# Prediction of the Ecological Behavior of Burkholderia gladiolus in Fresh Wet Rice Noodles at Different Temperatures and Its Correlation with Quality Changes

## TL;DR

This study models how a deadly bacteria grows in rice noodles at different temperatures and how it affects noodle quality.

## Contribution

A novel predictive model for Burkholderia gladiolus growth and Bongkrekic Acid production in rice noodles is developed.

## Key findings

- The Huang–Huang model best predicted bacterial growth with high accuracy.
- Bacterial growth increased pH and yellowing in rice noodles.
- Bongkrekic Acid production is most likely between 24°C and 30°C.

## Abstract

Burkholderia gladioli pathovar cocovenenans (BGC) is a highly lethal foodborne pathogen responsible for outbreaks of food poisoning with the highest recorded mortality rates among bacterial foodborne illnesses in China. In this study, the ecological behavior of BGC and its Bongkrekic Acid (BA) production dynamics in fresh wet rice noodles (FWRN) were investigated under isothermal conditions ranging from 4 °C to 37 °C. Growth kinetics were modeled using the Huang, Baranyi, and modified Gompertz primary models, with secondary models (Huang square root model and Ratkowsky square root model) describing the influence of temperature on growth parameters. Among these, the Huang–Huang model combination exhibited the best performance, with a root mean square error (RMSE) of 0.009 and bias factor (Bf) and accuracy factor (Af) values close to 1. Additionally, we examined the impact of BGC contamination on the quality attributes of FWRN, including pH, color (L*, a*, b*), hardness, and moisture content. The results indicated that BGC growth significantly increased pH and yellowing (b*) values, while changes in texture and moisture were less pronounced. A probabilistic model was further developed to predict BA production under various temperature scenarios, revealing that BA formation was most likely to occur between 24 °C and 30 °C. While this study provides valuable predictive tools for microbial risk assessment and quality control of FWRN, limitations include the exclusion of additional environmental factors such as oxygen and relative humidity, as well as the lack of direct investigation into the degradation behavior of BA. Future research will expand model parameters and include sensory evaluations and advanced microbiological analyses to enhance applicability under real-world storage and transportation conditions.