A novel fast quality control strategy for monitoring spoilage on mayonnaise based on modeling second-order front-face fluorescence spectroscopy data

Abstract

The potential of front-face fluorescence spectroscopy along with chemometric algorithms was investigated for the non-destructive evaluation of mayonnaise spoilage stored at 5 °C and 37 °C during six days. Fluorescence excitation spectra on homemade and commercial mayonnaise samples were recorded between 230 and 400 nm, and emission wavelengths from 300 to 600 nm. Fluorescence spectra data were analyzed using parallel factor analysis (PARAFAC) capturing the changes occurred in the data set. The best PARAFAC model was obtained with 3 components, having 52% core consistency values and 98.8% of the explained variance. A chromatographic analysis was performed to know the specific compounds. Three compounds were presented in all the samples: tyrosine, tryptophan and riboflavin. The results confirm the decrease of the tyrosine and tryptophan concentrations in the time evaluated at 37 °C while the changes at 5 °C were not observed. The results obtained were evaluated by N-way partial least square discriminant analysis (NPLS-DA) on data set formed by all the fluorescence spectra at 37 °C in order to test the ability of the matrices to discriminate between each storage times. The results showed that 100% of good classifications were obtained using 3 PLS factors. This research confirms that the excitation-emission matrices (EEMs) provide information related to the mayonnaise fluorescent molecular structure, allowing the classification of the samples as a function of storage time.