Three-dimensional food printing: Its readiness for a food and nutrition insecure world

Three-dimensional (3D) food printing is a rapidly emerging technology offering unprecedented potential for customised food design and personalised nutrition. Here, we evaluate the technological advances in extrusion-based 3D food printing and its possibilities to promote healthy and sustainable eating. We consider the challenges in implementing the technology in real-world applications. We propose viable applications for 3D food printing in health care, health promotion and food waste upcycling. Finally, we outline future work on 3D food printing in food safety, acceptability and economics, ethics and regulations. .

Collaborative design and feasibility assessment of computational nutrient sensing for simulated food-intake tracking in a healthcare environment

One in four older adults (65 years and over) are living with some form of malnutrition. This increases their odds of hospitalization four-fold and is associated with decreased quality of life and increased mortality. In long-term care (LTC), residents have more complex care needs and the proportion affected is a staggering 54% primarily due to low intake. Tracking intake is important for monitoring whether residents are meeting their nutritional needs however current methods are time-consuming, subjective, and prone to large margins of error. This reduces the utility of tracked data and makes it challenging to identify individuals at-risk in a timely fashion. While technologies exist for tracking food-intake, they have not been designed for use within the LTC context and require a large time burden by the user. Especially in light of the machine learning boom, there is great opportunity to harness learnings from this domain and apply it to the field of nutrition for enhanced food-intake tracking. Additionally, current approaches to monitoring food-intake tracking are limited by the nutritional database to which they are linked making generalizability a challenge. Drawing inspiration from current methods, the desires of end-users (primary users: personal support workers, registered staff, dietitians), and machine learning approaches suitable for this context in which there is limited data available, we investigated novel methods for assessing needs in this environment and imagine an alternative approach. We leveraged image processing and machine learning to remove subjectivity while increasing accuracy and precision to support higher-quality food-intake tracking. This thesis presents the ideation, design, development and evaluation of a collaboratively designed, and feasibility assessment, of computational nutrient sensing for simulated food-intake tracking in the LTC environment. We sought to remove potential barriers to uptake through collaborative design and ongoing end user engagement for developing solution concepts for a novel Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) system while implementing the technology in parallel. More specifically, we demonstrated the effectiveness of applying a modified participatory iterative design process modeled from the Google Sprint framework in the LTC context which identified priority areas and established functional criteria for usability and feasibility. Concurrently, we developed the novel AFINI-T system through the co-integration of image processing and machine learning and guided by the application of food-intake tracking in LTC to address three questions: (1) where is there food? (i.e., food segmentation), (2) how much food was consumed? (i.e., volume estimation) using a fully automatic imaging system for quantifying food-intake. We proposed a novel deep convolutional encoder-decoder food network with depth-refinement (EDFN-D) using an RGB-D camera for quantifying a plate’s remaining food volume relative to reference portions in whole and modified texture foods. To determine (3) what foods are present (i.e., feature extraction and classification), we developed a convolutional autoencoder to learn meaningful food-specific features and developed classifiers which leverage a priori information about when certain foods would be offered and the level of texture modification prescribed to apply real-world constraints of LTC. We sought to address real-world complexity by assessing a wide variety of food items through the construction of a simulated food-intake dataset emulating various degrees of food-intake and modified textures (regular, minced, puréed). To ensure feasibility-related barriers to uptake were mitigated, we employed a feasibility assessment using the collaboratively designed prototype. Finally, this thesis explores the feasibility of applying biophotonic principles to food as a first step to enhancing food database estimates. Motivated by a theoretical optical dilution model, a novel deep neural network (DNN) was evaluated for estimating relative nutrient density of commercially prepared purées. For deeper analysis we describe the link between color and two optically active nutrients, vitamin A, and anthocyanins, and suggest it may be feasible to utilize optical properties of foods to enhance nutritional estimation. This research demonstrates a transdisciplinary approach to designing and implementing a novel food-intake tracking system which addresses several shortcomings of the current method. Upon translation, this system may provide additional insights for supporting more timely nutritional interventions through enhanced monitoring of nutritional intake status among LTC residents

Super tasters and mighty movers: extending The Food Friends® messages into early elementary school

