Research Article
Yem formunun yakın kızılötesi yansıma spektroskopi metoduyla süt sığırı karma yemlerinin besin madde değerlerine etkisinin belirlenmesi
Abstract
Amaç: Bu araştırmada; toz, granül ve pelet formdaki süt sığırı karma yemlerinin ham besin madde içerikleri kimyasal ve yakın kızılötesi yansıma spektroskopi (NIRS) metodlarıyla analizleri yapılmış ve sonuçları değerlendirilmiştir.
Materyal ve Yöntem: Farklı zamanlarda her bir formdan alınmış 50 karma yem örneğinin ham besin madde analizleri her iki metotla yapılmıştır.
Araştırma Bulguları: Kuru madde değerleri hariç diğer besin madde değerleri bakımından, yem formlarının kimyasal analizi sonucundaki fark istatiksel olarak önemli çıkmamıştır. Ham protein (HP) ve ham yağ (HY) bakımından yem formları arasındaki farklar, NIRS metoduna göre analizde ise; ham protein (HP) ve ham yağ (HY) bakımından, yem formları arasındaki farklar istatistiksel olarak önemli olmadığı bulunmuştur. Oysan NIRS’a göre; KM, ham kül (HK), organik madde (OM) ve ham selüloz (HS) bakımından yem formları arasındaki farkın önemli olduğu görülmüştür (P<0.05). Kimyasal ve NIRS analizlerinin KM, OM ve HP değerleri arasında istatistiksel fark gözlenmezken, iki analiz metodunun HK, HY ve HS değerlerinde ise istatistiksel farklar görülmüştür (P<0.05).
Sonuç: Yemlerin iki farklı enerji formülüne göre kimyasal ve NIRS analiz sonuçlarına göre hesaplanmış metabolik enerji içerikleri karşılaştırılmış, sadece eşitlik 1’e göre istatiksel fark bulunmuştur (P<0.05).
Keywords
Supporting Institution
Aydın Adnan Menderes Üniversitesi
Project Number
ZRF-15075
Thanks
Bu çalışmadaki kimyasal ve spektrofotometrik analizler, Aydın Adnan Menderes Üniversitesi Tarımsal Biyoteknoloji ve Gıda Güvenliği Uygulama ve Araştırma Merkezi’ndeki (ADU- Tarbiyomer) cihazlarda yapılmıştır. ADU- Tarbiyomer Yetkililerine teşekkür ederiz
References
- Anonymous, 2019a. Global feed out put up 3% in 2018. All About Feed. 30 January 2019. https://www.allaboutfeed.net/Compound-Feed/Articles/2019/1/3-growth-in-compound-feed-in-2018-387470E/ Erişim: Ekim 2019
- Anonymous, 2019b. International feed industry federation annual report 2016/17. http://annualreport.ifif.org/#start Erişim: Ekim 2019
- AOAC., 1997. Association of official analytical chemists. 16th ed. Washington, D.C
- Coleman, S.W., S. Christiansen and J.S. Shenk. 1990. Prediction of botanical composition using NIRS calibrations developed from botanically pure samples. Crop Science, 30: 202-207.
- Conzen, J.P. 2006. Validation of chemometric models and analysis of unknown samples. In: Multivariate Calibration. A parctical guide for developing methods in the quantitative analytical chemisty. BrukerOptif GmbH, 2.nd English Edition. Pp. 13-70. Germany.
- Corson, D.C., G.C. Waghorn, M.J. Ulyattand and J. Lee. 1999. Nırs: forage analysis and livestock feeding. Proceedings of the New ZealandGrasslandAssociation, 61: 127–132.
- De Boever, J.L., B.G. Cottyn, J.M. Vanacker and C.V. Boucqu, 1995. The use of NIRS to predict the chemical composition and the energy value of compound feeds for cattle. Animal Feed Science and Technology, 5: 243-253
- De Jong, J.A., J.M. De Rouchey, M.D. Tokach, R.D. Goodband, J.C. Woodworth, S.S. Dritz, J. Erceg, L. McKinney and S. Smith. 2014. Formation of fines during the pelleted feed manufacturing process and the resulting differences in nutrient composition of fines and pellets. Kansas Agricultural Experiment Station ResearchReports, 0: 297-301.
- Decruyenaere, V., E. Froidmont, N. Bartiaux-Thill, A. Buldgen and D. Stilmant, 2012. Faecal near-infrared reflectance spectrometry (NIRS) compared with other techniques for estimating the in vivo digestibility and dry matter intake of lactating grazing dairy cows. Animal Feed Science and Technology, 173: 220-234.
