KAFKAS ÜNİVERSİTESİ VETERİNER FAKÜLTESİ DERGİSİ

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2 Bu dergi Kafkas Üniversitesi Veteriner Fakültesi Tarafından iki ayda bir yayımlanır. ISSN This journal is published bi-monthly, by the Faculty of Veterinary Medicine, University of Kafkas. Kafkas Üniversitesi Veteriner Fakültesi Adına Sahibi (OWNER) Prof.Dr. Şahin ASLAN Dekan (Dean) EDİTÖR (EDITOR-IN-CHIEF) Prof.Dr. İsa ÖZAYDIN YABANCI DİL EDİTÖRLERİ (ENGLISH EDITORS) Doç.Dr. H. Metin ERDOĞAN Doç.Dr. Ahmet ÜNVER EDİTÖR YARDIMCILARI (ASSOCIATE EDITORS) Prof.Dr. Çiğdem ALTINSAAT Doç.Dr. Mehmet ÇİTİL Doç.Dr. Özgür AKSOY Yrd.Doç.Dr. Oktay ÖZKAN YÜRÜTME KURULU (EXECUTIVE BOARD) Prof.Dr. Nurhayat GÜLMEZ Prof.Dr. Ahmet ÖNCÜER Prof.Dr. Abdullah DOĞAN Prof.Dr. Kemal IRMAK Prof.Dr. Sami ÖZCAN Prof.Dr. Yavuz ÖZTÜRKLER Doç.Dr. İsmail KAYA Doç.Dr. Hakan KOCAMIŞ Doç.Dr. Murat GÜLMEZ KAFKAS ÜNİVERSİTESİ VETERİNER FAKÜLTESİ DERGİSİ THE JOURNAL OF THE FACULTY OF VETERINARY MEDICINE UNIVERSITY OF KAFKAS DİZGİ (COMPOSITION) Dr. Pınar DEMİR BASKI (PRINT) ESER OFSET - ERZURUM CİLT VOLUME 15 SAYI 4 NUMBER YIL YEAR 2009

3 This journal is indexed and abstracted by Thomson Reuters Services beginning with Volume 13 (1) 2007 in the followings: Science Citation Index Expanded (also known as SciSearch ) Journal Citation Reports/Science Edition This journal is also indexed and abstracted in: CAB Abstracts ULAKBIM-TUBITAK DANIŞMA KURULU (ADVISORY BOARD) Prof. Dr. Kemal AK Prof. Dr. Belma ALABAY Prof. Dr. Feray ALKAN Prof. Dr. Metin BAYRAKTAR Prof. Dr. Burhan ÇETİNKAYA Prof. Dr. Hüdaverdi ERER Prof.Dr. Gül ERGÜN Prof. Dr. Ayhan FİLAZİ Prof. Dr. O. Sacit GÖRGÜL Prof. Dr. Ekrem GÜREL Prof.Dr. Ali İŞMEN Prof. Dr. Hakkı İZGÜR Prof. Dr. Zafer KARAER Prof. Dr. Arif KURTDEDE Prof. Dr. Kamil ÖCAL Prof.Dr. Engin SAKARYA Prof. Dr. Berrin SALMANOĞLU Prof. Dr. Yakup Can SANCAK Prof. Dr. Nesrin SULU Prof. Dr. Ş. Doğan TUNCER Prof. Dr. Halis YERLİKAYA İstanbul Üniversitesi Veteriner Fakültesi Ankara Üniversitesi Veteriner Fakültesi Ankara Üniversitesi Veteriner Fakültesi Fırat Üniversitesi Veteriner Fakültesi Fırat Üniversitesi Veteriner Fakültesi Selçuk Üniversitesi Veteriner Fakültesi Hacettepe Üniversitesi Fen Fakültesi Ankara Üniversitesi Veteriner Fakültesi Uludağ Üniversitesi Veteriner Fakültesi Abant İzzet Baysal Üniversitesi Fen Edebiyat Fakültesi Çanakkale Onsekiz Mart Üniversitesi Su Ürünleri Fakültesi Ankara Üniversitesi Veteriner Fakültesi Ankara Üniversitesi Veteriner Fakültesi Ankara Üniversitesi Veteriner Fakültesi Adnan Menderes Üniversitesi Veteriner Fakültesi Ankara Üniversitesi Veteriner Fakültesi Ankara Üniversitesi Veteriner Fakültesi Yüzüncü Yıl Üniversitesi Veteriner Fakültesi Ankara Üniversitesi Veteriner Fakültesi Ankara Üniversitesi Veteriner Fakültesi Fırat Üniversitesi Veteriner Fakültesi YAZIŞMA ADRESİ (ADDRESS FOR CORRESPONDENCE) Kafkas Üniversitesi Veteriner Fakültesi Dergisi Editörlüğü Kars / TÜRKİYE Phone: /1244 Fax: vetdergi@kafkas.edu.tr ELEKTRONİK BASKI (ELECTRONIC EDITION) ONLINE MAKALE GÖNDERME (ONLINE SUBMISSION)

4 İÇİNDEKİLER (CONTENTS) ARAŞTIRMA MAKALELERİ (RESEARCH ARTICLES) Comparison of two different methods to predict meat quality and prediction possibility using digital image analysis BOZKURT Y, ÖZKAYA S, KILIÇ B... Microbiological contamination model of staff hands employed at bakeries due to staff s life style and individual parameters DÜMEN E, SEZGİN F... Responses of broilers under cold conditioning (15ºC) to dietary triiodothyronine and iodine combined to antioxidants (selenium and vitamin C) SEVEN İ, TATLI SEVEN P, YILMAZ S... Repair of peroneal paralysis by muscle transposition in sheep KILIÇ E, AKSOY Ö, ÖZAYDIN İ, ÖZTÜRK S, SÖZMEN M, KURT B... Urtica dioica L. (Isırgan otu) nin metanol ve su ekstraktının 7,12-dimetilbenz(a)antrasen uygulanan tavşan tüylerindeki iz element seviyeleri üzerine etkileri TÜREL İ, ERTEKİN E, OTO G, ÇELİKEZEN FÇ, YAŞAR S... The effects of hen age, genotype, period and temperature of storage on egg quality BOZKURT Z, TEKERLİ M... İpekböceği (Bombycidae: Bombyx mori) nde juvenil ve ekdizon hormonları uygulaması sonucu olası değişimler TURGAY İZZETOĞLU G, ÖZKORKMAZ F, ZEKA Ö, ÖBER A... Establishing the optimum cutting date and additives for pasture grass silage and its mycotoxin levels ARSLAN C, EŞSİZ D... Seroprevalence of viral upper respiratory infections in dairy cattle DUMAN R, YAVRU S, KALE M, AVCI O... Kronik böbrek yetmezlikli kedilerde homosistein düzeyleri ÜREN N, FİDANCI UR, KIRMIZIGÜL AH, FİDANCI V, PEKCAN M... Variance components and genetic parameter estimates using random regression models on test day milk yields of Holstein Friesians TAKMA Ç, AKBAŞ Y... Koyun dalak doku arginazının bazı kinetik özellikleri KANDEMİR FM, ÖZDEMİR N... Effect of total isoflavones found in soybean on vitellogenin production in common carp TURKER H, BOZCAARMUTLU A... DMBA ile indüklenmiş kobayların beyin dokusundaki bazı elementler üzerine alfa lipoik asit in etkisi ÇİFTÇİ H, ÖZKAYA A, DAYANGAÇ A, ÖLÇÜCÜ A, ÇELİK S, ŞAHİN Z, ATEŞ S... Anaplasmosisli sığırlarda ısı şok protein (HSP), malondialdehit (MDA), nitrik oksit (NO) ve interlökin (IL-6, IL-10) düzeylerinin araştırılması ERGÖNÜL S, KONTAŞ AŞKAR T... Dikim sıklığının yerelmasının (Helianthus tuberosus L.) hasıl verimi ve silaj kalitesi üzerine etkilerinin belirlenmesi KARSLI MA, BİNGÖL NT... Sayfa (Page)