2013 Spring.Includes bibliographical references.The prevalence of childhood obesity has been increasing over the last thirty years for preschool aged children, two to five years of age, as well as among early elementary aged children, six to eleven years of age. The epidemic nature of this problem has led to the creation of multiple programs and intervention targeted at preschoolers aimed at preventing these upward trends into early elementary school and adulthood. The preschool years are particularly important for the development of eating habits along with the development of gross motor skills. Behavior change has been seen within this age group following interventions, but retention of such behaviors as the children progress into kindergarten and first grade has not been as well documented. The overall purpose of this project was to develop "booster" programming for kindergarten and first grade classrooms that extends the messages from The Food Friends®, preschool nutrition and physical activity programs, into early elementary school in an effort to sustain behavior changes made in preschool. To ascertain the best method for implementing a program into the classrooms, surveys and interviews were conducted with a convenient sample of kindergarten and first grade teachers. Survey questions were mailed; follow-up telephone interviews were conducted with a subsample of respondents. Findings guided the development and implementation of the "booster" programming in kindergarten classes. Process evaluation surveys were conducted to assess the fidelity of program and guide the development of the second year of programming and modifications to Year 1. The main themes found from the formative surveys and interviews included: 1) nutrition was not a consistent lesson topic; 2) physical activity was left for gym class and/or recess; and 3) the need for nutrition and activity messages/lessons to be incorporated into academic subject areas. A 5 unit "booster" program, based on Social Cognitive Theory, was developed utilizing The Food Friends® characters and themes of 'Super Tasters' and 'Mighty Movers'. Classroom-based lessons, with accompanying posters and banners for the cafeteria and gym, were implemented in two schools from December 2011 to April 2012. Process evaluation surveys were conducted online with teachers after each unit for fidelity and overall impressions of lessons/activities; interviews were conducted one-on-one with Extension agents. Findings included: 1) all agreed that they enjoyed the "booster" program; 2) it was helpful to have an Extension agent come to the classroom; and 3) few completed lessons intended to be taught by classroom teachers. Appropriate modifications to Year 1's program guided the development of Year 2 programming, slated for implementation in 2012-13 school year. The efficacy of the "booster" programming on behaviors will be evaluated as part of a larger longitudinal study. The ability to resonate messages of trying new foods and being more active within kindergarten and first grade students will contribute to the establishment of healthful behaviors at a young age, building the foundation of lifelong healthy lifestyles

Mom-O-Meter: A self-help pregnancy Android app

The goal of this project was to develop a self-help Android application to aid pregnant women in achieving healthy weight gain during pregnancy. Using Scrum, an agile software development approach, the team gathered requirements, designed, and implemented a smartphone application utilizing the Android and Google Health platforms. This application empowers women to take their health into their own hands, and has the potential of reducing short-term and long-term health risks associated with gestational weight gain for both mother and child. This application is an example of continued advancement of mobile technologies in healthcare, which drives the shift from a reactionary to preventative treatment paradigm

Characterization of Human Gut Microbiota Dynamics Using Model Communities in Gnotobiotic Mice

The human gut is colonized by a diverse array of microbes, collectively referred to as the microbiota. The microbiota\u27s complexity poses significant challenges in characterizing the rules dictating its assembly, inferring the functional roles of its component species, and understanding how communities sense and respond to changes in their habitat. We developed defined, representative model communities comprised of sequenced human gut bacteria that could be characterized in a highly controlled manner in gnotobiotic mice, plus a suite of scalable molecular tools for assaying community properties. These tools were first used to evaluate how the microbiota is impacted by probiotic bacterial strains found in fermented milk products: FMP). Introduction of a consortium of five FMP strains resulted in only minimal changes in the structural configuration of a 15-member model microbiota. However, RNA-Seq and follow-up mass spectrometry revealed numerous functional responses, many related to carbohydrate metabolism. Results from a study performed in monozygotic twin pairs confirmed many of our observations in the model microbiota, showing that lessons learned from preclinical models can inform the design and interpretation of human studies. In a second set of experiments, we evaluated the impact of food on both a model community and its constituent taxa by feeding gnotobiotic mice oscillating diets of disparate composition. In addition to prompt and reversible structural reconfigurations suggesting rules-based diet effects, we noted consistent, staggered changes in the representation of many functions within the metatranscriptome related to carbohydrate and amino acid metabolism. One prominent community member, Bacteroides cellulosilyticus WH2, was identified as an adaptive forager that tailors its versatile carbohydrate utilization strategy to the dietary polysaccharides available. The specific carbohydrates that trigger expression of many of this organism\u27s 113 predicted polysaccharide utilization loci were identified by RNA-Seq analysis during in vitro growth on 31 distinct carbohydrate substrates, aiding our interpretation of in vivo RNA-Seq and high resolution proteomics data. These results offer insight into how gut microbes adapt to dietary perturbations, both at a community level and from the perspective of a well-adapted symbiont with exceptional saccharolytic capabilities, and illustrate the value of studying defined models of the human gut microbiota

Ill. teach. home econ. (1973)

Description based on: Vol. 17, no. 2 (Nov.-Dec. 1973); title from cover.Education index 0013-1385 -1992Current index to journals in education 0011-3565Bibliography of agriculture 0006-153