Kaba Yem Kalitesinin Near Infrared Reflectance (NIR) Spektroskopisi ve Kemometrik Teknikler ile Tahmin Edilmesi

Yıl 2020, Cilt: 13 Sayı: 3, 234 - 240, 30.09.2020

Hasan Atalay Fatih Kahrıman

https://doi.org/10.30607/kvj.724124

Cited By: 2

Öz

Yakın Kızılötesi Spektroskopisi yem kalite değerlendirmelerinde yaygın olarak kullanılmaya başlanmıştır. Yapılan çalışmaların büyük kısmı lifli bileşenlerin NIR ile tespitine yönelik olarak yürütülmüştür. Kaba yem kalitesinin değerlendirilmesinde kullanılan Nisbi yem değeri (Relative Forage Value, RFV), Nisbi yem Kalitesi (Relative Forage Quality, RFQ) ve Net Enerji Laktasyon (Net Energy Lactation, NEL) gibi farklı kalite özelliklerine yönelik ise model geliştirme çalışmaları henüz yaygınlaşmamıştır. Bu çalışmada farklı kaba yem örneklerinde NFV, RFQ ve NEL değerlerinin NIR spektroskopisi ile tespiti ve dalgaboyu seçiminin model başarısı üzerine etkisinin araştırılması amaçlanmıştır. Araştırmada silaj, yonca kuru otu, yulaf kuru otu ve buğday samanı örneklerinde alınan spektral veriler ve laboratuvar analiz sonuçları kullanılarak kısmı en küçük kareler regresyon (PLSR) yöntemine göre tahmin modelleri geliştirilmiştir. Dalga boyu seçim yöntemi olarak variable importance projection (VIP) metodundan yararlanılmıştır. Araştırma bulgularına göre geliştirilen tahmin modellerinin RFV için VIP-PLS model kombinasyonundan (RMSE=12.7, Bias=0,000, R2=0,804, RPD=2,28) elde edilmiştir. VIP yöntemi bütün değişkenler için tahmin başarısını yükseltmiştir. Araştırma bulgularına dayanarak, kaba yem kalite değerlendirme parametrelerinde kullanılan hesaplamaların NIR ile tespitinin mümkün olduğu anlaşılmıştır.

Anahtar Kelimeler

Yakın Kızılötesi Yansıma, Kaba yem kalitesi, Dalga boyu seçimi, kemometri

Estimating Roughage Quality with Near Infrared Reflectance (NIR) Spectroscopy and Chemometric Techniques

Öz

Near-Infrared Spectroscopy has been commonly adopted in feed quality evaluations. The majority of studies have been performed on the detection of fibrous components with the help of NIR. Model development studies are not yet common for different roughage quality characteristics such as Relative Forage Value (RFV), Relative Forage Quality (RFQ), and Net Energy Lactation (NEL), which are used in the evaluation of roughage quality. The purpose of this study is the detection of RFV, RFQ, and NEL values with NIR spectroscopy in different roughage samples, and to investigate the effect of wavelength selection on the model's success. In this study, spectral data belonging to silage, dry alfalfa, dry oat, and wheat straw samples and laboratory analysis results were used to develop estimation models according to the partial least square regression (PLSR) method. A variable importance projection (VIP) method was used as the wavelength selection method. Estimation models, which were developed according to study results, were obtained from the VIP-PLS model combination (RMSE=12.7, Bias=0.000, R2=0.804, RPD=2.28) for RFV. VIP method has increased the estimation of success for all variables. Based on the study results, it was recognized that it is possible to use NIR in the calculations used in roughages quality evaluation parameters.

Anahtar Kelimeler

Near-İnfrared Reflectance (NIR), Wavelength Selection, Roughage quality, Chemometrics

