NIRS determination of non-structural carbohydrates, water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability

  • L. Campo CIAM-INGACAL. P.O. Box 10, 15080 A Coruña
  • A. B. Monteagudo CIAM-INGACAL. P.O. Box 10, 15080 A Coruña
  • B. Salleres CIAM-INGACAL. P.O. Box 10, 15080 A Coruña
  • P. Castro CIAM-INGACAL. P.O. Box 10, 15080 A Coruña
  • J. Moreno-Gonzalez CIAM-INGACAL. P.O. Box 10, 15080 A Coruña
Keywords: organic matter, partial least-squares, modified partial least-squares, coefficient of determination

Abstract

The aim of this work was to study the potential of near-infrared reflectance spectroscopy (NIRS) to predict non-structural carbohydrates (NSC), water soluble carbohydrates (WSC), in vitro organic dry matter digestibility (IVOMD), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and starch in samples of whole plant maize with a wide range of variability. The samples were analyzed in reflectance mode by a spectrophotometer FOSS NIRSystems 6500. Four hundred and fifty samples of wide spectrum from different origin were selected out of 3000 scanned for the calibration set, whereas 87 independent random samples were used in the external validation. The goodness of the calibration models was evaluated using the following statistics: coefficient of determination (R2), standard error of cross-validation (SECV), standard error of prediction for external validation (SEP) and the RPDCV and RPDP indexes [ratios of standard deviation (SD) of reference analysis data to SECV and SEP, respectively]. The smaller the SECV and SEP and the greater the RPDCV and RPDP, the predictions are better. Trait measurement units were g/100g of dry matter (DM), except for IVOMD (g/100g OM). The SECV and RPDCV statistics of the calibration set were 1.34 and 3.2 for WSC, 2.57 and 3 for NSC and 2.3 and 2.2 for IVOMD, respectively. The SEP and RPDP statistics for external validation were 0.74 and 4.7 for WSC, 2.14 and 2.5 for NSC and 1.68 and 1.6 for IVOMD respectively. It can be concluded that the NIRS technique can be used to predict WSC and NSC with good accuracy, whereas prediction of IVOMD showed a lesser accuracy. NIRS predictions of OM, CP, NDF, ADF and starch also showed good accuracy.

Downloads

Download data is not yet available.

References



Amari M, Abe A, 1997. Application of near infrared reflectance spectroscopy to forage analysis and prediction of TDN contents. Jpn Agric Res Q 31: 55-63. 

Barnes RJ, Dhanoa MS, Lister SJ, 1989. Standard normal variate and de-trending of near diffuse reflectance spectra. Appl Spectroscopy 43: 772-777.
http://dx.doi.org/10.1366/0003702894202201 

Campo Ramírez L, Castro García P, Moreno-González J, 2007. Calibración NIRS para estimar la digestibilidad de la materia orgánica de la planta entera de maíz en híbridos seleccionados para forraje. In: Los sistemas forrajeros: entre la producción y el paisaje. (SEEP, ed), Toledo, Spain. pp: 461-467.

 

Campo L, Castro P, Moreno-González J, 2010. Ecuaciones de calibración preliminares para la evaluación de la calidad de la biomasa en plantas de maíz por NIRS. 4ª Reunión Ibérica de Pastos y Cultivos, 3-6 mayo, Zamora-Miranda do Douro, Spain-Portugal. pp: 135-139.

 

Castro P, 1996. Efecto de tres temperaturas de secado sobre la composición química de forrajes y heces. Actas de la XXXVI Reunión Científica de la SEEP, pp: 365-368. 

Castro P, 2000. Determinación de carbohidratos no estructurales en forrajes. III Reunión Ibérica de Pastos y Forrajes, Consellería de Agricultura, Gandería e Política Agroalimentaria, Santiago de Compostela, Spain. pp: 447-453. 

Castro P, Flores G, González-Arráez A, Cardelle M, 2001. Predicción del valor nutritivo de ensilados de maíz mediante NIRS. XLI Reunión científica de la SEEP. Centro Iberoamericano de la Biodiversidad, Alicante, Spain. pp: 407-411. 

Castro P, Flores G, González-Arráez A, Castro J, Fernández-Lorenzo B, 2004. Análisis de ensilados de maíz mediante NIRS. In: Pastos y ganadería extensiva (García Criado B, García Ciudad A, Vázquez de Aldana BR, Zabalgogeazcoa I, eds). Sociedad Espa-ola para el Estudio de los Pastos, Salamanca, Spain. pp: 279-283.