5 Salvia verticillata ve Phlomis pungens in in vitro antibakteriyel etkinliğinin değerlendirilmesi ÖZKAN O, AYDIN H, BAĞCIGİL AF... A study on karyotypes of two species of Anoplius (Hymenoptera: Pompilidae) in Kars plateau, Turkey KIRPIK MA, GÜL S, NUR G, İNAK S, ÇİLİNGİR M, ALDEMİR A, BAĞRIAÇIK N... Immunohistochemical demonstration of nitrosative tissue damage in copper induced liver oxicity in Japanese quails KARAMAN M, ÖZEN H, ÖZCAN K, TUZCU M... Kuzeydoğu Anadolu Bölgesi sınır illerinde bulunan sığırlarda viral solunum sistemi enfeksiyonlarının seroprevalansı YILDIRIM Y, YILMAZ V, FARAJI MAJARASHIN AR... The problem of cystic echinococcosis in Van province YILMAZ H, CENGİZ ZT, ÇİÇEK M... Immunohistochemical distribution of insulin-, glucagon-, and somatostatin-containing cells in the pancreas of the rat (Wistar albino) KOCAMIŞ H, KARADAĞ SARI E, NAZLI M, GÜLMEZ N, ASLAN Ş, DEPREM T... Treatment of long bone fractures with acrylic external fixation in dogs and cats: Retrospective study in 30 cases ( ) ÖZAK A, YARDIMCI C, NİSBET HÖ, ŞİRİN YS... Effect of epidermal growth factor on in vitro maturation of cat oocytes recovered from ovaries at follicular and luteal stages POLAT B, BAKİ ACAR D, MACUN HC, KORKMAZ Ö, ÇOLAK A, BAŞTAN A, AKÇAY A... Metronidazole resistance of Campylobacter jejuni and Campylobacter coli from different animal species ICA T, CIFTCI A, SAVASAN S, DIKER KS... Sayfa (Page) OLGU SUNUMU (CASE REPORT) Accessory tongue in a calf TERİM KAPAKİN KA, SAĞLAM YS, KAPAKİN S... Swimming syndrome in two Labrador puppies YARDIMCI C, ÖZAK A, NİSBET HÖ, ŞİRİN YS DERLEME (REVIEW) İneklerde beslenme davranışları ARSLAN C EDİTÖRE MEKTUP (LETTER TO EDITOR) New data on bird haemoproteids and microfilariae in European Blackbird (Turdus merula) in Turkey BEYHAN YE, PEKMEZCI GZ, ERCIYAS K, UMUR S

6 Kafkas Univ Vet Fak Derg 15 (4): , 2009 DOI: /kvfd A RESEARCH ARTICLE Comparison of Two Different Methods to Predict Meat Quality and [1] [2] Prediction Possibility Using Digital Image Analysis Yalçın BOZKURT * Serkan ÖZKAYA * Birol KILIÇ ** [1] This study was presented at Annual Meeting of EAAP 2007 Congress in Ireland and the abstract was published in book of abstracts No: 13 pp. 139 of EAAP (European Association for Animal Production) [2] It was Financed by Suleyman Demirel University Research Fund (Project No: 1033-m-05) * Department of Animal Science, Faculty of Agriculture, Suleyman Demirel University, 32260, Isparta - TURKEY ** Department of Food Science, Faculty of Agriculture, Suleyman Demirel University, 32260, Isparta - TURKEY Makale Kodu (Article Code): 2009/014-A Summary The objective of this study was to determine muscle colour of beef carcasses using digital image analysis. Fourteen beef carcasses were selected from a slaughterhouse. The data collected on these carcasses included colourmeter measurements and digital images and measurements of muscle colour (L*, a*, b* values) and muscle ph from longissimus muscle at 24 hours after slaughtering. The discrepancies between colourmeter and digital image analysis values of L*, a*, b* were large (25.6±3.37, 3.01±3.38 and 2.25±3.56, respectively). There were significant differences between L* values (P<0.05) but there were non-significant differences between a* and b* values (P>0.05). The correlation coefficient was found significant (P<0.05) between ph and a* values (r=0.83). The results showed that prediction ability of digital image analysis was low for prediction of muscle colour. However, it was concluded that red value (a*) can be predicted by digital image analysis and there is a need for further studies in order to develop better techniques to use for prediction. Keywords: Beef carcass, Digital image analysis, Meat colour, Meat quality Özet Et Kalitesinin Tahmininde İki Farklı Metodun Karşılaştırılması ve Dijital Görüntü Analizi Yöntemi ile Tahmin Olasılığı Bu çalışmada sığır karkaslarının kas renginin dijital görüntü analizleri yöntemi kullanılarak belirlenmesi amaçlanmış ve 14 adet sığır karkası kullanılmıştır. Kesimden 24 saat sonra bu karkasların Longissimus kası alanından kas rengi ölçümleri (L*, a*, b* değerleri) hem kolorimetre ölçümleri ile hem de dijital görüntü analizi yöntemiyle alınmış ve ayrıca ph ölçümlerini içeren veriler de elde edilmiştir. Analizler sonucunda L*, a*, b* nin dijital görüntü ve kolorimetre değerleri arasında büyük farklılık bulunmuştur (sırasıyla 25.6±3.37, 3.01±3.38 ve 2.25±3.56). L* değerleri arasındaki farklılık istatistiki olarak önemli (P<0.05) fakat a* ve b* değerleri arasındaki farklılık istatistiki olarak önemsiz bulunmuştur (P>0.05). Ayrıca ph ve a* değeri arasındaki korelasyon (r=0.83) önemli bulunmuştur (P<0.05). Sonuçlar, kas renginin tahmininde dijital görüntü analizi yönteminin tahmin doğruluğunun düşük olduğunu göstermiştir. Dijital görüntü analizleri yöntemi ile kırmızı renk değerinin (a*) tahmin edilebileceği ancak tahminde kullanılmak üzere daha iyi tekniklerin geliştirilmesi ve bundan öte çalışmaların yapılmasına ihtiyaç olduğu sonucuna varılmıştır. Anahtar sözcükler: Sığır karkası, Dijital görüntü analizi, Et rengi, Et kalitesi INTRODUCTION Digital image analysis (DIA) was developed at 1990 s 1. McDonald and Ohen 2 initiated using this 1960 s to use in space research and currently started technique in beef quality and they distinguished meat to use in food science to evaluate food quality. This from fat on the base of reflection differences in muscle technique has been argued in poultry science since (Musculus longissimus dorsi). İletişim (Correspondence) ybozkurt@ziraat.sdu.edu.tr

