A single calibration of near-infrared spectroscopy to determine the quality of forage for multiple species
DOI:
https://doi.org/10.33448/rsd-v10i10.18990Keywords:
Chemometrics; Crude protein; Fiber; Pasture; Ruminants.Abstract
Near-infrared spectroscopy (NIRS) is an efficient and chemical-free technique for quickly assessing forage quality. However, calibration curves are usually validated for the forage of a single species, while few studies have reported on the forage of multiple species. Therefore, this work aimed to develop a broad system of calibrating curves by NIRS to predict neutral detergent fiber (NDF), acid detergent fiber (ADF) and crude protein (CP) values from single and mixed forage. To accomplish this, single and mixed forage (32 forage species) were sampled over six years (2013 to 2019) from different regions of Santa Catarina state in southern Brazil. Forage samples were chemically analyzed for NDF, ADF and CP levels, followed by performing spectroscopy. Next, calibration curves were calculated as Second Derivative for NDF, First Derivative + Multiplicative Scattering Correction for ADF, and, Multiplicative Scattering Correction for CP. Approximately 200 sample forage, resulted in determination coefficient (R2) values of 0.94, 0.95, and 0.98 and validation values of 0.94, 0.95, and 0.97 for NDF, ADF, and CP, respectively. Thus, calibration curves were properly developed for quality assessment of single or mixed forage for multiple species, resulting in a chemical-free and time-saving tool for routine laboratory use.
References
Andueza, D., Picard, F., Martin-Rosset, W., & Aufrère, J. (2016). Near-infrared spectroscopy calibrations performed on oven-dried green forages for the prediction of chemical composition and nutritive value of preserved forage for ruminants. Applied Spectroscopy, 70, 1321-1327. Retrieved from https://journals.sagepub.com/doi/pdf/10.1177/0003702816654056 DOI: 10.1177/0003702816654056.
Association of Official Analytical Chemists (1984). Official methods of analysis (14th ed.). Arlington: AOAC.
Association of Official Analytical Chemists (1995). Official methods of analysis (16th ed.) Arlington: AOAC.
Azzouz, T., Puigdoménech, A., Aragay, M., & Tauler, R. (2003). Comparison between different data pre-treatment methods in the analysis of forage samples using near-infrared diffuse reflectance spectroscopy and partial least-squares multivariate calibration method. Analytica Chimica Acta, 484, 121-134. DOI: 10.1016/S0003-2670(03)00308-8.
Barcellos, A. O., Ramos, A. K. B., Vilela, L., & Junior, G. B. M. (2008). Sustentabilidade da produção animal baseada em pastagens consorciadas e no emprego de leguminosas exclusivas, na forma de banco de proteína, nos trópicos brasileiros. Revista Brasileira de Zootecnia, 37, 51-67. Retrieved from https://www.scielo.br/j/rbz/a/KwNbj7GpY83JLJFfxWRGNxr/?lang=pt DOI: 10.1590/S1516-35982008001300008
Barros Neto, B., Scarminio, I.S., & Bruns, R. E. (2006). 25 anos de quimiometria no Brasil. Química Nova, 29, 1401-1406. Retrieved from https://www.scielo.br/j/qn/a/mQNsqf68QY9TmMw3KytvdvN/?lang=pt DOI: 10.1590/S0100-40422006000600042.
Bjorsvik, H. R., & Martens, H. (2001). Data Analysis: Calibration of NIR Instruments by PLS Regression. In D. A. Burns, & E. W. Ciurczak (Eds), Handbook of Near-infrared Analysis (pp. 185-208). New York: Marcel Dekker.
Bokobza, I. (2002). Origin of near-infrared absorption bands. In H. W. Siesler, Y. Ozaki, S. Kawata, & H. M. Heise (Eds), Near-Infrared Spectroscopy: Principles, Instruments, Applications (pp. 11-42). Weinheim: Wiley-VCH.
Decruyenaere. V., Lecomte, P., Demarquilly, C., Aufrere, J., Dardenne, P., Stilmant, D., & Buldgen, A. (2009). Evaluation of green forage intake and digestibility in ruminants using near infrared reflectance spectroscopy (NIRS): Developing a global calibration. Animal Feed Science Technology, 148, 138-156. DOI: 10.1016/j.anifeedsci.2008.03.007
Fernandes, C. O. M. (2012). Princípios da Produção de Leite a Pasto. In U. A. Córdova (Ed), Produção de leite à base de pasto em Santa Catarina (pp. 15-30). Florianópolis: Epagri.
Fontaneli, R. S., Scheffer-Basso, S. M., Dürr, J. W., Appelt, J.V., Bortolini, F., & Haubert, F. A. (2004). Predição da composição química de bermudas (Cynodon spp.) pela espectroscopia de reflectância no infravermelho proximal. Revista Brasileira de Zootecnia, 33, 838-842. Retrieved from https://www.scielo.br/j/rbz/a/WHhqdNqHKzWSKs6ks6nGyKQ/?format=pdf&lang=pt. DOI: 10.1590/S1516-35982004000400003
Frizon, C. N. T., Oliveira, G. A., Perussello, C. A., Peralta-Zamora, P. G., Camlofski, A. M. O., Rossa, U. B., & Hoffmann-Ribani, R. (2015). Determination of total phenolic compounds in yerba mate (Ilex paraguariensis) combining near infrared spectroscopy (NIR) and multivariate analysis. LWT-Food Science and Technology, 60, 795-801. DOI: 10.1016/j.lwt.2014.10.030
Kirkpinar, F., & Açikgöz, Z. (2018). Feeding. In B. Yücel, & T. Taşkin (Eds), Animal Husbandry and Nutrition (pp.97-114). London: IntechOpen.
