Sources and methods of manufacturing xanthan by fermentation of various carbon sources

Production and application of xanthan gum-prospects in the dairy and plant-based milk food industry: a review

Xanthan gum (XG) is an important industrial microbial exopolysaccharide. It has found applications in various industries, such as pharmaceuticals, cosmetics, paints and coatings, and wastewater treatment, but especially in the food industry. The thickening and stabilizing properties of XG make it a valuable ingredient in many food products. This review presents a comprehensive overview of the various potential applications of this versatile ingredient in the food industry. Especially in the plant-based food industries due to current interest of consumers in cheaper protein sources and health purposes. However, challenges and opportunities also exist, and this review aims to identify and explore these issues in greater detail. Overall, this article represents a valuable contribution to the scientific understanding of XG and its potential applications in the food industry.

Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

In this study, the focus was to produce xanthan gum from pineapple waste using Xanthomonas campestris. Six machine learning models were employed to optimize fermentation time and key metabolic stimulants (KH2PO4 and NH4NO3). The production of xanthan gum was optimized using two evolutionary optimization algorithms, particle swarm optimization, and genetic algorithm while the importance of input features was ranked using global sensitivity analysis. KH2PO4 was the most important input and was found to be beneficial for xanthan gum production, while a limited amount of nitrogen was needed. The extreme learning machine model was the most adequate for modeling xanthan gum production, predicting a maximum xanthan yield of 10.34 g/l (an 11.9 % increase over the control) at a fermentation time of 3 days, KH2PO4 of 15 g/l, and NH4NO3 of 2 g/l. This study has provided important insights into the intelligent modeling of a biostimulated process for valorizing pineapple waste.