Vegetable waste and by-products to feed a healthy gut microbiota: Current evidence, machine learning and computational tools to design novel microbiome-targeted foods

Trends in Food Science & Technology 118 (2021) 399–417 Available online 6 October 2021 0924-2244/© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Vegetable waste and by-products to feed a healthy gut microbiota: Current evidence, machine learning and computational tools to design novel microbiome-targeted foods Carlos Sabater a , b , In´ es Calvete-Torre a , b , Mar Villamiel c , F. Javier Moreno c , * , Abelardo Margolles a , b , ** , Lorena Ruiz a , b a Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos L´ acteos de Asturias-Consejo Superior de Investigaciones Científicas (IPLA-CSIC), Paseo Río Linares s/n, 33300, Villaviciosa, Asturias, Spain b Functionality and Ecology of Beneficial Microbes (MicroHealth) Group, Instituto de Investigaci´ on Sanitaria Del Principado de Asturias (ISPA), Oviedo, Asturias, Spain c Instituto de Investigaci´ on en Ciencias de La Alimentaci´ on (CIAL) (CSIC-UAM) CEI (CSIC+UAM), Madrid, Spain ARTICLE INFO Keywords: Vegetable food waste valorization Prebiotics Microbiome Machine learning Glycosidase activity Circular economy 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. 1. Introduction Over a third of the food production is wasted along the food supply chain, before reaching the consumer, with large associated environmental and economic impacts, which are increasing exponentially with the global rapid growth experienced by food production industries (FAO, 2019). This imposes an urgent need to develop strategies to achieve food waste reduction in food production in line with circular economy principles. This aspect is particularly critical in the case of horticultural commodities whose losses and waste have been reported to be the highest among all types of foods, reaching up to 60% (Sagar, Pareek, Sharma, Yahia, & Lobo, 2018). To date most agro-food waste and by-products have received little application, being mostly used for composting or livestock feeding. An attractive alternative to achieve food waste reduction is the valorization of food waste and by-products into novel value-added products with potential applications in * Corresponding author. Instituto de Investigaci´ on en Ciencias de La Alimentaci´ on (CIAL) (CSIC-UAM) CEI (CSIC+UAM), Madrid, Spain. ** Corresponding author. Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos L´ acteos de Asturias-Consejo Superior de Investigaciones Científicas (IPLA-CSIC), Paseo Río Linares s/n, 33300, Villaviciosa, Asturias, Spain. E-mail addresses: [email protected] (F.J. Moreno), [email protected] (A. Margolles). Contents lists available at ScienceDirect Trends in Food Science & Technology journal homepage: www.elsevier.com/locate/tifs https://doi.org/10.1016/j.tifs.2021.10.002 Received 21 May 2021; Received in revised form 27 September 2021; Accepted 3 October 2021 Trends in Food Science & Technology 118 (2021) 399–417 400 cosmetic, pharmaceutical or food industrial uses which can provide opportunities for earning additional income whereas contributing to sustainable food production schemes (Caldeira, De Laurentiis, Corrado, van Holsteijn, & Sala, 2019). Agro-food waste and by-products are rich sources of ingredients capable to provide physiological benefits beyond nutrition and, hence, hold valuable potential to develop novel functional foods. Indeed, there is currently an increasing consumer demand of such products reflecting their rising awareness on the power of nutrition to improve health (Cui et al., 2019). Vegetable waste and by-products are abundant in prebiotic substrates including polyphenols and a range of complex non-digestible carbohydrates under the definition of soluble dietary fibre (Table 1) (Sagar et al., 2018), whose consumption contribute to improve human health upon selective utilization by gut microbial populations (Belorkar & Gupta, 2016; Faustino et al., 2019; Gibson et al., 2017). Our intestinal environment harbors a densely packed ecosystem of microorganisms, collectively known as gut microbiota, which is acknowledged to strongly impact human health. The gut microbiota interacts closely with host epithelial and immune cells, dietary components, host signals and other microbial cells, impacting host nutrition, immune programming and resistance to pathogens (Qin et al., 2010). Accordingly, imbalances in the gut microbiota structure, so-called dysbiosis states, favour infection by opportunistic pathogens (e.g. Clostridium difficile) and chronic inflammation. This has been associated with a number of disorders, including autoimmune (e.g. systemic lupus erythematosus), inflammatory (e.g. inflammatory bowel disease) and metabolic diseases (e.g. metabolic syndrome, diabetes and obesity), some forms of cancer (e.g. colorectal cancer), and can even exacerbate health consequences of malnutrition on its different forms (e.g. undernourishment and overnourishment). In this scenario, the gut microbiota is a valuable source of disease biomarkers and targets to manage numerous health disorders which currently represent global challenges (Cheng, Qi, Zhuang, Fu, & Zhang, 2020; Littlejohn & Finlay, 2021; Tap et al., 2021). Evidence demonstrates that the main components of the diet can induce changes in the gut microbiota impacting human health in either a beneficial or detrimental manner. For instance, unbalanced diets enriched in ultra-processed foods or food contaminants, negatively modulate the microbiota with potential consequences for gut health (Asnicar et al., 2021; Jin, Beekmann, Ringø, Rietjens, & Xing, 2021). Contrarily, fibre supplementation of the habitual diet may help to address age-related comorbidities through microbiota modulation (Pellanda, P., Ghosh, T. S., & O’Toole, 2021), while polysaccharide administration may correct high-fat diet-induced gut dysbiosis (Guo et al., 2021). Hence, to date, diet is the best recognized strategy to effectively modulate the gut microbial composition, metabolism and interplay with the human host (Edwards et al., 2017). Accordingly, dietary interventions including consumption of prebiotic ingredients have emerged as promising approaches to achieve fine tune modulation of the gut microbiota as a mean to improve human health (Kolodziejczyk, Zheng, & Elinav, 2019; Li, Wang, Wang, Hu, & Chen, 2019). Prebiotic ingredients traditionally refer to non-digestible carbohydrates, which include a wide diversity of oligoand polysaccharides which are resistant to intestinal digestion and absorption (Ferreira-Lazarte, Moreno, & Villamiel, 2020). These carbohydrates reach the colon undigested or only partially digested, and at this location can be selectively fermented by gut microbial populations (Cockburn & Koropatkin, 2016). This selective effect is not observed for other compounds like substitutes for dietary sugars that do not alter microbial diversity or Table 1 Main dietary oligosaccharides present or derived from vegetable and fruit waste and by-products with prebiotic potential. Oligosaccharide Main structural backbone a [predominant glycosidic linkages] Precursor substrate/ manufacture method Main waste/by-product sources References FOS (Fru) n -Glc [β(2 → 1)] [β(2 → 6)] Inulin/hydrolysis Sucrose/ transfructosylation - Sucrose rich solutions (i.e., sugar cane molasses, beet molasses, agave syrups, date-fruit byproducts). de la Rosa et al. (2019) - Fruit peels (mango, banana, pineapple, orange, etc.). - Bagasse (sugar cane, agave, corn, coconut, cassava, etc.). - Leaves (banana, corn, sugar cane, etc.). - Pomaces (apple and grape). - Coffee processing by-products (pulp, husk and spent