7 486 Comparison of Two Different Methods... Prediction of meat yield has been the major focus of many studies in the developed countries. The value of beef cuts obtained from carcasses of different breeds of cattle is very essential for both consumers and marketing standards which emphasise quality, uniformity and consistency of the meat. Therefore, an evaluation procedure for predicting weights and yields of carcasses and beef retail cuts becomes of great importance for the beef industry 3. Digital image analysis has been considered to be one of the most promising methods for objective carcass evaluation 4. It has also been utilized for determination of colour and fat thickness 5, marbling scores in beef 6 and water retention capacity in beef 7. A grading instrument that would sort beef carcasses based on palatability characteristics of lean and fat (i.e., marbling, lean and fat colour, lean and fat texture, lean and fat firmness, etc.), and could allow enhancements to those characteristics currently assessed in beef because of the capability to quantify L*, a* and b* colour parameters of fat and lean-which are known to be correlated with eating quality of beef The objective of this study was to determine muscle colour of beef carcasses using Digital Image Analysis (DIA) and to compare with Minolta Colourmeter (MC) measurement techniques and to evaluate DIA technique in predicting meat quality and in using to determine the possible shelf life of meat products in markets. MATERIAL and METHODS This work was carried out at Evren Et Meat Processing Plant in Burdur, Turkey. Fourteen beef cattle carcasses were used and samples of Longissimus dorsi muscle were obtained at between 12 th and 13 th rib cross section area after the carcasses being dissectioned. Colour values (L*, a* and b*) of longissimus dorsi muscle were determined using minolta colourmeter (MC), L indicates lightness, a is redness and b is yellowness value indicator; and then the collected measurements were compared with Digital Image Analysis (DIA) measurements of the same muscles (n=14). Images were captured using a Canon digital camera MV850i. The camera was set on a standard quality ( pixel resolution) and kept the distance between the sample and the lens (50 cm), and photographed against a black background. Illumination conditions, location of camera and camera settings were tried to be the same and uniform for all samples. All influences of the varied lightening conditions (shadow, sun rays and lighting) in the slaughterhouse were eliminated. Digital images were downloaded from the camera to a computer file and processed using Image Pro Plus 5 software to obtain RGB (Red, Green, Blue) colour space components of meat from the images. This software can separate out different colours from irregularly shaped surfaces and be used to calculate relative areas that each colour represents within the video image, as well as provide feedback on Commission Internationale de l Eclairage (International Commission on Illumination; CIE) values for L* (psychometric lightness; dark = 0, white = 100), a* (red = + values; green = - values) and b* (yellow = + values; blue = - values) colour parameters or colour measurements that reflect how the human eye perceives colour. In carcasses, ph measurements were also taken from 24 h post mortem longissimus muscle (LM) samples with ph meter (Crison Instruments, Spain). Statistical Analysis The differences between MC and DIA L*, a* and b* values were examined by Students t test using the statistical package program Minitab v.13 for windows (Minitab12). The L*, a* and b* values determined by MC and DIA can also be defined as observed and predicted values respectively. The observed and predicted L*, a* and b* values were also compared using the Mean-Square Prediction Error (MSPE): Where n is the number of pairs of observed and predicted values being compared. i = (1, 2, 3,, n) Oi is the observed L*, a*, b* values with ith variable. Pi is the predicted L*, a*, b* values with ith variable. The MSPE can be considered as the sum of three components described by Rook et al. 13. Where, S O 2 and S P 2 are the variances of the observed and predicted LMs respectively. O and P are the means of the observed and predicted LMs, b is the slope of the regression of observed values on predicted and r is the correlation coefficient between O and P.

8 487 BOZKURT, ÖZKAYA, KILIÇ Besides common regression analysis, MSPE has been used to determine the prediction ability of regression models and sources of error components in many studies by Smoler et al. 14, Bozkurt and Ap Dewi 15, Fuentes-Pila et al. 16, Yan et al. 17, Bozkurt and Ozkaya 18 and Bozkurt 19,20. RESULTS Descriptive statistics of L*, a* and b* values measured by MC and DIA are presented in Table 1. Table 1. Descriptive statistics of L*, a* and b* values by MC and DIA Tablo 1. MC ve DIA ile elde edilen L*, a* ve b* değerlerine ait tanımlayıcı istatistikler Methods Variables (n=14) Mean±SE DIA MC SE: Standard Error L a b L a b ph 51.00± ± ± ± ± ± ±0.31 DIA predicted L* values 50% higher than the L* values determined by MC and differences were found to be statistically significant (P<0.05). The predicted L*, a* and b* values determined by two methods are compared in Table 2. Table 1. Descriptive statistics of L*, a* and b* values by MC and DIA Tablo 1. MC ve DIA ile elde edilen L*, a* ve b* değerlerine ait tanımlayıcı istatistikler Observed parameters Mean S.D. S.E. Variance R 2 r L* MC DIA a* MC DIA b* MC DIA S.D.: Standard Deviation, S.E.: Standard Error DIA produced twice as high prediction as MC method (51 v 25.4). Coefficient of determination (R 2 ) and correlation coefficient (r) values were determined as 7.2% and 0.27 for L* values respectively. It was observed that DIA predicted higher a* value (15.26) compared to MC (12.25). R 2 and r values for a* were 4.6% and Similarly, DIA method predicted slightly higher b* values (5.88) compared to b* values measured by MC (3.63). R 2 and r values for b* were 2.2% and 0.15 (Table 2). However, while there were significant differences (P<0.05) in L* values determined by DIA and MC methods, non-significant (P>0.05) differences were observed for a* and b* values determined by two methods (Table 2). Table 3. Mean bias, MSPE and proportion of MSPE (%) Tablo 3. Ortalama fark, MSPE ve MSPE oranları (%) L* a* b* N=14 Proportion of MSPE (%) Mean S.E. Mean Bias MSPE Bias Line Random MC DIA ± MC DIA ±3.38 ns MC DIA ±3.56 ns MSPE: Mean Square Prediction Error, MC: Minolta colourmeter S.E.: Standard Error, DIA: Digital Image Analysis Mean bias, MSPE and proportion of MSPE (%) between two prediction methods for L*, a* and b* values are given in Table 3. Mean bias was positive for L* values (25.6±3.37) and differences in L* values between two methods were statistically significant (P<0.05). MSPE value of predicted L* was and percentage values of bias, line and random error were 95, 3.9 and 1% as a proportion of MSPE (Table 3). The highest percentage was found in bias and the lowest percentage was found at random. Mean bias was also positive for a* values (3.01±3.38) and differences between two values were not statistically significant (P>0.05). MSPE value of predicted a* was and in terms of contribution of components to MSPE; the values of bias, line and random error were 21, 68.9 and 10.2% respectively (Table 3). The highest percentage was found at line and the lowest percentage was found at random. Similarly, mean bias was positive for b* values (2.25±3.56) and differences between two values were statistically not significant (P>0.05). MSPE value of predicted b* was and as contribution of components to MSPE; the values of bias, line and random error were 12, 31.4 and 56.9% respectively. The highest percentage was found at random and the lowest percentage was found at bias (Table 3). Correlation coefficients (r) between ph and L*, a* and b* values are shown in Table 4. Statistically significant correlation (P<0.05) was determined between a* values and ph of meat (P<0.05)

