| Title |
Vegetable waste and by-products to feed a healthy gut microbiota: Current evidence, machine learning and computational tools to design novel microbiome-targeted foods |
| ID_Doc |
26781 |
| Authors |
Sabater, C; Calvete-Torre, I; Villamiel, M; Moreno, FJ; Margolles, A; Ruiz, L |
| Title |
Vegetable waste and by-products to feed a healthy gut microbiota: Current evidence, machine learning and computational tools to design novel microbiome-targeted foods |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.tifs.2021.10.002 |
| Abstract |
Background: Food waste management is a key issue to global food security and friendly environmental governance. Worldwide, one-third of food produced for human consumption is lost or wasted along the food supply chain, primary production and food processing representing the most significant loses. Therefore, the need to achieve zero waste production schemes is becoming a priority to meet Sustainable Development Goals. Increasing evidence points towards vegetable food waste as a rich source of a wide array of carbohydrate structures and fibres providing the opportunity to identify and develop alternative approaches to valorize agrofood waste. Scope and approach: This review describes the valorization of vegetable waste and by-products via production of (novel) substrates targeted to gut microbiota modulation, emphasizing the importance of raw materials and structural-functional properties of carbohydrates. Furthermore, we propose a novel framework for the rational selection of vegetable sources with potential prebiotic activity, based on machine learning and other computational tools applied to available literature and public database information. Key findings and conclusions: Integration of the body of knowledge within the field of vegetable food waste valorization, from different perspectives, allows a rational selection of carbohydrate-based substrates with promising prebiotic activities. By exploring the interactions among dietary fibre and gut microbial ecosystems using computational tools fed with structural, functional and genomic data, we can identify substrates with potential to selectively stimulate gut commensals, in agreement with experimental evidence. Our approach establishes a new framework that can be extended to a wide range of commensal microbes and carbohydrate structures. |
| Author Keywords |
Vegetable food waste valorization; Prebiotics; Microbiome; Machine learning; Glycosidase activity; Circular economy |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000714869700001 |
| WoS Category |
Food Science & Technology |
| Research Area |
Food Science & Technology |
| PDF |
https://doi.org/10.1016/j.tifs.2021.10.002
|