Anthocyanin extraction methods: synthesis of morpho-anatomical knowledge for decision-making based on decision-tree

Abstract: Efficient anthocyanin extraction from emerging food matrices is essential in food technology and requires a precise, consistent, and clear extraction method. This study aimed to develop a decision-tree tool for selecting the optimal anthocyanin extraction technique. A comprehensive data synthesis covering the years 2018 to 2023 was conducted using leading academic databases, including Web of Science, Scopus, Medline, and SciELO. A combination of systematic and non-systematic approaches was employed to guide the decision-making process. The keywords used included “anthocyanin extraction methods,” and studies with more than 10 citations were prioritized, along with recent and relevant publications. Thirty-six articles were analyzed according to the PRISMA 2020 guidelines for systematic reviews. While ultrasound and microwave-assisted methods were predominantly featured, accounting for 46% of the reviewed studies, other methods such as enzyme-assisted extraction, deep eutectic solvents, and ionic liquid extraction were also evaluated for their comparative efficiency and suitability across various matrices. Fruits were the primary matrix, with a focus on the pericarp. While fruits, particularly the pericarp, was the primary matrix studied, the decision-tree tool is designed to be applicable across various food matrices, demonstrating its versatility and generalizability beyond fruits. The decision-tree tool was successfully applied to matrices with different structures, showcasing its adaptability. Integration of this tool could streamline selection processes, resulting in significant time and resource savings. In conclusion, this study highlights the influence of plant morpho-anatomical structures and extraction parameters on anthocyanin yield. It demonstrates how the decision-tree approach enhances efficiency and productivity, validated through blackberry and purple sweet potato matrices.