New Blue Pigment Produced by Pantoea agglomerans and Its Production Characteristics at Various Temperatures

Abstract

A bacterium capable of producing a deep blue pigment was isolated from the environment and identified as Pantoea agglomerans. The pigment production characteristics of the bacterium under various conditions were studied. The optimal agar plate ingredients for pigment production by the bacterium were first studied: the optimal ingredients were 5 g/liter glucose, 10 g/liter tryptic soy broth, and 40 g/liter glycerol at pH 6.4. Bacterial cells grew on the agar plate during the incubation, while the pigment spread into the agar plate, meaning that it is water soluble. Pigment production was affected by the initial cell density. Namely, at higher initial cell densities ranging from 10 6.3 to 10 8.2 CFU/cm 2 on the agar plate, faster pigment production was observed, but no blue pigment was produced at a very high initial density of 10 9.1 CFU/cm 2 . Thus, the cell population of 10 8.2 CFU/cm 2 was used for subsequent study. Although the bacterium was capable of growing at temperatures above and below 10°C, it could produce the pigment only at temperatures of >10°C. Moreover, the pigment production was faster at higher temperatures in the range of 10 to 20°C. Pigment production at various temperature patterns was well described by a new logistic model. These results suggested that the bacterium could be used in the development of a microbial temperature indicator for the low-temperature-storage management of foods and clinical materials. To our knowledge, there is no other P. agglomerans strain capable of producing a blue pigment and the pigment is a new one of microbial origin.

Development of a Competition Model for Microbial Growth in Mixed Culture

“…It could further predict the amount of s t a p h y l o c o c c a l e n t e r o t o x i n A p r o d u c e d b y Staphylococcus aureus in milk and a blue pigment caused by Pantoea agglomerans on agar plate Fujikawa and Morozumi, 2006;Fujikawa and Akimoto, 2011 . We now consider the NL model to be a…”

“…m and n are the parameters for the curvature of the deceleration phase and the period of the lag phase, respectively Fujikawa, 2010 . The NL model has been shown to describe and predict microbial growth in broth and on agar plates successfully at constant and dynamic temperatures Fujikawa et al, 2003 andFujikawa and Morozumi, 2005 . It could further predict the amount of s t a p h y l o c o c c a l e n t e r o t o x i n A p r o d u c e d b y Staphylococcus aureus in milk and a blue pigment caused by Pantoea agglomerans on agar plate Fujikawa and Morozumi, 2006;Fujikawa and Akimoto, 2011 . We now consider the NL model to be a…”

Evaluation of a new Maillard reaction type time-temperature integrator at various temperatures

“…The samples were stored in an incubator at constant temperatures or a programmable incubator at dynamic temperatures (Fujikawa & Akimoto, 2011). Three samples were taken at intervals and then the color change of the device was measured immediately with a Tristimulus colorimeter (CM-3500d, KonicaMinolta, Tokyo, Japan).…”

“…The mechanisms of those TTIs are based on irreversible changes and reactions; most of the changes are expressed as the color change (Ellouze et al, 2008;Mendoza et al, 2004;Shimoni, Anderson, & Labuza, 2001;Tsironi, Gogou, Velliou, & Taoukis, 2008;Welt, Sage, & Berger, 2003). Fujikawa and Akimoto (2011) also studied the production of a blue pigment by Pantoea agglomerans stored under various temperature patterns, suggesting that the bacterium could be used in the development of a biological TTI. However, one of the important problems with these chemical and biological TTIs is how to maintain suppression of the chemical reaction or microbial growth until the temperature measurement is to be started.…”

Prediction of Competitive Microbial Growth in Mixed Culture at Dynamic Temperature Patterns

“…The NL model has been shown to successfully describe and predict microbial growth at dynamic temperatures with various initial populations Fujikawa et al, 2004;Fujikawa and Morozumi, 2005 . It could further predict the amount of microbial metabolites that would be produced Fujikawa and Morozumi, 2006;Fujikawa and Akimoto, 2011 . However, the prediction with the model was for a single species.…”