| _version_ | 1867010230808739840 |
|---|---|
| author | Ali Khosravi Kevin A. Quiroz‐Suárez Kelvin A. Sanoja‐López Rafael Luque |
| author_facet | Ali Khosravi Kevin A. Quiroz‐Suárez Kelvin A. Sanoja‐López Rafael Luque Ali Khosravi Kevin A. Quiroz‐Suárez Kelvin A. Sanoja‐López Rafael Luque |
| collection | Wiley Open Access |
| contents | Advances in fermentative production of xanthan gum from agrofood industry wastes Ali Khosravi Kevin A. Quiroz‐Suárez Kelvin A. Sanoja‐López Rafael Luque Biofuels, Bioproducts and Biorefining Abstract Xanthan gum, a high molecular weight exopolysaccharide synthesized by Xanthomonas campestris , is employed extensively in the food, pharmaceutical, cosmetic, and petroleum industries due to its exceptional rheological properties, thermal and pH stability, and biodegradability. Despite its versatility, large‐scale industrial production remains constrained by the high costs associated with conventional carbon sources such as glucose and sucrose. This review provides a comprehensive analysis of cost‐effective and sustainable alternatives, emphasizing the valorization of agroindustrial and food‐processing residues, including molasses, whey, melon waste, jackfruit seed powder, rice bran, and olive mill effluents as viable substrates for xanthan gum fermentation. Numerous studies have demonstrated that these unconventional feedstocks, when subjected to suitable pretreatment protocols and nutrient optimization, can yield xanthan gum at levels comparable to or exceeding those obtained from traditional sources. Incorporating circular economy principles into the production chain not only enables significant cost reductions but also mitigates the environmental burden associated with organic waste disposal. Advances in artificial intelligence (AI), particularly through machine learning (ML) techniques such as artificial neural networks, support vector machines, and evolutionary algorithms, have further expanded the potential for xanthan gum production by accurately modeling and optimizing the complex parameters of bioprocesses. These data‐driven methodologies outperform traditional statistical tools in predicting sugar release, enhancing fermentation efficiency, and reducing process variability. By facilitating the efficient utilization of low‐cost substrates and improving process control in both hydrolysis and fermentation stages, AI‐based optimization contributes to the development of scalable, resilient, and environmentally sustainable bioprocesses. Ultimately, the synergistic integration of waste‐derived feedstocks, bioprocess engineering, and intelligent modeling aligns xanthan gum manufacturing with the broader objectives of sustainable development and the global bioeconomy. 10.1002/bbb.70113 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/bbb.70113 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_bbb_70113 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Advances in fermentative production of xanthan gum from agrofood industry wastes Ali Khosravi Kevin A. Quiroz‐Suárez Kelvin A. Sanoja‐López Rafael Luque Biofuels, Bioproducts and Biorefining Advances in fermentative production of xanthan gum from agrofood industry wastes Ali Khosravi Kevin A. Quiroz‐Suárez Kelvin A. Sanoja‐López Rafael Luque Biofuels, Bioproducts and Biorefining Abstract Xanthan gum, a high molecular weight exopolysaccharide synthesized by Xanthomonas campestris , is employed extensively in the food, pharmaceutical, cosmetic, and petroleum industries due to its exceptional rheological properties, thermal and pH stability, and biodegradability. Despite its versatility, large‐scale industrial production remains constrained by the high costs associated with conventional carbon sources such as glucose and sucrose. This review provides a comprehensive analysis of cost‐effective and sustainable alternatives, emphasizing the valorization of agroindustrial and food‐processing residues, including molasses, whey, melon waste, jackfruit seed powder, rice bran, and olive mill effluents as viable substrates for xanthan gum fermentation. Numerous studies have demonstrated that these unconventional feedstocks, when subjected to suitable pretreatment protocols and nutrient optimization, can yield xanthan gum at levels comparable to or exceeding those obtained from traditional sources. Incorporating circular economy principles into the production chain not only enables significant cost reductions but also mitigates the environmental burden associated with organic waste disposal. Advances in artificial intelligence (AI), particularly through machine learning (ML) techniques such as artificial neural networks, support vector machines, and evolutionary algorithms, have further expanded the potential for xanthan gum production by accurately modeling and optimizing the complex parameters of bioprocesses. These data‐driven methodologies outperform traditional statistical tools in predicting sugar release, enhancing fermentation efficiency, and reducing process variability. By facilitating the efficient utilization of low‐cost substrates and improving process control in both hydrolysis and fermentation stages, AI‐based optimization contributes to the development of scalable, resilient, and environmentally sustainable bioprocesses. Ultimately, the synergistic integration of waste‐derived feedstocks, bioprocess engineering, and intelligent modeling aligns xanthan gum manufacturing with the broader objectives of sustainable development and the global bioeconomy. 10.1002/bbb.70113 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Advances in fermentative production of xanthan gum from agrofood industry wastes |
| topic | Biofuels, Bioproducts and Biorefining |
| url | https://scijournals.onlinelibrary.wiley.com/doi/10.1002/bbb.70113 |