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Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy

Published online by Cambridge University Press:  27 March 2013

A. W. Canaza-Cayo ,
D. Alomar  and
E. Quispe
A. W. Canaza-Cayo
Affiliation:
Graduate School, Faculty of Agricultural Sciences, Universidad Austral de Chile, Valdivia 5090000, Chile
D. Alomar*
Affiliation:
Institute of Animal Production, Faculty of Agricultural Sciences, Universidad Austral de Chile, Campus Isla Teja, Valdivia, PO Box 567, Valdivia 5090000, Chile
E. Quispe
Affiliation:
Departamento de Zootecnia, Universidad Nacional de Huancavelica, Huancavelica 09000, Perú
*
†E-mail: [email protected]
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Abstract

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible–near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R2CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R2CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 μm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R2CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R2 of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 μm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.

Keywords

alpacafibre qualitydiameternear-infrared spectroscopyNIRS
Type
Product quality, human health and well-being
Information
animal , Volume 7 , Issue 7 , July 2013 , pp. 1219 - 1225
Copyright
Copyright © The Animal Consortium 2013 

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References

Alomar, D, Mera, M, Errandonea, J, Miranda, H 2010. Prediction of seed coat proportion in narrow-leafed and yellow lupins by near-infrared reflectance spectroscopy (NIRS). Crop & Pasture Science 61, 304–309.Google Scholar
Aylan-Parker, J, McGregor, BA 2002. Optimising sampling techniques and estimating sampling variance of fleece quality attributes in alpacas. Small Ruminant Research 44, 53–64.CrossRefGoogle Scholar
Barnes, RJ, Dhanoa, MS, Lister, SJ 1993. Correction to the description of standard normal variate (SNV) and de-trend (DT) transformations in practical spectroscopy with applications in food and beverage analysis –2nd edition. Journal of Near Infrared Spectroscopy 1, 185–186.Google Scholar
Bosco, GL 2010. Meeting Report. James L. Waters Symposium 2009 on Near-infrared Spectroscopy. Trends in Analytical Chemistry 29, 197–208.Google Scholar
Church, JS, O'Neill, JA 1999. The detection of polymeric contaminants in loose scoured wool. Vibrational Spectroscopy 19, 285–293.Google Scholar
Coleman, SW, Lupton, CJ, Pfeiffer, FA, Minikhiem, DL, Hart, SP 1999. Prediction of clean mohair, fiber diameter, vegetable matter, and medullated fiber with near infrared spectroscopy. Journal of Animal Science 77, 2594–2602.CrossRefGoogle ScholarPubMed
Cozzolino, D, Murray, I 2002. Effect of sample presentation and animal muscle species on the analysis of meat by near infrared reflectance spectroscopy. Journal of Near Infrared Spectroscopy 10, 37–44.Google Scholar
Cozzolino, D, Montossi, F, San Julian, R 2005. The use of visible (VIS) and near infrared (NIR) reflectance spectroscopy to predict fibre diameter in both clean and greasy wool samples. Animal Science 80, 333–337.Google Scholar
Frank, E, Hick, M, Gauna, C, Lamas, H, Renieri, C, Antonini, M 2006. Phenotypic and genetic description of fibre traits in South American domestic camelids (llamas and alpacas). Small Ruminant Research 61, 113–129.Google Scholar
Gishen, M, Cozzolino, D 2007. Feasibility study on the potential of visible and near infrared reflectance spectroscopy to measure alpaca fibre characteristics. Animal 1, 899–904.Google Scholar
