Differential sensing for the regio- and stereoselective identification and quantitation of glycerides.

Glycerides are of interest to the areas of food science and medicine because they are the main component of fat. From a chemical sensing perspective, glycerides are challenging analytes because they are structurally similar to one another and lack diversity in terms of functional groups. Furthermore, because animal and plant fat consists of a number of stereo- and regioisomeric acylglycerols, their components remain challenging analytes for chromatographic and mass spectrometric determination, particularly the quantitation of species in mixtures. In this study, we demonstrated the use of an array of cross-reactive serum albumins and fluorescent indicators with chemometric analysis to differentiate a panel of mono-, di-, and triglycerides. Due to the difficulties in identifying the regio- and stereochemistry of the unsaturated glycerides, a sample pretreatment consisting of olefin cross-metathesis with an allyl fluorescein species was used before array analysis. Using this simple assay, we successfully discriminated 20 glycerides via principal component analysis and linear discriminant analysis (PCA and LDA, respectively), including stereo- and regioisomeric pairs. The resulting chemometric patterns were used as a training space for which the structural characteristics of unknown glycerides were identified. In addition, by using our array to perform a standard addition analysis on a mixture of triglycerides and using a method introduced herein, we demonstrated the ability to quantitate glyceride components in a mixture.

The use of principal component analysis and discriminant analysis in differential sensing routines.

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.