Fundamental studies of sourdoughs fermented with Weissella cibaria and Lactobacillus plantarum: influence on baking characteristics, sensory profiles and in vitro starch digestibility of gluten-free breads

The application of sourdough can improve texture, structure, nutritional value, staling rate and shelf life of wheat and gluten-free breads. These quality improvements are associated with the formation of organic acids, exopolysaccharides (EPS), aroma or antifungal compounds. Initially, the suitability of two lactic acid bacteria strains to serve as sourdough starters for buckwheat, oat, quinoa, sorghum and flours was investigated. Wheat flour was chosen as a reference. The obligate heterofermentative lactic acid bacterium (LAB) Weissella cibaria MG1 (Wc) formed the EPS dextran (a α-1,6-glucan) from sucrose in situ with a molecular size of 106 to 107 kDa. EPS formation in all breads was analysed using size exclusion chromatography and highest amounts were formed in buckwheat (4 g/ kg) and quinoa sourdough (3 g/ kg). The facultative heterofermentative Lactobacillus plantarum FST1.7 (Lp) was identified as strong acidifier and was chosen due to its ubiquitous presence in gluten-free as well as wheat sourdoughs (Vogelmann et al. 2009). Both Wc and Lp, showed highest total titratable acids in buckwheat (16.8 ml; 26.0 ml), teff (16.2 ml; 24.5 ml) and quinoa sourdoughs (26.4 ml; 35.3 ml) correlating with higher amounts of fermentable sugars and higher buffering capacities. Sourdough incorporation reduced the crumb hardness after five days of storage in buckwheat (Wc -111%), teff (Wc -39%) and wheat (Wc -206%; Lp -118%) sourdough breads. The rate of staling (N/ day) was reduced in buckwheat (Ctrl 8 N; Wc 3 N; Lp 6 N), teff (Ctrl 13 N; Wc 9 N; Lp 10 N) and wheat (Ctrl 5 N; Wc 1 N; Lp 2 N) sourdough breads. Bread dough softening upon Wc and Lp sourdough incorporation accounted for increased crumb porosity in buckwheat (+10.4%; +4.7), teff (+8.1%; +8.3%) and wheat sourdough breads (+8.7%; +6.4%). Weissella cibaria MG1 sourdough improved the aroma quality of wheat bread but had no impact on aroma of gluten-free breads. Microbial shelf life however, was not prolonged in any of the breads regardless of the starter culture used. Due to the high prevalence of insulin-dependent diabetes mellitus particular amongst coeliac patients, glycaemic control is of great (Berti et al. 2004). The in vitro starch digestibility of gluten-free breads with and without sourdough addition was analysed to predict the GI (pGI). Sourdough can decrease starch hydrolysis in vitro, due to formation of resistant starch and organic acids. Predicted GI of gluten-free control breads were significantly lower than for the reference white wheat bread (GI=100). Starch granule size was investigated with scanning electron microscopy and was significantly smaller in quinoa flour (<2 μm). This resulted in higher enzymatic susceptibility and hence higher pGI for quinoa bread (95). Lowest hydrolysis indexes for sorghum and teff control breads (72 and 74, respectively) correlate with higher gelatinisation peak temperatures (69°C and 71°C, respectively). Levels of resistant starch were not increased by addition of Weissella cibaria MG1 (weak acidifier) or Lactobacillus plantarum FST1.7 (strong acidifier). The pGI was significantly decreased for both wheat sourdough breads (Wc 85; Lp 76). Lactic acid can promote starch interactions with gluten hence decreasing starch susceptibility (Östman et al. 2002). For most gluten-free breads, the pGI was increased upon sourdough addition. Only sorghum and teff Lp sourdough breads (69 and 68, respectively) had significantly decreased pGI. Results suggest that the increase of starch hydrolysis in gluten-free breads was related to mechanism other than presence of organic acids and formation of resistant starch.

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique.

CloudNeg: An autonomous multi issue negotiation system, with preference elicitation component, for trading cloud services

Cloud services provide its users with flexible resource provisioning. But in the current market, a user has to choose from a limited set of configurations at a fixed price. This paper presents an autonomous negotiation system termed CloudNeg for negotiating cloud services. CloudNeg provides buyers and sellers of cloud services with autonomous agents to negotiate on the specifications of a cloud instance, including price, on their behalf. These agents elicit their buyers’ time preferences and use them in negotiations. Further, this paper presents two artifacts: a negotiation algorithm and a prototype which together form CloudNeg.

Valuing maternity care: a comparison of stated preference methods with an application to cost-benefit analysis

This research investigates whether a reconfiguration of maternity services, which collocates consultant- and midwifery-led care, reflects demand and value for money in Ireland. Qualitative and quantitative research is undertaken to investigate demand and an economic evaluation is performed to evaluate the costs and benefits of the different models of care. Qualitative research is undertaken to identify women’s motivations when choosing place of delivery. These data are further used to inform two stated preference techniques: a discrete choice experiment (DCE) and contingent valuation method (CVM). These are employed to identify women’s strengths of preferences for different features of care (DCE) and estimate women’s willingness to pay for maternity care (CVM), which is used to inform a cost-benefit analysis (CBA) on consultant- and midwifery-led care. The qualitative research suggests women do not have a clear preference for consultant or midwifery-led care, but rather a hybrid model of care which closely resembles the Domiciliary Care In and Out of Hospital (DOMINO) scheme. Women’s primary concern during care is safety, meaning women would only utilise midwifery-led care when co-located with consultant-led care. The DCE also finds women’s preferred package of care closely mirrors the DOMINO scheme with 39% of women expected to utilise this service. Consultant- and midwifery-led care would then be utilised by 34% and 27% of women, respectively. The CVM supports this hierarchy of preferences where consultant-led care is consistently valued more than midwifery-led care – women are willing to pay €956.03 for consultant-led care and €808.33 for midwifery-led care. A package of care for a woman availing of consultant- and midwifery-led care is estimated to cost €1,102.72 and €682.49, respectively. The CBA suggests both models of care are cost-beneficial and should be pursued in Ireland. This reconfiguration of maternity services would maximise women’s utility, while fulfilling important objectives of key government policy.

Preference inference based on Pareto models

In this paper, we consider Preference Inference based on a generalised form of Pareto order. Preference Inference aims at reasoning over an incomplete specification of user preferences. We focus on two problems. The Preference Deduction Problem (PDP) asks if another preference statement can be deduced (with certainty) from a set of given preference statements. The Preference Consistency Problem (PCP) asks if a set of given preference statements is consistent, i.e., the statements are not contradicting each other. Here, preference statements are direct comparisons between alternatives (strict and non-strict). It is assumed that a set of evaluation functions is known by which all alternatives can be rated. We consider Pareto models which induce order relations on the set of alternatives in a Pareto manner, i.e., one alternative is preferred to another only if it is preferred on every component of the model. We describe characterisations for deduction and consistency based on an analysis of the set of evaluation functions, and present algorithmic solutions and complexity results for PDP and PCP, based on Pareto models in general and for a special case. Furthermore, a comparison shows that the inference based on Pareto models is less cautious than some other types of well-known preference model.