9 488 Comparison of Two Different Methods... Table 4. Correlation coefficients (r) between ph and L*, a* and b* values Tablo 4. ph ve L*, a* ve b* değerleri arasındaki korelasyon katsayıları (r) Meat Colours ph L* a* L* a* b* ns: nonsignificant (P>0.05) ns ns ns ns (r=0.83). Correlation between a* and L* was found to be statistically insignificant (P>0.05) (r=0.44). While there were negative and statistically not significant correlations between ph, L* and b* values, high positive correlation was found between ph and a* value, with increasing ph values a* values increased (Table 4). The reason for that, with increasing ph, denaturation of myoglobin decreases. The RGB values determined by digital image processing are shown in Table 5. Table 5. Descriptive statistics for RGB colour space values Tablo 5. RGB renk uzayına ait tanımlayıcı istatistikler Colour values (n=14) Means S.E. CV R G B CV: Coefficient of Variation, S.E.: Standard Error The highest R value in RGB colour space indicated more redness in muscle colour. The components of R and B values were higher than G values indicating darker coloured meat and also relatively increasing B values showed the change of colour towards purple and the higher values of R and G colour components showed the change of colour from redness to yellowness which may possibly be associated with fat content in meat. However, this should be verified by chemical analysis of fat content. DISCUSSION Objective measures of beef quality have been a long-time desire of the industry and there have been many research efforts in developing instruments. One popular, and obvious, approach has been to measure the mechanical properties as indicators of tenderness and meat colour. In previous studies colour had been efficiently described in the RGB colour space which is common for digital cameras and computer monitors. Each pixel has a certain amount of red, green and blue. The simplicity of the RGB space makes it highly suitable for arithmetic manipulations. Alternative colour spaces such as HSI (Hue, Saturation and Intensity) may better represent the workings of the human eye but are not as convenient as the RGB histograms. The lower R 2 values found between MC and DIA L*, a*, and b* in this study may be associated with the fact that a narrower range of colours were evaluated. Schutte et al. 21 indicated that high correlation was observed between colour values of DIA and that of well trained panalists (r=0.90). Dosiewicz et al. 22 showed that there is very strong relationship between marbling score and RGB values and they concluded that DIA can be used in chemical composition, texture and quality of meat. Illumination condition in obtaining images may have also affected the conversion from RGB color space to L*, a*, b* colour space, likely improving the precision of the colour predictions in this study. The results in this study showed that prediction ability of digital image analysis was low for prediction of muscle colour. However, it was concluded that colour image processing is a useful technique for meat quality evaluation. Quality attributes such as muscle colour, marbling, maturity and muscle texture can be effectively quantified and characterized and that red value (a*) can be predicted by digital image analysis. This technique can open up new dimensions in determining the shelf life of meat products for easy and fast detection in markets. Furthermore, there is a need for futher studies in order to develop better techniques, especially in segmentation of images to use for prediction. REFERENCES 1. Daley WDR, Babbitt SS: Machine vision: Quality control by computer. Misset World Poultry, 7 (4): 20-21, McDonald T, Chen YR: Separating connected muscle tissue in images of beef carcass rib eyes. Transactions of the ASAE, 33 (6): , Cross HR, Belk KE: Objective measurements of carcass and meat quality. Meat Sci, 36, , Gardner TL, Dolezal HG, Allen DM: Utilization of Video Image Analysis in Predicting Beef Carcass Lean Product Yields Animal Science Research Report-Oklahoma State. pp , Monin G: Recent methods for predicting quality of whole meat. Meat Sci, 49, , Albrecht E, Wegner J, Ender K: A new technique for objective evaluation of marbling in beef. Fleischwirtschaft, 4, , Irie M, Izumo A, Mohri S: Rapid method of determining

10 489 BOZKURT, ÖZKAYA, KILIÇ water holding capacity in meat using video image analysis and simple formulae. Meat Sci, 42, , Hodgson RR, Belk KE, Savell JW, Cross HR, Williams FL: Development of a quantitative quality grading system for mature cow carcasses. J Anim Sci, 70, 1840, Hilton GG, Tatum JD, Williams SE, Belk KE, Williams FL, Wise JW, Smith GC: An evaluation of current and alternative systems for quality grading carcasses of mature slaughter cows. J Anim Sci, 76, 2094, Tatum JD, George MH, Belk KE, Smith GC: Development of a "Palatability Assurance Critical Control Points" (PACCP) Model to Reduce the Incidence of Beef Palatability Problems- Final Report to the National Cattlemen s Beef Association. Colorado State University, Fort Collins, CO Wulf DM, O Connor SF, Tatum JD, Smith GC: Using objective measures of muscle color to predict beef longissimus tenderness. J Anim Sci, 75, 684, Minitab: Statistical Software Bortland Inc Rook AJ, Dhanoa MS, Gill M: Prediction of the voluntary intake of grass silages by beef cattle. 3. Percision of alternative prediction models. Anim Prod, 50, , Smoler E, Rook AJ, Sutton JD, Beever DE: Prediction of milk protein concentration from elements of metabolizable protein system. J Dairy Sci, 81, , Bozkurt Y, Ap Dewi I: An evaluation of equations based on metabolizable energy and ARC protein schemes to predict liveweight gain of housed beef cattle. Suranaree J Sci Techno, 8, 15-30, Fuentes-Pila J, Ibanez M, De Miguel JM: Predicting average feed intake of lactating Holstein cows fed totally mixed rations. J Dairy Sci, 86, , Yan T, Angnew RE, Murphy JJ, Ferris CP, Gordon FJ: Evaluation of different energy feding system with production data from lactating dairy cows offered grass silage-based diets. J Dairy Sci, 86, , Bozkurt Y, Ozkaya S: An assessment of the ARC metabolizable energy system to predict live weight gain of Brown Swiss cattle grown under feedlot conditions in Turkey. J Biol Sci, 5 (4): , Bozkurt Y: Prediction of body weight from body size measurements in Brown Swiss cattle fed under small-scale farming conditions. J Appl Anim Res, 29, Bozkurt Y: An evaluation of the ME system model to predict performance of different breeds of feedlot beef cattle fed under two different feeding periods. Asian J Anim Vet Adv, 3 (4): , Schutte BR, Biju N, Kranzler GA, Dolezal HG: Color video image analysis for augmenting beef carcass grading. Animal Research Report. pp edu/research/1998rr/07.html Accessed: Dasiewicz K, Slowinski M, Sakowski T, Oprzadek J, Wisnisch A, Dymnicki E, Sloniewski K: The attempt of video image analysis use for estimation of meat quality of beef breeds bulls. Food Science and Technology, 6 (2), Accessed:

11 Kafkas Univ Vet Fak Derg 15 (4): , 2009 DOI: /kvfd A RESEARCH ARTICLE [1] Microbiological Contamination Model of Staff Hands Employed at Bakeries Due to Staff s Life Style and Individual Parameters [1] * ** Emek DUMEN * Funda SEZGIN ** This study was supported by the Istanbul University Scientific Researches Project Unit with the Issue Number of 2052/ Istanbul University, School of Veterinary Medicine, Department of Food Hygiene and Technology, Avcılar, İstanbul - TURKEY Mimar Sinan Fine Arts University, Faculty of Science and Letter, Department of Statistics, Beşiktaş, İstanbul - TURKEY Summary Makale Kodu (Article Code): 2009/018-A In this study it is aimed how to determine correlations between gender, marital status, child status, hometown, education and position in the sales point variables of the bakery staff, employed in 5 different geographic regions of in Turkey at 100 franchise bakery shops belong same brand, effect the microbiological load of their hands by forming a microbiological contamination model using the statistical methods. We chose 30 sales points from Marmara region, 60 sales points from Aegean, Middle Anatolian and Mediterrenean regions, 20 sales points for each region, and 10 sales points for Black Sea regions. During 12 months 3 staff were has been chosen from each sales point and for the total time interval of the study the same staff were sampled except the conditions which are not due to study team as job quitting, illness problems, vacations and so on. The swab samples were analyzed in the point of wiev of counts of total aerobic mesophiles coliforms, Escherichia coli and Staphylococcus aureus. For establishing a microbiological contamination model due to how staff s individual and life style parameters effect the microbiological load of their hands all the parameters and variables were subjected to comparative logistic regression analysis. For all the geographical regions, the education and the marital status of the staff were determined as statistically important variables that affect the microbiological load of the staff hands which can risk consumers health. Keywords: Microbiological modeling, Bakery, Staff Pastanelerde İstihdam Edilen Personelin Ellerinin Mikrobiyolojik Yükünün Kişisel ve Yaşam Biçimleri Özelliklerine Göre Modellenmesi Özet Bu çalışmada, Türkiye nin beş değişik coğrafi bölgesinde bulunan ve aynı marka adı altında faaliyet gösteren toplam 100 adet satış noktasında istihdam edilen personelin cinsiyet, yaş, medeni durum, çocuk durumu, memleket kökeni, eğitim durum ve işletmedeki pozisyonunun, personelin ellerindeki mikrobiyolojik yükü nasıl etkilediğinin modellenmesi amaçlanmıştır. Toplam 100 adet satış noktasından 30 adedi Marmara bölgesinden, 20 şer adedi Ege, Akdeniz ve İç Anadolu bölgelerinden, 10 adedi ise Karadeniz bölgesinden seçilmiştir. Her satış noktasında yıl boyunca, işten ayrılma durumu hariç, aynı personel üzerinde çalışılmış ve her bir nokta için 3 adet değişik pozisyonlardaki personel seçilmiştir. Personelden alınan el sürüntüleri toplam mezofilik aerobik bakteri, toplam koliform grubu bakteri, Escherichia coli ve Staphylococcus aureus parametreleri yönünden analize alınmıştır. Personelin bireysel değişkenlerinin ellerindeki mikrobiyolojik yükü nasıl etkilediğinin modellemesi için, tüm parametreler karşılaştırmalı lojistik regresyon analizine tabi tutulmuştur. Tüm bölgeler için, personelin eğitim ve medeni durumu, tüm mikrobiyolojik parametreler açısından mikrobiyolojik kirlilik üzerine etkili olan ve istatistiki olarak önemli bireysel değişkenler olarak tespit edilmiştir. Anahtar sözcükler: Mikrobiyolojik modelleme, Pastane, Personel INTRODUCTION Food borne diseases (FBD), in particular gastrointestinal infections, represent a very large group of pathologies with a strong negative impact on the health of the population because of their widespread nature 1-5.Foods that are frequently associated with Staphylococcal and Escherichia coli food poisoning İletişim (Correspondence) /17154 emekdumen@yahoo.com

12 492 Microbiological Contamination Model of... include meat, salads, sandwich accoutrements, and especially cream-filled bakery items dairy products 6-8. The risk of food-borne illness due to contact with hands or surfaces depends on both the level of contamination as well as the probability of transfer and the importance of contaminated surfaces in relation to potential transmission of pathogens to food is apparent in food processing 9-15 According to the laws related to the new hygiene regulations of European Community (EU), it is a compulsory process for all kinds of food plants to apply food security systems at the all steps of the production and selling processes since 2005/ Coliforms and E. coli are the main indicator microorganism groups that determine fecal contamination and inadequate hygienic status of the foods The most important contamination source of the mentioned microorganism groups are the toilets that have inadequate hygienic status and the staff who do not apply hygiene processes especially after using the toilets. Staphylococcus aureus, is the main third strain cause to food poisoning in the world The toxins of S. aureus are thermo stabile molecules. Even the agent is inactivated; there may be a high possibility of contamination of the toxins to the food mechanically and also to the consumers, too, if the agent had enough time for producing enterotoxins 37. In this study, it is aimed to explore if gender, age, marital status, child status, hometown, education level and the position in the plant variables effect the microbiological load of the hands of staff employed at bakeries were explored by forming a statistical microbiological contamination model due to the parameters mentioned above. We also aimed to indicate which variable(s) plays predisposition role or facilitate to increase microbiological loads of the hands for E. coli, coliforms bacteria count, S. aureus and counts of total aerobic mesophiles parameters. So, we explored the relation between staff s individual variables and microbiological load of their hands by forming a statistical contamination model. MATERIAL and METHODS Material We used hand swabs of staff employed at the visited bakeries located in different regions and cities of Turkey as material in this study. Methods Swab Sampling from hands: Study team visited a total of 100 bakeries belonged to same brand located in 5 different geographical regions of Turkey (Marmara, Aegean, Mediterranean, Black Sea and Middle Anatolian) every month during the year We chose 30 sales points from Marmara region, 20 sales points from Aegean region, 20 sales points from Mediterranean region, 20 sales points from Middle Anatolian region and 10 sales points from Black Sea region as total of 100 sales points. We sampled 3 staff from each sales point and we tried to sample the same staff except special conditions that are not due to the study team as job quitting, illness problems and off days of the staff. The hand swab samples were analyzed for 4 microbiological parameters as counts of total aerobic mesophiles, coliforms, E. coli and S. aureus. During the study, team members were also responsible from recording the individual data of the staff whose hand swab samples were taken (gender, age, marital status, child status, hometown, educational status and the position in the plant were the data that are recorded). According to the individual data of the staff, we formed microbiologic contamination models to expose if the lifestyle and social status of the staff of the food plants affect the public health and the food security directly or indirectly. Microbiological Analysis: At the end of every sampling process, we transported the samples to laboratory immediately and the related analyses were made in the same day. All the samples taken from staff were analyzed according to the FDA-BAM norms for counts of total aerobic mesophiles, coliforms, E. coli and S. aureus 38. Logistic Regression Analysis: The relationship between each microbiological parameters (counts of total aerobic mesophiles, coliforms, Escherichia coli and Staphylococcus aureus) and the individual variables of the staff were analyzed was to expose if the microbiological parameters are the indicator of the individual variables or not. Acceptable and nonacceptable parameters were enumerated with 0 and 1 codes respectively. Forward Wald method was used for each single micro-biological parameter for logistic regression analysis at SPSS version RESULTS According to our results, microbiological contamination level of staff from different geographic

