local uniqueness, we refer to specific sensory characteristics from a certain location making the food product different from other food products within the same food category. In order to exploit the potential in locally produced food products, there is however a need to focus on the concept of sensory local uniqueness and how it is affected by its local production conditions (mesoclimate, soil, etc.) and practices. According to European Community (2006), the term geographical indication means: “the name of a region, a specific place or, in exceptional cases, a country, used to describe an agricultural product or a foodstuff: – originating in that region, specific place or country, and – which possesses a specific quality, reputation or other characteristics attributable to that geographical origin, and – the production and/or processing and/or preparation of which take place in the defined geographical area.” In making an attempt to better understand the meaning of geographical indication in terms of sensory local uniqueness, honey is an excellent model since its characteristics depend on several factors including botanical origin in the local area from where the bees gather the nectar and climatic conditions (Aparna & Rajalakshmi, 1999). It is also relevant from a consumer perspective, and González-Viñas, Moya, and Cabezudo (2003) state that not only do consumers demand high quality, but also they are aware of certificates addressing the honeys geographical and botanical origin.
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
Several studies have been carried out on honey with the aim to identify the geographical and botanical origin from the honey's physicochemical properties (Anklam, 1998; Krauze & Zalewski, 1991; Mateo & Bosch-Reig, 1998; Peña Crecente & Herrero Latorre, 1993; Pérez-Arquillué, Conchello, Ariño, Juan, & Herrera, 1995; Popek, 2002; Sanz, Perez, Herrera, Sanz, & Juan, 1995; Sporns, Plhak, & Friedrich, 1992; Zalewski, 1991). However, many authors have emphasised the importance of the need for the application of sensory analysis to identify and differentiate honeys from specific botanical origins and honey quality (Aparna & Rajalakshmi, 1999; Castro-Vázquez, Díaz-Moroto, González-Viñas, & Pérez-Coello, 2009; González Lorente, De Lorenzo Carretero, & Pérez Martin, 2008; Kaakeh & Gadelhak, 2005; Piana et al., 2004). Also, Persano Oddo and Piro (2004) state that a botanical declaration must include sensory analysis besides melissopalynological analysis and physicochemical analyses to complement each other for a verified declaration. Overall, sensory characterisation provides comprehensive and informative data about human perception of product quality. Until now, several non-Nordic honeys have received attention from a sensory perspective (Spanish honeys: Galán-Soldevilla, Ruiz-Pérez-Cacho, Serrano Jiménez, Jodral Villarejo, & Bentabol Manzanares, 2005; González Lorente et al., 2008; González-Viñas et al., 2003, Italian honeys: Esti, Panfili, Marconi, & Trivisonno, 1997, Indian honeys: Anupama, Bhat, & Sapna, 2003; Aparna & Rajalakshmi, 1999; Singh & Bath, 1997, Arab Gulf region honeys: Kaakeh & Gadelhak, 2005). However, the performed sensory profiles included few sensory descriptors or only overall ratings for descriptor groups like colour, texture, flavour, taste and aftertaste. The aim of the present study was to create a sensory vocabulary to describe and differentiate the characteristics of locally produced honeys in Denmark to substantiate if aspects of a definition of sensory local uniqueness can be ascribed. This could support the local producers in communicating and promoting their products. To explain causes of sensory flavour differences, information about location, botanical origin and mesoclimate in terms of rainfall and humidity in the local habitats was collected and linked to the sensory descriptive data. Furthermore, conceptual descriptors were linked to the sensory descriptive data to position the sensory experience of the honeys.
2767
2. Materials and methods 2.1. Experimental design A total of 21 Danish locally produced honeys representing a span in locations across Denmark were included (Fig. 1). All honeys were produced in 2009. For each location only one beekeeper delivered honey. Some of the honeys were harvested within the same local habitat but at different times of the year. For more details about the honeys, see Table 4. All the honeys were directly provided from the beekeepers, who all were members of Danish Beekeeper Association. The honeys were stored in darkness at room temperature until analyses. 2.2. Instrumental measures of honey quality Instrumental measurements of the honey quality including water content, invertase activity after Winkler and Hydroxymethylfurfural (HMF) were carried out by Institut für Bienenkunde Celle, Celle, Germany and in accordance with the guidelines of the International Honey Commission (Bogdanov, Martin, & Lüllmann, 1997). The water content influences the shelf life of the honeys. The lower water content the more stable and resistant the honey is to spoilage by yeast fermentation upon storage (Bogdanov et al., 1997). HMF and invertase activity are indicators of thermal treatment of the honeys. Heating prior to bottling is sometimes applied to avoid fermentation (Aparna & Rajalakshmi, 1999). However, the honey quality decreases by overexposure to heat and thus the magnitude of heating exposure is measured as a quality parameter. 2.3. Botanical declaration of the honeys The types and counts of pollen grains were determined by microscopic examination. For each honey, 500 pollen grains were counted to represent the variety in different types of pollen grains. During the counting, the non-nectar pollen grains were excluded. Furthermore, the pollen analysis allowed for over-represented and under-represented pollen grains. Also, electrical conductivity was performed according to the International Honey Commission (Bogdanov
Sæby (2 honeys) SæbyHeather (2 honeys)
Rønne (2 honeys)
Northern Jutland
Hobro (1 honeys) HobroHeather (2 honeys)
Lemvig (2 honeys) LemvigHeather (2 honeys)
Gjern (1 honey) Central Jutland Capital Zealand South Denmark
Fodby (1 honey) Næstved (2 honeys)
Aabenraa (2 honeys) Augustenborg (1 honey) Odense (1 honey) Fig. 1. Geographical distribution of the 21 included Danish locally produced honeys. “Heather” written after the location indicates that the honey is from the specified location but the beehive has been moved to the heather within the same location. Names in italic indicate the Danish regions.
2768
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
et al., 1997) to support the botanical declaration. The botanical declaration of the honeys was performed by Institut für Bienenkunde Celle, Celle, Germany. 2.4. Sensory descriptive analysis 2.4.1. Panel selection Panellists for the sensory descriptive analysis were selected from the external sensory panel of Faculty of Life Sciences, University of Copenhagen, Denmark. The external panel was recruited according to the selection criteria of ISO 3972 (1991). The sensory panel consisted of 6 women and 4 men (20–62 years of age). 2.4.2. Sample preparation The honeys were poured in polystyrene cups and closed with a lid. Each sample consisted of 30 g honey. Before serving, the samples were brought to room temperature (20 °C) to enhance the perceptible flavours in the honey. 2.4.3. Vocabulary development and profiling The sensory descriptive analysis consisted of four training sessions and four sensory profiling sessions each lasting 2 h. The aim of the training sessions was to develop a consensus sensory vocabulary and improve the panellists' cognitive clarity toward the descriptors discriminating the honeys and the use of the sensory intensity scale. Initially, the panellists individually generated sensory descriptors. Subsequently, a sorting task of the honeys was carried out to let the
panellists categorise stimuli based on their perceived similarity. The final vocabulary “honey language” consisted of 27 sensory descriptors (Table 1) developed via collaboration between the panellists and the panel leader (Byrne, Bak, Bredie, Bertelsen, & Martens, 1999; Byrne, Bredie, & Martens, 1999). For the sensory descriptive analysis, a 15-cm unstructured line scale was used. The sample set of 21 honeys was evaluated with three sensory replicates served across 4 days. Thus, the presentation order was unbalanced. However, it was ensured that the same honey only was served once for each profiling day. The samples were presented in randomised order to minimise systematic carry-over effects. The data was collected by Fizz Acquisition, Version 2.45A, Biosystems, France. The physical evaluation conditions for the sensory descriptive analysis followed the guidelines in ISO 13299 (2003).
2.5. Conceptual evaluation To move beyond the perception of the flavour in the honeys and achieve a better understanding of the sensory experience of the honeys, conceptual descriptors were included in the sensory descriptive analysis. Without prior training, the same panellists as used in the sensory descriptive analysis rated 9 conceptual descriptors (Table 2) on a 15-cm unstructured line scale after giving scores for the sensory descriptive terms. The descriptive analysis and conceptual evaluation were carried out as two consecutive sessions. These data was also collected by Fizz Acquisition, Version 2.45A, Biosystems, France.
Table 1 List of the sensory descriptors developed for the profiling. Sensory descriptora
Definition with reference materialsb
Scale
1 2 3
Appearance Brown-A Yellow-A Transparent-A
Appearance Degree of brown colour by visual evaluation Degree of yellow colour by visual evaluation Degree of transparency of the honey
None → extremely None → extremely None → extremely
4 5
Texture Liquid-TX Spreadability-TX
Texture associated with The degree of liquidity by stirring the honey The ease of spreading the honey on a plate
None → extremely Difficult → easy
6 7 8
Mouth feeling Grainy-MF Flour-MF Meltdown rate-MF
Mouth feeling associated with Amount of large grains (sugar crystals) during mastication Amount of small grains (sugar crystals) during mastication Length of time for the honey to melt in the mouth
None → extremely None → extremely Slowly → fast
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Flavour Raisin-F Fig-F Apricot-F Malt-F Wax-F Hop-F Smoked-F Menthol-F Vanilla-F Althea-F Anise-F Perfumed-F Flowers-F Elderflower-F Lemon-F
Aromatic taste sensation associated with Raisins extracted in 100 g/L sucrose solution Sun-dried figs Sun-dried apricots Malt drops dissolved in 100 g/L sucrose solution. Beeswax Hops extracted in 100 g/L sucrose solution Smoked malt extracted in 100 g/L sucrose solution Menthol pastille dissolved in 100 g/L sucrose solution Rooibos vanilla tea extracted in 100 g/L sucrose solution Althea drops (with bergamot oil) dissolved in 100 g/L sucrose Anise seeds extracted in 100 g/L sucrose solution Dried lavender extracted in 100 g/L sucrose solution Dried extracted in 100 g/L sucrose solution Elderflower squash Lemon juice dissolved in 100 g/L sucrose solution
None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely
24 25 26 27
Taste Sweet-T Sour-T Bitter-T Umami-T
Taste associated with 100 g/L sucrose solution 0.6 g/L Citric acid monohydrate 0.004 g/L Quinine Hydrochloride 0.7 g/L L-Glutamic acid Monosodium salt Monohydrate
None → extremely None → extremely None → extremely None → extremely
a b
Suffix to the sensory descriptors indicates the type of assessment by panellists: -A. appearance. -TX. texture. -MF. mouthfeel. -F. flavour. -T. taste. Definitions of sensory descriptors as derived during the vocabulary development.
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774 Table 2 List of conceptual descriptors. Conceptual descriptor
Definition
Scale
1 2 3 4 5 6 7 8 9
Functional attribute Abstract attribute Abstract attribute Abstract attribute Emotional attribute Emotional attribute Emotional attribute Emotional attribute Overall quality percept
Not Not Not Not Not Not Not Not Not
Aromatic Complex Balanced Unique Annoyed Disappointed Happy Desire Liking
at all → extremely at all → extremely at all → extremely at all → extremely at all → extremely at all → extremely at all → extremely at all → extremely at all → extremely
2.6. Mesoclimate data A data logger was placed on the different beehives to record min/ max temperature and min/max humidity. The beehives were inspected by the beekeepers and the data was daily recorded from April 1st to harvest time of the honey. In case several honeys were harvested from the same beehive over season the recording started from the last harvest.
