Predictive model for growth of Leuconostoc mesenteroides in Chinese cabbage juices with different salinities. (1st January 2023)

• Record Type: Journal Article
• Authors: Xing, Shaohua; Zhang, Xiru; Guan, Hui; Li, Huamin; Liu, Wenli
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 173(2023)
• Publication Date: 2023-01-01
• Subjects: Growth model -- Salinity -- Leuconostoc mesenteroides -- kimchi; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2022.114264
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: The purpose of this study is to develop a growth predictive model for Leuconostoc in different salinities of Chinese cabbage juices for kimchi fermentation. Leuconostoc mesenteroides (approximately 6.3 log CFU/g) was inoculated in sterile Chinese cabbage juices to determine the growth of Leuconostoc mesenteroides at different salinities from 0 to 6%. The Baranyi and Roberts model was used to fit growth data, the modified Ratkowsky root square model was used to fit maximum specific growth rates, the polynomial model was used to fit maximum cell concentration and the modified inverse Ratkowsky model was used to fit lag phase with respect to salinities. The results showed that the primary models fitted the growth data well (all R 2 values > 0.900). The R 2 and root mean square error ( RMSE ) of the secondary models were 0.9136–0.9657 and 0.0142–0.2352, respectively, indicating a good fit of the model. The validation experiments proved that the changes of Leuconostoc mesenteroides with different salinities could be predicted accurately by the model. The predictive model will assist producers in understanding how microbes change during kimchi fermentation at different salinities. Highlights: The growth of fermentation microorganisms was significantly affected by salinity. A prediction model of microorganisms change at different salinities established. The change of microorganisms could be predicted accurately by the prediction model.