1890 Research Article Received: 5 December 2008 Revised: 6 May 2009 Accepted: 7 May 2009 Published online in Wiley Interscience: 16 June 2009 (www.interscience.wiley.com) DOI 10.1002/jsfa.3669 Quantification of uncertainty using Bayesian and bootstrap models to simulate the impact of nitrogen fertilisation on β -glucan levels in barley Marta Fontana, a* Enda Cummins, b Stefano Buiatti c and Alessandro Sensidoni c Abstract BACKGROUND: β -Glucans have enjoyed renewed interest as a functional food ingredient, with current attention focused on optimising β -glucan levels in finished products without compromising final product quality. In order to measure the uncertainty about the level of β -glucans in barley, two different statistical methods (Bayesian inference and Bootstrap technique) were applied to measured levels of β -glucan in three different varieties of barley grain (n = 83). RESULTS: The resulting probability density distributions were similar for the full data set and also when applied to smaller sample sizes, highlighting the potential for either method in quantifying the total uncertainty in β -glucan levels. Bayesian inference was used to model the effect of nitrogen treatment on β -glucan and protein contents in barley. The model found that a low level of fertilisation (50 kg N ha -1 ) did not have a significant effect on β -glucan or protein content. However, fertilisation above this level did result in an increase in β -glucan and protein levels, the effect seeming to plateau at 100 kg N ha -1 . In addition, the uncertainty distributions were significantly different for two consecutive years of data, highlighting the potential environmental influence on β -glucan content. CONCLUSION: The model developed in this study could be a useful tool for processors to quantify the uncertainty about the initial level of β -glucan in barley and to evaluate the influence of environmental factors, thus enabling them to formulate their ingredient base to optimise levels of β -glucan without compromising final product quality. c  2009 Society of Chemical Industry Keywords: bootstrap; Bayesian; β -glucan; protein; nitrogen fertilisation INTRODUCTION β -Glucan β -Glucans are components of cell walls of cereal grains such as barley and oats. The principal uses of barley are for malting, brewing and as animal feed in the form of ground meal. Barley is a cereal under-utilised as an ingredient in processed human food, and β -glucan has a significant influence on the technological use of the cereal. In the brewing industry a high level of β -glucan forms highly viscous solutions, which affects filtration performance. Moreover, it is an antinutritive factor in animal feeds, in particular influencing nutrient uptake and body weight gain. Recent attention has focused on the potential use of β -glucan from barley as a functional dietary fibre. 1 Consumption of β -glucans has a positive effect on the human body, resulting in attenuation of blood glucose and insulin in humans while improving the glycaemic index (GI). 2 It also provides a reduction in total cholesterol and low-density lipoprotein (LDL) cholesterol in humans and animals. 3 The effect of β -glucan on blood glucose and insulin levels has been evaluated in a number of studies involving traditional processed foods (bread, pasta, flour, porridge). 4 Several technological processes have been employed over the years to enrich the level of β -glucan in flour. β -Glucan-enriched flour can be substituted for wheat flour in white bread and can result in significant health benefits for the consumer. Thus there has been considerable interest in the level of β -glucan required in order to be of significant nutritional benefit without compromising final product quality. 4,5 It is therefore important to accurately estimate the level of β -glucan in grain for two reasons: 1) to ensure an acceptable rheological quality and to produce baked foods such as pasta and bread with acceptable sensory properties; ∗ Correspondence to: Marta Fontana, Department of Agricultural and Environ- mental Sciences, University of Udine, Udine, Italy. E-mail: marta.fontana@uniud.it a Department of Agricultural and Environmental Sciences, University of Udine, Udine, Italy b School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Dublin, Ireland c Department of Food Science, University of Udine, Udine, Italy J Sci Food Agric 2009; 89: 1890–1896 www.soci.org c  2009 Society of Chemical Industry