- Fontaine, J. J. Hörr and B. Schirmer, 2001. Near-Infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fish meal, meat meal products, and poultry meal. Jornal of Agricultural and Food Chemistry, 49: 57-66
- Fontaine, J., B. Schirmer, And J.Hörr, 2002. Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat, barley, corn, triticale, wheat bran/middlings, rice bran, and sorghum. Jornal of Agricultural and Food Chemistry, 50, 3902-3911.
- Goldman, A., A. Genizi, A. Yulzari and N.G. Seligman, 1987. Improving the reliability of the two stage in vitro assay for ruminant feed digestibility by calibration against in vivo data from a wide range of sources. Anim. Feed Sci. Technol., 18: 233-245.
- Görgülü, M., 2014. Ruminant yemlerin SE, ME, TDN, NEm, NEg, NEL, değerlerinin ham besin maddelerinin hesaplanması. http://www.muratgorgulu.com.tr/altekran.asp?id=97 Erişim: Temmuz 2019
- Karaman, M. and S. Erdemir. 2018. Kanatlı hayvanların beslenmesinde kullanılan bazı karma yemlerin kimyasal kompozisyonunun near infrared reflektans spektroskopi (NIRS) İle belirlenmesi. Black Sea Journal of Agriculture, 1(2): 24-28.
- Osborne, B.G. and T. Feam. 1988. Near-infrared spectroscopy in food analysis. Longmans, Harlow, UK, 200 pp.
- Park, R.S., R.E. Agnew, F.J. Gordon and R.W.J. Steen. 1998. The use of near infrared reflectance spectroscopy (NIRS) on undried samples of grass silage to predict chemical composition and digestibility parameters. Anim. Feed Sci. Technol., 72(1-2): 155-167.
- Pehlevan, F ve M. Özdoğan 2015. Bazı alternatif yemlerin besin madde içeriğinin belirlenmesinde kimyasal ve yakın kızılötesi yansıma spektroskopi metotlarının karşılaştırılması. Tekirdağ Ziraat Fakültesi Dergisi, 2015: 12 (02):1-10
- Pehlevan, F. 2014. Bazı alternatif yemlerin kimyasal kompozisyonunun tahmini için nearinfraredreflektans spektroskopinin (NIRS) kullanımı. Adnan Menderes Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi, sayfa sayısı: , Aydın.
- Pérez-Marín, D.C., A. Garrido-Varo, J.E. Guerrero-Ginel and A. Gómez-Cabrera, 2004. Near-infrared reflectance spectroscopy (NIRS) for the mandatory labelling of compound feedingstuffs: chemical composition and open-declaration. Animal Feed Science and Technology, 116: 333–349.
- Quampah, A., Z.R. Huang, J.G. Wu, H.Y. Liu, J.R. Li, S.J. Zhu and C.H. Shi, 2012. Estimation of oil content and fatty acid composition in cottonseed kernel powder using near infrared reflectance spectroscopy. Journal of the American Oil Chemists’ Society, 89(4): 567-575
- SAS. 1999. The SAS System. Version 8. Copyright © 1999 by SAS Institute Inc., Carry NC, USA
- Shenk, J.S. and M.O. Westerhaus. 1985. Accuracy of NIRS instruments to analyse forage and grain. Crop Sci., 25, 1120–1122
- Soldado, A., J. R. Quevedo, A. Bahamonde, S. Modroño, A. Martinez-Fernandez, F. Vicente, D. Perez-Marin, A. Garrido-Varo, J. E. Guerrero, B., and D.L. Roza-Delgado. 2011. Validation of two discriminant strategies applied to NIRS data spectra for detection of animal meals in feedstuffs. Spanish Journal of AgriculturalResearch, 9(1):41-49.
- Stuth, J., A. Jama and D. Tolleson, 2003. Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy. Field Crops Research, 84: 45-56.
- TSE. 1991. Hayvan yemleri-metabolik (çevrilebilir) enerji tayini (kimyasal Metot). UDK 636.085. TS 9610. Türk Standartları Enstitüsü, Ankara.
- Ünlü, H.B., A. Kılıç and T. Ayyılmaz. 2015. Farklı düzeylerde öğütülmüş dane mısır ilavesinin yonca silajının yem değeri üzerine etkisi. Ege Üniversitesi Ziraat Fakültesi Dergisi. 52: 335-341.
- Williams, P.C. and D. Sobering. 1993. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. Journal of Near Infrared Spectroscopy, 1: 25-32.
