A comprehensive review of recent advances in the applications and biosynthesis of oxalic acid from bio-derived substrates.

Organic acids are important compounds with numerous applications in different industries. This work presents a comprehensive review of the biological synthesis of oxalic acid, an important organic acid with many industrial applications. Due to its important applications in pharmaceuticals, textiles, metal recovery, and chemical and metallurgical industries, the global demand for oxalic acid has increased. As a result, there is an increasing need to develop more environmentally friendly and economically attractive alternatives to chemical synthesis methods, which has led to an increased focus on microbial fermentation processes. This review discusses the specific strategies for microbial production of oxalic acid, focusing on the benefits of using bio-derived substrates to improve the economics of the process and promote a circular economy in comparison with chemical synthesis. This review provides a comprehensive analysis of the various fermentation methods, fermenting microorganisms, and the biochemistry of oxalic acid production. It also highlights key sustainability challenges and considerations related to oxalic acid biosynthesis, providing important direction for further research. By providing and critically analyzing the most recent information in the literature, this review serves as a comprehensive resource for understanding the biosynthesis of oxalic acid, addressing critical research gaps, and future advances in the field.

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.