Modeling the respiration rate of Golden papayas stored under different atmosphere conditions at room temperature

•Respiration rate was measured during the storage in different atmosphere conditions.•Modeling was performed using Michaelis–Menten and Nonlinear Regression models.•Kinetic parameters of the Michaelis-Menten model that change along the time. The present study aimed to measure the respiration rate of...

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Veröffentlicht in:Postharvest biology and technology 2018-02, Vol.136, p.152-160
Hauptverfasser: Barbosa, Nayara Cantarino, Mendonça Vieira, Ricardo Augusto, de Resende, Eder Dutra
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Sprache:eng
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Zusammenfassung:•Respiration rate was measured during the storage in different atmosphere conditions.•Modeling was performed using Michaelis–Menten and Nonlinear Regression models.•Kinetic parameters of the Michaelis-Menten model that change along the time. The present study aimed to measure the respiration rate of papayas (cv. Golden) stored under controlled atmosphere at room temperature, with decreasing O2 and increasing CO2 levels, in order to identify mathematical models capable of predicting respiration rate throughout storage. A model was proposed based on the Michaelis–Menten equation with uncompetitive inhibition and kinetic parameters that change during storage time. A second-order nonlinear regression model was used as reference for the mathematical approach. Nine experiments with three replicates were conducted under different controlled atmospheres to generate respiration data. A closed system method was used to measure the respiration rate at 2d intervals over 13d of storage at ambient temperature (23°C). Peel color measurements indicated total ripening of fruit stored in high O2 atmospheres, whereas ripening was minimal in atmospheres containing low O2 and high CO2 levels. The respiration rate remained at the lowest value, but gradually increased during storage at the lowest O2 level associated with the highest CO2 concentration. The nonlinear regression model obtained the lowest AICc value with VarPow variance, indicating a better fit than the Michaelis–Menten model. However, the latter, whose kinetic parameters change according to a polynomial second-order equation (MMQ), displayed a better fit than the nonlinear regression model evaluated by homoscedastic variance. Additionally, MMQ was more sensitive than nonlinear regression in detecting the real change in respiration rate in a biological system as a function of different gas compositions during storage.
ISSN:0925-5214
1873-2356
DOI:10.1016/j.postharvbio.2017.11.005