grain). α -GOS (Gal) n -Suc [ α (1 → 6)] Raffinose, stachyose and verbascose/extraction - Soybean whey. Martinez-Villaluenga and Frías (2014) - Sugar beet molasses. POS (GalA) n [ α (1 → 4)] and/or (GalA-Rha) n [ α (1 → 4)]; [ α (1 → 2)] GalA units can be partially esterified and Rha units ramified Pectin/hydrolysis - Citrus fruit peels (lemon, orange). Tan & Nie (2020) - Pomaces (apple). - Pulps (sugar beet and chicory). XOS (Xyl) n [β(1 → 4)] Xylan/hydrolysis - Corn stover and cobs. Santib´ a˜ nez et al. (2021) - Stalks (sorghum and grape). - Straws (wheat and rice). - Brans (wheat and barley). - Bagasse (sugarcane). AXOS (Xyl*) n -(Ara**) n Xyl residues are partially substituted at O-2 and/or O-3 position with α -L-Ara units *[β(1 → 4)] **[ α (1 → 3)] and [ α (1 → 2)] Arabinoxylan/ hydrolysis - Brans (corn, wheat, rice, barley and sorghum). Mathew et al. (2018) - Brewer’s spent grain. - Sugar beet pulp. COS (Glc) n [β(1 → 4)] Cellulose/(multi-stage enzymatic) hydrolysis - Corn stovers and corbs. Zhou et al. (2020) IMOS (Glc) n [ α (1 → 6)] Starch/hydrolysis and transglucosylation - Saccharified vegetable starch derived from corn, cassava, wheat, barley, peas, beans, lentils, oats, rice, potato processing waste. Madsen II et al. (2017) a Fru, fructose; Glc, glucose; Gal, galactose; Suc, sucrose; GalA, galacturonic acid; Rha, rhamnose; Xyl, xylose; Ara, arabinose. C. Sabater et al. Trends in Food Science & Technology 118 (2021) 399–417 401 composition (Serrano et al., 2021). The health promoting effects of prebiotics are usually the combined result of stimulating commensal and beneficial groups such as Bifidobacterium spp., Lactobacillus spp., some Ruminococcus species, and Lachnospiraceae members, among others; the generation of microbial metabolites with attributed functional traits, such as the short chain fatty acids (SCFAs) butyrate, propionate or acetate; and the inhibition of potential pathogenic bacteria (Carlson, Erickson, Lloyd, & Slavin, 2018). These beneficial effects are sometimes the result of cross-feeding mechanisms among cooperative bacteria in the human gut. For instance, the bifidogenic and butyrogenic effect of arabinoxylo-oligosaccharides (AXOS) is the result of cross-feeding mechanisms between bifidobacteria and eubacteria. Specifically, consumption of arabinose residues by Bifidobacterium longum releases acetate, which is further employed by Eubacterium rectale to produce butyrate; whereas xylose released from AXOS by E. rectale, results in bifidobacterial growth promotion (Moens, Weckx, & De Vuyst, 2016; Rivi` ere, Selak, Geirnaert, Van den Abbeele, & De Vuyst, 2018). This cooperation within the gut microbiota exists due to the high specialization required to metabolize some of the chemical structures present in complex fibre, and suggests that the response of a gut microbial community to a prebiotic intervention may be considerably influenced by the initial configuration of the gut microbial communities and by the particular chemical composition and structures of the prebiotic fibre under investigation. Indeed, some complex carbohydrate structures can only be directly accessible to a limited number of microbial species, yet they might provide benefits to various microbial populations within the community through cross-feeding mechanisms (Moens et al., 2016; Rivi` ere et al., 2018; Ze, Duncan, Louis, & Flint, 2012). In the frame of the blooming interest in exploring the relationships between diet, gut microbiota and health, valorizing agro-industrial waste and by-products via production of (novel) prebiotics, offers an opportunity to reduce waste in agro-industrial food manufacturing processes, while contributing to develop novel functional ingredients to improve human health (Campos, G´ omez-García, Vilas-Boas, Madureira, & Pintado, 2020; Ferreira-Lazarte, Kachrimanidou, Villamiel, Rastall, & Moreno, 2018; P´ erez-L´ opez, Cela, Costabile, Mateos-Aparicio, & Rup´ erez, 2016; Uerlings et al., 2020; Vazquez-Olivo, Guti´ errez-Grijalva, & Heredia, 2019). Technological advances in omic and meta-omic methodologies have stimulated a profound revolution in this research field, which has rapidly moved its focus from investigating the effects of a few prebiotic fibre structures on a limited range of traditionally recognized beneficial bacteria, to investigate the impact of a continuously growing range of prebiotic carbohydrate structures, on the whole configuration and activity of the gut microbial communities. Notwithstanding, efficient exploitation of the potential of agro-food waste and by-products components to achieve fine tune modulation of the gut microbiota through the formulation of (novel) prebiotics capable to improve human health in specific population groups requires integration of the whole body of knowledge generated in the fields of microbiology, microbial ecology, biochemistry and food technology. However, such integrative studies are scarce through literature and, thus, the potential of agro-food waste and by-products as a source of novel, sustainable and efficient prebiotics which could be incorporated into personalized nutrition strategies has been overlooked. This review will try to contribute to fill this gap exploring the available information on non-digestible carbohydrates diversity in vegetable (including fruit) food waste and by-products, with particular interest in oligosaccharides and their prebiotic potential, based on experimental data generated with various intestinal simulation models. This wealth of data will be integrated with advanced predictive computational approaches to aid exploiting the associations between the fine physico-chemical and structural properties of various prebiotic fibres and their impact on the gut microbiota, as a foundation to better define their prebiotic and health promoting effects and guide the best valorization approach of prebiotic rich agro-food waste. 1.1. Evolution of opportunities to rationally valorize vegetable waste and by-products in the omics era Since the introduction of the prebiotic concept, some vegetable waste and by-products already appeared as a rich source of fibre and complex carbohydrates which fit in the original prebiotic definition. However, for a few decades since then only a few categories of carbohydrates were considered to meet the prebiotic criteria, mainly including fructo-oligosaccharides (FOS) and galacto-oligosaccharides (GOS) structures. By that time, most studies deciphered the prebiotic potential of selected substrates or foodstuffs exclusively by investigating their capability to sustain in vitro the growth of a few representative bacterial groups, mainly including Lactobacillus, Lactococcus and Bifidobacterium species. It was soon noted that studying the effect of dietary substrates on single isolated bacterial strains was not the best option to predict the effect these might have on complex gut microbial communities, which can encompass hundreds or thousands of different species/ strains and their innumerable combinations. In this regard, in vitro fecal fermentations aimed at cultivating a complex microbial population under tightly controlled conditions, started to be conducted as a routine laboratory approach to evaluate the effect of selected prebiotics on the human fecal microbiota without ethical constraints (Pham & Mohajeri, 2018). These fermentation models range from very simplified batch systems to highly elaborated continuous and dynamic systems, such as the simulators simgi, SHIME, TIM, PolyFermS, etc, designed to more accurately mimic the whole digestion and/or fermentation processes that take place at one or various phases of the gastrointestinal tract (Verhoeckx et al., 