Consuelo Díaz-Moreno

The nutritional and bioactive aptitude of bee pollen for a solid-state fermentation process

Interest in bioprocess-based products has increased in recent years due to their applications and advantages. Solid-state fermentation, as a part of this technology, has been used in different industries, including food. Among the products produced with this type of process, bee bread is one of the most important. Bee pollen is a valuable raw material because of its nutritional characteristics, in particular its protein (10–52%), lipid (.15–20%), dietary fiber (.3–20%), and mineral contents, as well as its bioactive qualities related to the presence of vitamins and antioxidants, which ultimately should allow micro-organism development during fermentation processes. Pollen grains possess, however, a natural protection that greatly limits nutrient transportation to the exterior of the grain. In fact, bees naturally induce bioprocesses in the interior of their hive to make pollen nutrients more bioavailable. The objective of this work is therefore to review the characteristics of bee pollen, and its aptitude as a raw material for a solid-state fermentation process.

Analysis of Multifloral Bee Pollen Pellets by Advanced Digital Imaging Applied to Functional Food Ingredients

Bee pollen is a hive product, resulting from floral pollen agglutination by worker bees and it is characterized by its excellent bioactive and nutritional composition. Currently, research is focused on bee pollen applications on food industry, because this product has been considered an excellent source of compounds for human nutrition. It is also important in some industries, where color and particle size are important characteristics for production. Due to the granular nature of bee pollen, conventional colorimetry does not allow describing color correctly; thus, digital image analysis is a better alternative. This technique could also allow classifying bee pollen according to its appearance beyond the color. Consequently, the aim of this work was to develop a novel methodology for image data processing to classify bee pollen as ingredient in food industry. Seven color groups in samples were established regarding harvest month and particle size. It was possible to calculate the percentage of each color group in all samples. This methodology also allowed selecting each fraction for different applications in food industry using colorimetry, granulometry and the relationship between both of them.