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

• Published in: Food research international 2024-03, Vol.179, p.113958-113958, Article 113958
• Main Authors: Breda, Leandra Schuastz, de Melo Nascimento, José Elton, Alves, Vandressa, de Alencar Arnaut de Toledo, Vagner, de Lima, Vanderlei Aparecido, Felsner, Maria Lurdes
• Format: Article
• Language: English
• Subjects: Animals; Bee pollen; Bees; Crude protein; Dietary Supplements; Digital image analysis; Environmentally friendly method; Greenness metrics; Machine learning; Pollen - chemistry; Proteins - analysis; Random Forest
• ISSN: 0963-9969; 1873-7145
• DOI: 10.1016/j.foodres.2024.113958

[Display omitted] •Novel use of digital image processing (DIP) and Random Forest (RF) in bee pollen.•Indication of high crude protein contents in bee pollen using image data and PCA.•Combining DIP-RF resulted in an efficient predictive model for crude protein analysis.•Greenness of the methodologies for crude protein analysis in bee pollenwasevaluated. 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.