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Neuro-fuzzy modeling of pulp temperature in rapid cooling chamber
Ítalo Emannuel Dos Anjos Santos1, Willian Minoru Okita1, Dian Lourençoni1
1Collegiate of Agricultural and Environmental, Federal University of the São Francisco Valley (UNIVASF), Av. Antônio C. Magalhães, 510 - Santo Antonio, Juazeiro, BA Brazil.
Abstract:
Post-harvest fruit losses in Brazil can reach up to 40%, with inadequacies in the cold chain being one of the primary causes. This study proposes the development of a neuro-fuzzy model to predict the pulp temperature of mangoes in rapid cooling chambers, aiming to enhance the efficiency of the cooling process. The experiment was conducted on a commercial mango farm in Petrolina, Pernambuco. The results demonstrated that the neuro-fuzzy model can accurately estimate the pulp temperature of mangoes (R² = 0.98), thereby aiding decision-making related to optimal rapid cooling times. Implementing this model could significantly reduce post-harvest losses and help ensure the quality of the final product.