2769
applied as well to give all variables the same variance. To derive significance indications from the sensory data and linkages between the sensory data and pollen grains, data was analysed by derived estimated regression coefficients and jack-knife uncertainty testing based on full cross validation (Martens & Martens, 2000). 2.7.3. Mesoclimate data The daily recordings of the humidity and rainfall were considered as repeated measurements. The Diggle model handles repeated measurements and takes into account different length of time intervals and serial correlation structure within each honey (Diggle, 1988). Thus, the Diggle method was used to elucidate possible dependent effects of location or/and time on the profiles of temperature and humidity. The analysis was run by R version 2.10.1 (www.r-project.org). The model was a linear mixed model including a random honey effect to account for inter-honey variation. An additional Spatial Gaussian serial correlation structure described the dependency between the response variables within honeys that are close in time i.e. time where the bees are gathering honey. 3. Results and discussion
2.7. Data analysis
3.1. Honey composition
2.7.1. Pollen grains Statistical analysis on pollen grains counts was carried out in R version 2.10.1 (www.r-project.org). Each identified pollen grain was considered as a response variable and it was tested whether location and/or botanical origin influenced the presence of the pollen grain. LM-tests were used for model reduction and variables were removed based on backward elimination with a 5% cutoff value.
All the Danish locally produced honeys fulfilled the required standards of maximum 20% water content — though maximum 23% water content for heather honeys, and maximum 40 mg/kg HMF (BEK 836, 09/10/2003-) and minimum invertase activity of 50 U/kg (Persano Oddo, Piazza, & Pulcini, 1999). The pollen analysis and electrical conductivity resulted in a clear botanical declaration of the honeys (Council Directive, 2001/110/EC). The pollen analysis showed diverse patterns of pollen grains in the honeys and the predominant species are listed in Table 3. The identified pollen grains are common for the vegetation in Denmark. In a study by Persano Oddo et al. (2004), the authors summarised all the botanical species contributing
2.7.2. Sensory data The progress in panel performance over training sessions was evaluated by PanelCheck version 1.3.2 (Nofima, Norway) using the applications Tucker1-plot, profile plots, Eggshell plots and p-MSE plots. For the sensory raw profiling data, the panel performance was further studied with focus on sensory descriptor and panellist interactions by using SensoMineR in R package version 2.10.1 (Husson & Lê, 2009). Since significant interactions were revealed, the sensory raw profiling data was pre-processed to correct for idiosyncratic line scale usage by applying Generalised Procrustes Analysis (GPA) in MATLAB 7.9.0.529 R2009b (The MathWorks, US). Subsequently, the sensory profiling data was ready for multivariate data analysis in Unscrambler, Version 10.0.1 (CAMO ASA, Norway). Principal Component Analysis (PCA) including the full sensory vocabulary of honeys was established to achieve a total overview of the sensory sample variation. Next, the sensory data was analysed by ANOVA Partial Least Squares Regression (APLSR). APLSR visualises and determines the structure and degree of each design variable's (honey samples) contribution to the variation in sensory data. Thus, APLSR was performed with the X-matrix as 0/1 design variables and the Ymatrix as the sensory descriptors (Martens, Bredie, & Martens, 2000). It was of relevance to know which pollen grains contributed to description of the sensory variation within the honeys. Thus, a Partial Least Squares Regression (PLSR) model was made to elucidate possible relationships between the pollen grains and sensory descriptors. To obtain a more precise interpretation, the honeys were analysed in groups according to season. To achieve an indication of how the conceptual descriptors were associated to the flavour descriptors and to understand the direction of consumer response, a PCA including flavour descriptors and conceptual descriptors was established. The multivariate data analysis was based on averaged values over panellists and sensory replicates. The data was mean-centred and in case of different measuring units standardising of the data was
Table 3 Identified predominant species of pollen grains and influenced parameters. Identified pollen grains
Significantlya dependent parameter(s)
Acer (maple) Location × botanical origin Archillea-type (yarrow-type) Allium (onion) Brassica (rape)/Sinapis-type (mustard-type) Phacelia (fiddleneck) Pyrus-type (pomaceous fruits) Pyrus-type (pomaceous fruits)/Prunus-type (stone fruits)/ Rubus (raspberry/blackberry) Tilia (lime) Trifolium respens (white clover)/Melilotus (melilot) Trifolium respens (white clover)/Trifolium pratense (red clover) Brassica (rape) Centaurea cyanus (blue bonnet- cornflower) Trifolium respens (white clover) Other less represented pollen grains
Botanical origin
Trifolium respens (white clover)/ Trifolium hybriddum-type (hybrid clover-type)
Location
Pyrus-type (pomaceous fruits)/Prunus-type (stone fruits) Salix (willow)
No effect of location and botanical origin
Culluna vulgaris (heather)b a
P b 0.05. Honeys having predominating amount of Culluna vulgaris were enriched with bee bread, a mix of pollen and honey, and thus not possible to determine the relative frequency of this pollen. b
2770
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
Principal Component 2 (Y-explained variance 26%)
to unifloral honey in Europe. In total, 116 different botanical species were listed, and among these 10 botanical species were identified as predominating in the Danish produced honeys including Allium, Brassica, Centaurea cyanus, Prunus-type, Rubus, Salix, Tilia, Trifolium pratense, Trifolium respens and Culluna vulgaris (see Table 3). Thus, the other botanical species found in the Danish local honeys contributed to making these unique in relation to location and reflected the local habitat of the bees. This confirms that most of the botanical species in Europe only have local importance as also stated by Persano Oddo et al. (2004). It is furthermore confirmed by the present study showing that the majority of the pollen grains were either significantly dependent on location or an interaction between location and botanical origin (Table 3). This is in agreement with Anklam (1998) stating that pollen analysis is useful for the determination of the pollen profiles and can also be used for the identification of location. For some of the local honeys a single nectar source was so predominant that a corresponding botanical declaration was allowed (Council Directive, 2001/110/EC). For example the spring honey from Lemvig contained 88% Brassica allowing the declaration as unifloral “Rape honey”. In total, two rape honeys, two clover honeys, two heather honeys and one summer honey with cornflower were declared. It is not surprising that rape, clover and heather honeys are identified as the unifloral honeys among the local honeys since these are the most common ones in Denmark. It is in agreement with Persano Oddo and Piro (2004) that state that heather is widely distributed in the North of Europe and is one of the most important sources for late summer honeys and rape honey is largely cultivated in Europe for seed to oil production and a very important source for the bees during the spring. The sources to variations for the pollen profiles were studied from the mesoclimate in terms of rainfall and humidity for the specific locations. However, missing data in collection of mesoclimate appeared for five honeys. The mean humidity was significantly dependent on the location (P b 0.05) and time of the year (P b 0.001) while the mean temperature was only dependent on the time of the year (P b 0.001). These findings were confirmed by data from the Danish Meteorological Institute. Anklam (1998) and Aparna and Rajalakshmi (1999) confirm this finding by stating that even though
1.0
the honeys come from the same floral source they can differ because of seasonal climatic conditions or different location. However, the degree of influence on the humidity on the local flora remains unclear. Furthermore, many other uncontrolled factors might have affected the local flora as well as the soil condition and light exposure. 3.2. Sensory local uniqueness of Danish honeys 3.2.1. Sensory differentiation The Danish local honeys were studied to investigate if sensory local uniqueness could be defined. A sensory vocabulary was created to identify the sensory characteristics of the local honeys (Table 1). Hereby, a specific and well-defined sensory profile was obtained for each studied honey. The suitability of sensory descriptive analysis to characterise honey is confirmed by González Lorente et al. (2008). By multivariate data analysis, it was found that the local honeys differed in their sensory profiles and the explained variance accounted for 57% by the first two Principal Components (PCs). Also, other authors found multivariate data strategies as an appropriate method to define authenticity (Krauze & Zalewski, 1991; López et al., 1996; Zalewski, 1991). Especially, the appearance, texture and mouthfeel of the honeys spanned the sensory variation within the Danish local honeys. It was similarly found by González-Viñas et al. (2003) showing that panellists placed greater emphasis on the texture and appearance than the aroma and taste in evaluation of honeys. Since most textural and visual properties are not solely depending on the origin but probably also influenced by processing method (Piana et al., 2004), it was decided only to include the flavour and taste descriptors in further data analysis. It coincides with the major interest to study if flavours/tastes could be used to define sensory local uniqueness for the Danish honeys. It is furthermore in agreement with Aparna and Rajalakshmi (1999) stating that flavour is the most important sensory property of honey and reflects the botanical origin and its authenticity. 3.2.2. The sensory flavour variation mainly described by seasonal effect The honeys could be categorised into three main season groups: spring honeys, summer honeys and late summer honeys, based on their sensory characteristic. The spring honeys included honeys
Fig-F Hop-F Anise-F Smoked-F Altheadrop-F Perfumed-F Umami-T Vanilla-F Augustenborg Malt-F
0.5
Gjern Bitter-T
Næstved
Sweet-T
Elderflower-F Aabenraa-CLOVER Sour-T Lemon-F
0.0 Menthol-F Odense Wax-F
Apricot-F
Flowers-F Hobro
Rønne
-0.5
Raisin-F Lemvig-CLOVER
-1.0 -1.0
-0.5
0.0
0.5
1.0
Principal Component 1 (Y-explained variance 40%) Fig. 2. Summer honeys: ANOVA Partial Least Squares Regression (APLSR) correlation loading plot of PC1 versus PC2 showing the differences in the sensory characteristics of summer honeys for each location. The sample indicators were in the X-matrix and the sensory descriptors in the Y-matrix. Capitals in the sample indicator correspond to the honey declaration. The inner and outer ellipses represent r2 = 50% and 100%, respectively.
