Quantification of uncertainty using Bayesian and bootstrap models to simulate the impact of nitrogen fertilisation on β‐glucan levels in barley

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. Copyright © 2009 Society of Chemical Industry