Suitability of faecal near-infrared reflectance spectroscopy (NIRS) predictions for estimating gross calorific value

  1. De la Roza-Delgado, Begoña 1
  2. Modroño, Sagrario 1
  3. Vicente, Fernando 1
  4. Martínez-Fernández, Adela 1
  5. Soldado, Ana 1
  1. 1 SERIDA. Dept. Nutrition, Grasslands and Forages. PO Box 13, 33300 Villaviciosa (Asturias)
Revista:
Spanish journal of agricultural research

ISSN: 1695-971X,  2171-9292

Año de publicación: 2015

Volumen: 13

Número: 1

Tipo: Artículo

DOI: 10.5424/SJAR/2015131-6959 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Spanish journal of agricultural research

Objetivos de desarrollo sostenible

Resumen

A total of 220 faecal pig and poultry samples, collected from different experimental trials were employed with the aim to demonstrate the suitability of Near Infrared Reflectance Spectroscopy (NIRS) technology for estimation of gross calorific value on faeces as output products in energy balances studies. NIR spectra from dried and grounded faeces samples were analyzed using a Foss NIRSystem 6500 instrument, scanning over the wavelength range 400-2500 nm. Validation studies for quantitative analytical models were carried out to estimate the relevance of method performance associated to reference values to obtain an appropriate, accuracy and precision. The results for prediction of gross calorific value (GCV) of NIRS calibrations obtained for individual species showed high correlation coefficients comparing chemical analysis and NIRS predictions, ranged from 0.92 to 0.97 for poultry and pig. For external validation, the ratio between the standard error of cross validation (SECV) and the standard error of prediction (SEP) varied between 0.73 and 0.86 for poultry and pig respectively, indicating a sufficiently precision of calibrations. In addition a global model to estimate GCV in both species was developed and externally validated. It showed correlation coefficients of 0.99 for calibration, 0.98 for cross-validation and 0.97 for external validation. Finally, relative uncertainty was calculated for NIRS developed prediction models with the final value when applying individual NIRS species model of 1.3% and 1.5% for NIRS global prediction. This study suggests that NIRS is a suitable and accurate method for the determination of GCV in faeces, decreasing cost, timeless and for convenient handling of unpleasant samples.

Referencias bibliográficas

  • Barnes RJ, Dhanoa MS, Lister SJ, 1989. Standard normal variate transformation and de-trending of near infrared diffuse reflectance spectra. Appl Spectrosc 43: 772-777. http://dx.doi.org/10.1366/0003702894202201
  • Castrillo C, Baucells M, Vicente F, Muñoz F, Andueza D, 2005. Energy evaluation of extruded compound foods for dogs by near-infrared spectroscopy. J Anim Physiol Anim Nutr 89: 194-198. http://dx.doi.org/10.1111/j.1439-0396.2005.00557.x
  • De la Roza B, Martínez A, Modroño S, Argamentería A, 2002. Measurement of metabolic parameters in lactating dairy cows by near infrared reflectance spectroscopy analysis using cattle faecal samples. Proc. 10th Int Conf Near Infrared Spectroscopy (Davies AMC & Cho RK, eds), pp: 371-374.
  • Dixon R, Coates D, 2009. Review: Near infrared spectroscopy of faeces to evaluate the nutrition and physiology of herbivores. J Near Infrared Spectrosc 17: 1-31. http://dx.doi.org/10.1255/jnirs.822
  • Fanchone A, Archimède H, Boval M, 2009. Comparison of fecal crude protein and fecal near-infrared reflectance spectroscopy to predict digestibility of fresh grass consumed by sheep. J Anim Sci 87: 236-243. http://dx.doi.org/10.2527/jas.2007-0817
  • Mark H, Workman J, 1991. Statistics in spectroscopy. Academic Press Inc, San Diego, CA, USA. 309 pp.
  • Murray I, 1993. Forage analysis by near infra-red spectroscopy. In: Sward measurement handbook (Davies A, Baker RD, Grant SA, Laidlaw AS, eds), NIR Publications. Chichester, UK. pp: 155-177.
  • Neumeister N, Jaross W, Henker J, Kaltenborn G, 1998. Simultaneous determination of fecal fat, nitrogen and water by Fourier transform near infrared reflectance spectroscopy through a polyethylene/polyamine film. J Near Infrared Spectrosc 6: 265-272. http://dx.doi.org/10.1255/jnirs.206
  • Núñez-Sánchez N, Martínez Marín AL, Pérez Hernández M, Carrion D, Gómez Castro G, Pérez Alba LM, 2012. Faecal near infrared spectroscopy (NIRS) as a tool to asses rabbit’s feed digestibility. Livest Sci 150: 386-390. http://dx.doi.org/10.1016/j.livsci.2012.07.030
  • Sánchez MT, de la Haba MJ, Serrano I, Pérez-Marín D, 2013. Application of NIRS for nondestructive measurement of quality parameters in intact oranges during on-tree ripening and at harvest. Food Anal Methods 6: 826-837. http://dx.doi.org/10.1007/s12161-012-9490-7
  • Savenije B, Geesink GH, van der Palen JGP, Hemke G, 2006. Prediction of pork quality using visible/near-infrared reflectance spectroscopy. Meat Sci 73:18-184. http://dx.doi.org/10.1016/j.meatsci.2005.11.006
  • Shenk JS, Westerhaus MO, 1991a. Population definition sample selection and calibration procedures for near infrared spectra and modified partial least squares regression. Crop Sci 31: 469-474. http://dx.doi.org/10.2135/cropsci1991.0011183X003100020049x
  • Shenk JS, Westerhaus MO, 1991b. Population structuring of near infrared spectra and modified partial least squares regression. Crop Sci 31: 1548-1555. http://dx.doi.org/10.2135/cropsci1991.0011183X003100060034x
  • Shenk JS, Westerhaus MO, 1995a. Analysis of agriculture and food products by near infrared reflectance spectroscopy. Monograph, NIR Systems Inc, Silver Spring, MD, USA.
  • Shenk JS, Westerhaus MO, 1995b. Routine operation, calibration, development and network system management manual. NIR Systems Inc, Silver Spring, MD, USA.
  • Shenk JS, Workman Jr. JJ, Westerhaus MO, 2001. Application of NIR spectroscopy to agricultural products. In: Handbook of near infrared analysis (Burns DA & Ciurczak EW, eds), Practical spectroscopy series, vol. 27, 2nd ed. Marcel Dekker, NY, pp: 419-473.
  • Van den Neucker A, Bijleveld ChMA, Wolthers BG, Swaaneburg JCJM, Kester ADM, van Kreel B, Forget PPh, 2002. Comparison of near infrared reflectance analysis of fecal fat, nitrogen and water with conventional methods, and fecal energy content. Clin Biochem 35: 29-33. http://dx.doi.org/10.1016/S0009-9120(01)00283-1
  • Van Soest, PJ, 1994. Nutritional ecology of the ruminant, 2nd ed. Cornell University Press, Ithaca, NY, USA.
  • Williams PC, 2001. Implementation of nearinfrared technology. In: Near-infrared technology in the agricultural and food industries (Williams PC & Norris KH, eds). AACC Inc, St. Paul, MN, USA. pp: 145-169.
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