Givens, DI, De Boever, JL, Deaville, ER 1997. The principles, practices and some future applications of near infrared spectroscopy for predicting the nutritive value of foods for animals and humans. Nutrition Research Reviews 10, 83–114.CrossRefGoogle ScholarPubMed
Hammersley, MJ, Townsend, PE, Graystone, GF, Ranford, SL 1995. Visible/near infrared spectroscopy of scoured wool. Textile Research Journal 65, 241–246.Google Scholar
Huanca, T, Apaza, N, Lazo, A 2007. Evaluación del diámetro de fibra en alpacas de las comunidades de los distritos de Cojata y Santa Rosa, Puno. Retrieved May 14, 2012, from http://www.produccionbovina.com/produccion_de_camelidos/Alpacas/142-HUANCA-Diametro.pdfGoogle Scholar
Hunter, L 1993. Mohair: a review of its properties, processing and applications. CSIR Division of Textile Technology, Port Elizabeth, South Africa.Google Scholar
Lupton, CJ, McColl, A, Stobart, RH 2006. Fiber characteristics of the Huacaya Alpaca. Small Ruminant Research 64, 211–224.CrossRefGoogle Scholar
McGregor, BA 2006. Production, attributes and relative value of alpaca fleeces in southern Australia and implications for industry development. Small Ruminant Research 61, 93–111.Google Scholar
Montes, M, Quicaño, I, Quispe, R, Quispe, E, Alfonso, L 2008. Quality characteristics of Huacaya alpaca fibre produced in the Peruvian Andean Plateau region of Huancavelica. Spanish Journal of Agricultural Research 6, 33–38.Google Scholar
Murray, I 1986. Near infrared reflectance analysis of forages. In Recent advances in animal nutrition (ed. W Heresign and DJA Cole), pp. 141–156. Butterworths, London, UK.CrossRefGoogle Scholar
Oria, I, Quicaño, I, Quispe, E, Alfonso, L 2009. Variabilidad del color de la fibra de alpaca en la zona altoandina de Huancavelica-Perú. Animal Genetic Resources Information 45, 79–84.CrossRefGoogle Scholar
Ozeki, H, Ito, S, Wakamatsu, K, Thody, AJ 1996. Spectrophotometric characterization of eumelanin and pheomelanin in hair. Pigment Cell Research 9, 265–270.Google Scholar
Rippon, JA 1992. The structure of wool. In Wool dyeing (ed. DM Lewis), pp. 1–51. Society of Dyers and Colourists, Bradford, UK.Google Scholar
Robert, P, Devaux M, F, Bertrand, D 1996. Beyond prediction: extracting relevant information from near infrared spectra. Journal of Near Infrared Spectroscopy 4, 75–84.CrossRefGoogle Scholar
Shenk, JS, Westerhaus, MO 1993. Analysis of agriculture and food products by near infrared reflectance spectroscopy. Infrasoft International (ISI) Monograph, Port Matilda, PA, USA.Google Scholar
Shenk, J, Westerhaus, M 1994. The application of near infrared reflectance spectroscopy (NIRS) to forage analysis. In Forage quality, evaluation, and utilization (ed. GC Fahey), pp. 406–449. ASA, CSSA, SSSA, Madison WI, USA.Google Scholar
Shenk, J, Westerhaus, M 1996. Calibration the ISI way. In Near infrared spectroscopy: the future waves (ed. AMC Davies and P Williams), pp. 198–202. NIR Publications, Chichester, UK.Google Scholar
Shenk, JS, Workman, JJ, Westerhaus, MO 2008. Application of NIR spectroscopy to agricultural products. In Handbook of near infrared analysis (ed. DA Burns and EW Ciurczak), 3rd edition, pp. 347–386. CRC Press, Boca Raton FL, USA.Google Scholar
Slack-Smith, T, Fong, D, Douglas, SAS 1979. The potential application of near infrared reflectance to estimate the alcohol extractable matter content of scoured wool. Journal of Textile Institute 70, 1.Google Scholar
Stobart, RH, Russell, WC, Larsen, SA, Johnson, CL, Kinnison, JL 1986. Sources of variation in wool fiber diameter. Journal of Animal Science 62, 1181–1186.CrossRefGoogle Scholar
Williams, PC 2001. Implementation of near infrared technology. In Near infrared technology in the agricultural and food industries (ed. PC Williams and KH Norris), pp. 145–171. AACC, St Paul, MM, USA.Google Scholar