Nature has inspired an emergent supramolecular field of synthetic receptor arrays and assays for the pattern-based recognition of various bioanalytes and metal species. The synthetic receptors are not necessarily selective for a particular analyte, but the combined signal response from the array is diagnostic for the analyte. This tutorial review describes recent work in the literature for this emerging supramolecular field and details basic array and assay design principles. We review the analytes targeted, signaling types used, and pattern recognition. Developing specific receptors for the solution-based analysis of complex analytes and mixtures is a daunting task. A solution to this difficult task has been inspired by nature's use of arrays of receptors in the senses of taste and smell. An emerging field within supramolecular chemistry is the use of synthetic and readily available receptors in array formats for the detection of analytes in solution. Each receptor in a differential array does not necessarily have selectivity for a particular analyte, but the combined fingerprint response can be extracted as a diagnostic pattern visually, or using chemometric tools. This new genre of molecular recognition is advancing rapidly with several groups developing novel array platforms and receptors.

Statistical analysis techniques such as principal component analysis (PCA) and discriminant analysis (DA) have become an integral part of data analysis for differential sensing. These multivariate statistical tools, while extremely versatile and useful, are sometimes used as “black boxes”. Our aim in this paper is to improve the general understanding of how PCA and DA process and display differential sensing data, which should lead to the ability to better interpret the final results. With various sets of model data, we explore several topics, such as how to choose an appropriate number of hosts for an array, selectivity compared to cross-reactivity, when to add hosts, how to obtain the best visually representative plot of a data set, and when arrays are not necessary. We also include items at the end of the paper as general recommendations which readers can follow when using PCA or DA in a practical application. Through this paper we hope to present these statistical analysis methods in a manner such that chemists gain further insight into approaches that optimize the discriminatory power of their arrays.

Supramolecular chemistry has branched into many different subfields, finding practical applications in sensing, molecular imaging, metal extraction, and drug delivery.