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774 Table 4 Sensory flavour uniqueness for the locally produced Danish honeys responding to the individual honey variety identification. Significance of APLSR derived regression coefficients for the linkage of the sensory descriptors to the sample indicators as derived by jack-knife uncertainty testing. Season
Location
Significant sensory descriptors
Spring
Sæby
Spring Spring Spring
RAPEa — Sæby RAPE-Lemvig Aabenraa
apricot (P b 0.001), hop (P b 0.001), flower (P b 0.01), malt (P b 0.01), sour (P b 0.01), fig(P b 0.05), anise (P b 0.05) menthol (P b 0.001)
Spring
Næstved
Spring
Fodby
Spring
Rønne
Summer Summer
Hobro Gjern
Summer
CLOVER — Lemvig
Summer
CLOVER-Aabenraa
Summer
Augustenborg
Summer Summer Summer
Odense Næstved Rønne
Late HobroHeather1 summer Late HobroHeather2 summer Late HEATHER—SæbyHeather summer
raisin (P b 0.01), apricot (P b 0.01), flower (P b 0.01), lemon (P b 0.05) fig (P b 0.001), malt (P b 0.001), hop (P b 0.001), vanilla (P b 0.01), anise (P b 0.01) sweet (P b 0.01), umami (P b 0.01), elderflower (P b 0.05), bitter(P b 0.05) althea (P b 0.01), perfumed (P b 0.01), wax (P b 0.05) althea (P b 0.001), anise (P b 0.01), fig (P b 0.05), malt (P b 0.05), smoked (P b 0.05), vanilla (P b 0.05) menthol (P b 0.001), raisin (p b 0.01), wax (P b 0.05) hop (P b 0.001), lemon (P b 0.001), fig (P b 0.05), elderflower (P b 0.05), sour (p b 0.05) fig (P b 0.001), malt (P b 0.001), anise (P b 0.001), perfumed (P b 0.001), umami (P b 0.001), smoked (P b 0.01), vanilla (P b 0.01), hop (P b 0.05), althea (P b 0.05) raisin (P b 0.05) fig (P b 0.01), anise (P b 0.05) apricot (P b 0.001), lemon (P b 0.001), sour (P b 0.001) vanilla (P b 0.001), umami (P b 0.001), wax (P b 0.01) hop (P b 0.05), smoked (P b 0.05)
hop (P b 0.001), anise (P b 0.001), perfumed (P b 0.001), sour (P b 0.001), bitter (P b 0.001), smoked (P b 0.01), fig (P b 0.05) Late CORNFLOWER — SæbyHeather lemon (P b 0.001), raisin (P b 0.01), summer elderflower (P b 0.01) Late LemvigHeather sweet (P b 0.01), apricot (P b 0.05) summer Late HEATHER — LemvigHeather smoked (P b 0.05) summer
a
The capital letters indicate that the honey is declared as stated.
harvested until the third week of July, summer honeys included honeys harvested in the period third week of July to the middle of August, and late summer honeys were harvested after this date. Over these seasons the honeys changed in sensory characteristics from displaying flavours of raisin (P b 0.001), wax (P b 0.001), menthol (P b 0.001), flowers (P b 0.001) and althea (P b 0.01) to over the summer moving towards more fruity flavour notes such as elderflower (P b 0.001), apricot (P b 0.001), lemon (P b 0.001), hop (P b 0.001), anise (P b 0.001) and sour (P b 0.001). Honeys harvested in the late summer have an aromatic flavour described with “dark” sensory descriptors including smoked (P b 0.001), malt (P b 0.001), fig (P b 0.001), hop (P b 0.001), vanilla (P b 0.001), perfumed (P b 0.001), bitter (P b 0.001) and umami (P b 0.01). More sensory descriptors were significantly correlated to the late summer honeys compared to the spring honey. Thus, the honeys increase in flavour complexity over the season. This might be explained by larger diversity in the flora over the year.
2771
3.2.3. Sensory local uniqueness As season is not an indicator of local uniqueness, APLSR models were separately established for each main season group to visualise the location's contribution to the variation in the sensory flavour profile of the honeys (Fig. 2, summer honeys; figures of spring and late summer honeys not shown). Overall, it was found that the location influenced the sensory characteristics of the honeys within each season. Honey from different locations in Denmark displayed specific sensory flavour and taste characteristics indicating variability in location and botanical origin (Table 4). Thus, the present study provides evidence that the honeys are representative of their botanical origin and location in terms of their unique flavours. In addition, honeys from similar locations could also differ in characteristics within the same season. Specifically for the summer honeys (see Fig. 2 and Table 4), it was found that clover honeys from Aabenraa and Lemvig were very different in sensory characteristics. Even though both honeys were mainly based on nectar from clover, the remaining nectar gathered in the local habitat made the honeys unique in relation to their origin of location. Anklam (1998) confirms that even though honeys come from the same floral source they can differ because of different locations. Also, Castro-Vázquez, DíazMaroto, de Torres, and Pérez-Coello (2010) found that location of production influenced and differentiated the sensory and aroma profile of Spanish chestnut honeys. No division of the sensory local uniqueness was observed within Denmark. For example the sensory characteristics of the summer honeys from the Zealand town Næstved in the Eastern part of Denmark and Gjern located in Central Jutland (see Fig. 1) were almost similar even though the two locations are far away from each other. Thus, the local sensory characteristics could not be systematically indexed onto the specific regions in Denmark. In comparison with previous studies on honey, the present study provided a more detailed sensory characterisation. For examples, Galán-Soldevilla et al. (2005) developed an odour and flavour vocabulary describing the locally produced honeys in the Spanish Andalucía region and the “La Vega del Guadalquivir” with a final sensory list only containing 3 flavour descriptors including overall intensity, floral and ripe fruit. Esti et al. (1997) characterised the flavour of honeys from the Molise region in Italy and characterised these as light fruity and having a certain aromatic character. Aparna and Rajalakshmi (1999) described Indian honey having a pleasing aroma, flavour and sweet taste. González Lorente et al. (2008) described artisan honeys from the same geographical area in Spain by applying 7 sensory descriptors including sweetness, adhesiveness, viscosity, acidity, bitterness, crystallisation and colour. Anupama et al. (2003) also described the Indian honeys using 8 flavour descriptors herein flowery and waxy. However, the Danish local honey might be considered to be unique since the sensory descriptors floral, wax, acidity, bitterness and sweetness were the only few overlaps found between the Danish local honeys and the other mentioned sensory works on honeys. 3.3. Relationship between the sensory characteristics and pollen grains The sensory variation within the honeys was mirrored by pollen analysis (Fig. 3) and as illustrated the identified pollen grains were related to the sensory variation of the honeys. The linkage between the sensory descriptors and pollen grains was highly complex and groupings of pollen grains were apparent indicating interplay between pollen grains and flavour characteristics (Fig. 3). This is confirmed from the statistical analysis where the main part of the pollen grains significantly (P b 0.05) contributed to more than one sensory flavour note. For example Brassica (rape) significantly dependent on botanical origin (cf. Table 3), was significantly (Pb 0.05) correlated to menthol in the spring honeys. Brassica also significantly contributed to vanilla flavour (Pb 0.01) and umami taste (P b 0.001) in
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
Principal Component 2 (Y-explained variance 15%)
2772
1.0 Aabenraa
Pyrus-type/Prunus-type
Trifolium respens Salix
Smoked-F Wax-F
0.5
Other pollen grains
Elderflower-F Althea-F Rønne Fodby Sæby-RAPE
Raisin-F Flowers-F
0.0
Umami-T
Anise-F
Lemon-F
Trifolium respens/Melilotus Perfumed-F
Apricot-F
Menthol-F
Malt-F Acer
Sweet-T Næstved
Sæby Centaurea cyanus
Pyrus-type/Prunus-type/Rubus
Fig-F
Sour-T
-0.5
Hop-F
Brassica
Vanilla-F
Allium
Bitter-T
Phacelia Brassica/Sinapis-type Pyrus-type
Lemvig-RAPE
-1.0 -1.0
-0.5
0.0
0.5
1.0
Principal Component 1 (Y-explained variance 22%) Fig. 3. Spring honeys: Partial Least Squares Regression (PLSR) correlation loading plot of the first two PCs. The design setup: pollen grains in the X-matrix and the sensory descriptors in the Y matrix. The X-matrix and Y-matrix were weighted by 1/std. However, the sample indicators in bold were pacified. The inner and outer ellipses represent r2 = 50% and 100%, respectively.
the spring honeys. In the summer honeys, Brassica still significantly contributed to vanilla flavour (Pb 0.05) and umami taste (Pb 0.01) but also the flavour of fig (Pb 0.001), hops (Pb 0.05), smoked (P b 0.05), althea (P b 0.05), anise (P b 0.05), perfumed (P b 0.01), lemon (Pb 0.001), and sour taste (Pb 0.001). According to Persano Oddo and Piro (2004) Brassica honeys have a medium aroma intensity described as floralfresh fruit, warm, spoiled and vegetal and with a medium sweetness and weak acidity. Pérez-Arquillué et al. (1995) describe Spanish Brassica honey as having a strong-flavour. However, the Danish Brassica honeys appeared to have a different flavour. The different flavour could be explained by the additional pollen grains in the Brassica honeys.
It was not possible to find any significant relationships between the pollen grains and sensory descriptors for the late summer honeys. This was also expected since the heather honeys are enriched with bee bread, a mix of pollen and honey. 3.4. Association between flavour characteristics and conceptual descriptors of the honeys To move beyond the perception of the flavour in the honeys, evaluation of the conceptual descriptors was linked to the sensory descriptive analysis. The conceptual descriptors contribute to an
1.0 Lemon-F Apricot-F
Principal Component 2 (20%)
Elderflower-F Liking
0.5
Sour-T
Desire
Happy
Unique
Balanced Menthol-F
0.0
Hop-F
Raisin-F Sweet-T
Anise-F
Flowers-F Althea-F
Wax-F
-0.5
Vanilla-F Umami-T Annoyed
Aromatic Complex Perfumed-F Fig-F Bitter-T Smoked-F
Disappointed Malt-F
-1.0 -1.0
-0.5
0.0
0.5
1.0
Principal Component 1 (34%) Fig. 4. All honeys: Principal Component Analysis (PCA) plot of the first two PCs. X matrix: flavour descriptor and hedonic descriptors. The inner and outer ellipses represent r2 = 50% and 100%, respectively.
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
understanding of how the different flavour are perceived and associated by the panellists. Initially in the analysis, it was found that all panellists were able to use the conceptual descriptors significantly (P b 0.001) to differentiate the samples. Thus, reliability and validity of asking conceptual descriptors to a trained panel after evaluating sensory descriptive terms were equally applicable as the sensory descriptors in describing the variation between the products. Furthermore, it was analysed and ensured that the panellists were not systematically affected by the intense sweetness of the honeys in their evaluating of liking during increased consumption over a session. An association between the conceptual descriptors and the flavour/taste descriptors was found (Fig. 4), and thus a fit to additionally describe the local honeys from a conceptual perspective was achieved. The flavour descriptors menthol, raisin, lemon, apricot and elderflower were strongly positively correlated to each other and perceived as balanced and elicited desire, happiness and high liking. As examples the menthol flavour and the elderflower flavour were found in the declared rape honey from Sæby and the spring honey from Fodby, respectively (see Table 4). The conceptual descriptors aromatic, complex and unique were positively correlated to the sensory flavour descriptors fig, hop, smoked, sour and lemon. However, it was interesting to notice that these descriptors were not related to liking, happiness and desire. Instead, the descriptors elicited emotional responses as disappointed and annoyed. However, the lemon flavour was positively correlated to liking. The flavour descriptors fig, hop, smoked, sour might be perceived as different from familiar honeys and the complexity perceived too high such as the positive hedonic response decreases according to the Wundt curve (Berlyne, 1970). The Wundt curve is based on that a food product is mostly liked when it is in accordance with an individual's level of arousal (e.g. complexity, novelty). Thus, stimuli with an arousal potential above and below the optimal level would be less liked the more they deviated from it.
