Prediction models of the nutritional quality of fresh and dry Brachiaria brizantha cv. Piatã grass by near infrared spectroscopy

References

• Alomar D, Fuchslocher R, Cuevas J, Mardones R, Cuevas E. 2009. Prediction of the composition of fresh pastures by near infrared reflectance or interactance-reflectance spectroscopy. Chil J Agric Res. 69(2):198–206.
   Web of Science ®Google Scholar
• AOAC. 2003. Official methods of analysis. 17th ed., 2nd revision. Gaithersburg (MD): Association of Official Analytical Chemists.
   Google Scholar
• AOAC. 2005. Official methods of analysis. 18th ed. Gaithersburg (MD): Association of Official Analytical Chemists.
   Google Scholar
• Arzani H, Sanaei A, Barker AV, Ghafari S, Motamedi J. 2015. Estimating nitrogen and acid detergent fiber contents of grass species using Near Infrared Reflectance Spectroscopy (NIRS). J Rangel Sci. 5(4):260–268.
   Google Scholar
• Burns DA, Ciurczak EW. 2007. Handbook of near- infrared analysis. Boca Raton (FL): CRC Press.
   Google Scholar
• Cappelle ER, Valadares Filho SC, Silva JFC, Cecon PR. 2001. Estimativa do valor energético a partir de características químicas e bromatológicas dos alimentos [Estimates of the energy value from chemical characteristics of the feedstuffs]. Rev Bras de Zootec. 30(6):1837–1856. Portuguese.
   Web of Science ®Google Scholar
• Cozzolino D. 2014. Use of infrared spectroscopy for in-field measurement and phenotyping of plant properties: instrumentation, data analysis, and examples. Appl Spectrosc Rev. 49(7):564–584.
   Web of Science ®Google Scholar
• Deepa K, Senthilkumar S, Kalpana K, Suganya T, Sasikumar P, Thirumalaisamy G, Sureshkumar R, Vasanthakumar P. 2016. NIRS in animal sciences. Int J Sci Environ Technol. 5(2):605–610.
   Google Scholar
• Detmann E, Souza MA, Valadares Filho SC, Queiroz AC, Berchielli TT, Saliba EOS, Cabral LS, Pina DS, Ladeira MM, Azevedo JAG. 2012. Métodos para Análise de Alimentos [Methods for food analysis]. Visconde do Rio Branco (MG): UFV. Portuguese.
   Google Scholar
• Dias DG, Pegaroro RF, Alves DD, Porto EMV, Santos Neto JA, Aspiazú I. 2015. Produção do capim Piatã submetido a diferentes fontes de fósforo [Production of Piata grass under diferente phosphorus sources]. Rev Bras Eng Agric Ambient. 19(4):330–335. Portuguese.
   Google Scholar
• Fekadu D, Bediye S, Kehaliw A, Daba T, Kitaw G, Assefa G. 2010. Near infrared spectroscopy (NIRS) for determination of chemical entities of natural pasture from Ethiopia. Agric Biol J N Am. 1(5):919–922.
   Google Scholar
• EMBRAPA – Empresa Brasileira de Pesquisa Agropecuária. BRS Piatã. [Accessed 21 September 2022]. Available from: <http://amaranto.cpac.embrapa.br/agrobsb/Arquivos/forrageiras/BRSPiata.pdf>.
   Google Scholar
• Fernandes AMF. 2015. Uso da espectroscopia de reflectância do infravermelho próximo (NIRS) para previsão da composição bromatológica de vagens de algaroba e palma forrageira [dissertation]. Sobral (CE): Universidade Estadual Vale do Acaraú.
   Google Scholar
• Goldshleger N, Chudnovsky A, Ben-binyam R. 2013. Predicting salinity in tomato using soil reflectance spectra. Int J Remote Sens. 34(17):6079–6093.
   Web of Science ®Google Scholar
• Kennard RW, Stone LA. 1969. Computer aided design of experiments. Technometrics. 11(1):137–148.
   Web of Science ®Google Scholar
• Kragten SA, Wyss U. 2014. Les fourrages à la lumière du proche infrarouge (NIRS) [Forages in the light of NIRS]. Rech Agron em Suisse. 5(5):204–2011. French.
   Google Scholar
• Lobos I, Gou P, Hube R, Saldaña R, Alfaro M. 2013. Evaluation of potencial NIRS to predict pastures nutritive value. J Soil Sci Plant Nutr. 13(2):463–468.
   Web of Science ®Google Scholar
• Lugassi R, Chudnovsky A, Zaady E, Dvash L, Goldshleger N. 2015. Estimating pasture quality of fresh vegetation based on spectral slope of mixed data of dry and fresh vegetation – method development. Remote Sens. 7(6):8045–8066.
