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.

Yerba Mate (Ilex paraguariensis) Processing and Extraction: Retention of Bioactive Compounds.

Ilex paraguariensis is a native tree from South America known for the presence of bioactive compounds, and its processed leaves are consumed as hot and cold infusions. After harvest (step 1), the leaves are subjected to flame blanching to inactive the enzymes (step 2), followed by drying and milling (step 3). The impacts of I. paraguariensis processing on leaf composition were investigated by extracting the major compounds (chlorogenic and isochlorogenic acids (3-CQA, 4-CQA, 5-CQA, 3,4-DQA, 3,5-DQA and 4,5-DQA), p-coumaric acid, caffeine and rutin) using different ratios of ethanol and water as extraction solvent (EW 25:75, 50:50, and 75:25 (w/w)). The solvent ratio of EW 50:50 was more effective in extracting the chlorogenic acids isomers, with retention of chlorogenic acids of 3463, 9485, and 9516 µg mL- 1 for steps 1, 2, and 3, respectively. Rutin and p-coumaric acid exhibited similar behavior with the increment of processing steps; however, p-coumaric acid was only detected in steps 2 and 3 for the solvent ratios EW 50:50 and 25:50. The caffeine extraction from I. paraguariensis varied from 936 to 1170 µg mL- 1 for all processing steps, with emphasis on its concentration extracted in step 1. The evolution of processing steps led to a higher retention of phenolic compounds from I. paraguariensis, which was not observed when using different solvent ratios, and the solvent ratio EW 50:50 was more effective for the extraction of chlorogenic acids. The successful extraction of chlorogenic acids from I. paraguariensis in this study proved to be a promising alternative for the use of yerba mate beyond the cuia cup.