3.5. Perspectives on sensory local uniqueness Identification of local food uniqueness implies linkage between the product, location and culture (Committee of the Regions, 1996). It was fulfilled by sensory analysis, botanical declaration and physicochemical measurements as also stated by Persano Oddo and Piro (2004) for a verified declaration. In the present, study a sensory vocabulary was developed to characterise Danish local honeys. The vocabulary was a helpful tool to identify and recognise the sensory local uniqueness for these. Hereby, a sensory profiles of the honeys from different locations were defined within a relatively small geographical area and provided empirical evidence that specific flavours exist. Specific sensory lexicons already exist for other product categories e.g. cheeses (Rétiveau, Chambers, & Esteve, 2005). According to González-Viñas et al. (2003) not only do consumers demand high quality but also they are aware of certificates addressing the honeys geographical and botanical origin. It is suggested to develop more sensory vocabularies for local products in order to facilitate comprehensive and detailed information about the sensory local uniqueness. It is in correspondence with the Committee of the Regions (1996) stating that if the quality of local products has to be promoted it requires identification and differentiation between the products on a regional basis. Also, Rétiveau et al., (2005) state that sensory vocabularies are suitable in promoting products when focussing on the products' unique characteristics. Overall, for the success of local food products recognition of sensory local uniqueness is of outmost importance. An objective sensory evaluation results in a valid product specification of the local foods. It is valuable for formulating precise product descriptions in promoting strategy towards the consumers in respect to communicating the unique properties of locally produced food products.
2773
4. Conclusions Seasonal variation influenced the sensory characteristics of the honeys as a consequence of floral changes. Moreover, location influenced the sensory characteristics of the honeys. Honey from different locations in Denmark displayed specific characteristics. In addition, there was evidence that honeys from similar locations could also differ within their characteristics even though they were harvested within same time frame. The sensory variation within the honeys due to the flora in the local habitat of the bees was mirrored by pollen analysis showing diverse pollen profiles. The link between the sensory descriptors and pollen grains was highly complex and grouping of pollen grains was apparent indicating interplay of the pollen grains resulting in correlation to more than one flavour characteristic. Overall, the study assists the beekeepers to understand the sensory quality of their products and provides them with sensory descriptors that may be used in formulating precise product descriptions for communicating the sensory local uniqueness in their promoting strategy towards the consumers. Hereby, recognition of the unique properties of local honeys can be established. Acknowledgement This work was financially supported by The Danish Food Industry Agency — The Ministry of Food, Agriculture and Fisheries — through the project “Local foods in Denmark”. The Danish Beekeeper Association is thanked for the critical review of the manuscript on pollen data. Technical staff at sensory laboratory, University of Copenhagen, is thanked for their assistance. The FoodUnique Project is also acknowledged. References Anklam, E. (1998). A review of analytical method to determine the geographical and botanical origin of honey. Food Chemistry, 63, 549–562. Anupama, D., Bhat, K. K., & Sapna, V. K. (2003). Sensory and physico-chemical properties of commercial samples of honey. Food Research International, 36, 183–191. Aparna, A. R., & Rajalakshmi, D. (1999). Honey — Its characteristics, sensory aspects, and applications. Food Review International, 15, 455–471. BEK 836 (09/10/2003--). Declaration from the Danish Ministry of Food, Agriculture and Fisheries. Berlyne, D. E. (1970). Novelty, complexity, and hedonic value. Perception & Psychophysics, 8, 279–286. Bogdanov, S., Martin, P., & Lüllmann, C. (1997). Harmonised methods of the European Honey Commission. Apidologie, 1–59 extra issue. Byrne, D. V., Bak, L. S., Bredie, W. L. P., Bertelsen, G., & Martens, M. (1999). Development of a sensory vocabulary for warmed-over-flavor: Part I. In porcine meat. Journal of Sensory Studies, 14, 47–65. Byrne, D. V., Bredie, W. L. P., & Martens, M. (1999). Development of a sensory vocabulary for warmed-over flavour: Part II. In chicken meat. Journal of Sensory Studies, 12, 67–78. Castro-Vázquez, L., Díaz-Maroto, M. C., de Torres, C., & Pérez-Coello, M. S. (2010). Effect of geographical origin on the chemical and sensory characteristics of chestnut honeys. Food Research International, 43, 2335–2340. Castro-Vázquez, L., Díaz-Moroto, M. C., González-Viñas, M. A., & Pérez-Coello, M. S. (2009). Differentiation of monofloral citrus, rosemary, eucalyptus, lavender, thyme and heather honeys based on volatile composition and sensory descriptive analysis. Food Chemistry, 112, 1022–1030. Committee of the Regions (1996). Opinion of the Committee of the Regions of 18 September 1996 on Promoting and Protecting Local Products — Trump-card for the Regions. Diggle, P. J. (1988). An approach to the analysis of repeated measurements. Biometrics, 44, 959–971. European Community (2006). Council regulation no. 510/2006 of 20 March 2006 on the protection of geographical indications and designation of origin for agricultural products and foodstuffs. Official Journal of the European Union. Council Directive (2001/110/ECC). 20 December 2001 Relating to Honey — Official Journal of the European Communities. Esti, M., Panfili, G., Marconi, E., & Trivisonno, M. C. (1997). Valorization of the honeys from the Molise region through physico-chemical, organoleptic and nutritional assessment. Food Chemistry, 58, 125–128. Galán-Soldevilla, H., Ruiz-Pérez-Cacho, M. P., Serrano Jiménez, S., Jodral Villarejo, M., & Bentabol Manzanares, A. (2005). Development of a preliminary sensory lexicon for floral honeys. Food Quality and Preference, 16, 71–77.
2774
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
González Lorente, M., De Lorenzo Carretero, C., & Pérez Martin, R. A. (2008). Sensory attributes and antioxidant capacity of Spanish honeys. Journal of Sensory Studies, 23, 293–302. González-Viñas, M. A., Moya, A., & Cabezudo, M. D. (2003). Description of the sensory characteristics of Spanish unifloral honeys by free choice profiling. Journal of Sensory Studies, 18, 103–113. Guerrero, L., Guárdia, M. D., Xicola, J., Verbeke, W., Vanhonacker, F., Zakowska-Biemans, S., et al. (2009). Consumer-driven definition of traditional food products and innovation in traditional foods. A qualitative cross-cultural study. Appetite, 52, 345–354. Husson, F., & Lê, S. (2009). SensoMineR: Sensory data analysis with R. R package version 1.10. http://CRAN.R-project.org/package=SensoMineR ISO 3972 (1991). Sensory analysis — Methodology — Method of investigating sensitivity of taste. International Organisation for Standardisation. ISO 13299 (2003). Sensory analysis — Methodology — General guidance for establishing a sensory profile. International Organisation for Standardisation. Kaakeh, W., & Gadelhak, G. G. (2005). Sensory evaluation and chemical analysis of Apis mellifera honey from the Arab Gulf region. Journal of Food and Drug Analysis, 13, 331–337. Krauze, A., & Zalewski, R. (1991). Classification of honeys by principal component analysis on the basis of chemical and physical parameters. Zeitschrift für Lebensmittel-Untersuchung und -Forschung, 192, 19–23. López, B., Latorre, M. J., Fernández, M. I., García, M. A., García, S., & Heerero, C. (1996). Chemometric classification of honeys according to their type based on quality control data. Food Chemistry, 55, 281–287. Hellström, E., Dahlgren, M., Valimaki, H., Lauterbach, E., Redzepi, R., Örvarsson, H., et al. (2004). Manifesto for the New Nordic Kitchen. Martens, H., & Martens, M. (2000). Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Food Quality and Preference, 11, 5–16. Martens, M., Bredie, W. L. P., & Martens, H. (2000). Sensory profiling data studied by partial least squares regression. Food Quality and Preference, 11, 147–149.
Mateo, R., & Bosch-Reig, F. (1998). Classification of Spanish unifloral honeys by discriminant analysis of electrical conductivity, color, water content, sugars, and pH. Journal of Agricultural and Food Chemistry, 46, 393–400. Peña Crecente, R., & Herrero Latorre, C. (1993). Pattern recognition analysis applied to classification of honeys from two geographic origin. Journal of Agricultural and Food Chemistry, 41, 560–564. Pérez-Arquillué, C., Conchello, P., Ariño, A., Juan, T., & Herrera, A. (1995). Physiochemical attributes and pollen spectrum of some unifloral Spanish honeys. Food Chemistry, 54, 167–172. Persano Oddo, L., Piazza, M. G., & Pulcini, P. (1999). Invertase activity in honey. Apidologie, 30, 57–65. Persano Oddo, L., & Piro, R. (2004). Main European uniforal honeys: Descriptive sheets. Apidologie, 35, S38–S81. Persano Oddo, L., Piana, L., Bogdanov, S., Bentabol, A., Gotsiou, P., Kerkvliet, J., et al. (2004). Botanical species giving unifloral honey in Europe. Apidologie, 35, S82–S93. Piana, M. L., Persano Oddo, L., Bentabol, A., Bruneau, E., Bogdanov, S., & Guyot Declerck, C. (2004). Sensory analysis applied to honey: State of the art. Apidologie, 35, S26–S37. Popek, S. (2002). A procedure to identify a honey type. Food Chemistry, 79, 401–406. Rétiveau, A., Chambers, D. H., & Esteve, E. (2005). Developing a lexicon for the flavour description of French cheeses. Food Quality and Preference, 16, 517–527. Sanz, S., Perez, C., Herrera, A., Sanz, M., & Juan, T. (1995). Application of a statistical approach to the classification of honey by geographic origin. Journal of the Science of Food and Agriculture, 69, 135–140. Singh, N., & Bath, P. K. (1997). Quality evaluation of different types of Indian honeys. Food Chemistry, 58, 129–133. Sporns, P., Plhak, L., & Friedrich, J. (1992). Alberta honey composition. Food Research International, 25, 93–100. Zalewski, R. I. (1991). Authentication of honey sample via test set sample and Principal Component analysis. Food Quality and Preference, 3, 223–227.
Contents lists available at ScienceDirect
Food Research International j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / f o o d r e s
Sensory local uniqueness of Danish honeys Sandra Stolzenbach ⁎, Derek V. Byrne, Wender L.P. Bredie Department of Food Science, Faculty of Life Sciences, University of Copenhagen, Rolighedsvej 30, DK-1958, Frederiksberg C, Denmark
a r t i c l e
i n f o
Article history: Received 25 January 2011 Accepted 2 June 2011 Keywords: Local foods Danish honey Sensory descriptive analysis Uniqueness Location
a b s t r a c t Sensory characteristics of 21 locally produced Danish honeys representing a span in seasonal variation and location across Denmark were studied. Seasonal variation influenced the sensory characteristics predominantly as a consequence of changes in the flora of the honeybees' habitat. However, honeys from different locations displayed specific sensory characteristics within same season. Thus, the Danish honeys had distinct and unique flavours related to origin of location — sensory local uniqueness. In addition, honeys from similar locations could also differ in sensory profiles. The sensory variation within the honeys due to the flora in the local habitat was mirrored by pollen analysis showing diverse pollen profiles. This finding assists the beekeeper in formulating precise product descriptions for communication and creation of recognition of the products sensory uniqueness in their promotion strategy of the honeys towards the consumers. © 2011 Elsevier Ltd. All rights reserved.