2015). Although such sophisticated and complex model systems have been proposed to resemble in vivo processes, several important limitations to accurately mimic complex carbohydrates digestion have been highlighted (Hern´ andez-Hern´ andez, 2019). Thus, to date most approaches to evaluate the prebiotic potential of selected ingredients or foodstuffs have been focused on fecal fermentation models as briefly described below. Fecal fermentations range from simplified batch fermentations conducted with no control of pH or additional influx of fresh media which are incubated anaerobically, to continuous and computer-controlled vessels that tightly control atmosphere and pH, and automatically feed fresh media to the system. Such systems are used to evaluate over a relatively short period of time (up to 48–72 h) the effect of selected dietary substrates on the microbial populations and their metabolism. Another advantage is that these systems enable to study specifically the effect of a given prebiotic intervention on different population groups, depending on the characteristics of the sample donor. Thus, they might be key to rationally select functional ingredients for specific population groups (Dey, 2017; Fehlbaum et al., 2018). Fecal fermentations based on these in vitro systems coupled to molecular approaches targeted to quantify specific bacterial groups (such as qPCR or FISH) probably represent the most widely used model to evaluate the prebiotic potential of vegetable waste and by-products (Table 2 illustrates the effect of several fermentable carbohydrate fractions on microbial growth, assessed by culture-independent techniques). Most investigations following this approach have only monitored a few defined target microbial populations, including some representative groups of commensal and potentially beneficial bacteria (lactobacilli, Lactococcus, Bifidobacterium, etc.), as well as SCFAs production which result from the microbial metabolism of complex carbohydrates. While our comprehension of the complexity of the intestinal ecosystem began to increase thanks to large international research efforts that exploited next-generation sequencing (NGS) approaches to decipher its composition and functionalities, including MetaHIT, HumanMicrobiome and MyNewGut projects among others, the list of commensal and potentially beneficial bacterial groups started to expand. Thereafter, other target microbial groups started to be tracked while evaluating the prebiotic character of defined substrates in these in vitro models. Among these, Atopobium, Bacteroides/Prevotella group, Clostridium cluster IV and XIVa, C. Sabater et al. Trends in Food Science & Technology 118 (2021) 399–417 402 Table 2 Studies reporting carbohydrate composition (%) and fermentability of vegetable by-products assessed by culture-independent techniques included in the artificial neural network model developed in the current work (Fig. 1 and Supplementary Figs. S1–S3). Reference Carbohydrate Source Extraction Gal Ara Rha Man Fuc Xyl Glc Fru Raf Stch Vrb UA GOS XOS AraOs GlcOs Berger et al. (2014) AXOS Rye Enzymatic 0.90 8.50 0.00 0.00 0.00 28.00 24.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Gull´ on, Gonz´ alez-Mu˜ noz, and Paraj´ o (2011) XOS Barley Hydrothermal 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.30 29.70 51.30 5.10 0.00 Gull´ on et al. (2011) XOS Barley Enzymatic 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.30 29.70 51.30 5.10 0.00 Gull´ on et al. (2011) XOS Barley Enzymatic 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.30 29.70 51.30 5.10 0.00 G´ omez et al. (2014) POS Citrus Chemical 13.43 19.10 0.73 1.79 0.00 0.57 2.41 0.00 0.00 0.00 0.00 42.75 0.00 0.00 0.00 0.00 Leijdekkers et al. (2014) POS Beet Commercial 17.00 16.00 12.00 1.00 1.00 1.00 5.00 0.00 0.00 0.00 0.00 47.00 0.00 0.00 0.00 0.00 Manderson et al. (2005) POS Citrus Chemical 9.59 31.19 2.13 0.00 0.24 2.44 48.12 0.00 0.00 0.00 0.00 6.29 0.00 0.00 0.00 0.00 Ferreira-Lazarte et al. (2018) POS Artichoke Enzymatic 8.20 18.90 7.60 1.00 0.00 1.10 16.70 0.00 0.00 0.00 0.00 46.50 0.00 0.00 0.00 0.00 Ferreira-Lazarte et al. (2018) POS Artichoke Enzymatic 21.10 10.70 5.40 1.20 0.00 2.30 3.90 0.00 0.00 0.00 0.00 55.50 0.00 0.00 0.00 0.00 Ferreira-Lazarte et al. (2018) POS Sunflower Chemical 4.30 1.10 3.20 0.10 0.00 2.20 0.90 0.00 0.00 0.00 0.00 88.10 0.00 0.00 0.00 0.00 Ferreira-Lazarte et al. (2018) POS Sunflower Enzymatic 12.20 2.30 3.20 1.30 0.00 0.90 1.80 0.00 0.00 0.00 0.00 78.20 0.00 0.00 0.00 0.00 G´ omez, Míguez, Veiga, Paraj´ o, and Alonso (2015) AXOS Barley Hydrothermal 0.00 1.50 0.00 0.00 0.00 1.00 5.50 0.00 0.00 0.00 0.00 4.80 0.00 56.20 15.30 5.30 Berger et al. (2014) AXOS Rye Enzymatic 0.90 6.40 0.00 0.00 0.00 15.00 27.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Berger et al. (2014) AXOS Rye Enzymatic 0.80 5.40 0.00 0.00 0.00 14.00 27.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 D´ avila, Gull´ on, Alonso, Labidi, and Gull´ on (2019) Hemicellulose Vine Hydrothermal 0.00 0.05 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.04 0.11 0.37 0.00 0.01 D´ avila et al. (2019) Hemicellulose Vine Hydrothermal 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.04 0.16 0.48 0.00 0.01 D´ avila et al. (2019) Hemicellulose Vine Hydrothermal 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.04 0.17 0.51 0.00 0.01 D´ avila et al. (2019) Hemicellulose Vine Hydrothermal 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.05 0.19 0.57 0.00 0.02 Gull´ on et al. (2014) AXOS Wheat Hydrothermal 0.00 0.06 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.25 0.14 0.20 Gull´ on et al. (2014) AXOS Wheat Hydrothermal 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.30 0.14 0.24 Gull´ on et al. (2014) AXOS Wheat Hydrothermal 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.32 0.15 0.00 Gull´ on et al. (2014) AXOS Wheat Hydrothermal 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.37 0.17 0.00 Di et al. (2017) POS Citrus Chemical 20.20 44.20 3.56 0.00 0.23 2.69 5.77 0.00 0.00 0.00 0.00 23.26 0.00 0.00 0.00 0.00 Li, Xia, Nie, and Shan (2016) POS Citrus Enzymatic 14.80 17.60 3.20 0.00 0.00 1.30 36.90 2.00 0.00 0.00 0.00 24.20 0.00 0.00 0.00 0.00 Berger et al. (2014) AXOS Oat Enzymatic 0.40 4.10 0.00 0.00 0.00 7.00 19.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Berger et al. (2014) AXOS Oat Enzymatic 0.40 3.10 0.00 0.00 0.00 6.00 15.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Berger et al. (2014) AXOS Barley Enzymatic 0.70 2.90 0.00 0.00 0.00 18.00 28.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Campos et al. (2020) Fibre Pineapple Milling 0.66 0.00 0.00 0.00 0.00 0.00 0.11 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Uerlings et al. (2019) Fructans Chicory Commercial 1.49 1.63 0.27 7.72 0.00 0.47 12.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Uerlings et al. (2019) Fructans Chicory Commercial 4.54 8.09 0.93 1.81 0.00 1.91 27.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Uerlings et al. (2019) Fructans and POS Beet Commercial 4.34 0.01 1.15 0.60 0.00 1.36 6.44 0.00 0.04 0.00 0.03 0.00 0.00 0.00 0.00 0.00 Uerlings et al. (2019) Fructans and POS Citrus Commercial 3.56 5.85 1.23 3.06 0.03 1.51 22.55 0.00 0.01 0.00 0.19 0.00 0.00 0.00 0.00 0.00 Uerlings et al. (2019) Fructans and POS Citrus Commercial 3.14 0.00 1.07 0.81 0.07 2.73 2.91 0.00 0.00 0.03 0.02 0.00 0.00 0.00 0.00 0.00 Uerlings et al. (2019) Fructans and POS Citrus Commercial 4.03 6.69 1.35 2.82 0.01 1.87 13.96 0.00 0.02 0.00 0.21 0.00 0.00 0.00 0.00 0.00 Uerlings et al. (2019) Fructans and POS Citrus Commercial 5.06 7.59 1.49 4.15 0.04 2.26 9.62 0.00 0.08 0.29 0.21 0.00 0.00 0.00 0.00 0.00 Uerlings et al. (2019) Fructans and POS Citrus Commercial 5.17 7.77 1.44 4.07 0.04 2.34 8.79 0.00 0.01 0.04 0.13 0.00 0.00 0.00 0.00 0.00 Uerlings et al. (2019) Fructans and POS Apple Commercial 2.53 5.39 0.71 1.19 0.01 2.80 8.17 0.00 0.00 0.04 0.08 0.00 0.00 0.00 0.00 0.00 Uerlings et al. (2019) Fructans and POS Apple Commercial 4.70 7.86 0.77 1.11 0.01 3.65 11.92 0.00 0.00 0.05 0.10 0.00 0.00 0.00 0.00 0.00 Mandalari et al. (2007) POS Bergamot Enzymatic 5.74 10.54 1.90 2.17 0.23 1.28 5.82 0.00 0.00 0.00 0.00 48.26 0.00 0.00 0.00 0.00 P´ erez-L´ opez et al. (2016) Fibre Soy Hydrothermal 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Berger et al. (2014) AXOS Barley Enzymatic 1.00 3.50 0.00 0.00 0.00 18.00 30.