Kaynakça

• AOAC Association of Official Analytical Chemists, 16th ed. Washington, D.C. 1997, USA.
• Asekova S, Han SI, Choı HJ, Park SJ, Shın DH, Kwon CH, Shannon JG, Lee JD. Determination of forage quality by near-infrared reflectance spectroscopy in soybean, Turk J Agric For. 2016; 40: 45-52.
• Cécillon L, Barthès BG, Gomez C, Ertlen D, Genot V, Hedde M, Stevens A, and Brun JJ. Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS). Eur. J. Soil Sci. 2009; 60: 770–784.
• Doğusoylu CE, Bayram İ. Estabilishing A calibration for Neutral Detergent Fiber (NDF) value by using Near Infrared Spectroscopy (NIR) in corn grain. 6TH International Multidisciplinary Studies Congress Health Sciences, Veterinary and Sports Sciences, Conference Paper, 551-559, April 2019, Gaziantep.
• Ergün A, Tuncer ŞD, Çolpan İ, Yalçın S, Yıldız G, Küçükersan K, Küçükersan S, Şehu A. Yemler Yem Hijyeni ve Teknolojisi, Pozitif Matbaacılık, 2004; ANKARA.
• Galvão RKH, Araújo MCU, Fragoso WD, Silva EC, José GE, Soares SFC, Paiva HM. A variable elimination method to improve the parsimony of mlr models using the successive projections algorithm, Chemometrics and Intelligent Laboratory Systems, 2008; 92 (1): 83–91.
• Gerhardt,Analytical Systems Documents Gerhardt GmbH & Co.KG Cäsariusstraße 97, D-53639, 2012; Königswinter, www.gerhardt.de.
• Güney M, Bingöl NT, Aksu T. Kaba Yem Kalitesinin Sınıflandırılmasında Kullanılan Göreceli Yem Değeri (GYD) ve Göreceli Kaba Yem Kalite İndeksi (GKKİ), Atatürk Üniversitesi Vet. Bil. Derg. 2016; (11): 254-258.
• ISO 12099 Animal feding stuffs, cereals and milled cereal products – Guidelines for the application of near infrared spectrometry.
• Jaranyama P, Garcia AD. Understanding relative feed value (RFV) and relative forage quality (RFQ). College of Agric and Biological Sci, South Dakota State University, 2004; USDA.
• Jones GM, Wade NS, Baker JP, Ranck EM. Use of Near Infrared Reflectance Spectroscopy in Forage Testing, J Dairy Sci. 1976; 70: 1086-1091.
• Kahrıman F, Egesel CÖ. Using Near Infrared (NIR) Spectroscopy in the Analysis of Cereal Products: The Example of Maize, in: Recent Researches in Science and Landscape Management, Efe R., Zencirkiran M., Curebal İ., Eds., Cambridge Scholars Publishing, 2018; Newcatsle, pp.507-521.
• Kahrıman F, Öner F, Türk F, Gökçe A, Düzen E, Onaç İ, Egesel CÖ. Efficiency of different chemometric methods for determination of oil content in maize by NIR spectroscopy, Agrosym 2017, Jahorina, Bosna Hersek, 5 Ekim - 8 Aralık 2017, pp 788-793.
• Li H, Xu Q-S, Liang Y. libPLS: An integrated library for partial least squares regression and discriminant analysis. PeerJ PrePrints. 2014; 2:e190v1.
• Marten GC, Halgerson JL, Cherney JH. Quality prediction of small grain forages by near infrared reflectance spectroscopy. Crop Sci.1983; 23: 94-96.
• Nielsen SN, Stubbs TL, Gerland-Campbell KA, Carter AH. Rapid Estimation of Wheat Straw Decomposition Constituents Using Near-Infrared Spectroscopy. Agronomy 9: 462, 2019; doi.org/10.3390/agronomy9080462.
• Norris KH, Barnes RP, Moore JE, and Shenk JS. Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sei. 1976; 43: 889-897.
• NRC (National Research Council) Nutrient Requirements of Dairy Cattle. 7th rev. ed. National Academy Press, 2001; Washington, DC.
• Pehlevan F, Özdoğan M. Comparison between chemical and near ınfrared reflectance spectroscopy methods for determining of nutrient content of some alternative feeds. Journal of Tekirdag Agricultural Faculty, 2015; 12(2): 1-10.
• Romero, JJ, Castillo MS, Burns JC, Moriel P, Davidson S. Forage Quality Concepts and Practices, NC State University College of Agriculture and Life Sciences, 2014; Published by North Carolina Cooperative Extension.
• Rushing JB, Saha UK, Lemus R, Sonon L, and Baldwin BS. Analysis of some ımportant forage quality attributes of southeastern wildrye (Elymus glabriflorus) using near ınfrared reflectance spectroscopy. American Journal of Analytical Chemistry 7. 2016; 642- 662.
• Samiei A, Liang JB, Ghorbani GR, Hirooka H, Mahyari SA, Sadri H, Tufarelli V. Relationship between dietary energy level, silage butyric acid and body condition score with subclinical ketosis ıncidence in dairy Cows, Advances in Animal and Veterinary Sciences, 2015; 3(6): 354-361.
• Shenk JS, Landa I, Hover RH, Westerhaus MO. Description and evaluation of near, infrared reflectance spectro-computer for forage and grain analysis. Crop Sci.1981; 21: 355-358.
• Sinnaeve G, Dardenne P, Agneesens R, Biston R. The use of near infrared spectroscopy for the analysis of fresh grass silage, Journal of near infrared spectroscopy, 1984; 2: 79-84.
• Undersander D. & J. Moore. Relative Forage Quality (RFQ) - Indexing legumes and grasses for forage quality In: Proceedings, National Alfalfa Symposium, 13- 15 December, 2004, San Diego, CA, UC Cooperative Extension, University of California, Davis 95616.
• Undersander D. “Uses and Abuses of NIR for Feed Analysis”, Florida Ruminant Nutrition Symposium, Gainseville.[Online, accessed July 13, 2011] URL:http:// dairy.ifas.ufl.edu/rns/2006/Undersander.pdf
• Van Dyke NJ, Anderson PM. Interpreting a forage analysis. Alabama cooperative extension. 2000; Circular ANR-890.
• Van Soest PJ, Robertson JB, Lewis BA. Methods for dietery fiber, neıtral detergent fiber and nonstarchpolysaccharides in relation to animal nutrition. J. Dairy Sci. 1991; 74: 3583-3597.
• Wold S, Johansson A, Cochi M. PLS-partial least squares projections to latent structures Leiden: Escom Science Publishers, 1993; 523-550 p.
• Yang Z, Nie G, Pan L, Zhang Y, Huang L, Ma X, and Zhang X. Development and validation of near- infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum, PeerJ. 2017; 5:e3867; DOI 10.7717/peerj.3867