A lot of effort has been made in times to improve the  understanding and functioning of the human gastrointestinal tract and to  engineer novel in-vitro models with  greater biomechanical and biochemical relevance (Boulby et al. 1999; Faas et  al. 2002; Kunz et al. 2005; Goetze et al. 2007, 2009; Kwiatek et al. 2006;  Marciani et al. 2001a, 2007, 2012; Marciani 2011; Schwizer et al. 2002, 2006;  Steingoetter et al. 2005; Treier et al. 2006; Mackie et al. 2013).Without physical  models that can closely represent digestion “near real”, in-vitro to in-vivo  correlation will leave a gap that will cloud the true understanding of the digestion  processes. In-vitro digestion studies are highly desired because of their  uses in food and nutrition (Frei et al., 2003; Hur et al., 2011; Guerra et al.,  2012) and highly advantageous in saving time and cost, and have demonstrated  some levels of reproducibility when compared to clinical studies (in-vivo) in (Kong  and Singh 2008a, 2008b; Kong and Singh 2010). Performing in-vitro  digestion can take the path of either a static or dynamic simulation (Guerra et  al., 2012 and Alegria et al., 2015). The static approach is very simple, and do  not simulate the physical processes of digestion (Alegria et al., 2015) such  as, shearing, mixing, hydration and peristalsis (Fernandez-Garcia et al, 2009  and Wickham et al., 2009), whereas the dynamic simulation does. More recent,  there are strong approaches to standardized (Minekus et al., 2014) and  harmonize (Egger et al., 2016) in-vitro  digestion using static models, where the main task is to improve comparing  results across research teams. As such, proposal has been made to address on  static digestion in-vitro models to  remove barriers such as using enzymes from different origins, food bolus to  secretion ratios (intestinal fluids) and variations in pH. However, even with  this effort, dynamic digestion models are still favored, because of the ability  to closely mimic digestion to model the gastrointestinal complexities that will  aid in better interpretation of results (Hur et al., 2011; Guerra et al.,  2012).     Absorption of nutrients are critical for health and  sustain functionality of cells and human wellbeing (Borgström et al., 1957;  Guyton and Hall, 2015). This phenomenon takes place in the small intestine  where as little as 10% of the total micro-nutrients from bulk  digest escapes the absorption process (Guerra et al., 2012). The mechanisms  removing up to 9/10ths of the nutrient content from bulk across the epithelium  border during digestion in the body are not clearly understood. The challenge  to global health is clear, and absorption of any nutrient in less or large  amounts can be detrimental to health, leading to diet-related diseases, such as  diabetes (Jenkins et al., 2002) and obesity (WHO, 2000). However, in order to  optimize foods for health benefits, knowledge underlying digestion on the  molecular scale is needed to understand how foods are degraded (Norton, 2007)  to mobilise nutrients for absorption (bio-accessible and bioavailable) during  the digestion process in the bowels (Jenkins et al., 1981; Englyst et al., 1996).      A lot of in-vitro procedures simulate intestinal digestion  by simply mixing food and intestinal fluids using an over-head mixer (Oomen and  others 2003), a shaking bath (Muir and O’Dea 1992), or magnetic stirrer (De  Boever and others 2001). Clearly, these procedures oversimplify the mixing  process, and will not reproduce the fluid mechanics and the mechanical forces  that the digesta would face in the small intestine resulting from contractions  of the gut wall. Evidence strongly supports that, the environment brought about from fluid-mechanical events  during digestion have a crucial function on the material response to making  nutrient becoming biologically available (Dikeman et al. 2006; Lentle and  Janssen, 2010). Similarly, until recently, most dynamic models which were  used to perform bowel digestions, had rarely account for the interaction effects  of segmentation and peristaltic forces on the food digestibility and nutrient  transport (Guerra et al., 2012;  Gouseti et al., 2014; Tharakan et al., 2010), when reporting product  transportation and intestinal absorption.     Some of these  complex dynamic models able to simulate aspects of intestinal digestion include  the TIM, a gastro intestinal model, simulating peristalsis and absorption (Minekus  et al., 1995 and Kheadr et al., 2010), the soft tubular bio-inspired reactor  model, which simulate peristalsis frequency and amplitude to understand mass  transfer and mixing (Deng et al., 2016), and the human duodenum model (HDM),  which mimics the geometry of the human duodenum, consisting of ascending and  descending regions capable of irregular segmenting patterns to understand  transit rate (Wright et al., 2016). These models clearly simulate one or two  aspects of intestinal digestion, but not all or almost real. A small intestine  model (SIM), developed by Tharakan (2010) was improved by Gouseti (2014) (the  Dynamic Duo) which was further improved to the dynamic duodenal model (DDM). It  has enhanced features and is able to simulate most aspects of small intestinal  digestion in-vitro (Tharakan et al.,  2010 and Gouseti et al., 2014). Longland (1991) has stated that an effective in-vitro model should incorporate the  following: sequential and physiological relevant use of enzymes; appropriate pH  and simulated GI fluids; the elimination of the digested product; suitable  biological transit times and mixing at each compartment for each step of  digestion. The improved dynamic duodenal model was designed with these  functionalities.     If the digested  contents were to simply propel through the small intestine, then the digestion  and absorption processes would have been poorly performed, as admix of enzymes  to digesta would be partial, and bulk digesta would rarely come in contact with  epithelium cells for absorption to take place (Hall., 2015; Ganong and Ganong, 1995).  Mixing is crucial to the digestion process in the small intestine bowel, facilitating these reactions and  transportation (Dikeman et al.  2006; Lentle and Janssen, 2010). Mixing is facilitated by two main  processes, a combination of segmentation or peristalsis or either (Hall.,  2015). However, segmentation is known to have a more powerful effect on mixing  (Stoll et all, 2000; Ganong and Ganong, 1995 and Barrett et al., 2010) than do  peristalsis, and is often overlook in most dynamic in-vitro models (Gouseti et al., 2014). This research explores these  effects on these contributions on digestibility and absorption while varying  the digest’s viscosity. Manipulating the luminal content’s viscosity may affect  digestibility, and has shown to alter physiologic responses (Dikeman et al.,  2007; Dorota et al., 2012; Edmund et al., 2014; Pernille et al., 2013). The  soft tubular material of the dynamic duodenal model vessel allows the walls and  inner lumen to be actively engaging in the mixing and reactions and simulated  mass transfer during digestion. It also has at pancreatic duct positioned to  release intestinal fluids onto the incoming pyloric outflow.     Overall the  model was developed, and the new tool attempts to create a realistic  environment to perform intestinal digestion. To date, the dynamic duodenal  model has been used to investigate digestibility changes experienced by  different food products during digestion, and also to study the effect of  intestinal motility on the outcomes of the digestion processes, such as glycaemic  indices.