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C. Sabater et al. Trends in Food Science & Technology 118 (2021) 399–417 403 Reference Main groups of bacteria stimulated Technique Berger et al. (2014) Akkermansia and Bifidobacterium qPCR Gull´ on et al. (2011) Atopobium, Bacteroides, Bifidobacterium, Clostridium and Eubacterium FISH Gull´ on et al. (2011) Atopobium, Bacteroides, Bifidobacterium, Clostridium and Eubacterium FISH Gull´ on et al. (2011) Atopobium, Bacteroides, Bifidobacterium, Clostridium and Eubacterium FISH G´ omez et al. (2014) Atopobium, Bacteroides, Clostridium, Enterococcus, Eubacterium, Faecalibacterium, lactobacilli, Prevotella and Roseburia FISH Leijdekkers et al. (2014) Bacteroides 16S Manderson et al. (2005) Bacteroides and Bifidobacterium FISH Ferreira-Lazarte et al. (2018) Bacteroides, Bifidobacterium, Enterococcus, lactobacilli and Prevotella FISH Ferreira-Lazarte et al. (2018) Bacteroides, Bifidobacterium, Enterococcus, lactobacilli and Prevotella FISH Ferreira-Lazarte et al. (2018) Bacteroides, Bifidobacterium, Enterococcus, lactobacilli and Prevotella FISH Ferreira-Lazarte et al. (2018) Bacteroides, Bifidobacterium, Enterococcus, lactobacilli and Prevotella FISH G´ omez et al. (2015) Bacteroides, Bifidobacterium, Enterococcus, lactobacilli and Prevotella FISH Berger et al. (2014) Bifidobacterium qPCR Berger et al. (2014) Bifidobacterium qPCR D´ avila et al. (2019) Bifidobacterium FISH D´ avila et al. (2019) Bifidobacterium FISH D´ avila et al. (2019) Bifidobacterium FISH D´ avila et al. (2019) Bifidobacterium FISH Gull´ on et al. (2014) Bifidobacterium qPCR +FISH Gull´ on et al. (2014) Bifidobacterium qPCR +FISH Gull´ on et al. (2014) Bifidobacterium qPCR +FISH Gull´ on et al. (2014) Bifidobacterium qPCR +FISH Di et al. (2017) Bifidobacterium and Eubacterium FISH Li et al. (2016) Bifidobacterium and lactobacilli qPCR Berger et al. (2014) Bifidobacterium and lactobacilli qPCR Berger et al. (2014) Bifidobacterium and lactobacilli qPCR Berger et al. (2014) Bifidobacterium and lactobacilli qPCR Campos et al. (2020) Bifidobacterium and lactobacilli RT-PCR Uerlings et al. (2019) Bifidobacterium, Clostridium and lactobacilli qPCR Uerlings et al. (2019) Bifidobacterium, Clostridium and lactobacilli qPCR Uerlings et al. (2019) Bifidobacterium, Clostridium and lactobacilli qPCR Uerlings et al. (2019) Bifidobacterium, Clostridium and lactobacilli qPCR Uerlings et al. (2019) Bifidobacterium, Clostridium and lactobacilli qPCR Uerlings et al. (2019) Bifidobacterium, Clostridium and lactobacilli qPCR Uerlings et al. (2019) Bifidobacterium, Clostridium and lactobacilli qPCR Uerlings et al. (2019) Bifidobacterium, Clostridium and lactobacilli qPCR Uerlings et al. (2019) Bifidobacterium, Clostridium and lactobacilli qPCR Uerlings et al. (2019) Bifidobacterium, Clostridium and lactobacilli qPCR Mandalari et al. (2007) Bifidobacterium, Eubacterium and lactobacilli FISH P´ erez-L´ opez et al. (2016) Enterococcus and lactobacilli FISH Berger et al. (2014) lactobacilli qPCR C. Sabater et al. Trends in Food Science & Technology 118 (2021) 399–417 404 Faecalibacterium, Lachnospira and Roseburia groups are remarkable. The steady price reduction of NGS techniques and the widest dimension of the data offered from the metataxonomic and metagenomic study of complex microbial communities, has led to its application to examine the global effects that selected ingredients, foodstuffs or drugs can have on the whole microbial community configuration in in vitro fecal fermentation systems. Data derived from these communitybased studies provides a more clear depiction of the effects that the tested ingredients might have not only on a few particular taxa, but on the configuration of the whole ecosystem. This information, when coupled to appropriate genomic and computation tools, can be extremely powerful, enabling even to predict cross-feeding events, that might explain why not all individuals or microbiota configurations respond identically to the same prebiotic intervention (Leshem, Segal, & Elinav, 2020). Similarly, it can aid to identify crucial aspects related to either the fine molecular structure of prebiotics or the basal gut microbiota configuration that might help predict whether an individual’s microbiota will be modulated effectively by a given prebiotic intervention. This knowledge may be pivotal to promote personalized functional nutrition strategies, based on the wide complexity of prebiotic carbohydrate structures and the response of an individual’s microbiota to a given dietary intervention (Kolodziejczyk et al., 2019). Notwithstanding, few studies to date have taken advantage of these multidimensional approximations to study the prebiotic potential of vegetables waste, by-products or ingredients derived thereof, although these will likely represent the most important source of novel prebiotics as the focus on sustainability and circular economy scales (Esposito, Sessa, Sica, & Malandrino, 2020). Some examples in the literature that have demonstrated their utility to guide more rationally the selection of novel functional ingredients from selected agro-food sources have included, for instance, the evaluation of pectins from different citrus fruits or sugarbeet, or sugarcane derived xylo-oligosaccharides (XOS) (Larsen et al., 2019; Leijdekkers et al., 2014; Venema, K., Verhoeven, J., Verbruggen, S., & Keller, 2020). Blooming in the application of NGS techniques in microbiome studies have led to the generation of a vast amount of high-throughput omics data, which is often heterogeneous and difficult to interpret. Therefore, the development of new bioinformatic tools to process results from sequencing experiments and analyze them in an integrative way, taking into consideration as many factors as possible, is needed (Moreno-Indias et al., 2021). In this sense, application of machine learning models, comprising several families of powerful pattern-recognition algorithms, to assist metagenome analysis have been reported (Marcos-Zambrano et al., 2021). Machine learning is widely used for dimensionality reduction of complex metagenomics data in order to assess the distribution of microbial communities across the samples studied (Kobak & Berens, 2019). In addition, machine learning-based classification methods are often used in taxonomic assignment of metagenomic reads to annotate genome sequences (Moreno-Indias et al., 2021), obtainment of metagenome-assembled genomes (Murovec, Deutsch, & Stres, 2020), and classification of biological samples in diagnostic studies where human microbiome can be used as a tool to predict a certain disease (Aryal, Alimadadi, Manandhar, Joe, & Cheng, 2020) or to predict the responsiveness to a given intervention (McCoubrey, Elbadawi, Orlu, Gaisford, & Basit, 2021). Moreover, machine learning allows integrating results from microbiome studies with those from other omics techniques like proteomics and metabolomics that may contain information both from the microbiome