 

Cozzolino D, Fassio A, Gimenez A, 2001. The use of near-infrared reflectance spectroscopy (NIRS) to predict the composition of whole maize plants. J Sci Food Agri 81: 142-146.
http://dx.doi.org/10.1002/1097-0010(20010101)81:1<142::AID-JSFA790>3.0.CO;2-I 

Cozzolino D, Fassio A, Fernandez E, 2003. Uso de la espectroscopía de reflectancia en el infrarrojo cercano para el análisis de calidad de ensilaje de maíz. Agric Tec [online] 63(4): 387-393. 

Cozzolino D, Fassio A, Fernández E, Restainaoe, La Manna A, 2006. Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy. Anim Feed Sci Technol 129: 329-336.
http://dx.doi.org/10.1016/j.anifeedsci.2006.01.025 

De Boever JL, Cottyn BG, Debrabander DL, Vanacker JM, Boucque CV, 1997. Prediction of the feeding value of maize silages by chemical parameters, in vitro digestibility and NIRS. Anim Feed Sci Technol 66(1-4): 211-222.
http://dx.doi.org/10.1016/S0377-8401(96)01101-7 

Edney MJ, Morgan JE, Williams PC, Campbell LD, 1994. Analysis of feed barley by near infrared reflectance spectroscopy. J Near Infrared Spectrosc 2: 33-41.
http://dx.doi.org/10.1255/jnirs.29 

Flores G, 2004. Factores que afectan a la calidad del ensilaje de hierba y a la planta de maíz forrajero en Galicia y evaluación de métodos de laboratorio para la predicción de la digestibilidad in vivo de la materia orgánica de estos forrajes ensilados. Doctoral Thesis. Universidad Politécnica de Madrid, Spain.

 

Flores Calvete G, González Arráez A, Castro González J, Castrom García P, Cardelle M, Fernández Lorenzo B, Valladaes Alonso J, 2003. Evaluación de métodos de laboratorio para la predicción de la digestibilidad in vitro de la materia orgánica de ensilajes de hierba y planta entera de maíz (revisión científica). Pastos XXXIII(1): 5-99. 

Garrido A, Guerrro JE, Gómez-Cabrera A, 1993. Posibilidades y limitaciones de la aplicación de la técnica NIRS en la evaluación nutricional de alimentos para el ganado. In: Nuevas fuentes de alimentos para la producción animal IV (Gómez-Cabrera A, De-Pedro-Sanz EJ, eds). Consejería de Agricultura y Pesca, Junta de Andalucía, Sevilla, Spain. pp: 243-255. 

Goering HK, Van Soest PJ, 1970. Forage fibre analysis (apparatus reagents, procedures and some applications). Agriculture Handbook no. 379. USDA-ARS, Albany, USA. 

ISI, 2000. WinISI 1.5 Near-infrared software, the complete software solution for routine analysis, robust calibration and networking. InfraSoft International LLC, Port Matilda, PA, USA. 

Kennedy CA, Shelford JA, Williams PC, 1996. Near infrared spectroscopy analysis of intact grass silage and fresh grass for dry matter, crude protein and acid detergent fiber. In: Near infrared spectroscopy (Davies AMC, Williams P, eds). NIR Publ, Chichester, UK. pp: 524-530. 

Lovett DK, Deaville ER, Mould F, Givens DI, Owen E, 2004. Using near infrared reflectance spectroscopy (NIRS) to predict the biological parameters of maize silage. Anim Feed Sci Technol 115: 179-187.
http://dx.doi.org/10.1016/j.anifeedsci.2004.02.007 

MacGregor CA, Stokes MR, Hoover WH, Leonard HA, Junking LL, Sniffen CJ, Mailman RW, 1983. Effect of dietary concentration of total nonstructural carbohidrate on energy and nitrogen metabolism and milk production of dairy cows. J Dairy Sci 66(1): 39-50.
http://dx.doi.org/10.3168/jds.S0022-0302(83)81751-2 

Mechin V, Argillier O, Hebert Y, Guingo E, Moreau L, Charcosset A, Barriere Y, 2001. Genetic analysis and QTL mapping of cell wall digestibility and lignification in silage maize. Crop Sci 41: 690–697.
http://dx.doi.org/10.2135/cropsci2001.413690x 

