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* MATFORSK Norwegian Food Research Institute, 1430 Ås, Norway
TINE Norwegian Dairies BA, 4358 Kleppe, Norway
Corresponding author:
J. P. Wold; e-mail:
jens.petter.wold{at}matforsk.no.
The purpose of this paper is to demonstrate the potential of solid-sample fluorescence spectroscopy in nondestructive assessment of light-induced oxidation in different dairy products such as Swiss cheese, cream cheese, and sour cream. Analytical and quantitative spectral properties of fluorescence were elucidated by use of principal component analysis with designed experiments involving different levels of air and light exposure.
A significant reduction in fluorescence intensity at approximately 525 nm, and a corresponding increase in the region 415 to 490 nm as a result of illumination was observed on all the products. The effect was ascribed to photodegradation of riboflavin. Variation in two smaller peaks at approximately 620 nm and 630 nm was an interaction effect between exposure to light and air. A pronounced interaction effect between light and air produced intense blue fluorescence and off-flavors on Swiss-like Jarlsberg cheese. High correlations (0.83 to 0.93) between fluorescence spectra and sensory measured off-flavors were obtained for cream cheese. Results indicate that solid-sample fluorescence can be used as a nondestructive and rapid tool to measure the degree of light-induced degradation of riboflavin as well as sensory properties connected to storage of dairy products. Images of fluorescence can be used to visualize the intensity and propagation of this process. The simplicity and rapidity of the method offer rich opportunities for efficient evaluation of factors affecting light-induced oxidation in dairy products, such as packaging materials, light sources, exposure time, and temperature.
Key Words: light-induced oxidation dairy product fluorescence image analysis
Abbreviation key: ESR = electronic spin resonance, PC = principal component, PCA = PC analysis, PLSR = partial least squares regression, RMSECV = root mean square error of cross validation
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