and the host, as well as relevant demographic, health or diet metadata. This integrative approach is of special interest to assess biological relevance of features and to discover novel biomarkers (Moreno-Indias et al., 2021). Specific applications of machine learning within the field of probiotics and prebiotics include the optimization of probiotic therapeutics in an artificial human gastrointestinal tract (Westfall et al., 2021), sequence analysis to identify gastrointestinal diseases in patients (Fukui et al., 2020), determination of prebiotic effect of GOS and FOS on human microbiota (Liu et al., 2017), and in silico simulation of colonic fermentation of novel prebiotic structures by elucidating potential mechanisms of action (Sabater, Blanco-Doval, Margolles, Corzo, & Montilla, 2021). However to our knowledge these models have not been applied before to guide novel prebiotics formulation from agro-food waste and by-products. In this review, we will take advantage of advanced predictive computational approaches to integrate the wealth of data retrieved from public databases and scientific literature, in order to reveal associations between fine physico-chemical and structural properties of various prebiotic fibres encountered in vegetable food waste and by-products, and the enzymatic machinery available in gut commensal microbial communities. The ultimate aim is to provide a framework to aid in the rationale design of sustainable and personalized next-generation prebiotic ingredients from agro-food waste and by-products. 2. Revisiting prebiotic carbohydrate structures from vegetable waste streams and by-products: from traditional to emergent prebiotics Non-digestible carbohydrates have been the most studied prebiotic ingredient in waste and by-products derived from agro-food manufacturing processes. These are derived from plant polysaccharides which serve as energy reserves (starch or inulin), structural elements (cellulose, hemicellulose and pectin) or water retaining elements (pectins and alginates), among others. They are abundant in the non-edible parts or in those not utilized during fruit and vegetables transformation into commercial products (leaves and roots, fruit peel, seeds, unused pulp, pomace, hulls, husk, etc.) (Vazquez-Olivo et al., 2019). The prebiotic carbohydrate structures most frequently present in such products include polysaccharides and derived oligosaccharides (OS) which contain from 2 to 13 monosaccharide units bond by O-glycosidic linkages. These OS are classified according to the type of O-glycosidic bond, monomeric composition, branching level, degree of polymerization (DP) or chemical modifications such as methylation or acetylation. Structural properties of OS determine their physical-chemical and prebiotic properties and the specific health benefits exerted, and are thus pivotal so as to consolidate prebiotics incorporation into novel, efficient and even personalized nutrition strategies aimed at modulating the gut microbiota environment (Belorkar & Gupta, 2016). Besides, their abundance in vegetable waste and by-products makes them an affordable source for its manufacturing. The most studied OS from vegetable sources include fructooligosaccharides (FOS); as well as a rapidly growing range of emergent and candidate prebiotic OS with diverse composition and chemical structures. The latest are receiving increasing interest, as they could expand the functional properties and health promoting applications of vegetable-derived OS and include xylo-oligosaccharides (XOS), arabinoxylo-oligosaccharides (AXOS), isomalto-oligosaccharides (IMOS), α -galacto-oligosaccharides ( α -GOS), pectin-derived oligosaccharides (POS) or cello-oligosaccharides (COS), among others (Table 1). In addition to OS, some polysaccharides like pectin have also been considered as emerging prebiotics. Most of these carbohydrates are naturally encountered in vegetable products as described in the following sections, although they can also be chemically/enzymatically modified or rationally selected to further improve their functional properties. For this purpose, a deep knowledge on the microbial communities required to cooperatively ferment these ingredients is crucial. The main structural characteristics, sources and manufacture methods of the most common dietary OS found in vegetable and fruit waste and by-products with evidence on gut microbiota modulatory effects are compiled in Table 1 and briefly described below. FOS and β-GOS have probably received the largest attention as traditional prebiotic carbohydrates, although their effect has been mainly studied on specific target microbial groups, including bifidobacteria and lactobacilli. FOS are result of the transfructosylation of C. Sabater et al. Trends in Food Science & Technology 118 (2021) 399–417 405 sucrose or of the partial hydrolysis of inulin, a fructan polymer with a high DP, widely encountered in wheat, onions, bananas, asparagus, chicory, garlic and leek, being frequently extracted from artichoke and chicory containing waste, among others. Structurally, they are the only prebiotic carbohydrates predominantly composed of fructose units, a sugar moiety non-frequently fermented by gut microbial species. Concerning FOS production, de la Rosa et al. (2019) have recently highlighted a great variety of agro-food waste and by-products that could be exploited either for the FOS cost-efficient production or to produce FOS synthesizing enzymes (Tables 1 and 2). On the other hand, β-GOS are the result of lactose extension, though different GOS structures have been described from various vegetable sources such as α -GOS, from soya beans or sugar beet molasses (Martinez-Villaluenga & Frías, 2014). Whereas structural variations in the DP, branching or chemical modifications of FOS or GOS have been described, the impact of such variations on their modulatory properties on the human gut microbiota has received limited attention (Carlson et al., 2018). An expanded range of prebiotic carbohydrates abundant in agro-food waste and structurally diverse is receiving increasing interest in recent years as emergent and candidate prebiotics. Among those, pectin, the main constituent of vegetable cell walls, and POS resulting from its depolymerization, represent a group of highly heterogeneous carbohydrate structures with promising functional attributes (Babbar, Dejonghe, Gatti, Sforza, & Elst, 2016). These are particularly abundant in waste and by-products derived from manufacturing of citrus fruits, apple pomace, or sugar beet, among others (Tables 1–3). The general structure of pectins is a galacturonic acid chain of variable length that can be disrupted by rhamnose alternating with this acid. The former domain is the homogalacturonan (HG) and the latter, that include ramifications of different monomeric composition such as arabinans, galactans and arabinogalactans, is the rhamnogalacturonan I (RG-I). Additional chemical modifications of this structure include partial methyl-esterification at C6 and partial acetyl-esterification at O2 and O3 in the galacturonic acid, that modifies the physical-chemical and functional properties of the corresponding pectin and POS (Larsen et al., 2019). According to its methyl-esterification degree, pectin can be classified as low-methoxyl pectins (<50%, LM) and high-methoxyl pectins (>50%, HM), with different functional properties in terms of gelation and microbiota modulatory capacity. For instance, a low degree of methyl-esterification and high neutral sugar contents has been reported to enhance pectin and POS fermentative properties (Ferreira-Lazarte et al., 2018; G´ omez, Gull´ on, Y´ a˜ nez, Schols, & Alonso, 