Characterization of Lavandula spp. Honey Using Multivariate Techniques

Abstract

Traditionally, melissopalynological and physicochemical analyses have been the most used to determine the botanical origin of honey. However, when performed individually, these analyses may provide less unambiguous results, making it difficult to discriminate between mono and multifloral honeys. In this context, with the aim of better characterizing this beehive product, a selection of 112 Lavandula spp. monofloral honey samples from several regions were evaluated by association of multivariate statistical techniques with physicochemical, melissopalynological and phenolic compounds analysis. All honey samples fulfilled the quality standards recommended by international legislation, except regarding sucrose content and diastase activity. The content of sucrose and the percentage of Lavandula spp. pollen have a strong positive association. In fact, it was found that higher amounts of sucrose in honey are related with highest percentage of pollen of Lavandula spp.. The samples were very similar for most of the physicochemical parameters, except for proline, flavonoids and phenols (bioactive factors). Concerning the pollen spectrum, the variation of Lavandula spp. pollen percentage in honey had little contribution to the formation of samples groups. The formation of two groups regarding the physicochemical parameters suggests that the presence of other pollen types in small percentages influences the factor termed as "bioactive", which has been linked to diverse beneficial health effects.

Fig 1. Shepard diagrams of the NMDS results.

(A) physicochemical data (normalized) and (B) melissopalynological data (normalized). Dashed line signifies a perfect linear relationship between calculated and ordination distances.

Fig 2. NMDS biplot of a chord distance matrix of the physicochemical data in honey (stress = 0.04).

Sites: Mogadouro (1–10); Mirandela (11–20); Chaves (21–30); Castelo-Branco (31–40); Fafe (41–50); Lousã (51–60); Santarém (61–70); Odemira (71–80); Monchique (81–90); Montemor-o-Novo (91–100) and Amieira (101–112).

Fig 3

Bar plots showing the average silhouette widths for physicochemical data (A) and melissopalynological data (B). The best partition by this method is the one with the largest average silhouette width. For further details refer to [27].

Fig 4. UPGMA clustering of a matrix of chord distance among sites in a NMDS ordination plot for physicochemical data (CCC = 0.72).

Sites: Mogadouro (1–10); Mirandela (11–20); Chaves (21–30); Castelo-Branco (31–40); Fafe (41–50); Lousã (51–60); Santarém (61–70); Odemira (71–80); Monchique (81–90); Montemor-o-Novo (91–100) and Amieira (101–112).

Fig 5. NMDS biplot of a chord distance matrix of the melissopalynological data in honey (stress = 0.19). Genera plants added using weighted averages.

Sites: Mogadouro (1–10); Mirandela (11–20); Chaves (21–30); Castelo-Branco (31–40); Fafe (41–50); Lousã (51–60); Santarém (61–70); Odemira (71–80); Monchique (81–90); Montemor-o-Novo (91–100) and Amieira (101–112).

Fig 6. UPGMA clustering of a matrix of chord distance among sites in a NMDS ordination plot for melissopalynological data (CCC = 0.75).

Sites: Mogadouro (1–10); Mirandela (11–20); Chaves (21–30); Castelo-Branco (31–40); Fafe (41–50); Lousã (51–60); Santarém (61–70); Odemira (71–80); Monchique (81–90); Montemor-o-Novo (91–100) and Amieira (101–112).