Neuro-fuzzy modeling of pulp temperature in rapid cooling chamber

IF 2.701
Ítalo Emannuel dos Anjos Santos, Willian Minoru Okita, Dian Lourençoni, Magno do Nascimento Amorim, Ana Carolina de Sá Silva Lins, Isadora Benevides Miranda, Sílvia Helena Nogueira Turco
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引用次数: 0

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.

快速冷却室纸浆温度的神经模糊建模
在巴西,收获后的水果损失高达40%,冷链不足是主要原因之一。本研究提出了一种神经模糊模型来预测芒果在快速冷却室中的果肉温度,旨在提高冷却过程的效率。该实验在伯南布哥省Petrolina的一个商业芒果农场进行。结果表明,神经模糊模型可以准确地估计芒果果肉温度(R²= 0.98),从而有助于决策最佳快速冷却时间。实施这种模式可以大大减少收获后的损失,并有助于确保最终产品的质量。
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期刊介绍: The Journal of Food Science and Technology (JFST) is the official publication of the Association of Food Scientists and Technologists of India (AFSTI). This monthly publishes peer-reviewed research papers and reviews in all branches of science, technology, packaging and engineering of foods and food products. Special emphasis is given to fundamental and applied research findings that have potential for enhancing product quality, extend shelf life of fresh and processed food products and improve process efficiency. Critical reviews on new perspectives in food handling and processing, innovative and emerging technologies and trends and future research in food products and food industry byproducts are also welcome. The journal also publishes book reviews relevant to all aspects of food science, technology and engineering.
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