2016). Less frequent pectin structures include rhamnogalacturonan II (RGII), which contains galacturonic acid, rhamnose, galactose and unusual sugars, such as apiose, aceric acid, 3-deoxy-lyxo-2-heptuloasaric (DHA) and 3-deoxy-manno-2-octulosonic acid; and xylo-galacturonan (XGA) (Zandleven et al., 2007). On the other hand, POS can be obtained by partial depolymerization of pectins by physical-chemical procedures (hydrothermal treatments, with high temperature/pressure conditions; acid hydrolysis), enzymatic or combined processes (Babbar et al., 2016; Mu˜ noz-Almagro, Rico-Rodriguez, Wilde, Montilla, & Villamiel, 2018). Source and depolymerization methods of pectins are the factors most influencing their molecular structure and, consequently, their bioactivity (Sabater et al., 2021). Other structural characteristics including DP, degree of methyl-esterification, ramification level, and chemical substitutions determine the particular gut microbiota modulatory activities and, the overall response of the community (Larsen et al., 2019). To highlight this point, Bacteroides group seems to be preferentially promoted by LM pectins as opposed to HM pectins, whereas F. praustnizii appears to grow preferably on fractions containing HG over RG-I fractions. Similarly, the presence of residues such as arabinose, which is utilized by a limited amount of gut commensals, can modulate species containing arabinofuranosides activities, such as some Prevotella, Lachnospira or Bifidobacterium species (Larsen et al., 2019). In this sense, it has been described that arabinose-rich rhamnogalacturonic acid present in pectic compounds from sunflower and artichoke play a major role in stimulating bifidobacteria while enzymatic reduction of the pectin molecular weight led to an increase in the stimulation of Bifidobacterium and Lactobacillus (Ferreira-Lazarte et al., 2018). Moreover, studies performed on a dynamic gastrointestinal simulator revealed that fermentation of citrus pectin stimulated the growth of Bifidobacterium spp., Bacteroides spp. and Faecalibacterium prausnitzii (Ferreira-Lazarte, Moreno, Cueva, Gil-S´ anchez, & Villamiel, 2019). However, apart from these examples, to date limited efforts have attempted to establish solid causal relationships between particular structural characteristics of pectin and their accessibility and fermentability by different members of the gut microbial ecosystem. Other examples of emergent prebiotics include XOS, which are naturally present in bamboo shoots, honey, fruits and vegetables (Table 1) although at concentrations generally too low to show any prebiotic effects. Instead, they are industrially produced by xylan hydrolysis of a wide variety of lignocellulosic biomass by chemical and enzymatic methods, being the latter preferred in the food industry because of the lack of undesirable side reactions and products (Vazquez, Alonso, Domınguez, & Parajo, 2000). The production of XOS from agricultural waste or by-products offers great scope to the nutraceutical and food industries as the raw material is cheap and abundantly available with a large number of sources (Tables 1–3) (Samanta et al., 2015). XOS are considered as emerging or candidate prebiotics (Santib´ a˜ nez et al., 2021) and the International Association of Probiotics and Prebiotics (ISAPP) recognized XOS as prebiotic OS in the latest update of its prebiotic definition (Gibson et al., 2017). Human and animal studies have demonstrated that XOS are efficient prebiotics, as doses as low as 1.4 and 2.8 g per day in adults can significantly change the microbiota producing significant increases in Bifidobacterium counts as compared to the placebo subjects (Finegold et al., 2014; Na & Kim, 2013). Meanwhile, total anaerobic and Bacteroides fragilis group counts were significantly higher in the 2.8 g per day XOS group and no significant differences in the counts of Lactobacillus, Enterobacteriaceae and Clostridium were found at any of the two XOS doses (Finegold et al., 2014). Finally, XOS, with a DP ranging from 2 to 7, obtained from corn cobs via enzyme-catalysed hydrolysis, have recently received the EFSA approval regarding the safety of their use as novel foods (EFSA Panel on Dietetic Products, Nutrition and Allergies, 2018). AXOS represent another group of emergent prebiotics highly abundant in lignocellulosic materials, such as by-products derived from wheat, oat, corn and bran manufacturing such as brewer’s spent grain (Tables 1–3). They are obtained by the hydrolysis of arabinoxylans, or “heteroxylans”, which constitute the major hemicellulosic component in the cell wall of some cereal plants, such as wheat, rice and barley, among others (Mathew, Aronsson, Karlsson, & Adlercreutz, 2018). Their basic backbone is represented by a β(1 → 4) linked xylose polymer which can either contain simple arabinosyl branches or more complex side groups including glucuronic acid or its 4-O-methyl derivative, acetyl groups, galactose or xylose residues. The presence of these modifications mainly depends on the xylan source and overall, may confer different biological properties to the polymer (Aachary & Prapulla, 2011). Whereas there exists limited information on the particular effect that different AXOS structures may have on the human gut microbiota, a few research works have provided evidence towards this point. For instance, Mendis and colleagues have demonstrated that, even close bacterial species such as those belonging to the genus Bacteroides, exhibited different substrates specificity and capacity to utilize varied AXOS structures (Mendis, Martens, & Simsek, 2018). Indeed, AXOS include complex and heterogeneous structures thus the cooperative action of various xylan degrading activities is required to achieve its complete degradation including β-D-xylosidases (EC 3.2.1.37), α -L-arabinofuranosidases (EC 3.2.1.55), α -D-glucuronidases (EC 3.2.1.139), and several esterases (Pollet, Delcour, & Courtin, 2010). Thus, while few commensal species hold full xylanolytic capacity, AXOS access by first degraders such as specific Bacteroides species, would release products that may support C. Sabater et al. Trends in Food Science & Technology 118 (2021) 399–417 406 Table 3 Studies including information on fractions from vegetable by-products reported in the literature, where carbohydrate composition of substrates was not properly determined but vegetable waste source, types of polyand oligosaccharides present, treatments selected and fermentability assessed by culture-independent techniques were reported. Each entry in the table refers to each one of the by-products fractions tested in the referenced studies. This Table was used to generate Fig. 2 and Supplementary Material Figs. S4–S6. Reference Carbohydrate Source Extraction Main groups of bacteria stimulated Technique Bindels et al. (2015) POS Commercial Commercial Anaeroplasma, Anaerostipes, Bacteroides, Bifidobacterium, Roseburia qPCR Vandeputte et al. (2017) Fructans Commercial Commercial Anaerostipes, Bifidobacterium, Bilophila 16S Hoving et al. (2018) MOS Commercial Commercial Bacteroides 16S Monteagudo-Mera et al. (2016) GOS Synthesis Enzymatic Bacteroides, Bifidobacterium, Clostridium, Lactobacillus 16S Reis et al. (2014) AXOS Barley Ultrasound Bacteroides, Bifidobacterium, Prevotella qPCR Reis et al. (2014) AXOS Barley Chemical Bacteroides, Bifidobacterium, Prevotella qPCR Van den Abbeele et al. (2018) AXOS Commercial Commercial Bacteroides, Bifidobacterium, Prevotella 16S Van den Abbeele et al. (2018) Fructans Commercial Commercial Bacteroides, Bifidobacterium, Prevotella 16S Míguez, Vila, Venema, Paraj´ o, and Alonso (2020) Galactoglucomannooligosaccharides Pinus Hydrothermal