Li, Z., Xu, G., Wang, J., Du, G., Cai, W., & Shao, X. (2016). Outlier detection for multivariate calibration in near infrared spectroscopic analysis by model diagnostics. Chinese Journal of Analytical Chemistry, 44, 305-309. DOI: 10.1016/S1872-2040(16)60907-6
Lobos, I., Gou, P., Hube, S., Saldaña, R., & Alfaro M. (2013). Evaluation of potential nirs to predict pastures nutritive value. Journal of Soil Science and Plant Nutrition, 13 (2), 463-468. Retrieved from https://scielo.conicyt.cl/pdf/jsspn/v13n2/aop3613.pdf. DOI: 10.4067/S071895162013005000036
Manley, M., & Baeten, V. (2018). Spectroscopic technique: Near infrared (NIR) spectroscopy. In D. V. Sun (Ed.). Modern techniques for food authentication (pp. 51-102). Cambridge: Academic Press.
Modroño, S., Soldado, A., Martínez-Fernández, A., & Roza-Delgado, B. (2017). Handheld NIRS sensors for routine compound feed quality control: Real time analysis and field monitoring. Talanta, 162, 597-603. DOI: 10.1016/j.talanta.2016.10.075
Molano, M. L., Cortés, M. L., Ávila, P., Martens, S. D., & Muñoz, L. S. (2016). Ecuaciones de calibración en espectroscopía de reflectancia en el infrarrojo cercano (NIRS) para predicción de parâmetros nutritivos en forrajes tropicales. Tropical Grasslands - Forrajes Tropicales, 4, 139-145.
Monrroy, M., Gutiérrez, D., Miranda, M., Hernández, K., & García, J. R. (2017). Determination of brachiaria spp. forage quality by near-infrared spectroscopy and partial least squares regression. Journal of the Chilean Chemical Society, 62 (2), 3472-3477. Retrieved from https://scielo.conicyt.cl/pdf/jcchems/v62n2/art10.pdf. DOI: 10.4067/S071797072017000200010
Neves, A. C. O., Soares, G. M., Morais, S. C., Costa, F. S. L., Porto, D. L., & Lima, K. M. G. (2012). Dissolution testing of isoniazid, rifampicin, pyrazinamide and ethambutol tablets using near-infrared spectroscopy (NIRS) and multivariate calibration. Journal of Pharmaceutical and Biomedical Analysis, 57, 115-119. DOI: 10.1016/j.jpba.2011.08.029
Norman, H. C., Hulm, E., Humphries, A. W, Hughes, S. J., & Vercoe, P. E. (2020). Broad near-infrared spectroscopy calibrations can predict the nutritional value of >100 forage species within the Australian feedbase. Animal Production Science, 60, 1111-1122. Retrieved from https://www.publish.csiro.au/an/pdf/AN19310. DOI: 10.1071/AN19310
Parrini, S., Acciaioli, A., Crovetti, A., & Bozzi, R. (2018). Use of FT-NIRS for determination of chemical components and nutritional value of natural pasture. Italian Journal of Animal Science, 17, 87-91. Retrieved from https://www.tandfonline.com/doi/full/10.1080/1828051X.2017.1345659. DOI: 10.1080/1828051X.2017.1345659
Pasquini, C. (2003). Near infrared spectroscopy: fundamentals, practical aspects and analytical applications. Journal of the Brazilian Chemical Society, 14, 198-219. Retrieved from https://www.scielo.br/j/jbchs/a/R8Z76mVbzwxk6RCYCLGkSnz/?format=pdf&lang=en. DOI: 10.1590/S0103-50532003000200006
Pereira, S. A., Shitsuka, D. M., Parreira, F. J. & Shitsuka, R. (2018). Metodologia da pesquisa científica. UFSM.
Ramirez, J. A., Posada, J. M., Handa, I.T., Hoch, G., Vohland, M., Messier, C., & Reu, B. (2015). Near‐infrared spectroscopy (NIRS) predicts non‐structural carbohydrate concentrations in different tissue types of a broad range of tree species. Methods in Ecology and Evolution, 6, 1018-1025. Retrieved from https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.12391. DOI: 10.1111/2041-210X.12391
Sánchez, M. T., Pérez-Marín, D., Torres, I., Gil, B., Garrido-Varo, A., & De la Haba, M. J. (2017). Use of NIRS technology for on-vine measurement of nitrate content and other internal quality parameters in intact summer squash for baby food production. Postharvest Biology and Technology, 125, 122-128. DOI: 10.1016/j.postharvbio.2016.11.011
Siesler, H. W., Ozaki, Y., Kawata, S., & Heise, H. M. (2008). Near-infrared spectroscopy: principles, instruments, application. Weinheim: Wiley-VCH.
Ullmann, I., Herrmann, A., Hasler, M., & Taube, F. (2017). Influence of the critical phase of stem elongation on yield and forage quality of perennial ryegrass genotypes in the first reproductive growth. Field Crops Research, 205, 23-33. DOI: 10.1016/j.fcr.2017.02.003
Van Soest, P. J., Robertson, J. B., & Lewis, B. A. (1991). Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science, 74, 3583-3597. DOI: 10.3168/jds.S0022-0302(91)78551-2.
Williams, P. (2014). The RPD statistic: a tutorial note. NIR News, 25, 22-26. Retrieved from https://journals.sagepub.com/doi/pdf/10.1255/nirn.1419. DOI: 10.1255/nirn.1419
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 multiflorum. PeerJ, 5, 3867. Retrieved from https://peerj.com/articles/3867/. DOI: 10.7717/peerj.3867.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Caroline Massignani; Bruna Búrigo Vandresen; Júlia Vargas Marques; Ricardo Kazama; Milene Puntel Osmari; Daniele Cristina da Silva-Kazama
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
