A simple method for the determination of enantiomeric excess and identity of chiral carboxylic acids.

The association between an achiral copper(II) host (1) and chiral carboxylate guests was studied using exciton-coupled circular dichroism (ECCD). Enantiomeric complexes were created upon binding of the enantiomers of the carboxylate guests to the host, and the sign of the resultant CD signal allowed for determination of the configuration of the studied guest. The difference in magnitudes and shapes of the CD signals, in conjunction with linear discriminant analysis (LDA), allowed for the identity of the guest to be determined successfully. A model was created for the host-guest complexes which successfully predicts the sign of the observed CD signal. Further, Taft parameters were used in the model, leading to rationalization of the observed magnitudes of the CD signals. Finally, the enantiomeric excess (ee) of unknown samples of three chiral carboxylic acid guests was determined with an average absolute error of ±3.0%.

A food freshness sensor using the multistate response from analyte-induced aggregation of a cross-reactive poly(thiophene).

A single cross-reactive conjugated polymer generates a multidimensional response capable of identifying and differentiating between 22 structurally similar and biologically relevant amines with 97% accuracy in a highly competitive aqueous environment. Statistical analysis on an array of wavelengths was used to assess the viability of this approach. In a separate investigation, the multidimensional response from a single cross-responsive poly(thiophene) has been analyzed using a different ratiometric method to quantify the amount of biogenic amine present in a fish matrix, thereby evaluating the quality of the food.

Multi-layered analyses using directed partitioning to identify and discriminate between biogenic amines.

Multiple layers of statistical analyses were used to decipher the response from a single, cross-reactive conjugated polymer (1) providing enhanced classification accuracies over traditional multivariate statistical approaches. This analysis was demonstrated by classifying a series of seven biologically relevant, nonvolatile amines (i.e. biogenic amines). If only a single layer of analysis was employed (linear discriminant analysis), 89% classification accuracy was achieved lacking any concentration information. However, using this multi-layered, group-ungroup method, the analytes were first categorized based on general class of molecule (directed partitioning), i.e. aromatic, aliphatic, polyamines, with 98% accuracy. In a second analysis layer, these sub-groups were broken down into the individual molecular components, with the aliphatic and aromatic amines classifying near 99%, while the polyamine identification accuracy approached 90%. In the third layer of analysis, the concentration of the analytes in question was determined in the biologically relevant range within approximately 10% accuracy by following trends in the principle component analysis output.

Cross-reactive conjugated polymers: analyte-specific aggregative response for structurally similar diamines.

A single cross-reactive conjugated polymer (poly(thiophene) 1) generates unique spectral patterns in response to structurally similar diamines. Multivariate statistics are used to deconvolute subtle variations in these spectral responses, allowing for identification and quantification of the analytes with >99% accuracy.