   Google Scholar
• Melo JCR. 2017. Uso da espectroscopia de reflectância do infravermelho próximo (NIRS) para previsão da composição bromatológica da torta de algodão e feijão guandu [Use of near infrared reflectance spectroscopy (NIRS) to predict the bromatological composition of cotton cake and Guandu beans] [dissertation]. Sobral (CE): Universidade Estadual Vale do Acaraú.
   Google Scholar
• Monrroy M, Gutiérrez D, Miranda M, Hernández K, García JR. 2017. Determination of Brachiaria spp. forage quality by near-infrared spectroscopy and partial least squares regression. J Chil Chem Soc. 62(2):3472–3477.
   Web of Science ®Google Scholar
• Morón A, García A, Sawchik J, Cozzolino D. 2007. Preliminary study on the use of near-infrared reflectance spectroscopy to assess nitrogen content of undried wheat plants. J Sci Food Agric. 87(1):147–152.
   Web of Science ®Google Scholar
• Parrini S, Acciaioli A, Franci O, Pugliese C, Bozzi R. 2019. Near infrared spectroscopy technology for prediction of chemical composition of natural fresh pastures. J Appl Anim Res. 47(1):514–520.
   Web of Science ®Google Scholar
• Parrini S, Acciaioli A, Crovetti A, Bozzi R. 2018. Use of FT-NIRS for determination of chemical components and nutritional value of natural pasture. Ital J Anim Sci. 17(1):87–91.
   Web of Science ®Google Scholar
• Pereira LFS. 2017. Espectroscopia no NIR e processamento de imagens digitais para classificação de mamão papaia (Carica papaya L.) Golden [NIR spectroscopy and digital images processing to Golden papaia (Carica papaya L.) grading] [dissertation]. Campinas (SP): Universidade Estadual de Campinas.
   Google Scholar
• Rambo MKD, Ferreira MMC. 2018. Análise de resíduos lignocelulósicos por espectroscopia NIR associada a pré-tratamentos multivariados dentro do contexto de química verde [Analysis of lignocellulosic residues by NIR spectroscopy associated to multivariate pre-treatments in the context of green chemistry]. Rev Virtual Quim. 10(2):421–431. Portuguese.
   Google Scholar
• Ribeiro AF, Messana JD, Dian PHM, Reis RA, Ruggieri AC, Malheiros EB, Berchielli TT. 2014. Chemical composition, in vitro digestibility and gas production of Brachiaria managed under different forage allowances. Ital J Anim Sci. 13(1):36–43.
   Web of Science ®Google Scholar
• Serafim CC, Guerra GL, Mizubuti IY, Castro FAB, Prado-Calixto OP, Galbeiro S, Parra ARP, Bumbieris Junior VH, Pértile SFN, Rego FCA. 2021. Use of near-infrared spectroscopy f chemical composition of Tifton 85 grass. Semin Cienc Agrar. 42(3):1287–1302.
   Web of Science ®Google Scholar
• Shenk JS, Westerhaus MO. 1991. Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Sci. 31(2):469–474.
   Web of Science ®Google Scholar
• Souza DM, Madari BE, Guimarães FF. 2012. Aplicação de técnicas multivariadas e inteligência artificial na análise de espectros de infravermelho para determinação de matéria orgânica em amostras de solos [Application of multivariate calibration and artificial intelligence in the analysis of infrared spectra to quantify organic matter in soil samples]. Quim Nova. 35(9):1738–1745. Portuguese.
   Google Scholar
• Tran H, Salgado P, Tillard E, Dardenne P, Nguyen XT, Lecomte P. 2010. “Global” and “local” predictions of dairy diet nutritional quality using near infrared reflectance spectroscopy. J Dairy Sci. 93(10):4961–4975.
   PubMed Web of Science ®Google Scholar
• Van Soest PJ, Robertson JB, Lewis BA. 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J Dairy Sci. 74(10):3583–3597.
   PubMed Web of Science ®Google Scholar
• Williams PC, Sobering DC. 1993. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J Near Infrared Spectrosc. 1(1):25–33.
   Google Scholar
• Windham WR, Mertens DR, Barton FE. 1989. Protocol for NIRS calibration: sample selection and equation development and validation. In: Marten GC, Shenk JS, Barton FE II, editor. Near infrared reflectance spectroscopy (NIRS): analysis of forage quality. Washington (DC): USDA-Agricultural Research Service; p. 96–103.
   Google Scholar
• Yang Z, Nie G, Pan L, Zhang Y, Huang L, Ma X, Zhang X. 2017. Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiforum. PeerJ Comput Sci. 5(10):1–20.
   Google Scholar