Binaphthalene Boronic Acid Sensor for Saccharides and d‐Fructose Determination in Beverages

Abstract

The presence of electron rich compounds such as amines added to the fluorescent methoxybinaphthalene boronic acid results in a dramatic increase in affinity of diols to the aryl boronic acid as well as in the augmented fluorescence response. This is likely the result of the change in boron geometry upon coordination with electron donor which facilitate the diols binding. Here, we demonstrate the role of amino alcohol additive in binding of saccharides by boronic acid‐based fluorescent sensor. We show that this strategy allows a poorly responsive sensor to become a highly sensitive probe for the detection of sugars, which could be used for classification of saccharides as well as for quantitative analysis in DMSO‐water solutions. The simple binaphthalene boronic acid sensor was particularly sensitive to d‐fructose (Ka=2.08×106), which allowed for identification of commercial sweetened beverages based on their d‐fructose content. The same method was successfully used for the quantitative analysis of d‐fructose in soft beverages.

A Highly Accurate pH Detection Method for Sweat Analysis using a Printed 96‐Microwell Colorimetric Sensor Array

“…The collected data matrix was treated by the Student's t-test to exclude 2 repetitive data as outliers from 8 repetitive data. The inset data was subsequently data-processed by LDA, [27][28][29] which enables classifying the components of inset datasets based on the recognition of similarities among the components along with the decreasing of multi-dimensional inset data. Figure 3 displayed pH-dependent LDA canonical score plots containing 6 repetitive datasets.…”

“…[23][24][25][26] The color gradient of pH indicators on the 96-microwell PCSAD upon changing pH conditions is quickly captured by a portable digital recorder, followed by data processing based on imaging analysis. The acquired dataset is subsequently treated by computational methods (linear discriminant analysis (LDA), [27][28][29] support vector machine (SVM), [30,31] etc. ), and unknown pH values in sweat could be predicted precisely.…”