1. Introduction The globalisation of the food supply chain that has happened over the last decades has resulted in a diminished knowledge of the origin of foods and the raw materials they are made from. As a consequence of this, a new trend in local foods has appeared with increasing interest in food origin. This is confirmed by Guerrero et al. (2009) who found that food origin was important for consumers in all countries studied including Belgium, France, Norway, Poland, Spain and Italy. According to the Committee of the Regions (1996), products with regional identity are demanded by consumers in all Member States of the Community. Additionally, a growing demand for authentic, exclusive, differentiated and value-added food products has been observed in most industrialised countries. An example in Scandinavia is the increasing attention to Nordic foods. In Denmark, initiatives have been taken by the Ministry of Food, Agriculture and Fisheries — The Danish Food Industry Agency — with the project “Taste Denmark” focussing on improved food quality, taste and food experiences. Also, an association called “the Taste of Denmark” has been established to improve the cooperation between the many local food producer networks in Denmark. As impetus to this Nordic focusing, a declaration of intents by Nordic chefs in which the keywords are simplicity, purity, freshness and ethical standards (Hellström et al., 2004) has been developed. Furthermore, the New Nordic Food programme build on this manifesto supporting business innovation,
⁎ Corresponding author. Tel.: + 45 3533 1018; fax: + 45 3533 3509. E-mail address: [email protected] (S. Stolzenbach). 0963-9969/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodres.2011.06.006
product development of local and regional food producers and the Nordic food tourism sector has been established. However, few studies have been addressing the sensory local uniqueness of food products from the Nordic region. In the present paper, by the phrase sensory local uniqueness, we refer to specific sensory characteristics from a certain location making the food product different from other food products within the same food category. In order to exploit the potential in locally produced food products, there is however a need to focus on the concept of sensory local uniqueness and how it is affected by its local production conditions (mesoclimate, soil, etc.) and practices. According to European Community (2006), the term geographical indication means: “the name of a region, a specific place or, in exceptional cases, a country, used to describe an agricultural product or a foodstuff: – originating in that region, specific place or country, and – which possesses a specific quality, reputation or other characteristics attributable to that geographical origin, and – the production and/or processing and/or preparation of which take place in the defined geographical area.” In making an attempt to better understand the meaning of geographical indication in terms of sensory local uniqueness, honey is an excellent model since its characteristics depend on several factors including botanical origin in the local area from where the bees gather the nectar and climatic conditions (Aparna & Rajalakshmi, 1999). It is also relevant from a consumer perspective, and González-Viñas, Moya, and Cabezudo (2003) state that not only do consumers demand high quality, but also they are aware of certificates addressing the honeys geographical and botanical origin.
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
Several studies have been carried out on honey with the aim to identify the geographical and botanical origin from the honey's physicochemical properties (Anklam, 1998; Krauze & Zalewski, 1991; Mateo & Bosch-Reig, 1998; Peña Crecente & Herrero Latorre, 1993; Pérez-Arquillué, Conchello, Ariño, Juan, & Herrera, 1995; Popek, 2002; Sanz, Perez, Herrera, Sanz, & Juan, 1995; Sporns, Plhak, & Friedrich, 1992; Zalewski, 1991). However, many authors have emphasised the importance of the need for the application of sensory analysis to identify and differentiate honeys from specific botanical origins and honey quality (Aparna & Rajalakshmi, 1999; Castro-Vázquez, Díaz-Moroto, González-Viñas, & Pérez-Coello, 2009; González Lorente, De Lorenzo Carretero, & Pérez Martin, 2008; Kaakeh & Gadelhak, 2005; Piana et al., 2004). Also, Persano Oddo and Piro (2004) state that a botanical declaration must include sensory analysis besides melissopalynological analysis and physicochemical analyses to complement each other for a verified declaration. Overall, sensory characterisation provides comprehensive and informative data about human perception of product quality. Until now, several non-Nordic honeys have received attention from a sensory perspective (Spanish honeys: Galán-Soldevilla, Ruiz-Pérez-Cacho, Serrano Jiménez, Jodral Villarejo, & Bentabol Manzanares, 2005; González Lorente et al., 2008; González-Viñas et al., 2003, Italian honeys: Esti, Panfili, Marconi, & Trivisonno, 1997, Indian honeys: Anupama, Bhat, & Sapna, 2003; Aparna & Rajalakshmi, 1999; Singh & Bath, 1997, Arab Gulf region honeys: Kaakeh & Gadelhak, 2005). However, the performed sensory profiles included few sensory descriptors or only overall ratings for descriptor groups like colour, texture, flavour, taste and aftertaste. The aim of the present study was to create a sensory vocabulary to describe and differentiate the characteristics of locally produced honeys in Denmark to substantiate if aspects of a definition of sensory local uniqueness can be ascribed. This could support the local producers in communicating and promoting their products. To explain causes of sensory flavour differences, information about location, botanical origin and mesoclimate in terms of rainfall and humidity in the local habitats was collected and linked to the sensory descriptive data. Furthermore, conceptual descriptors were linked to the sensory descriptive data to position the sensory experience of the honeys.
2767
2. Materials and methods 2.1. Experimental design A total of 21 Danish locally produced honeys representing a span in locations across Denmark were included (Fig. 1). All honeys were produced in 2009. For each location only one beekeeper delivered honey. Some of the honeys were harvested within the same local habitat but at different times of the year. For more details about the honeys, see Table 4. All the honeys were directly provided from the beekeepers, who all were members of Danish Beekeeper Association. The honeys were stored in darkness at room temperature until analyses. 2.2. Instrumental measures of honey quality Instrumental measurements of the honey quality including water content, invertase activity after Winkler and Hydroxymethylfurfural (HMF) were carried out by Institut für Bienenkunde Celle, Celle, Germany and in accordance with the guidelines of the International Honey Commission (Bogdanov, Martin, & Lüllmann, 1997). The water content influences the shelf life of the honeys. The lower water content the more stable and resistant the honey is to spoilage by yeast fermentation upon storage (Bogdanov et al., 1997). HMF and invertase activity are indicators of thermal treatment of the honeys. Heating prior to bottling is sometimes applied to avoid fermentation (Aparna & Rajalakshmi, 1999). However, the honey quality decreases by overexposure to heat and thus the magnitude of heating exposure is measured as a quality parameter. 2.3. Botanical declaration of the honeys The types and counts of pollen grains were determined by microscopic examination. For each honey, 500 pollen grains were counted to represent the variety in different types of pollen grains. During the counting, the non-nectar pollen grains were excluded. Furthermore, the pollen analysis allowed for over-represented and under-represented pollen grains. Also, electrical conductivity was performed according to the International Honey Commission (Bogdanov
Sæby (2 honeys) SæbyHeather (2 honeys)
Rønne (2 honeys)
Northern Jutland
Hobro (1 honeys) HobroHeather (2 honeys)
Lemvig (2 honeys) LemvigHeather (2 honeys)
Gjern (1 honey) Central Jutland Capital Zealand South Denmark
Fodby (1 honey) Næstved (2 honeys)
Aabenraa (2 honeys) Augustenborg (1 honey) Odense (1 honey) Fig. 1. Geographical distribution of the 21 included Danish locally produced honeys. “Heather” written after the location indicates that the honey is from the specified location but the beehive has been moved to the heather within the same location. Names in italic indicate the Danish regions.
2768
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
et al., 1997) to support the botanical declaration. The botanical declaration of the honeys was performed by Institut für Bienenkunde Celle, Celle, Germany. 2.4. Sensory descriptive analysis 2.4.1. Panel selection Panellists for the sensory descriptive analysis were selected from the external sensory panel of Faculty of Life Sciences, University of Copenhagen, Denmark. The external panel was recruited according to the selection criteria of ISO 3972 (1991). The sensory panel consisted of 6 women and 4 men (20–62 years of age). 2.4.2. Sample preparation The honeys were poured in polystyrene cups and closed with a lid. Each sample consisted of 30 g honey. Before serving, the samples were brought to room temperature (20 °C) to enhance the perceptible flavours in the honey. 2.4.3. Vocabulary development and profiling The sensory descriptive analysis consisted of four training sessions and four sensory profiling sessions each lasting 2 h. The aim of the training sessions was to develop a consensus sensory vocabulary and improve the panellists' cognitive clarity toward the descriptors discriminating the honeys and the use of the sensory intensity scale. Initially, the panellists individually generated sensory descriptors. Subsequently, a sorting task of the honeys was carried out to let the
panellists categorise stimuli based on their perceived similarity. The final vocabulary “honey language” consisted of 27 sensory descriptors (Table 1) developed via collaboration between the panellists and the panel leader (Byrne, Bak, Bredie, Bertelsen, & Martens, 1999; Byrne, Bredie, & Martens, 1999). For the sensory descriptive analysis, a 15-cm unstructured line scale was used. The sample set of 21 honeys was evaluated with three sensory replicates served across 4 days. Thus, the presentation order was unbalanced. However, it was ensured that the same honey only was served once for each profiling day. The samples were presented in randomised order to minimise systematic carry-over effects. The data was collected by Fizz Acquisition, Version 2.45A, Biosystems, France. The physical evaluation conditions for the sensory descriptive analysis followed the guidelines in ISO 13299 (2003).
2.5. Conceptual evaluation To move beyond the perception of the flavour in the honeys and achieve a better understanding of the sensory experience of the honeys, conceptual descriptors were included in the sensory descriptive analysis. Without prior training, the same panellists as used in the sensory descriptive analysis rated 9 conceptual descriptors (Table 2) on a 15-cm unstructured line scale after giving scores for the sensory descriptive terms. The descriptive analysis and conceptual evaluation were carried out as two consecutive sessions. These data was also collected by Fizz Acquisition, Version 2.45A, Biosystems, France.
Table 1 List of the sensory descriptors developed for the profiling. Sensory descriptora
Definition with reference materialsb
Scale
1 2 3
Appearance Brown-A Yellow-A Transparent-A
Appearance Degree of brown colour by visual evaluation Degree of yellow colour by visual evaluation Degree of transparency of the honey
None → extremely None → extremely None → extremely
4 5
Texture Liquid-TX Spreadability-TX
Texture associated with The degree of liquidity by stirring the honey The ease of spreading the honey on a plate
None → extremely Difficult → easy
6 7 8
Mouth feeling Grainy-MF Flour-MF Meltdown rate-MF
Mouth feeling associated with Amount of large grains (sugar crystals) during mastication Amount of small grains (sugar crystals) during mastication Length of time for the honey to melt in the mouth
None → extremely None → extremely Slowly → fast
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Flavour Raisin-F Fig-F Apricot-F Malt-F Wax-F Hop-F Smoked-F Menthol-F Vanilla-F Althea-F Anise-F Perfumed-F Flowers-F Elderflower-F Lemon-F
Aromatic taste sensation associated with Raisins extracted in 100 g/L sucrose solution Sun-dried figs Sun-dried apricots Malt drops dissolved in 100 g/L sucrose solution. Beeswax Hops extracted in 100 g/L sucrose solution Smoked malt extracted in 100 g/L sucrose solution Menthol pastille dissolved in 100 g/L sucrose solution Rooibos vanilla tea extracted in 100 g/L sucrose solution Althea drops (with bergamot oil) dissolved in 100 g/L sucrose Anise seeds extracted in 100 g/L sucrose solution Dried lavender extracted in 100 g/L sucrose solution Dried extracted in 100 g/L sucrose solution Elderflower squash Lemon juice dissolved in 100 g/L sucrose solution
None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely None → extremely
24 25 26 27
Taste Sweet-T Sour-T Bitter-T Umami-T
Taste associated with 100 g/L sucrose solution 0.6 g/L Citric acid monohydrate 0.004 g/L Quinine Hydrochloride 0.7 g/L L-Glutamic acid Monosodium salt Monohydrate
None → extremely None → extremely None → extremely None → extremely
a b
Suffix to the sensory descriptors indicates the type of assessment by panellists: -A. appearance. -TX. texture. -MF. mouthfeel. -F. flavour. -T. taste. Definitions of sensory descriptors as derived during the vocabulary development.