Bacteroides, Blautia, Desulfovibrio, Mogibacterium, Oscillospira, Methanobrevibacter, Ruminococcus, Suterella 16S Míguez et al. (2020) Galactoglucomannooligosaccharides Pinus Hydrothermal Bacteroides, Blautia, Mogibacterium 16S Larsen et al. (2019) Pectin Commercial Commercial Bacteroides, Butyrivibrio, Citrobacter, Clostridium, Coprococcus, Desulfovibrio, Enterobacter, Enterococcus, Kleibsella, Lachnospira, Mogibacterium, Oscillospira, Prevotella 16S Larsen et al. (2019) Pectin Commercial Commercial Bacteroides, Butyrivibrio, Citrobacter, Clostridium, Coprococcus, Lachnospira, Mogibacterium, Oscillospira, Prevotella 16S Larsen et al. (2019) Pectin Commercial Commercial Bacteroides, Butyrivibrio, Citrobacter, Clostridium, Desulfovibrio, Enterobacter, Enterococcus, Kleibsella, Lachnospira, Mogibacterium, Oscillospira, Prevotella 16S Larsen et al. (2019) Pectin Commercial Commercial Bacteroides, Citrobacter, Clostridium, Desulfovibrio, Enterobacter, Enterococcus, Kleibsella, Mogibacterium, Oscillospira, Ruminococcus 16S Larsen et al. (2019) Pectin Commercial Commercial Bacteroides, Citrobacter, Clostridium, Desulfovibrio, Enterococcus, Lachnospira, Mogibacterium, Oscillospira, Prevotella 16S S´ ayago-Ayerdi, Zamora-Gasga, and Venema (2020) Fructans Agave Ultrafiltration Bacteroides, Faecalibacterium, Fusicatenibacter, Mogibacterium 16S Nsor-Atindana et al. (2020) Celullose Commercial Ultrasound Bifidobacterium 16S Nsor-Atindana et al. (2020) Celullose Commercial Ultrasound Bifidobacterium 16S Nsor-Atindana et al. (2020) Celullose Commercial Ultrasound Bifidobacterium 16S Nsor-Atindana et al. (2020) Celullose Commercial Ultrasound Bifidobacterium 16S S´ ayago-Ayerdi, Zamora-Gasga, and Venema (2019) Fibre Mango Enzymatic Bifidobacterium 16S Fehlbaun et al. (2018) GOS Commercial Commercial Bifidobacterium 16S Fehlbaun et al. (2018) GOS Commercial Commercial Bifidobacterium 16S Fehlbaun et al. (2018) XOS Sugarcane Commercial Bifidobacterium 16S Wu et al. (2017) IMOS Commercial Commercial Bifidobacterium 16S Kjølbæk et al. (2020) AXOS Commercial Commercial Bifidobacterium, Blautia, Dorea, Eubacterium, Faecalibacterium 16S Venema, Verhoeven, Verbruggen, and Keller (2020) XOS Sugarcane Commercial Bifidobacterium, Blautia, Faecalibacterium, Lachnospira, Paraprevotella, Roseburia, Ruminococcus 16S S´ ayago-Ayerdi et al. (2020) Fructans Hibiscus Ultrafiltration Bifidobacterium, Catenibacterium, Collinsella, Fusicatenibacter, Mogibacterium 16S Azcarate-Peril et al. (2017) GOS Commercial Commercial Bifidobacterium, Christensenellaceae, Faecalibacterium, Lachnospira, Lactobacillus 16S Bindels et al. (2015) Fructans Commercial Commercial Bifidobacterium, Coprobacillus, Roseburia qPCR Wu et al. (2017) IMOS Commercial Commercial Bifidobacterium, Dialister 16S Saman et al. (2017) ND Rice Enzymatic Bifidobacterium, Lactobacillus FISH Wang et al. (2017) GOS Commercial Commercial Bifidobacterium, Lactobacillus 16S Wang et al. (2017) IMOS Commercial Commercial Bifidobacterium, Lactobacillus 16S Wang et al. (2017) FOS Commercial Commercial Bifidobacterium, Lactobacillus, Parabacteroides 16S Fehlbaun et al. (2018) Fructans Commercial Commercial Bifidobacterium, Ruminococcus 16S Larsen et al. (2019) Pectin Commercial Commercial Butyrivibrio, Citrobacter, Clostridium, Coprococcus, Lachnospira, Mogibacterium, Oscillospira, Prevotella 16S Larsen et al. (2019) Pectin Commercial Commercial Butyrivibrio, Citrobacter, Clostridium, Enterobacter, Enterococcus, Faecalibacterium, Lachnospira, Mogibacterium, Oscillospira, Prevotella 16S Larsen et al. (2019) Pectin Commercial Commercial Butyrivibrio, Citrobacter, Clostridium, Faecalibacterium, Lachnospira, Mogibacterium, Oscillospira, Prevotella 16S Larsen et al. (2019) Pectin Commercial Commercial Butyrivibrio, Citrobacter, Desulfovibrio, Faecalibacterium, Lachnospira, Mogibacterium, Oscillospira, Prevotella 16S Larsen et al. (2019) Pectin Commercial Commercial Butyrivibrio, Citrobacter, Enterobacter, Faecalibacterium, Lachnospira, Mogibacterium, Oscillospira, Prevotella 16S Míguez et al. (2020) POS Citrus Hydrothermal Faecalibacterium 16S Maukonen et al. (2017) Fibre Barley Enzymatic Lachnospira, Ruminococcus qPCR Fehlbaun et al. (2018) β-glucan Oat Commercial Prevotella, Ruminococcus 16S C. Sabater et al. Trends in Food Science & Technology 118 (2021) 399–417 407 other neighboring species within the ecosystem, such as Bifidobacterium, that cannot grow on xylan but can cross-feed on sugars/metabolites released by xylanolytic bacteria (Zeybek, Rastall, & Buyukkileci, 2020). In this way, based on their specific structural characteristics AXOS hold interesting capacity to sustain a large number of members in the gut microbial ecosystem, yet limited efforts have attempted to establish solid associations between their fine structural characteristics and their bioactivity on the gut microbiota. COS are linear OS consisting of 2–6 glucose moieties bonded by β(1 → 4) linkages that can be obtained from lignocellulosic biomass (cellulose fraction) residues (such as corn cobs) through a controlled enzymatic hydrolysis process (Karnaouri, Matsakas, Krikigianni, Rova, & Christakopoulos, 2019). However, COS industrial production in high yield and with proper DP control remains challenging (Zhong, Luley-Goedl, & Nidetzky, 2019). From a bioactivity perspective, COS are drawing increasing attention as recent evidence has shown they can exhibit favorable prebiotic effects on certain species, such as Clostridium butyricum, Lactococcus lactis subsp. lactis, Lactobacillus paracasei subsp. paracasei, and Lb. rhamnosus (Zhong, Ukowitz, Domig, & Nidetzky, 2020). IMOS are branched α -gluco-oligosaccharides obtained from a great variety of saccharified vegetable starch, one of the most abundant and renewable polysaccharides available (Madsen, Stanley, Swann, & Oswald, 2017). Depending on the starch source, the enzymes used and the process conditions, the IMOS preparations can vary in type of glycosidic linkage and DP. Recent evidence has demonstrated that IMOS are slowly fermentable fibres with the ability to modulate the activity and composition of the human colonic microbiota (Gu et al., 2018; Wu et al., 2017). 3. Data mining for the rationale selection of prebiotic structures from sustainable sources: examples of its application The abundance in vegetable waste of non-digestible carbohydrates, capable to sustain and specifically promote cornerstone microbial populations in the human gut, based on their particular chemical composition, makes them a convenient source for the rationale design of novel, sustainable and flexible prebiotics. Available evidence suggests that prebiotic effects might be strongly dependent on the particular physicalchemical structure of complex carbohydrates, which can vary based on their source and methodology for extraction. In any case, most studies have neglected the fine physico-chemical properties of the substrates tested and have not performed comparative analyses to assess the effect of variable structures of a given prebiotic family. Besides, there is currently an underexploited wealth of data scattered through literature, concerning physico-chemical properties of prebiotic substrates and novel OS isolated from vegetable by-products, information on their effects on gut microbial populations and whole genome sequences of a growing list of gut commensals with potential probiotic traits. Translating all this information into well-designed further research that may support knowledge-based formulation of functional ingredients derived from food waste and by-products, urges for integrative analyses which at the moment are sorely lacking. In this regard, advanced computational methods could be tools of great relevance to develop a framework towards elucidating structure-activity relationships that may explain differences in the modulatory effect of a wide range of vegetable byproducts on gut microbiota. Within this frame, in this work we have taken advantage of a series of advanced computational tools to conduct an in-depth analysis of the available information in the literature and genome publicly available datasets to help advance our understanding on the potential of vegetable waste as a source of emergent, sustainable, robust and flexible prebiotics. 