13 493 DUMEN, SEZGIN regions were significantly different from each other (P<0.05). Table 1 shows the results of the differences among geographical regions of the microbiological contamination level of the staff. One way variance analysis method (MANOVA) was used to form the model of contamination according to the geographical regions parameter. One way variance analysis indicates the statistically significance differences two or above variables for a single parameter 39. Logistic regression analysis method was used to form the microbiological contamination model according to the chosen parameters for the staff s individual variables. For the counts of total aerobic mesophiles parameter, education variable of the analyzed staff was effective. Education level was inversely proportional with the contamination level of the indicated parameter. Table 2 shows the logistic regression analyses results for counts of total aerobic mesophiles parameter. The marital status and education variables of the staff were designative for coliforms parameter. Married staff was cleaner than divorced and single staff. Table 3 shows the logistic regression analysis results for coliforms parameter. Parallel to coliforms parameter, the marital status and the education variables again were the effective variables for E. coli and S. aureus. Table 4 and Table 5 show the logistic regression analysis results for E. coli and S. aureus parameters respectively. Table 1. MANOVA results of the differences among the geographic regions of the microbiological pollution level of staff Tablo 1. Personel ellerindeki mikrobiyolojik kirlilik seviyesinin coğrafi bölgelere göre farklılıklarının MANOVA sonuçları Variables Effect Value F Hypothesis df Error df Sig. Intercept Pillai s Trace Wilks Lambda Hotelling s Trace Roy s Largest Root a a a a *.000*.000*.000* Region Pillai s Trace Wilks Lambda Hotelling s Trace Roy s Largest Root b *.000*.000*.000* a: Exact statistics b: The statistic is an upper bound on F that yields a lower bound on the significance level. α= 0.05 significant level, statistical differences among the geographical regions was determined because P<0.05 Table 2. Logistic regression analyses results for counts of total aerobic mesophiles parameter Tablo 2. Toplam mezofilik aerobic bakteri sayısı parametresi için lojistik regresyon analizi Variables B S.E. Wald df Sig. Exp (B) Sex Age Marrital Status Child Status Hometown Education Position in the Plant Constant α = 0.05 * signed parameters are significant statistically at 0.05 level For the goodness of fit ג 2 was used. Model is significant with ג 2 = 97.1 P= * Table 3. Logistic regression analysis results for coliforms parameter Tablo 3. Toplam koliform grubu bakteri sayısı parametresi için lojistik regresyon analizi Variables B S.E. Wald df Sig. Exp (B) Sex Age Marrital Status Child Status Hometown Education Position in the Plant Constant α = 0.5 * signed parameters are significant statistically at 0.05 level For the goodness of fit ג 2 was used. Model is significant with ג 2 = 56.3 P= * *.000*

14 494 Microbiological Contamination Model of... Table 4. Logistic regression analysis results for E. coli Tablo 4. E. coli için lojistik regresyon analizi Variables B S.E. Wald df Sig. Exp (B) Sex Age Marrital Status Child Status Hometown Education Position in the Plant Constant α = 0.5 * signed parameters are significant statistically at 0.05 level For the goodness of fit ג 2 was used. Model is significant with ג 2 = 41. P= * * Table 5. Logistic regression analysis results for S. aureus Tablo 5. S. aureus için lojistik regresyon analizi Variables B S.E. Wald df Sig. Exp (B) Sex Age Marrital Status Child Status Hometown Education Position in the Plant Constant α = 0.5 * signed parameters are significant statistically at 0.05 level For the goodness of fit ג 2 was used. Model is significant with ג 2 = 87. P= * * DISCUSSION According to our results, significant differences determined among all the geographical regions at P<0.05 level in spite of all visited bakeries were the franchise of the same brand (data not shown). We think that this is because there are not homogenous employment conditions in the visited sales points and inadequate education applications. The marital status, hometown and education positions of the staff were highly variable even at the same sales point. We can also say that staff hygiene, consequently the product hygiene and the consumers health is directly related with the education. In a study, Bas et al. 40 made an investigation at 115 food plants and pointed out that, critical control points of the food plants vary according to the demographic structure and regional differences. In our results, too, demographic structure is the factor that determines the differences among the geographical regions. We also think that various traditions that change due to geographic regions (crowded family, culture, dominant work sectors in some regions, etc.) form the microbiological contamination differences of the staff hands among the geographical regions basically. The education variable was statistically significant in our model for counts of total aerobic mesophiles parameter at P<0.05 level. Other individual variables were not statistically significant due to our results. According to the odds ratio values in Exp (B) column, education variable for counts of total aerobic mesophiles parameter is 5 times effective on microbiological contamination of the staff hands for our microbiological model. In the classification table, dirty decided staff were estimated at a rate of 93.3% and clean decided staff were estimated at a rate of 94.9%. All the microbiological paramters appropriateness evaluation was decided according to the criterion of floury food products stated in Turkish Codex Alimentarius 41, Codex Alimantarius 17 and FDA-BAM 38,42. Centers for Disease Control and Prevention 43 reports that hands including the enteric viruses, are the main contamination sources of the food borne diseases 44. We also want to state that the hands are also the most important contamination way of hepatitis A virus outbreaks in spite of mentioned parameter was not investigated in our study 43. We directly relate hand washing habit to education levels of the staff. Appropriate hand washing processes eliminate soil sourced pathogens 45. It is very important to use the right chemicals (non-