Mouazen AM, Saeys W, Xing J., De Baerdemaeke J, Ramon H, 2005. Near infrared spectroscopy for agricultural materials: an instrument comparison. J Near Infrared Spectrosc 13: 87-97.
http://dx.doi.org/10.1255/jnirs.461 

Murray SC, Rooney WL, Mitchell SE, Sharma A, Klein PE, Mullet JE, Kresovich S, 2008. Genetic improvement of sorghum as a biofuelfeedstock: II. QTL for stem and leaf structural carbohydrates. Crop Sci 48: 2180-2193.
http://dx.doi.org/10.2135/cropsci2008.01.0068 

Norris KH, Barnes RF, Moore JE, Shenk JS, 1976. Predicting forage quality by near infrared reflectance spectroscopy. J Anim Sci 43: 889-897. 

Park HS, Lee JK, Ko HJ, Lee HY, Kil DY, 2005. Prediction of the values of maize silage by near infrared spectroscopy. XX Int Grassland Cong (O'Mara FP, Wilkins RJ, Mannetje L't, Lovett DK, Rogers PAH, Boland TM, eds), Ireland. p: 263. 

Reeves JB, Blosser TH, Colenbrander VF, 1989. Near infrared reflectance spectroscopy for analyzing undried silage. J Dairy Sci 72(1): 79-88.
http://dx.doi.org/10.3168/jds.S0022-0302(89)79082-2 

Riboulet C, Lefevre B, Denoue D, Barriere Y, 2008. Genetic variation in maize cell wall for lignin content, lignin structure, p-hydroxycinnamic acid content, and digestibility in set of 19 lines at silage harvest maturity. Maydica 53: 11-19. 

Saeys W, Mouazen AM, Ramon H, 2005. Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosystems Eng 91: 393-402.
http://dx.doi.org/10.1016/j.biosystemseng.2005.05.001 

SAS, 2008. SAS/Stat user’s guide v. 9.2. SAS Institute Inc, Cary, NC, USA.

 

Shenk JS, Westerhaus MO, 1995. The application of near Iifrared reflectance spectroscopy (NIRS) to forage analysis. In: Forage quality, evaluation and utilization (Fahey GC, ed). Madison, WI, USA. pp: 406-449. 

Shenk JS, Westerhaus MO, Hoover MR, 1976. Analysis of forages by infrared reflectance. J Dairy Sci 62: 807-812.
http://dx.doi.org/10.3168/jds.S0022-0302(79)83330-5 

Smith D, 1973. The nonstructural carbohydrates. In: Chemistry and biochemistry of herbage (V 1) (Butler GW, Bailey RW, eds). Acad Press, London, UK. pp: 106-155. 

Tilley JMA, Terry RA, 1963. A two stage technique for the in vitro digestion of forage crops. J Br Grassland Soc 18: 104-111.
http://dx.doi.org/10.1111/j.1365-2494.1963.tb00335.x 

Valdes EV, Hunter RB, Pinter L, 1987. Determination of quality paremeters by near infrared reflectance spectroscopy in whole-plant corn silage. Can J Sci 67: 747-754.
http://dx.doi.org/10.4141/cjps87-102 

Vásquez DR, Abadia B, Arreaza LC, 2004. Aplicación de la espectroscopía de reflectancia en el infrarrojo cercano (NIRS) para la caracterización nutricional del pasto de guinea y del grano de maíz. Revista Corpoica 5(1): 49-55. 

Williams PC, 2001. Implementation of near-infrared technology. In: Near infrared technology in the agricultural and food industries. (Williams PC, Norris KH, eds), Am Assoc Cereal Chemist, St. Paul, MN, USA. pp: 145-169 

Williams PC, 2007. Near-infrared technology - Getting the best out of light, edition 5.1. PDK Projects Inc., Nanaimo, Canada.

 

Williams PC, Sobering DC, 1996. How do we do it: a brief summary of the methods we use in developing near infrared calibrations. In: Near infrared spectroscopy: the future waves (Davies AMC, Williams PC, eds). NIR Publ, Chichester, UK. pp: 185-188.

Published
2013-05-09
How to Cite
CampoL., MonteagudoA. B., SalleresB., CastroP., & Moreno-GonzalezJ. (2013). NIRS determination of non-structural carbohydrates, water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability. Spanish Journal of Agricultural Research, 11(2), 463-471. https://doi.org/10.5424/sjar/2013112-3316
Section
Plant production (Field and horticultural crops)