3.1. Analysis of the influence of carbohydrate sources and composition on microbial growth In order to demonstrate the applicability of our proposed approach for data mining in this field, a novel data analysis based on two complementary approaches is proposed focused on both experimental data on OS composition and fermentability, and on genomics data analyses. For the first set of analyses, we collected from the literature composition and fermentability data of fermentable carbohydrates present in vegetable by-products, comprising both polyand OS (Tables 2 and 3), most of which have also exhibited potential prebiotic effects. Then, machine learning algorithms were used to elucidate characteristic patterns in the carbohydrate composition that may relate to the observed stimulation of specific groups of commensal and potentially beneficial bacteria. On the second approach based on genomics analysis, functional annotation of a relatively large set of microbial genomes (n =210) was performed to investigate novel glycosidase domains that may explain differences in the fermentation profiles of several commensal and potentially beneficial species. As explained, the first data analysis strategy relies on mining data obtained through in vitro fermentation experiments performed by previous authors (Tables 2 and 3), to investigate associations between carbohydrate characteristics and selective stimulation of commensal and potentially beneficial bacteria. To gather complete datasets containing structure-activity information, 15 articles reporting both detailed carbohydrate composition of vegetable by-products and microbial growth determined by culture-independent techniques (FISH, qPCR and 16S rRNA sequencing) were retrieved (Table 2). Since most of these works describe in vitro fermentation assays of several fractions isolated from the same by-product, this selection resulted in a total of 41 fractions analyzed which were included in our comparative study. By-product composition data following hydrolysis were collected considering up to 16 carbohydrates: arabinose (Ara), fructose (Fru), fucose (Fuc), galactose (Gal), glucose (Glc), mannose (Man), raffinose (Raf), rhamnose (Rha), stachyose (Stch), verbascose (Vrb), xylose (Xyl), uronic acids (UA), arabino-oligosaccharides (AraOs), α -galacto-oligosaccharides (GOS), β-gluco-oligosaccharides (GlcOs), and xylooligosaccharides (XOS) (Table 2). It should be noted that monosaccharide contents included in this study refer to the monomeric composition values determined following hydrolysis of complex and likely undigestible polysaccharides present in the agro-industrial byproducts considered, and thus these contents do not correspond to free sugar values. In addition, by-product fractions could be divided into 13 major groups according to the genera promoted during fermentation: 1) mainly Bifidobacterium (Bif); 2) mainly lactobacilli (Lac); 3) Bifidobacterium and lactobacilli (Bif-Lac); 4) Akkermansia and Bifidobacterium (Akk-Bif); 5) Atopobium, Bacteroides, Bifidobacterium, Clostridium and Eubacterium (Ato-Bac-Bif-Clo-Eub); 6) Atopobium, Bacteroides, Clostridium, Enterococcus, Eubacterium, Faecalibacterium, lactobacilli, Prevotella and Roseburia (Ato-Bac-Clo-Ent-Eub-Fae-Lac-Pre-Ros); 7) mainly Bacteroides (Bac); 8) Bacteroides and Bifidobacterium (Bac-Bif); 9) Bacteroides, Bifidobacterium, Enterococcus, lactobacilli and Prevotella (BacBif-Ent-Lac-Pre); 10) Bifidobacterium, Clostridium and lactobacilli (BifClo-Lac); 11) Bifidobacterium and Eubacterium (Bif-Eub); 12) Bifidobacterium, Eubacterium and lactobacilli (Bif-Eub-Lac); 13) Enterococcus and lactobacilli (Ent-Lac) (Table 2). Composition patterns leading to these 13 fermentation profiles were elucidated through machine learning algorithms, specifically artificial neural network-based principal component analysis (PCA) (Fig. 1). In this analysis, principal components (PC) are new variables that are constructed as linear combinations or mixtures of the initial variables (i. e. carbohydrate composition of by-products). Neural network-based PCA has been designed to reconstruct experimental data (e.g. carbohydrate composition data reported in the bibliography, Table 2) by combining classical PCA technique with artificial neural networks (Stacklies, Redestig, Scholz, Walther, & Selbig, 2007). Briefly, artificial C. Sabater et al. Trends in Food Science & Technology 118 (2021) 399–417 414 commensal microbes as well as their ability to metabolyse complex carbohydrate structures. In our work, we have shown that substrates showing high uronic acid contents like pectin may selectively stimulate the growth of Bacteroides, Enterococcus and Prevotella. In addition, we propose that pectin promotes beneficial bacteria that could exert synergistic metabolic interactions with Citrobacter, Mogibacterium and Oscillospira. Furthermore, glycosidase profiles characterized by the presence of polygalacturonases, rhamnosidases, rhamnogalacturonases, pectin lyases and pectin methyland acetyl esterases are associated with reference genomes from species belonging to the genera Bacteroides, Enterococcus and Prevotella. Remarkably, both computational analyses provide complementary interpretations of these results that are in agreement with experimental evidence. Indeed, these approaches can shed some light on the potential microbiota-modulatory activities of the diverse range of carbohydrates that could be represented in diet and also on the presence of cross-feeding mechanisms among co-existing bacterial species, as well as about the structure-function relationship among prebiotic carbohydrates. Finally, we would like to emphasize that the application of machine learning and computational methods in the field of prebiotics and microbiota allows us to envisage a future scenario where the approaches that we have collated in this review can be scaled up and applied to an extensive number of gut commensal populations and valorizable prebiotic sources. Thus, we will be able to make a rational selection of the most suitable prebiotic substrates for specific microbial groups, or even symbiotic formulations, based on genomic and metagenomic data, prediction of metabolic maps and the structural characteristics of the substrates. This will make possible to carry out preclinical and clinical tests aimed at promoting the growth of microorganisms of interest without the need to perform previous in vitro fermentation tests. These methods could also be applied to evaluate not only the composition of the microbiota but also its activity, such as the production of bioactive metabolites. This approach would represent a significant advance to formulate personalized nutrition strategies. Declaration of competing interest There is no conflict of interest in this article. Acknowledgements The work in our research groups was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 818368 (MASTER), and the grants RTI 2018-095021-JI00 (funded by (MCIU/AEI/FEDER, UE), AGL 2017-84614-C2-1-R and AGL 2016-78311-R (funded by (MINECO/AEI/FEDER, UE). 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