- Xiccato , G., A. Trocino, J.L. De Boever, L. Maertens, R. Carabaño, J.J. Pascual, J.M. Perez, T. Gidenne , L. Falcao-E-Cunha. 2003. Prediction of chemical composition, nutritive value and ingredient composition of European compound feeds for rabbits by near infrared reflectance spectroscopy (NIRS). Animal Feed Science and Technology, 104(2003):153-168.
Determination of the effect of feed form on nutrient values of dairy cattle mixed feeds by near-infrared reflectance spectroscopy method
Abstract
Objective: In this study, Crude nutrients contents of dairy cattle mixed feeds in powder, crumble, and pellet form were analyzed by chemical and near-infrared reflectance spectroscopy (NIRS) methods, and the results of analyzes were evaluated.
Material and Methods: Crude nutrient analysis of 50 mixed feed samples taken from each form at different times, was done by both methods.
Results: As a result of the chemical analysis of the feed forms, the difference among the samples in terms of other nutrient values except for dry matter (DM) values was statistically insignificant. According to the analysis of feed forms on the NIRS method, it was found that the difference among the feed forms in terms of crude protein (CP) and ether extract (EE) values was not statistically significant. On the other hand, it was found that the differences among the feed forms in terms of the DM, crude ash (ash),organic matter (OM) and crude fiber (CF) in NIRS method were statistically significant (P<0.05). While no statistical difference was observed between DM, OM and CP values of chemical and NIRS analyzes, it was seen that the differences between two methods at ash, EE and CF values were statistically significant (P<0.05).
Conclusion: The metabolisable energy values of feeds calculated according to two different energy formulas between the chemical and NIRS methods were compared, only the value of equality 1 was found statistical difference (P<0.05).
Keywords
Project Number
ZRF-15075
References
- Anonymous, 2019a. Global feed out put up 3% in 2018. All About Feed. 30 January 2019. https://www.allaboutfeed.net/Compound-Feed/Articles/2019/1/3-growth-in-compound-feed-in-2018-387470E/ Erişim: Ekim 2019
- Anonymous, 2019b. International feed industry federation annual report 2016/17. http://annualreport.ifif.org/#start Erişim: Ekim 2019
- AOAC., 1997. Association of official analytical chemists. 16th ed. Washington, D.C
- Coleman, S.W., S. Christiansen and J.S. Shenk. 1990. Prediction of botanical composition using NIRS calibrations developed from botanically pure samples. Crop Science, 30: 202-207.
- Conzen, J.P. 2006. Validation of chemometric models and analysis of unknown samples. In: Multivariate Calibration. A parctical guide for developing methods in the quantitative analytical chemisty. BrukerOptif GmbH, 2.nd English Edition. Pp. 13-70. Germany.
- Corson, D.C., G.C. Waghorn, M.J. Ulyattand and J. Lee. 1999. Nırs: forage analysis and livestock feeding. Proceedings of the New ZealandGrasslandAssociation, 61: 127–132.
- De Boever, J.L., B.G. Cottyn, J.M. Vanacker and C.V. Boucqu, 1995. The use of NIRS to predict the chemical composition and the energy value of compound feeds for cattle. Animal Feed Science and Technology, 5: 243-253
- De Jong, J.A., J.M. De Rouchey, M.D. Tokach, R.D. Goodband, J.C. Woodworth, S.S. Dritz, J. Erceg, L. McKinney and S. Smith. 2014. Formation of fines during the pelleted feed manufacturing process and the resulting differences in nutrient composition of fines and pellets. Kansas Agricultural Experiment Station ResearchReports, 0: 297-301.
- Decruyenaere, V., E. Froidmont, N. Bartiaux-Thill, A. Buldgen and D. Stilmant, 2012. Faecal near-infrared reflectance spectrometry (NIRS) compared with other techniques for estimating the in vivo digestibility and dry matter intake of lactating grazing dairy cows. Animal Feed Science and Technology, 173: 220-234.
- Fontaine, J. J. Hörr and B. Schirmer, 2001. Near-Infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fish meal, meat meal products, and poultry meal. Jornal of Agricultural and Food Chemistry, 49: 57-66
- Fontaine, J., B. Schirmer, And J.Hörr, 2002. Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat, barley, corn, triticale, wheat bran/middlings, rice bran, and sorghum. Jornal of Agricultural and Food Chemistry, 50, 3902-3911.
- Goldman, A., A. Genizi, A. Yulzari and N.G. Seligman, 1987. Improving the reliability of the two stage in vitro assay for ruminant feed digestibility by calibration against in vivo data from a wide range of sources. Anim. Feed Sci. Technol., 18: 233-245.