15 495 DUMEN, SEZGIN toxic to foods, must not leave any residues on the hands, must provide antisepsis condition) while washing hands as for proper washing habit It is pretty hard to remove the permanent microorganisms from hands by friction movements because they are protected by the glands that secrete fats 49,50. In this respect, even to wash hands frequently may not be effective for eliminating soil sourced pathogens if the right chemical agents are not used. We think that high educated staff would increase the effectiveness of the hygiene applications and food security systems. According to the results we got, while the microbiological pollution model of the staff hands was being formed at the 2nd step of the study, 86.3% of 300 staff (259 staff, data not shown) had values above 103 cfu/10 cm 2 for counts of total aerobic mesophiles parameter. At some staff, the microbiological load of counts of total aerobic mesophiles on hands was 106 cfu/10 cm 2 and above values (132 of 300 staff, 44%, data not shown). The marital status and the education variables were effective on the microbiological pollution model of the staff hands due to categorical data for coliforms. E. coli and coliforms parameters are related to the same individual variables however much coliforms include additional different species than E. coli. According to odds ratio values in the column of Exp (B), the marital status variable was 4.1 times effective on coliforms while the education variable was effective on the same parameter 5.1 times. Insufficient education level (primary and secondary school levels) and being non-married (single or divorced) increased the level of microbiological contamination. Hometown variable effectiveness was determined as 1 time. For the hometown variable, Middle Anatolian, East Anatolian and Southeast Anatolian regions originated staff increased the dirtiness. In the classification table, dirty decided staff were estimated at a rate of 90.7% and clean decided staff were estimated at a rate of 89.8%. Coliforms parameter also state the existence of the microorganisms included in Enterobactericeae family such as Salmonella, Shigella, Yersinia, Proteus and Klebsiella on behalf of E. coli and all the microorganisms mentioned above are important risk factors for the food security 51. De Wit & Rombouts 52 indicate that high values of coliforms points inadequate hygienic applications and non-applied toilet hygiene procedures and hand washing rules. Similarly, the effective factors for coliforms parameter were the marital status and the education variables in our study. We think that, to gain hand washing habit and to apply hand washing rules (to use correct and suitable chemicals, sufficient time and drying hands) is directly related with educational status. Besides, we can say that, unconsciousness about the probable damages of pathogens is too, directly related with the education level due to our findings. The marital status variable (divorcement and being single) was 4.1 times effective on coliforms parameter. According to the study team, to continue a regular life and integrity of family life is quite effective on individual s hygiene. Also we think that general hygiene of a person is due to stableness of psychological situation as much as education. For E. coli parameter, detailed results of categorical data indicate that the marital status and the education variables were effective on the microbiological contamination model of the staff s hands. The education variable (education level at primary or secondary school) and divorcement increased the contamination of the staff s hands and was evaluated as critical individual variables that risk consumers health and product hygiene for our model. Due to the model outputs, the marital status and the education variables were statistically significant on E. coli parameter at P<0.05 level. According to odds ratio values in the column of Exp (B), the marital status variable was 4.6 times effective on coliforms while the education variable was effective on the same parameter 5.4 times. In the classification table, dirty decided staff were estimated at a rate of 92.1% and clean decided staff were estimated at a rate of 93.3%. Legnani et al. 53, indicate that the values of E. coli for staff hands and surfaces must not be above the value of 1 cfu/cm 2. Similarly in our study, we accepted 0 cfu/cm -2 value as hygienic and acceptable for our regional data and microbiological contamination model. The values above the mentioned levels were decided as non-acceptable for both two parts of the study and evaluated as a risk factor. Moreover, we want to remind that E. coli (+) staff may have a high potential risk of carrying pathogen serotypes like E. coli O111 and E. coli O157:H7 in spite of serotyping analysis were not performed by the study team. Low education level increased the microbiological contamination for E. coli in our study, and our results are parallel to the findings of Aycicek et al. 54. The scientists indicate that, they analyzed directly food contact staff at 30 food plants and found 7.8% of E. coli (+) staff. The same scientists also point out that E. coli (+) staff s had a low degree of socio-economic position 54. Aktan et al. 55, also stated that staff with

16 496 Microbiological Contamination Model of... low education level have a high risk of carrying E. coli and in a study of them, they isolated E. coli with a rates of 37.5% and 41.7% at the staff before and during work respectively in hospital kitchens established at rural areas. For S. aureus parameter, again, the marital status and the education variables were effective. The mentioned variables were determined as significant at P<0.05 level. However, gender, age, children, hometown and position variables were not determined as significant for S. aureus (P>0.05). According to odds ratio values in the column of Exp (B), the marital status variable was 2 times effective on coliforms while the education variable was effective on the same parameter 4 times. In the classification table, dirty decided staff were estimated at a rate of 92.7% and clean decided staff were estimated at a rate of 91.5%. S. aureus, is known to be predominant species for food borne intoxication cases caused by Staphylococci 56. Microorganisms living on human skin basically separate into two groups. The first group is permanent agents and the second group is temporary microorganisms. S. aureus, is the only pathogen and permanent microorganism on human skin 54, We think that the education and the marital status variables effects hand washing habit. Even effective and frequent hand washing and taking shower transactions can not totally eliminate S. aureus. The individual variables match the contamination ways of S. aureus. According to Moore et al. 61, direct food contact surfaces and the staff is the most important contamination way of S. aureus. According to the t test results, we can arrange the regions generally in order from clean to dirty due to the microbiological parameters analyzed; Marmara, Aegean, Blacksea, Mediterrenean and Middle Anatolian (data not shown). When the geographical regions analyzed two by two, again the region that has the highest microbiological contamination rate due to the staff hands was Middle Anatolian region. According to the data of Turkish Government Instute of Statistics 62, the Middle Anatolian is the region that includes the maximum count of families that have 10 and above household in the same house in the five geographical region of Turkey (109,195 families). This is even higher than the Marmara region (54,951 families), that include İstanbul, the most crowded and biggest city of Turkey 62. According to these data, we think that the demographic construction and the number of household parameters can indirectly effect the public health and food hygiene negatively. To determine microbiological contamination at the last form of the food products is a very important process for consumers health, but unfortunately this process is not able to prevent the contamination at the process stage and cause economic losses. 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19 Kafkas Univ Vet Fak Derg 15 (4): , 2009 DOI: /kvfd A * ** *** RESEARCH ARTICLE Responses of Broilers under Cold Conditioning (15ºC) to Dietary Triiodothyronine and Iodine Combined to Antioxidants (Selenium and Vitamin C) İsmail SEVEN * Pınar TATLI SEVEN ** Seval YILMAZ *** University of Firat, Vocation School of Sivrice, Elazig - TURKEY University of Firat, Faculty of Veterinary Medicine, Department of Animal Nutrition and Nutritional Diseases, Elazig - TURKEY University of Firat, Faculty of Veterinary Medicine, Department of Biochemistry, Elazig - TURKEY Summary Makale Kodu (Article Code): 2009/022-A This experiment was conducted to determine the influence of dietary antioxidants (selenium (Se) and vitamin C), and triiodothyronine (T3) to iodine on biochemical parameters, malondialdehyde (MDA), catalase (CAT) in broilers exposed cold. In the study, 150, one-day-old, broiler chicks (Ross 308) were used. Chicks were randomized into 1 control and 4 treated groups each containing 30 birds and each experimental group comprised 3 replicates of 10 birds. The experimental groups were as follow: control was fed with basal diet; group I was fed with basal diet supplemented with 1 mg/kg selenium as sodium selenite plus 2 mg/kg iodine as calcium iodate; group II was fed with basal diet supplemented 1 mg/kg selenium plus 1 mg/kg T3; group III was fed with basal diet supplemented with 250 mg/kg vitamin C as ascorbic acid plus 1 mg/kg iodine; group IV was fed with basal diet supplemented with 250 mg/kg vitamin C plus 1 mg/kg T3. Plasma T3, triglyceride and SGPT were significantly different among the groups. MDA level in heart tissue of control group was found significantly higher than those of other groups (P<0.01). MDA levels in liver (P<0.01) and abdominal fat (P<0.001) tissues of control and group I were determined significantly higher than those of other groups, whereas MDA level in lung tissue was similar found in all groups. The CAT activity of liver of control and group I was found significantly the highest (P<0.001). Results showed that cold exposure in broilers induced oxidative damage in tissues, but this damage decreased partly in supplement groups, except group I. Keywords: Cold conditioning, Antioxidant enzymes, Iodine, Ascorbic acid, Selenium, Triiodothyronine, Broiler Özet Soğuk Koşullar Altındaki (15ºC) Broylerlerin Diyetsel Antioksidanlarla (Selenyum ve Vitamin C) Kombine Edilen Triiodotiroidinin ve İyodine Tepkileri Bu araştırma soğuğa maruz kalan broylerlerde diyetsel antioksidanların (selenyum (Se) ve vitamin C) ve triiodotiroidinin (T3) biyokimyasal parametreler, malondialdehit (MDA) ve katalaz (CAT) üzerine etkilerini belirlemek için yapıldı. Çalışmada bir günlük yaşta 150 adet broyler civcivler (Ross 308) kullanıldı. Civcivler her biri 30 civcivden oluşan 1 kontrol ve 4 deneme grubuna tesadüfi olarak ayrıldı ve her deneme grubu 10 civcivden oluşan 3 tekerrür grubu ihtiva etti. Deneme grupları şöyledir; kontrol grubuna bazal diyet yedirildi, grup 1 e sodyum selenit olarak 1 mg/kg selenyum + kalsiyum iyodat olarak 2 mg/kg iyot katkılı basal diyet yedirildi; grup II ye 1 mg/kg selenyum + 1 mg/kg T3 katkılı bazal diyet yedirildi; grup III e askorbik asit olarak 250 mg/kg vitamin C + 1 mg/kg iyot katkılı bazal diyet yedirildi; grup IV e askorbik asit olarak 250 mg/kg vitamin C + 1 mg/kg T3 katkılı bazal diyet yedirildi. Plazma T3, trigliserit ve SGPT gruplar arasında önemli oranda farklıydı. Kontrol grubunun kalp dokusundaki MDA düzeyi (P<0.01) diğer gruplarınkinden önemli oranda yüksek bulundu (P<0.01). Akciğer dokusunda MDA düzeyi tüm gruplarda benzer bulunurken, kontrol ve grup I in karaciğer (P<0.01) ve kalp (P<0.001) dokularında MDA düzeyi diğer gruplarınkinden önemli oranda yüksek tespit edildi. Kontrol ve grup I in karaciğer CAT aktivitesi önemli oranda en yüksek bulundu (P<0.001). Sonuçlar gösterdi ki, broylerlerde soğuk maruziyeti dokularda oksidatif hasarı indükledi, ancak grup I dışındaki katkı gruplarında bu hasar kısmen azaldı. Anahtar sözcükler: Soğuk koşullar, Antioksidan enzimler, İyot, Askorbik asit, Selenyum, Triiodotiroidinin, Broyler İletişim (Correspondence) /3934 pintatli@hotmail.com

