Fluorescence sensor array for identification of commercial milk samples according to their thermal treatments.

Identification of processed milk is of importance for commercial and legal concerns. The fluorescence response patterns induced by fluorophore/protein interactions allow a possible discrimination of processed milk samples corresponding to their thermal treatment. The fluorescence responses of 4 fluorophores upon addition of commercial milk samples in 96-well plate are measured in the range of 400-600 nm using the excitation wavelength at 375 nm. The pattern recognition of the 53,126 fluorescence responses (4 fluorophores×41 wavelengths×4 thermally processed milks×3 brands×3 lots×3 bottles×3 repeats) are analyzed by multivariate statistical methods. Linear discriminant analysis (LDA) successfully recognizes the milk samples according to their thermal processing, i.e. pasteurized milk, sterilized milk, UHT fresh milk and recombined milk (UHT milk having milk powder), with 100% classification accuracy in a cross validation using a leave-one-out technique.

Protein discrimination by fluorescent sensor array constituted of variously charged dendritic phenylene-ethynylene fluorophores.

A protein fluorescent sensor array based on variously charged dendritic fluorophores is developed. The variation of charge is achieved by different combinations of cationic trimethylammonium, anionic carboxylate and non-ionic methyl ester or N,N-dimethylamino on the peripheries of the fluorophores. Nine phenylene-ethynylene dendritic fluorophores, seven zeroth generation bearing charges varied from -3 to +3 and two first generation bearing -6 and +6 charges, are synthesized from triiodotriphenylamine through series of Sonogashira coupling. In phosphate buffer saline pH 7.4, five out of these nine compounds fluorogenically response to eight protein analytes distinctively. The data set of fluorescent intensities obtained from 5 fluorophores×8 proteins×9 replicated measurements can be statistically sorted into eight clusters corresponding to each protein by principal component analysis (PCA). Factorial discriminant analysis (FDA) cross-validation is applied to locate the optimum detection wavelength and reduce the number of sensing elements from nine to two with 100% discriminating accuracy. The method described should be generally practical for the development of electronic tongue for protein related food analysis and medical diagnosis.