Santos Ítalo Emannuel Dos Anjos, Okita Willian Minoru, Lourençoni Dian, Amorim Magno do Nascimento, de Sá Silva Lins Ana Carolina, Miranda Isadora Benevides, Turco Sílvia Helena Nogueira
Collegiate of Agricultural and Environmental, Federal University of the São Francisco Valley (UNIVASF), Av. Antônio C. Magalhães, 510 - Santo Antonio, Juazeiro, BA Brazil.
Luiz de Queiroz College of Agriculture, University of Sao Paulo (USP), Av. Pádua Dias, 11 - Agronomia, Piracicaba, SP Brazil.
J Food Sci Technol. 2025 Jun;62(6):1110-1115. doi: 10.1007/s13197-024-06109-7. Epub 2024 Oct 10.
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.
巴西收获后水果损失率可达40%，冷链不完善是主要原因之一。本研究提出开发一种神经模糊模型，用于预测快速冷却室内芒果的果肉温度，旨在提高冷却过程的效率。实验在伯南布哥州佩特罗利纳的一个商业芒果农场进行。结果表明，神经模糊模型能够准确估计芒果的果肉温度（R² = 0.98），从而有助于做出与最佳快速冷却时间相关的决策。应用该模型可显著减少收获后损失，并有助于确保最终产品的质量。
