Author: Herr Dr. Amoneit
Last saved by: Gürbüzoglu, Meltem

Aplication of Synchronous Fluorescence Spectroscopy with Multivariate Data Analysis for the Detection of Cheese Adulteration
Anna Dankowska,  Maria Małecka,  
Faculty of Commodity Science, Poznań University of Economics, 
Poznań, Poland
"Candidate for SFEL Travel Grant"

Milk fat is one of the most expensive commodity fat on the market, therefore adulteration of cheese is practiced for economical purposes and the detection of foreign fat in milk fat is a real issue. For legal, medical and ethical reasons, cheeses should be correctly labeled. 
The potential of fluorescence spectroscopy for detection of cheese adulteration with plant oils was investigated. Synchronous fluorescence spectra of 21 cheeses, 
6 cheese like products and their mixtures (n=22)  were collected in the region of 
240 – 700 nm with different wavelength intervals (10, 30, 60 and 80 nm).  Successive Projections Algorithm was applied to retain for further chemometric analysis the most informative wavelengths from the spectra. Applied technique is useful for detection of the addition of plant fat to cheese and the lowest detection limits of adulteration were observed for application of wavelength intervals 60 and  80 nm equaled 3.0 and 4.4%, respectively. The Multiple Linear Regression models were used to calculate the level of  adulteration with the lowest Root Mean Square Error of Prediction and Root Mean Square Error of Cross Validation equaled 1.5 and 1.8% for the measurement acquired at the  wavelength interval equaled 60 nm. Lower classification errors were obtained for SPA-LDA methods than for PCA-LDA. The  lowest classification errors equaled 3.8% (∆λ =10 and 30 nm) and 0.0% (∆λ=60 nm) for the PCA-LDA and SPA-LDA classification method, respectively.