Food literacy : time for a new term or just another buzzword?

Eating is an essential everyday life activity that has fascinated, captivated and defined society since time began. We currently exist in a society where over-consumption of food is an established risk factor chronic disease, the rate of which is increasing alarmingly. 'Food literacy' is an emerging term used to describe what we, as individuals and as a community know and understand about food and how to use it to meet our need, and thus potentially support and empower citizens to make healthy food choices. What exactly the components of food literacy are and how they influence food choice are poorly defined and understood, but increasingly gaining interest among health professionals, policy makers, community workers, educators and members of the public. This paper will build the argument for why concepts of 'food literacy' need to extend beyond existing terms and measures used in the literature to describe the food skills and knowledge needed to make use of public health nutrition messages.

Pet food from bovine origin drying by green heat pump technology and fluidization

Bovine intestine was dried in a heat pump fluid bed combination. Minimum fluidisation velocity was calculated by Ergun Equation and some relations were established.

HCI & sustainable food culture : a design framework for engagement

The current food practices around the world raises concerns for food insecurity in the future. Urban / suburban / and peri-urban environments are particularly problematic in their segregation from rural areas where the natural food sources are grown and harvested. Soaring urban population growth only deteriorates the lack of understanding in and access to fresh produce for the people who live, work, and play in the city. This paper explores the role of Human-Computer Interaction (HCI) design in encouraging individual users to participate in creating sustainable food cultures in urban environments. The paper takes a disciplinary perspective of urban informatics and presents five core constituents of the HCI design framework to encourage sustainable food culture in the city via ubiquitous technologies: the perspective of transdisciplinarity; the domains of interest of people, place, and technology; and the perspective of design.

Advancing design for sustainable food cultures

Our paper, “HCI & Sustainable Food Culture: A Design Framework for Engagement,” presented at the 2010 NordiCHI conference, introduced a design framework for understanding engagement between people and sustainable food cultures (Choi and Blevis, 2010). Our goal for this chapter “Advancing Design for Sustainable Food Cultures” is to expand our notion of this design framework and the programme of research it implies. This chapter presents the three elements of design framework for sustainability: (i) engagement across disciplines; (ii) engagement with and amongst users/non-users and; (iii) engagement for sustained usability. The uses a corresponding sample of photographic records of experiences that reflect three key issues in the current sustainable food domain: respectively, (i) context of food cultures, (ii) farmers’ markets, and (iii) producing food.

The relationship between substrate metabolism, exercise and appetite control : does glycogen availability influence the motivation to eat, energy intake or food choice?

The way in which metabolic fuels are utilised can alter the expression of behaviour in the interests of regulating energy balance and fuel availability. This is consistent with the notion that the regulation of appetite is a psychobiological process, in which physiological mediators act as drivers of behaviour. The glycogenostatic theory suggests that glycogen availability is central in eliciting negative feedback signals to restore energy homeostasis. Due to its limited storage capacity, carbohydrate availability is tightly regulated and its restoration is a high metabolic priority following depletion. It has been proposed that such depletion may act as a biological cue to stimulate compensatory energy intake in an effort to restore availability. Due to the increased energy demand, aerobic exercise may act as a biological cue to trigger compensatory eating as a result of perturbations to muscle and liver glycogen stores. However, studies manipulating glycogen availability over short-term periods (1-3 days) using exercise, diet or both have often produced equivocal findings. There is limited but growing evidence to suggest that carbohydrate balance is involved in the short-term regulation of food intake, with a negative carbohydrate balance having been shown to predict greater ad libitum feeding. Furthermore, a negative carbohydrate balance has been shown to be predictive of weight gain. However, further research is needed to support these findings as the current research in this area is limited. In addition, the specific neural or hormonal signal through which carbohydrate availability could regulate energy intake is at present unknown. Identification of this signal or pathway is imperative if a casual relationship is to be established. Without this, the possibility remains that the associations found between carbohydrate balance and food intake are incidental.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Less food for thought : impact of attentional instructions on intrusive thoughts about snack foods