20 500 Responses of Broilers under Cold... INTRODUCTION Ambient temperature is an important factor in poultry breeding. The suitable temperature for poultry is between 16-25ºC 1,2. Environmental stress declines the production and moreover it may increase the death rate in poultry 1,3. Cold condition, a physical environmental stressor, has been shown to have variable modulator effects on cells of the immune system in animals. Animal exposure to cold environment is associated with increased serum triiodothyronine (T3) levels, which is felt to be main factor responsible for the sustained phase of cold adaptation characterized by increased heat production. During such a phase, the heat is produced by nonshivering thermogenesis, a process involving an array of changes in metabolic activity of the whole organism 4. High concentrations of thyroid hormones stimulate free radical formation in mitochondria by affecting oxygen metabolism 5. Thyroid hormones act on mitochondria by regulating energy metabolism, and mitochondria are a major source of intracellular free radials. Data from in vivo and in vitro studies indicate that thyroid hormones have a considerable impact on oxidative stress. Thyroid hormones are unique in that they require the trace element iodine (I) for biological activity 6. Selenium (Se) is an essential trace element that regulates a major component of the antioxidant defense mechanism in all living tissues 7. Types I, II, and III iodothyronine monodeiodinases are now known to be selenoenzymes. Vitamin C supplementation leads to strengthening of the antioxidative defence and consequent lowering of the degree of oxidative stress 8. In thyroid disorders, it should been taken supplements containing molecules with antioxidant properties, such as vitamin C, Vitamin E, and coenzyme Q10. Especially vitamin C is very important. The play an important role in bringing about an improvement in the general functioning of the individual s immune system along with the functioning of the thyroid gland itself 9. In addition, several works revealed a beneficial effect of Vitamin C supplementation in stressed-laying hens and broilers 6,10,11. The present study was conducted by using triiodothyronine (T3) hormone, Se, I and vitamin C combinations on biochemical parameters and antioxidant enzyme activities of broilers exposed to cold at 15ºC. MATERIAL and METHODS The experiment was in accordance with animal welfare, and was conducted under protocols by the Veterinary Faculty in Elazig-Turkey. In this study, 150 one-day-old, broiler chicks (Ross 308) were used. Chicks were randomized into 1 control and 4 experimental groups, each containing 30 birds and each treatment group comprised of 3 replicates of 10 birds. Corn and soybean meal-based feeds were formulated according to the requirements suggested by National Research Council 12. Diets were formulated as starter, grower and finisher diets (Table I). The experimental groups were as follows; Group I (control) was fed with basal diet, Group II (Se+I) was fed with basal diet supplemented with 1 mg/kg Se as sodium selenite plus 2 mg/kg I as calcium iodate, Group III (Se + T3) was supplemented with 1 mg/kg Se plus 1 mg/kg T3 (Sigma, 2877), Group IV (Vitamin C + I) was supplemented with 250 mg/kg Vitamin C as ascorbic acid plus 1 mg/kg I, Group V (Vitamin C+T3) was diet supplemented with 250 mg/kg Vitamin C plus 1 mg/kg T3. Small amounts of the basal diet were first mixed with the respective amounts of Se, I, Vitamin C and T3 as a small batch, then with a larger amount of the basal diet so that the total amount of the respective diets was homogeneously mixed. This process was Table 1. Composition of the basal diet, % Tablo 1. Temel diyetin bileşimi, % Ingredients Starter Grower Finisher Corn Soybean meal Fish meal Soybean oil Limestone Dicalcium phosphate L-Lysine hydrochloride Vitamin-mineral premix 1 DL- Methionin Sodium chloride ME, kcal/kg CP, % Calcium, % Total phosphorus,% Selenium ppm (analysed) Iodine, ppm (analysed) Calculated nutrient content : Vitamin and mineral premix provided per kilogram of diet: Vitamin A, IU; Cholecalciferol, IU; Vitamin E, 30 mg; Vitamin K3, 5 mg; Vitamin B1, 3 mg; Vitamin B2, 6 mg; Vitamin B6, 5 mg; Vitamin B12, 30 mg; Ca-D-pantothenate, 10 mg; Folic acid, 0.75 mg; D-biotin, 0.08 mg; Mn, 80 mg; Zn, 60 mg; Fe, 40 mg; Cu, 5 mg; Se, 0.15 mg; Co, 0.1 mg; I, 0.4 mg 2 : Based on NRC (1994) feed composition tables