D. Varga, A. Szabó, L. Locsmándi, Cs. Hancz, R. Romvári NEAR INFRARED SPECTROSCOPY ANALYSIS OF RAW AND SMOKED SILVER CARP FILLETS D. Varga, A. Szabó, L. Locsmándi, Cs. Hancz, R. Romvári Kaposvár University, Faculty of Animal Science, H-7400, Kaposvár, Guba S. str. 40. Hungary Introduction In the Hungarian market fish product meant a market-sized live fish till present. In parallel with the modification of consumers’ expectations the proportion of processed products is increasing, giving a more expressed basis for the research concerning fish meat quality. It is important to identify the processed products, assessing the quality and discriminate to different categories. The near infrared spectroscopy (NIRS) has a possibility with wide range of applications (Prevolnik et al., 2004; Bechmann et al. 1998; Uddin et al. 2005). The advantages of NIRS are as follows: cheap, fast, produces no dangerous waste and can be set in the manufacturing process (on-line analysis). The purpose of this study was to test the possibilities of discrimination different products of silver carp. Conclusions Near infrared spectroscopy analysis is suitable to discriminate raw and smoked fish fillets with different origin or harvesting time. The test of the sensitivity of SRT method based on NIR spectroscopy was successful. So it is possible to discriminate and identify fish fillet samples during the meat quality control. Table 2. Results of raw and smoked fillets NIR spectra discrimination by different harvesting time Materials and Methods Silver carp (Hypophthalmichthys molitrix) raw and smoked fillets were analyzed in the experiment. Half of the fishes were raised in pond and the other part came from natural water. The harvesting happened at three different times: spring, summer and autumn. Fillets of individual animals were homogenized (IKA A11 basic), and approximately 20 g of sample was analyzed. The near-infrared spectrums were measured with NIRSystem 6500 (Foss NIRSystem, Silver Spring, MD, USA) spectrometer in reflectance mode, 1100-2500 nm wavelength range at 2 nm increments, and then they are recorded log (1 / R) format. The so-called trials “Small Ring Cup” sample holder was used. The WinISI II 1.5 software was used for the instrument control and data management. In the basic tests we analyzed the raw spectra first derivatives. In the latter case, four "gap" and "smooth" values were used with standard MSC (normal multiplicative scatter correction), in the spectrum 1100-2500 nm wavelength range. The spring (n=20) and summer (n=10) samples were stored frozen till the analyses, the autumn samples (n=50) were analyzed after 24 h storage on 4°C. All the smoked samples (n=20, n=9, n=12) were stored frozen and analyzed after thawing. Results Firstly the effect of the origin was analyzed. Table I shows the results of the samples harvested at different times. The best method for discriminate the natural and farmed fish samples was using 400-2500 nm wavelength interval for the raw, and 800-1100 nm wavelength interval for the smoked fish products. Our second aim was to investigate the effect of harvesting time on the product. In the case of the raw samples only the spring and summer samples were comparable, because the autumn samples were analyzed at fresh state. All the smoked fillets were analyzed together. The discrimination was the most successfully when the all recorded spectra intervals were used. Figure 1. Smoked silver carp fillets Table 1. Results of raw and smoked fillets NIR spectra discrimination by different origin References Bechmann, I., Jorgensen, M.B. (1998): Rapid assessment of quality parameters for frozen cod using near infrared spectroscopy. Lebensm. Wiss. U. Technol. 31: 648-652. Prevolnik M., Candek-Potokar M., Skorjanc D., (2004): Ability of NIR spectroscopy to predict meat chemical composition and quality – a review, Czech J. Anim. Sci., 49, (11): 500-510. Uddin M., Okazaki E., Turza S., Yumiko Y., Tanaka M., Fukuda Y. (2005): Non-destructive visible/NIR spectroscopy for differentiation of fresh and frozen-thawed fish, Journal of Food Science, 70, 8: 506-510.