Intrusive thoughts about food may play a role in unhealthy eating behaviours. Food-related thoughts that capture attention can lead to craving and further intrusive thoughts (Kavanagh, Andrade, & May, 2005). We tested whether diverting attention to mental images or bodily sensations would reduce the incidence of intrusive thoughts about snack foods. In two experiments, participants reported their thoughts in response to probes during three 10 min periods. In the Baseline and Post-task period, participants were asked to let their mind wander. In the middle, Experimental, period, participants followed mind wandering (Control), thought diversion, or Thought Suppression instructions. Self-directed or Guided Imagery, Mindfulness-based Body Scanning, and Thought Suppression all reduced the proportion of thoughts about food, compared to Baseline. Following Body Scanning and Thought Suppression, food thoughts returned to Baseline frequencies Post-task, rather than rebounding. There were no effects of the interventions upon craving, although overall, craving and thought frequency were correlated. Thought control tasks may help people to ignore thoughts about food and thereby reduce their temptation to snack.

Developments in texture-based vector field visualisation techniques

Vector field visualisation is one of the classic sub-fields of scientific data visualisation. The need for effective visualisation of flow data arises in many scientific domains ranging from medical sciences to aerodynamics. Though there has been much research on the topic, the question of how to communicate flow information effectively in real, practical situations is still largely an unsolved problem. This is particularly true for complex 3D flows. In this presentation we give a brief introduction and background to vector field visualisation and comment on the effectiveness of the most common solutions. We will then give some examples of current development on texture-based techniques, and given practical examples of their use in CFD research and hydrodynamic applications.

Household food expenditure and its contribution to socioeconomic inequalities in purchasing foods consistent with Australian dietary guideline recommendations

Aim: To examine the amount of money spent on food by household income, and to ascertain whether food expenditure mediates the relationship between household income and the purchase of staple foods consistent with Australian dietary guideline recommendations. ----- ----- Methods: In face-to-face interviews (n = 1003, 66.4% response rate), households in Brisbane, Australia were asked about their purchasing choices for a range of staple foods, including grocery items, fruits and vegetables. For each participant, information was obtained about their total weekly household food expenditure, along with their sociodemographic and household characteristics. ----- ----- Results: Household income was significantly associated with food expenditure; participants residing in higher-income households spent more money on food per household member than those from lower-income households. Lower income households were less likely to make food purchasing choices of dietary staples that were consistent with recommendations. However, food expenditure did not attenuate the relationship between household income and the purchase of staple foods consistent with dietary guideline recommendations. ----- ----- Conclusions: The findings suggest that food expenditure may not contribute to income inequalities in purchasing staple foods consistent with dietary guideline recommendations: instead, other material or psychosocial factors not considered in the current study may be more important determinants of these inequalities. Further research should examine whether expenditure on non-staple items and takeaway foods is a larger contributor to socioeconomic inequalities in dietary behavior.

Digital image processing techniques for pavement macro-texture analysis

Road surface macro-texture is an indicator used to determine the skid resistance levels in pavements. Existing methods of quantifying macro-texture include the sand patch test and the laser profilometer. These methods utilise the 3D information of the pavement surface to extract the average texture depth. Recently, interest in image processing techniques as a quantifier of macro-texture has arisen, mainly using the Fast Fourier Transform (FFT). This paper reviews the FFT method, and then proposes two new methods, one using the autocorrelation function and the other using wavelets. The methods are tested on pictures obtained from a pavement surface extending more than 2km's. About 200 images were acquired from the surface at approx. 10m intervals from a height 80cm above ground. The results obtained from image analysis methods using the FFT, the autocorrelation function and wavelets are compared with sensor measured texture depth (SMTD) data obtained from the same paved surface. The results indicate that coefficients of determination (R2) exceeding 0.8 are obtained when up to 10% of outliers are removed.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.