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774 Table 2 List of conceptual descriptors. Conceptual descriptor
Definition
Scale
1 2 3 4 5 6 7 8 9
Functional attribute Abstract attribute Abstract attribute Abstract attribute Emotional attribute Emotional attribute Emotional attribute Emotional attribute Overall quality percept
Not Not Not Not Not Not Not Not Not
Aromatic Complex Balanced Unique Annoyed Disappointed Happy Desire Liking
at all → extremely at all → extremely at all → extremely at all → extremely at all → extremely at all → extremely at all → extremely at all → extremely at all → extremely
2.6. Mesoclimate data A data logger was placed on the different beehives to record min/ max temperature and min/max humidity. The beehives were inspected by the beekeepers and the data was daily recorded from April 1st to harvest time of the honey. In case several honeys were harvested from the same beehive over season the recording started from the last harvest.
2769
applied as well to give all variables the same variance. To derive significance indications from the sensory data and linkages between the sensory data and pollen grains, data was analysed by derived estimated regression coefficients and jack-knife uncertainty testing based on full cross validation (Martens & Martens, 2000). 2.7.3. Mesoclimate data The daily recordings of the humidity and rainfall were considered as repeated measurements. The Diggle model handles repeated measurements and takes into account different length of time intervals and serial correlation structure within each honey (Diggle, 1988). Thus, the Diggle method was used to elucidate possible dependent effects of location or/and time on the profiles of temperature and humidity. The analysis was run by R version 2.10.1 (www.r-project.org). The model was a linear mixed model including a random honey effect to account for inter-honey variation. An additional Spatial Gaussian serial correlation structure described the dependency between the response variables within honeys that are close in time i.e. time where the bees are gathering honey. 3. Results and discussion
2.7. Data analysis
3.1. Honey composition
2.7.1. Pollen grains Statistical analysis on pollen grains counts was carried out in R version 2.10.1 (www.r-project.org). Each identified pollen grain was considered as a response variable and it was tested whether location and/or botanical origin influenced the presence of the pollen grain. LM-tests were used for model reduction and variables were removed based on backward elimination with a 5% cutoff value.
All the Danish locally produced honeys fulfilled the required standards of maximum 20% water content — though maximum 23% water content for heather honeys, and maximum 40 mg/kg HMF (BEK 836, 09/10/2003-) and minimum invertase activity of 50 U/kg (Persano Oddo, Piazza, & Pulcini, 1999). The pollen analysis and electrical conductivity resulted in a clear botanical declaration of the honeys (Council Directive, 2001/110/EC). The pollen analysis showed diverse patterns of pollen grains in the honeys and the predominant species are listed in Table 3. The identified pollen grains are common for the vegetation in Denmark. In a study by Persano Oddo et al. (2004), the authors summarised all the botanical species contributing
2.7.2. Sensory data The progress in panel performance over training sessions was evaluated by PanelCheck version 1.3.2 (Nofima, Norway) using the applications Tucker1-plot, profile plots, Eggshell plots and p-MSE plots. For the sensory raw profiling data, the panel performance was further studied with focus on sensory descriptor and panellist interactions by using SensoMineR in R package version 2.10.1 (Husson & Lê, 2009). Since significant interactions were revealed, the sensory raw profiling data was pre-processed to correct for idiosyncratic line scale usage by applying Generalised Procrustes Analysis (GPA) in MATLAB 7.9.0.529 R2009b (The MathWorks, US). Subsequently, the sensory profiling data was ready for multivariate data analysis in Unscrambler, Version 10.0.1 (CAMO ASA, Norway). Principal Component Analysis (PCA) including the full sensory vocabulary of honeys was established to achieve a total overview of the sensory sample variation. Next, the sensory data was analysed by ANOVA Partial Least Squares Regression (APLSR). APLSR visualises and determines the structure and degree of each design variable's (honey samples) contribution to the variation in sensory data. Thus, APLSR was performed with the X-matrix as 0/1 design variables and the Ymatrix as the sensory descriptors (Martens, Bredie, & Martens, 2000). It was of relevance to know which pollen grains contributed to description of the sensory variation within the honeys. Thus, a Partial Least Squares Regression (PLSR) model was made to elucidate possible relationships between the pollen grains and sensory descriptors. To obtain a more precise interpretation, the honeys were analysed in groups according to season. To achieve an indication of how the conceptual descriptors were associated to the flavour descriptors and to understand the direction of consumer response, a PCA including flavour descriptors and conceptual descriptors was established. The multivariate data analysis was based on averaged values over panellists and sensory replicates. The data was mean-centred and in case of different measuring units standardising of the data was
Table 3 Identified predominant species of pollen grains and influenced parameters. Identified pollen grains
Significantlya dependent parameter(s)
Acer (maple) Location × botanical origin Archillea-type (yarrow-type) Allium (onion) Brassica (rape)/Sinapis-type (mustard-type) Phacelia (fiddleneck) Pyrus-type (pomaceous fruits) Pyrus-type (pomaceous fruits)/Prunus-type (stone fruits)/ Rubus (raspberry/blackberry) Tilia (lime) Trifolium respens (white clover)/Melilotus (melilot) Trifolium respens (white clover)/Trifolium pratense (red clover) Brassica (rape) Centaurea cyanus (blue bonnet- cornflower) Trifolium respens (white clover) Other less represented pollen grains
Botanical origin
Trifolium respens (white clover)/ Trifolium hybriddum-type (hybrid clover-type)
Location
Pyrus-type (pomaceous fruits)/Prunus-type (stone fruits) Salix (willow)
No effect of location and botanical origin
Culluna vulgaris (heather)b a
P b 0.05. Honeys having predominating amount of Culluna vulgaris were enriched with bee bread, a mix of pollen and honey, and thus not possible to determine the relative frequency of this pollen. b
2770
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
Principal Component 2 (Y-explained variance 26%)
to unifloral honey in Europe. In total, 116 different botanical species were listed, and among these 10 botanical species were identified as predominating in the Danish produced honeys including Allium, Brassica, Centaurea cyanus, Prunus-type, Rubus, Salix, Tilia, Trifolium pratense, Trifolium respens and Culluna vulgaris (see Table 3). Thus, the other botanical species found in the Danish local honeys contributed to making these unique in relation to location and reflected the local habitat of the bees. This confirms that most of the botanical species in Europe only have local importance as also stated by Persano Oddo et al. (2004). It is furthermore confirmed by the present study showing that the majority of the pollen grains were either significantly dependent on location or an interaction between location and botanical origin (Table 3). This is in agreement with Anklam (1998) stating that pollen analysis is useful for the determination of the pollen profiles and can also be used for the identification of location. For some of the local honeys a single nectar source was so predominant that a corresponding botanical declaration was allowed (Council Directive, 2001/110/EC). For example the spring honey from Lemvig contained 88% Brassica allowing the declaration as unifloral “Rape honey”. In total, two rape honeys, two clover honeys, two heather honeys and one summer honey with cornflower were declared. It is not surprising that rape, clover and heather honeys are identified as the unifloral honeys among the local honeys since these are the most common ones in Denmark. It is in agreement with Persano Oddo and Piro (2004) that state that heather is widely distributed in the North of Europe and is one of the most important sources for late summer honeys and rape honey is largely cultivated in Europe for seed to oil production and a very important source for the bees during the spring. The sources to variations for the pollen profiles were studied from the mesoclimate in terms of rainfall and humidity for the specific locations. However, missing data in collection of mesoclimate appeared for five honeys. The mean humidity was significantly dependent on the location (P b 0.05) and time of the year (P b 0.001) while the mean temperature was only dependent on the time of the year (P b 0.001). These findings were confirmed by data from the Danish Meteorological Institute. Anklam (1998) and Aparna and Rajalakshmi (1999) confirm this finding by stating that even though
1.0
the honeys come from the same floral source they can differ because of seasonal climatic conditions or different location. However, the degree of influence on the humidity on the local flora remains unclear. Furthermore, many other uncontrolled factors might have affected the local flora as well as the soil condition and light exposure. 3.2. Sensory local uniqueness of Danish honeys 3.2.1. Sensory differentiation The Danish local honeys were studied to investigate if sensory local uniqueness could be defined. A sensory vocabulary was created to identify the sensory characteristics of the local honeys (Table 1). Hereby, a specific and well-defined sensory profile was obtained for each studied honey. The suitability of sensory descriptive analysis to characterise honey is confirmed by González Lorente et al. (2008). By multivariate data analysis, it was found that the local honeys differed in their sensory profiles and the explained variance accounted for 57% by the first two Principal Components (PCs). Also, other authors found multivariate data strategies as an appropriate method to define authenticity (Krauze & Zalewski, 1991; López et al., 1996; Zalewski, 1991). Especially, the appearance, texture and mouthfeel of the honeys spanned the sensory variation within the Danish local honeys. It was similarly found by González-Viñas et al. (2003) showing that panellists placed greater emphasis on the texture and appearance than the aroma and taste in evaluation of honeys. Since most textural and visual properties are not solely depending on the origin but probably also influenced by processing method (Piana et al., 2004), it was decided only to include the flavour and taste descriptors in further data analysis. It coincides with the major interest to study if flavours/tastes could be used to define sensory local uniqueness for the Danish honeys. It is furthermore in agreement with Aparna and Rajalakshmi (1999) stating that flavour is the most important sensory property of honey and reflects the botanical origin and its authenticity. 3.2.2. The sensory flavour variation mainly described by seasonal effect The honeys could be categorised into three main season groups: spring honeys, summer honeys and late summer honeys, based on their sensory characteristic. The spring honeys included honeys
Fig-F Hop-F Anise-F Smoked-F Altheadrop-F Perfumed-F Umami-T Vanilla-F Augustenborg Malt-F
0.5
Gjern Bitter-T
Næstved
Sweet-T
Elderflower-F Aabenraa-CLOVER Sour-T Lemon-F
0.0 Menthol-F Odense Wax-F
Apricot-F
Flowers-F Hobro
Rønne
-0.5
Raisin-F Lemvig-CLOVER
-1.0 -1.0
-0.5
0.0
0.5
1.0
Principal Component 1 (Y-explained variance 40%) Fig. 2. Summer honeys: ANOVA Partial Least Squares Regression (APLSR) correlation loading plot of PC1 versus PC2 showing the differences in the sensory characteristics of summer honeys for each location. The sample indicators were in the X-matrix and the sensory descriptors in the Y-matrix. Capitals in the sample indicator correspond to the honey declaration. The inner and outer ellipses represent r2 = 50% and 100%, respectively.
