Development of a prediction model for the pericarp CIE a* value of mature green tomato at different storage temperatures as a function of cumulative ethylene production. (August 2020)

• Record Type: Journal Article
• Authors: Ciptaningtyas, Drupadi; Kagoshima, Wakana; Iida, Rei; Umehara, Hitomi; Johkan, Masafumi; Nakamura, Nobutaka; Orikasa, Takahiro; Thammawong, Manasikan; Shiina, Takeo
• Is Part Of: Journal of food engineering. Volume 278(2020)
• Publication Date: 2020-08
• Subjects: Color -- Ethylene -- Mature green stage -- Modeling -- Ripening -- Sigmoid function -- Tomato; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2020.109945
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: An estimation model following the sigmoid function of pericarp color based on the cumulative ethylene production was proposed. In the first part of the experiment, the same quintuplet samples were stored at 15 and 25 °C, respectively. In the second part, 114 tomatoes were stored at 12, 15, and 20 °C, and different triplet samples were used every time. It was concluded that the proposed model well described the behavior of the change in pericarp color in different storage temperatures. Moreover, the goodness of fit was better when the same sample was used. Another estimation method of parameters α, β, and γ of the sigmoid function at 15 °C was performed based on a linear regression using parameters α, β, and γ obtained at 12 and 20 °C. It suggested that the improvement of the goodness of fit will be achieved by the addition of data at different storage temperatures. Highlights: CIE a* of MG tomato during storage was modeled by cumulative ethylene. A sigmoid function based model was development at 12, 15, 20 and 25 °C. Proposed model is applicable not only at 25 °C but also at other temperatures. Model parameters at 15 °C were estimated based on parameters at 12 and 20 °C. Addition of data at different storage temperatures will improve the goodness of fit.