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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: dalomar@uach.cl
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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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