A mathematical model to predict the color change of fresh dough sheets under fluctuation temperatures. (1st June 2022)

• Record Type: Journal Article
• Authors: Xing, Shaohua; Liu, Lu; Zhang, Xiru; Guan, Hui; Gong, Hansheng; Li, Huamin; Liu, Wenli
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 162(2022)
• Publication Date: 2022-06-01
• Subjects: Dough sheets -- Green tea -- Turmeric -- Color -- Kinetics modelling; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2022.113447
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: The objective of this work was to establish a mathematical model to predict the color change of fresh dough sheets under fluctuation temperatures. The changes of color expressed in the parameters (L*, a*, b* and TCD) of green tea and turmeric dough sheets were measured at 5, 10, 20, 30 and 40 °C, respectively, and then the change models were established by zero- and first-order reaction kinetics model. The results showed that the first-order reaction kinetics model (all R 2 > 0.9) fitted well the change of color parameter L* while the other parameters were not well fitted by zero- and first-order reaction kinetics model. Furthermore, the temperature-dependent reaction rate was assessed by the Arrhenius equation and showed better performance. Finally, a dynamic prediction model of parameter L* combining the first-order reaction kinetics model with the Arrhenius equation was established under fluctuation temperatures. The validation results showed that changes of parameter L* under fluctuation temperatures could be predicted accurately by the model. This study will may be useful to determine consumption time and shelf life under the dynamic product distribution channel by providing real-time indication of quality of fresh noodle products. Highlights: The color of fresh dough sheets was significantly affected by temperature. A prediction model of color values under fluctuation temperatures established. Color change could be predicted accurately by the prediction model.