Green and fast prediction of crude protein contents in bee pollen based on digital images combined with Random Forest algorithm.

Bee pollen is considered an excellent dietary supplement with functional characteristics, and it has been employed in food and cosmetics formulations and in biomedical applications. Therefore, understanding its chemical composition, particularly crude protein contents, is essential to ensure its quality and industrial application. For the quantification of crude protein in bee pollen, this study explored the potential of combining digital image analysis and Random Forest algorithm for the development of a rapid, cost-effective, and environmentally friendly analytical methodology. Digital images of bee pollen samples (n = 244) were captured using a smartphone camera with controlled lighting. RGB channels intensities and color histograms were extracted using open source softwares. Crude protein contents were determined using the Kjeldahl method (reference) and in combination with RGB channels and color histograms data from digital images, they were used to generate a predictive model through the application of the Random Forest algorithm. The developed model exhibited good performance and predictive capability for crude protein analysis in bee pollen (R2 = 80.93 %; RMSE = 1.49 %; MAE = 1.26 %). Thus, the developed analytical methodology can be considered environmentally friendly according to the AGREE metric, making it an excellent alternative to conventional analysis methods. It avoids the use of toxic reagents and solvents, demonstrates energy efficiency, utilizes low-cost instrumentation, and it is robust and precise. These characteristics indicate its potential for easy implementation in routine analysis of crude protein in bee pollen samples in quality control laboratories.

Landscape and land use affect composition and nutritional values of bees' food.

Bees are primary pollinators across various terrestrial biomes and rely heavily on floral resources for sustenance. The composition of landscapes can influence bee foraging behavior, while human activities can directly affect both the composition and nutritional value of bee food. We aimed to assess how landscape structure and land use practices can impact the composition and nutritional value of food sources for two generalist social bee species, Apis mellifera and Scaptotrigona postica. Food samples were collected from twenty-five colonies of A. mellifera and thirteen of S. postica to examine how food composition and nutritional value may vary based on the extent of human land use and the composition of landscapes surrounding beekeeping sites. The pollen composition and nutritional value of A. mellifera were influenced by both land use practices and landscape heterogeneity. The number of patches determined total sugar and lipid content. Landscape heterogeneity affected pollen composition in S. postica, primarily due to the number of patches, while total sugar was affected by landscape diversity. Pollen nutritional value in S. postica was linked to land use, mainly meadow and vegetation, which influenced total sugar and dry matter. S. postica showed a higher sensitivity to land use changes compared to A. mellifera, which was more affected by landscape heterogeneity. Assuring landscape heterogeneity by preserving remaining forest patches around apiaries and meliponaries is crucial. Thoughtful land use planning is essential to support beekeeping activities and ensure an adequate quantity and quality of bee food resources.