Autofluorescence changes of tomato surface tissues during overripening.

We investigated the autofluorescence of tomato surface tissues during overripening at 25 °C for 13 days. Microscopic images and fluorescence spectra of tissues, including the epidermis and cuticle, were examined (excitation at 360 nm), revealing that the autofluorescence changes were related to the epidermis, particularly the fluorophores in the cuticle.

Rapid evaluation of quality deterioration and freshness of beef during low temperature storage using three-dimensional fluorescence spectroscopy.

We investigated three-dimensional (3-D) fluorescence spectroscopy for its potential to evaluate beef quality deteriorative changes and freshness. The fluorescence characteristics of heme, conjugated Schiff base and amino acids, could be indicators of internal biochemical reactions associated with beef deterioration, including color changes, lipid oxidation, and protein degradation, as well as a measure of freshness decline. To classify beef quality in terms of color (sensory index) and pH (chemical index), cluster analysis method (CA) was used. Three classes were identified: "fresh", "acceptable", "spoiled". We then developed a qualitative model to classify stored beef into these three classes using 3-D front-face excitation-emission matrices (EEMs) of fat tissue, combined with a parallel factor analysis (PARAFAC) algorithm. The resulting model had calibration and validation accuracies of 95.56% and 93.33%, respectively. These results demonstrate the potential of fluorescence spectroscopy to accurately and non-destructively monitor beef quality decline.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra.

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models' performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.