Neuro-fuzzy modeling of pulp temperature in rapid cooling chamber

被引：0

作者：

Santos, italo Emannuel dos Anjos ^{[1
]}

Okita, Willian Minoru ^{[1
]}

Lourenconi, Dian ^{[1
]}

Amorim, Magno do Nascimento ^{[2
]}

de Sa Silva Lins, Ana Carolina ^{[3
]}

Miranda, Isadora Benevides ^{[4
]}

Turco, Silvia Helena Nogueira ^{[1
]}

机构：

[1] Fed Univ Sao Francisco Valley UNIVASF, Collegiate Agr & Environm, Ave Antonio C Magalhaes,510 St Antonio, Juazeiro, BA, Brazil

[2] Univ Sao Paulo, Luiz De Queiroz Coll Agr, Ave Padua Dias, 11-Agron, Piracicaba, SP, Brazil

[3] Univ Fed Lavras, Agr Engn Dept, Lavras, MG, Brazil

[4] Univ Fed Campina Grande, Agr Engn Dept, R Aprigio Veloso,882 Univ, Campina Grande, PB, Brazil

来源：

JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE | 2024年

关键词：

Post-harvest losses; Cold chain; Neuro-fuzzy model; Mango; Rapid cooling chambers; INFERENCE SYSTEM; PERFORMANCE;

D O I：

10.1007/s13197-024-06109-7

中图分类号：

TS2 [食品工业];

学科分类号：

0832 ;

摘要：

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 (R-2 = 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.

引用

页数：6

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