The pet food industry is an important sector of the pet care market that is growing rapidly. Whilst the number of new and innovative products continues to rise, research and development to assess product performance follows traditional palatability methodology. Pet food palatability research focuses on the amount of food consumed through use of one-bowl and two-bowl testing, but little understanding is given to why differences are observed, particularly at a fundamental ingredient level. This review will highlight the key differences in feeding behaviour and nutritional requirements between dogs and cats. The dominant pet food formats currently available and the ingredients commonly included in pet foods are also described. The current methods used for assessing pet food palatability and their limitations are outlined. The opportunities to utilise modern analytical methods to identify complete foods that are more palatable and understand the nutritional factors responsible for driving intake are discussed.

Human gut microbiota can provide comprehensive information about the health of a host but the tools to collect microbiome samples are not currently available. A standalone wireless robotic capsule that has been developed in this study, collects the microbiota both from lumen (capsule surrounding) and intestinal wall (mucosa layer) for the first time. First, a two-way shape memory alloy (SMA) spring actuation system was developed by tackling the high-drain current requirement of SMAs. The actuator can produce up to 800 mN force that was sufficient to collect samples. Second, successful encapsulation of the collected sample to avoid contamination was realised by testing 3 main sealing materials. Third, the robotic capsule was tested in a gut simulator that mimics in-vivo environment to ensure successful and safe travel of the capsule along the gastrointestinal tract. Finally an in vitro experimental setup that keeps an intestine alive for 6 hours was used to optimise the sample collection. The capsule collected 128 $\mu$ L and 107 $\mu$ L samples (which are sufficient quantities for microbiome analysis) from duodenual and ileal tissues of a sheep. The proposed robotic capsule has a potential to become a vital apparatus for clinicians to sample human and animal gut in the future.

The diet of the domestic dog has changed significantly from that of its wolf ancestor, with to date only two studies having examined macronutrient self‐selection in dogs. Whilst the first focused solely on protein intake, determining an intake of 30% metabolisable energy (ME), the second investigated dietary protein, fat and carbohydrate (PFC), indicating an intake ratio of 30:63:7% by energy. This study's aim was to further elucidate macronutrient intake by providing greater macronutrient range, energy content, and to investigate over a longer duration than previous studies. Fifteen adult dogs were given access to three wet diets providing 500% of daily ME, twice daily over 10 days. The diets were nutritionally complete and formulated using the same four ingredients in different proportions to supply high levels of protein (58% ME), fat (86% ME) or carbohydrate (54% ME). Overall fat and carbohydrate consumption significantly declined from 6,382 to 917 kcals per day (p < 0.001) and 553 to 214 kcals day−1 (p < .01) respectively. Protein intake, however, remained constant over the study and ranged from 4,786 to 4,156 kcals day−1. Such results impacted on percentage total energy intake, with fat decreasing from 68% to 52% (p < .001) and protein increasing from 29% to 44% (p < .01). Our findings suggest that dogs still possess a “feast or famine” mentality, wherein energy dense fat is prioritised over protein initially. With continued feeding over 10 days, a transition to a more balanced energy contribution from both macronutrients is evident. The study also shows that given the option, dogs do not select carbohydrate to be a significant portion of the diet. The health implications of such dietary selection are of interest.

Using robotic capsules for assessing gut health has been an emerging field since the early 2000s with researchers attempting to perform diagnosis, monitoring, and therapeutic functions inside the gut. The knowledge of peristaltic forces inside the intestine are crucial for designing the actuation mechanism of robotic capsules, however, the impact of peristalsis on a capsule has not yet been quantified. In this work, an analytical model is presented to study the peristaltic movement of the small intestine. For the first time, finite element simulations were conducted in COMSOL multiphysics to generate intestinal peristaltic forces, and analyze their impact on a robotic capsule. A capsule prototype (30 × φ12 mm) was developed to measure the peristaltic forces from living intestinal tissue, while an embedded system was used simultaneously to record the live data from the capsule-intestine interaction. In in vitro experiments, the intestine applied an average axial force of 226 mN and contraction cycles of 9 times/min, while the capsule prototype experienced maximum radial force of 180 mN. A specialized in vitro setup is developed to keep fresh ex vivo intestine samples alive for up to 6 h, while the capsule prototype measured the intestinal forces from the living tissue. This in vitro experimental setup provided an excellent model for the in vivo environment in terms of generating peristaltic movements, hence this force analysis will help in developing efficient prototypes for locomotion, anchoring, localization, biopsy, drug delivery, and sampling mechanisms for robotic capsules.

Animal behaviour can be an indicator of health and welfare. Monitoring behaviour through visual observation is labour-intensive and there is a risk of missing infrequent behaviours. Twelve healthy domestic shorthair cats were fitted with triaxial accelerometers mounted on a collar and harness. Over seven days, accelerometer and video footage were collected simultaneously. Identifier variables (n = 32) were calculated from the accelerometer data and summarized into 1 s epochs. Twenty-four behaviours were annotated from the video recordings and aligned with the summarised accelerometer data. Models were created using random forest (RF) and supervised self-organizing map (SOM) machine learning techniques for each mounting location. Multiple modelling rounds were run to select and merge behaviours based on performance values. All models were then tested on a validation accelerometer dataset from the same twelve cats to identify behaviours. The frequency of behaviours was calculated and compared using Dirichlet regression. Despite the SOM models having higher Kappa (>95%) and overall accuracy (>95%) compared with the RF models (64–76% and 70–86%, respectively), the RF models predicted behaviours more consistently between mounting locations. These results indicate that triaxial accelerometers can identify cat specific behaviours.