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774 Table 4 Sensory flavour uniqueness for the locally produced Danish honeys responding to the individual honey variety identification. Significance of APLSR derived regression coefficients for the linkage of the sensory descriptors to the sample indicators as derived by jack-knife uncertainty testing. Season
Location
Significant sensory descriptors
Spring
Sæby
Spring Spring Spring
RAPEa — Sæby RAPE-Lemvig Aabenraa
apricot (P b 0.001), hop (P b 0.001), flower (P b 0.01), malt (P b 0.01), sour (P b 0.01), fig(P b 0.05), anise (P b 0.05) menthol (P b 0.001)
Spring
Næstved
Spring
Fodby
Spring
Rønne
Summer Summer
Hobro Gjern
Summer
CLOVER — Lemvig
Summer
CLOVER-Aabenraa
Summer
Augustenborg
Summer Summer Summer
Odense Næstved Rønne
Late HobroHeather1 summer Late HobroHeather2 summer Late HEATHER—SæbyHeather summer
raisin (P b 0.01), apricot (P b 0.01), flower (P b 0.01), lemon (P b 0.05) fig (P b 0.001), malt (P b 0.001), hop (P b 0.001), vanilla (P b 0.01), anise (P b 0.01) sweet (P b 0.01), umami (P b 0.01), elderflower (P b 0.05), bitter(P b 0.05) althea (P b 0.01), perfumed (P b 0.01), wax (P b 0.05) althea (P b 0.001), anise (P b 0.01), fig (P b 0.05), malt (P b 0.05), smoked (P b 0.05), vanilla (P b 0.05) menthol (P b 0.001), raisin (p b 0.01), wax (P b 0.05) hop (P b 0.001), lemon (P b 0.001), fig (P b 0.05), elderflower (P b 0.05), sour (p b 0.05) fig (P b 0.001), malt (P b 0.001), anise (P b 0.001), perfumed (P b 0.001), umami (P b 0.001), smoked (P b 0.01), vanilla (P b 0.01), hop (P b 0.05), althea (P b 0.05) raisin (P b 0.05) fig (P b 0.01), anise (P b 0.05) apricot (P b 0.001), lemon (P b 0.001), sour (P b 0.001) vanilla (P b 0.001), umami (P b 0.001), wax (P b 0.01) hop (P b 0.05), smoked (P b 0.05)
hop (P b 0.001), anise (P b 0.001), perfumed (P b 0.001), sour (P b 0.001), bitter (P b 0.001), smoked (P b 0.01), fig (P b 0.05) Late CORNFLOWER — SæbyHeather lemon (P b 0.001), raisin (P b 0.01), summer elderflower (P b 0.01) Late LemvigHeather sweet (P b 0.01), apricot (P b 0.05) summer Late HEATHER — LemvigHeather smoked (P b 0.05) summer
a
The capital letters indicate that the honey is declared as stated.
harvested until the third week of July, summer honeys included honeys harvested in the period third week of July to the middle of August, and late summer honeys were harvested after this date. Over these seasons the honeys changed in sensory characteristics from displaying flavours of raisin (P b 0.001), wax (P b 0.001), menthol (P b 0.001), flowers (P b 0.001) and althea (P b 0.01) to over the summer moving towards more fruity flavour notes such as elderflower (P b 0.001), apricot (P b 0.001), lemon (P b 0.001), hop (P b 0.001), anise (P b 0.001) and sour (P b 0.001). Honeys harvested in the late summer have an aromatic flavour described with “dark” sensory descriptors including smoked (P b 0.001), malt (P b 0.001), fig (P b 0.001), hop (P b 0.001), vanilla (P b 0.001), perfumed (P b 0.001), bitter (P b 0.001) and umami (P b 0.01). More sensory descriptors were significantly correlated to the late summer honeys compared to the spring honey. Thus, the honeys increase in flavour complexity over the season. This might be explained by larger diversity in the flora over the year.
2771
3.2.3. Sensory local uniqueness As season is not an indicator of local uniqueness, APLSR models were separately established for each main season group to visualise the location's contribution to the variation in the sensory flavour profile of the honeys (Fig. 2, summer honeys; figures of spring and late summer honeys not shown). Overall, it was found that the location influenced the sensory characteristics of the honeys within each season. Honey from different locations in Denmark displayed specific sensory flavour and taste characteristics indicating variability in location and botanical origin (Table 4). Thus, the present study provides evidence that the honeys are representative of their botanical origin and location in terms of their unique flavours. In addition, honeys from similar locations could also differ in characteristics within the same season. Specifically for the summer honeys (see Fig. 2 and Table 4), it was found that clover honeys from Aabenraa and Lemvig were very different in sensory characteristics. Even though both honeys were mainly based on nectar from clover, the remaining nectar gathered in the local habitat made the honeys unique in relation to their origin of location. Anklam (1998) confirms that even though honeys come from the same floral source they can differ because of different locations. Also, Castro-Vázquez, DíazMaroto, de Torres, and Pérez-Coello (2010) found that location of production influenced and differentiated the sensory and aroma profile of Spanish chestnut honeys. No division of the sensory local uniqueness was observed within Denmark. For example the sensory characteristics of the summer honeys from the Zealand town Næstved in the Eastern part of Denmark and Gjern located in Central Jutland (see Fig. 1) were almost similar even though the two locations are far away from each other. Thus, the local sensory characteristics could not be systematically indexed onto the specific regions in Denmark. In comparison with previous studies on honey, the present study provided a more detailed sensory characterisation. For examples, Galán-Soldevilla et al. (2005) developed an odour and flavour vocabulary describing the locally produced honeys in the Spanish Andalucía region and the “La Vega del Guadalquivir” with a final sensory list only containing 3 flavour descriptors including overall intensity, floral and ripe fruit. Esti et al. (1997) characterised the flavour of honeys from the Molise region in Italy and characterised these as light fruity and having a certain aromatic character. Aparna and Rajalakshmi (1999) described Indian honey having a pleasing aroma, flavour and sweet taste. González Lorente et al. (2008) described artisan honeys from the same geographical area in Spain by applying 7 sensory descriptors including sweetness, adhesiveness, viscosity, acidity, bitterness, crystallisation and colour. Anupama et al. (2003) also described the Indian honeys using 8 flavour descriptors herein flowery and waxy. However, the Danish local honey might be considered to be unique since the sensory descriptors floral, wax, acidity, bitterness and sweetness were the only few overlaps found between the Danish local honeys and the other mentioned sensory works on honeys. 3.3. Relationship between the sensory characteristics and pollen grains The sensory variation within the honeys was mirrored by pollen analysis (Fig. 3) and as illustrated the identified pollen grains were related to the sensory variation of the honeys. The linkage between the sensory descriptors and pollen grains was highly complex and groupings of pollen grains were apparent indicating interplay between pollen grains and flavour characteristics (Fig. 3). This is confirmed from the statistical analysis where the main part of the pollen grains significantly (P b 0.05) contributed to more than one sensory flavour note. For example Brassica (rape) significantly dependent on botanical origin (cf. Table 3), was significantly (Pb 0.05) correlated to menthol in the spring honeys. Brassica also significantly contributed to vanilla flavour (Pb 0.01) and umami taste (P b 0.001) in
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
Principal Component 2 (Y-explained variance 15%)
2772
1.0 Aabenraa
Pyrus-type/Prunus-type
Trifolium respens Salix
Smoked-F Wax-F
0.5
Other pollen grains
Elderflower-F Althea-F Rønne Fodby Sæby-RAPE
Raisin-F Flowers-F
0.0
Umami-T
Anise-F
Lemon-F
Trifolium respens/Melilotus Perfumed-F
Apricot-F
Menthol-F
Malt-F Acer
Sweet-T Næstved
Sæby Centaurea cyanus
Pyrus-type/Prunus-type/Rubus
Fig-F
Sour-T
-0.5
Hop-F
Brassica
Vanilla-F
Allium
Bitter-T
Phacelia Brassica/Sinapis-type Pyrus-type
Lemvig-RAPE
-1.0 -1.0
-0.5
0.0
0.5
1.0
Principal Component 1 (Y-explained variance 22%) Fig. 3. Spring honeys: Partial Least Squares Regression (PLSR) correlation loading plot of the first two PCs. The design setup: pollen grains in the X-matrix and the sensory descriptors in the Y matrix. The X-matrix and Y-matrix were weighted by 1/std. However, the sample indicators in bold were pacified. The inner and outer ellipses represent r2 = 50% and 100%, respectively.
the spring honeys. In the summer honeys, Brassica still significantly contributed to vanilla flavour (Pb 0.05) and umami taste (Pb 0.01) but also the flavour of fig (Pb 0.001), hops (Pb 0.05), smoked (P b 0.05), althea (P b 0.05), anise (P b 0.05), perfumed (P b 0.01), lemon (Pb 0.001), and sour taste (Pb 0.001). According to Persano Oddo and Piro (2004) Brassica honeys have a medium aroma intensity described as floralfresh fruit, warm, spoiled and vegetal and with a medium sweetness and weak acidity. Pérez-Arquillué et al. (1995) describe Spanish Brassica honey as having a strong-flavour. However, the Danish Brassica honeys appeared to have a different flavour. The different flavour could be explained by the additional pollen grains in the Brassica honeys.
It was not possible to find any significant relationships between the pollen grains and sensory descriptors for the late summer honeys. This was also expected since the heather honeys are enriched with bee bread, a mix of pollen and honey. 3.4. Association between flavour characteristics and conceptual descriptors of the honeys To move beyond the perception of the flavour in the honeys, evaluation of the conceptual descriptors was linked to the sensory descriptive analysis. The conceptual descriptors contribute to an
1.0 Lemon-F Apricot-F
Principal Component 2 (20%)
Elderflower-F Liking
0.5
Sour-T
Desire
Happy
Unique
Balanced Menthol-F
0.0
Hop-F
Raisin-F Sweet-T
Anise-F
Flowers-F Althea-F
Wax-F
-0.5
Vanilla-F Umami-T Annoyed
Aromatic Complex Perfumed-F Fig-F Bitter-T Smoked-F
Disappointed Malt-F
-1.0 -1.0
-0.5
0.0
0.5
1.0
Principal Component 1 (34%) Fig. 4. All honeys: Principal Component Analysis (PCA) plot of the first two PCs. X matrix: flavour descriptor and hedonic descriptors. The inner and outer ellipses represent r2 = 50% and 100%, respectively.
