Lipid metabolism is a growing area of biochemical research because understanding these pathways could lead to treatments for metabolic disorders such as obesity and type 2 diabetes. To study lipid metabolism, researchers need tools to identify and quantitate glycerides, the main component of animal fat. However, it can be difficult to tell one glyceride apart from another subtly different glyceride using current analytical methods such as mass spectrometry. Thus, we developed a method of discriminating glycerides using an array of cross-reactive proteins in conjunction with pattern recognition algorithms. By incorporating an olefin metathesis pretreatment step, we were able to distinguish glyceride regio- and stereoisomers and to predict these structural features. Finally, we achieved quantitation of glycerides in mixtures. 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.