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The advent of the alternative sweeteners market has signaled a demand for chemosensors which target multiple saccharides and saccharide derivatives, in aqueous media at physiological pH. This demand has largely been unmet as existing molecular receptors for saccharides have generally not shown sufficient degrees of affinity and selectivity in aqueous media. A chemosensor array for saccharides and saccharide derivatives, fully operational in aqueous media at physiological pH, has been developed and is reported herein. Boronic acid based peptidic receptors, derived from a combinatorial library, served as the cross-reactive sensor elements in this array. The binding of saccharides to these receptors was assessed colorimetrically using an indicator uptake protocol in the taste-chip platform. The differential indicator uptake rates of these receptors in the presence of saccharides were exploited in order to identify patterns within the data set using linear discriminant analysis. This chemosensor array is capable of classifying disaccharides and monosaccharides as well as discriminating compounds within each saccharide group. Disaccharides have also been distinguished from closely related reduced-calorie counterparts. This linear discriminant analysis set was then employed as a training set for identifying a specific saccharide in a real-world beverage sample. The methodology developed here augurs well for use in other real-world samples involving saccharides as well as for sensing other desired analytes.

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The paper proposes two Bayesian approaches to non-parametric monotone function estimation. The first approach uses a hierarchical Bayes framework and a characterization of smooth monotone functions given by Ramsay that allows unconstrained estimation. The second approach uses a Bayesian regression spline model of Smith and Kohn with a mixture distribution of constrained normal distributions as the prior for the regression coefficients to ensure the monotonicity of the resulting function estimate. The small sample properties of the two function estimators across a range of functions are provided via simulation and compared with existing methods. Asymptotic results are also given that show that Bayesian methods provide consistent function estimators for a large class of smooth functions. An example is provided involving economic demand functions that illustrates the application of the constrained regression spline estimator in the context of a multiple-regression model where two functions are constrained to be monotone. Copyright (c) 2009 Royal Statistical Society.

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