Prediction of Bioactive Compounds and Antioxidant Activity in Bananas during Ripening Using Non-Destructive Parameters as Input Data

Author(s)

de Souza, Angela Vacaro ; Favaro, Vitoria Ferreira da Silva ; de Mello, Jessica Marques ; Canato, Vinicius ; Sartori, Diogo de Lucca ; Putti, Fernando Ferrari ; Tadayozzi, Yasmin Saegusa ; Salgado, Douglas D'Alessandro

• Document type: Journal article
• Source: FOODS; v. 13, n. 14, p. 14-pg., 2024-07-01.

Abstract

Vegetable quality parameters are established according to standards primarily based on visual characteristics. Although knowledge of biochemical changes in the secondary metabolism of plants throughout development is essential to guide decision-making about consumption, harvesting and processing, these determinations involve the use of reagents, specific equipment and sophisticated techniques, making them slow and costly. However, when non-destructive methods are employed to predict such determinations, a greater number of samples can be tested with adequate precision. Therefore, the aim of this work was to establish an association capable of modeling between non-destructive-physical and colorimetric aspects (predictive variables)-and destructive determinations-bioactive compounds and antioxidant activity (variables to be predicted), quantified spectrophotometrically and by HPLC in 'Nanic & atilde;o' bananas during ripening. It was verified that to predict some parameters such as flavonoids, a regression equation using predictive parameters indicated the importance of R-2, which varied from 83.43 to 98.25%, showing that some non-destructive parameters can be highly efficient as predictors. (AU)

• FAPESP's process: 21/06706-9 - Antimicrobial peptides: expanding the horizons for animal production performance optimization
• Grantee: Eduardo Festozo Vicente
• Support Opportunities: Research Grants - Young Investigators Grants - Phase 2
• FAPESP's process: 20/01711-1 - Criação de um classificador de banana nanicão a partir da avaliação de parâmetros de qualidade dos frutos
• Grantee: Jéssica Marques de Mello
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• FAPESP's process: 21/08901-3 - MODELING OF ARTIFICIAL NEURAL NETWORKS APPLIED TO THE MATURATION PREDICTION FROM BANANAS QUALITY PARAMETERS
• Grantee: Vitória Ferreira da Silva Fávaro
• Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
• FAPESP's process: 20/14166-1 - Modeling of artificial neural networks applied to the maturation prediction from bananas quality parameters
• Grantee: Angela Vacaro de Souza
• Support Opportunities: Regular Research Grants