- Görgülü, M., 2014. Ruminant yemlerin SE, ME, TDN, NEm, NEg, NEL, değerlerinin ham besin maddelerinin hesaplanması. http://www.muratgorgulu.com.tr/altekran.asp?id=97 Erişim: Temmuz 2019
- Karaman, M. and S. Erdemir. 2018. Kanatlı hayvanların beslenmesinde kullanılan bazı karma yemlerin kimyasal kompozisyonunun near infrared reflektans spektroskopi (NIRS) İle belirlenmesi. Black Sea Journal of Agriculture, 1(2): 24-28.
- Osborne, B.G. and T. Feam. 1988. Near-infrared spectroscopy in food analysis. Longmans, Harlow, UK, 200 pp.
- Park, R.S., R.E. Agnew, F.J. Gordon and R.W.J. Steen. 1998. The use of near infrared reflectance spectroscopy (NIRS) on undried samples of grass silage to predict chemical composition and digestibility parameters. Anim. Feed Sci. Technol., 72(1-2): 155-167.
- Pehlevan, F ve M. Özdoğan 2015. Bazı alternatif yemlerin besin madde içeriğinin belirlenmesinde kimyasal ve yakın kızılötesi yansıma spektroskopi metotlarının karşılaştırılması. Tekirdağ Ziraat Fakültesi Dergisi, 2015: 12 (02):1-10
- Pehlevan, F. 2014. Bazı alternatif yemlerin kimyasal kompozisyonunun tahmini için nearinfraredreflektans spektroskopinin (NIRS) kullanımı. Adnan Menderes Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi, sayfa sayısı: , Aydın.
- Pérez-Marín, D.C., A. Garrido-Varo, J.E. Guerrero-Ginel and A. Gómez-Cabrera, 2004. Near-infrared reflectance spectroscopy (NIRS) for the mandatory labelling of compound feedingstuffs: chemical composition and open-declaration. Animal Feed Science and Technology, 116: 333–349.
- Quampah, A., Z.R. Huang, J.G. Wu, H.Y. Liu, J.R. Li, S.J. Zhu and C.H. Shi, 2012. Estimation of oil content and fatty acid composition in cottonseed kernel powder using near infrared reflectance spectroscopy. Journal of the American Oil Chemists’ Society, 89(4): 567-575
- SAS. 1999. The SAS System. Version 8. Copyright © 1999 by SAS Institute Inc., Carry NC, USA
- Shenk, J.S. and M.O. Westerhaus. 1985. Accuracy of NIRS instruments to analyse forage and grain. Crop Sci., 25, 1120–1122
- Soldado, A., J. R. Quevedo, A. Bahamonde, S. Modroño, A. Martinez-Fernandez, F. Vicente, D. Perez-Marin, A. Garrido-Varo, J. E. Guerrero, B., and D.L. Roza-Delgado. 2011. Validation of two discriminant strategies applied to NIRS data spectra for detection of animal meals in feedstuffs. Spanish Journal of AgriculturalResearch, 9(1):41-49.
- Stuth, J., A. Jama and D. Tolleson, 2003. Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy. Field Crops Research, 84: 45-56.
- TSE. 1991. Hayvan yemleri-metabolik (çevrilebilir) enerji tayini (kimyasal Metot). UDK 636.085. TS 9610. Türk Standartları Enstitüsü, Ankara.
- Ünlü, H.B., A. Kılıç and T. Ayyılmaz. 2015. Farklı düzeylerde öğütülmüş dane mısır ilavesinin yonca silajının yem değeri üzerine etkisi. Ege Üniversitesi Ziraat Fakültesi Dergisi. 52: 335-341.
- Williams, P.C. and D. Sobering. 1993. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. Journal of Near Infrared Spectroscopy, 1: 25-32.
- Xiccato , G., A. Trocino, J.L. De Boever, L. Maertens, R. Carabaño, J.J. Pascual, J.M. Perez, T. Gidenne , L. Falcao-E-Cunha. 2003. Prediction of chemical composition, nutritive value and ingredient composition of European compound feeds for rabbits by near infrared reflectance spectroscopy (NIRS). Animal Feed Science and Technology, 104(2003):153-168.
There are 28 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Articles |
| Authors | |
| Project Number | ZRF-15075 |
| Publication Date | June 30, 2021 |
| Submission Date | December 2, 2019 |
| Acceptance Date | January 26, 2021 |
| Published in Issue | Year 2021 Volume: 58 Issue: 2 |