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
understanding of how the different flavour are perceived and associated by the panellists. Initially in the analysis, it was found that all panellists were able to use the conceptual descriptors significantly (P b 0.001) to differentiate the samples. Thus, reliability and validity of asking conceptual descriptors to a trained panel after evaluating sensory descriptive terms were equally applicable as the sensory descriptors in describing the variation between the products. Furthermore, it was analysed and ensured that the panellists were not systematically affected by the intense sweetness of the honeys in their evaluating of liking during increased consumption over a session. An association between the conceptual descriptors and the flavour/taste descriptors was found (Fig. 4), and thus a fit to additionally describe the local honeys from a conceptual perspective was achieved. The flavour descriptors menthol, raisin, lemon, apricot and elderflower were strongly positively correlated to each other and perceived as balanced and elicited desire, happiness and high liking. As examples the menthol flavour and the elderflower flavour were found in the declared rape honey from Sæby and the spring honey from Fodby, respectively (see Table 4). The conceptual descriptors aromatic, complex and unique were positively correlated to the sensory flavour descriptors fig, hop, smoked, sour and lemon. However, it was interesting to notice that these descriptors were not related to liking, happiness and desire. Instead, the descriptors elicited emotional responses as disappointed and annoyed. However, the lemon flavour was positively correlated to liking. The flavour descriptors fig, hop, smoked, sour might be perceived as different from familiar honeys and the complexity perceived too high such as the positive hedonic response decreases according to the Wundt curve (Berlyne, 1970). The Wundt curve is based on that a food product is mostly liked when it is in accordance with an individual's level of arousal (e.g. complexity, novelty). Thus, stimuli with an arousal potential above and below the optimal level would be less liked the more they deviated from it.
3.5. Perspectives on sensory local uniqueness Identification of local food uniqueness implies linkage between the product, location and culture (Committee of the Regions, 1996). It was fulfilled by sensory analysis, botanical declaration and physicochemical measurements as also stated by Persano Oddo and Piro (2004) for a verified declaration. In the present, study a sensory vocabulary was developed to characterise Danish local honeys. The vocabulary was a helpful tool to identify and recognise the sensory local uniqueness for these. Hereby, a sensory profiles of the honeys from different locations were defined within a relatively small geographical area and provided empirical evidence that specific flavours exist. Specific sensory lexicons already exist for other product categories e.g. cheeses (Rétiveau, Chambers, & Esteve, 2005). According to González-Viñas et al. (2003) not only do consumers demand high quality but also they are aware of certificates addressing the honeys geographical and botanical origin. It is suggested to develop more sensory vocabularies for local products in order to facilitate comprehensive and detailed information about the sensory local uniqueness. It is in correspondence with the Committee of the Regions (1996) stating that if the quality of local products has to be promoted it requires identification and differentiation between the products on a regional basis. Also, Rétiveau et al., (2005) state that sensory vocabularies are suitable in promoting products when focussing on the products' unique characteristics. Overall, for the success of local food products recognition of sensory local uniqueness is of outmost importance. An objective sensory evaluation results in a valid product specification of the local foods. It is valuable for formulating precise product descriptions in promoting strategy towards the consumers in respect to communicating the unique properties of locally produced food products.
2773
4. Conclusions Seasonal variation influenced the sensory characteristics of the honeys as a consequence of floral changes. Moreover, location influenced the sensory characteristics of the honeys. Honey from different locations in Denmark displayed specific characteristics. In addition, there was evidence that honeys from similar locations could also differ within their characteristics even though they were harvested within same time frame. The sensory variation within the honeys due to the flora in the local habitat of the bees was mirrored by pollen analysis showing diverse pollen profiles. The link between the sensory descriptors and pollen grains was highly complex and grouping of pollen grains was apparent indicating interplay of the pollen grains resulting in correlation to more than one flavour characteristic. Overall, the study assists the beekeepers to understand the sensory quality of their products and provides them with sensory descriptors that may be used in formulating precise product descriptions for communicating the sensory local uniqueness in their promoting strategy towards the consumers. Hereby, recognition of the unique properties of local honeys can be established. Acknowledgement This work was financially supported by The Danish Food Industry Agency — The Ministry of Food, Agriculture and Fisheries — through the project “Local foods in Denmark”. The Danish Beekeeper Association is thanked for the critical review of the manuscript on pollen data. Technical staff at sensory laboratory, University of Copenhagen, is thanked for their assistance. The FoodUnique Project is also acknowledged. References Anklam, E. (1998). A review of analytical method to determine the geographical and botanical origin of honey. Food Chemistry, 63, 549–562. Anupama, D., Bhat, K. K., & Sapna, V. K. (2003). Sensory and physico-chemical properties of commercial samples of honey. Food Research International, 36, 183–191. Aparna, A. R., & Rajalakshmi, D. (1999). Honey — Its characteristics, sensory aspects, and applications. Food Review International, 15, 455–471. BEK 836 (09/10/2003--). Declaration from the Danish Ministry of Food, Agriculture and Fisheries. Berlyne, D. E. (1970). Novelty, complexity, and hedonic value. Perception & Psychophysics, 8, 279–286. Bogdanov, S., Martin, P., & Lüllmann, C. (1997). Harmonised methods of the European Honey Commission. Apidologie, 1–59 extra issue. Byrne, D. V., Bak, L. S., Bredie, W. L. P., Bertelsen, G., & Martens, M. (1999). Development of a sensory vocabulary for warmed-over-flavor: Part I. In porcine meat. Journal of Sensory Studies, 14, 47–65. Byrne, D. V., Bredie, W. L. P., & Martens, M. (1999). Development of a sensory vocabulary for warmed-over flavour: Part II. In chicken meat. Journal of Sensory Studies, 12, 67–78. Castro-Vázquez, L., Díaz-Maroto, M. C., de Torres, C., & Pérez-Coello, M. S. (2010). Effect of geographical origin on the chemical and sensory characteristics of chestnut honeys. Food Research International, 43, 2335–2340. Castro-Vázquez, L., Díaz-Moroto, M. C., González-Viñas, M. A., & Pérez-Coello, M. S. (2009). Differentiation of monofloral citrus, rosemary, eucalyptus, lavender, thyme and heather honeys based on volatile composition and sensory descriptive analysis. Food Chemistry, 112, 1022–1030. Committee of the Regions (1996). Opinion of the Committee of the Regions of 18 September 1996 on Promoting and Protecting Local Products — Trump-card for the Regions. Diggle, P. J. (1988). An approach to the analysis of repeated measurements. Biometrics, 44, 959–971. European Community (2006). Council regulation no. 510/2006 of 20 March 2006 on the protection of geographical indications and designation of origin for agricultural products and foodstuffs. Official Journal of the European Union. Council Directive (2001/110/ECC). 20 December 2001 Relating to Honey — Official Journal of the European Communities. Esti, M., Panfili, G., Marconi, E., & Trivisonno, M. C. (1997). Valorization of the honeys from the Molise region through physico-chemical, organoleptic and nutritional assessment. Food Chemistry, 58, 125–128. Galán-Soldevilla, H., Ruiz-Pérez-Cacho, M. P., Serrano Jiménez, S., Jodral Villarejo, M., & Bentabol Manzanares, A. (2005). Development of a preliminary sensory lexicon for floral honeys. Food Quality and Preference, 16, 71–77.
2774
S. Stolzenbach et al. / Food Research International 44 (2011) 2766–2774
González Lorente, M., De Lorenzo Carretero, C., & Pérez Martin, R. A. (2008). Sensory attributes and antioxidant capacity of Spanish honeys. Journal of Sensory Studies, 23, 293–302. González-Viñas, M. A., Moya, A., & Cabezudo, M. D. (2003). Description of the sensory characteristics of Spanish unifloral honeys by free choice profiling. Journal of Sensory Studies, 18, 103–113. Guerrero, L., Guárdia, M. D., Xicola, J., Verbeke, W., Vanhonacker, F., Zakowska-Biemans, S., et al. (2009). Consumer-driven definition of traditional food products and innovation in traditional foods. A qualitative cross-cultural study. Appetite, 52, 345–354. Husson, F., & Lê, S. (2009). SensoMineR: Sensory data analysis with R. R package version 1.10. http://CRAN.R-project.org/package=SensoMineR ISO 3972 (1991). Sensory analysis — Methodology — Method of investigating sensitivity of taste. International Organisation for Standardisation. ISO 13299 (2003). Sensory analysis — Methodology — General guidance for establishing a sensory profile. International Organisation for Standardisation. Kaakeh, W., & Gadelhak, G. G. (2005). Sensory evaluation and chemical analysis of Apis mellifera honey from the Arab Gulf region. Journal of Food and Drug Analysis, 13, 331–337. Krauze, A., & Zalewski, R. (1991). Classification of honeys by principal component analysis on the basis of chemical and physical parameters. Zeitschrift für Lebensmittel-Untersuchung und -Forschung, 192, 19–23. López, B., Latorre, M. J., Fernández, M. I., García, M. A., García, S., & Heerero, C. (1996). Chemometric classification of honeys according to their type based on quality control data. Food Chemistry, 55, 281–287. Hellström, E., Dahlgren, M., Valimaki, H., Lauterbach, E., Redzepi, R., Örvarsson, H., et al. (2004). Manifesto for the New Nordic Kitchen. Martens, H., & Martens, M. (2000). Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Food Quality and Preference, 11, 5–16. Martens, M., Bredie, W. L. P., & Martens, H. (2000). Sensory profiling data studied by partial least squares regression. Food Quality and Preference, 11, 147–149.
Mateo, R., & Bosch-Reig, F. (1998). Classification of Spanish unifloral honeys by discriminant analysis of electrical conductivity, color, water content, sugars, and pH. Journal of Agricultural and Food Chemistry, 46, 393–400. Peña Crecente, R., & Herrero Latorre, C. (1993). Pattern recognition analysis applied to classification of honeys from two geographic origin. Journal of Agricultural and Food Chemistry, 41, 560–564. Pérez-Arquillué, C., Conchello, P., Ariño, A., Juan, T., & Herrera, A. (1995). Physiochemical attributes and pollen spectrum of some unifloral Spanish honeys. Food Chemistry, 54, 167–172. Persano Oddo, L., Piazza, M. G., & Pulcini, P. (1999). Invertase activity in honey. Apidologie, 30, 57–65. Persano Oddo, L., & Piro, R. (2004). Main European uniforal honeys: Descriptive sheets. Apidologie, 35, S38–S81. Persano Oddo, L., Piana, L., Bogdanov, S., Bentabol, A., Gotsiou, P., Kerkvliet, J., et al. (2004). Botanical species giving unifloral honey in Europe. Apidologie, 35, S82–S93. Piana, M. L., Persano Oddo, L., Bentabol, A., Bruneau, E., Bogdanov, S., & Guyot Declerck, C. (2004). Sensory analysis applied to honey: State of the art. Apidologie, 35, S26–S37. Popek, S. (2002). A procedure to identify a honey type. Food Chemistry, 79, 401–406. Rétiveau, A., Chambers, D. H., & Esteve, E. (2005). Developing a lexicon for the flavour description of French cheeses. Food Quality and Preference, 16, 517–527. Sanz, S., Perez, C., Herrera, A., Sanz, M., & Juan, T. (1995). Application of a statistical approach to the classification of honey by geographic origin. Journal of the Science of Food and Agriculture, 69, 135–140. Singh, N., & Bath, P. K. (1997). Quality evaluation of different types of Indian honeys. Food Chemistry, 58, 129–133. Sporns, P., Plhak, L., & Friedrich, J. (1992). Alberta honey composition. Food Research International, 25, 93–100. Zalewski, R. I. (1991). Authentication of honey sample via test set sample and Principal Component analysis. Food Quality and Preference, 3, 223–227.