Feasibility study on the potential of visible and near infrared reflectance spectroscopy to measure alpaca fibre characteristics

Gishen, Mark; Cozzolino, Daniel

doi:10.1017/s1751731107000146

Cited by 10 publications

(29 citation statements)

References 21 publications

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“…In general, the absorption bands in the sample set were similar to those reported for sheep wool (Cozzolino et al, 2005) and alpaca fibre in Australia (Gishen and Cozzolino, 2007). The different absorption bands observed are consistent with different components expected in fibres.…”

Section: Spectrasupporting

confidence: 85%

“…Likewise, MFD variations between 17 and 31 mm in alpacas from Huancavelica were reported by Montes et al (2008). In other countries, moderate ranges between 23 and 34 mm in Huacaya alpacas have been found (Lupton et al, 2006;Gishen and Cozzolino, 2007). Differences in MFD can be attributed to genetic and non-genetic effects such as age of the animals and the environment in which they are raised.…”

Section: Discussionmentioning

confidence: 87%

“…Associated to proteins are signals from disulfide bonds in the range of 1890 to 1920 nm. Several of these absorption bands have been detected for alpaca fibre (Gishen and Cozzolino, 2007) and sheep wool (Cozzolino et al, 2005) samples. Loadings for different PCs are coincident in few bands, for example, all positive (and strong) at around 1900 nm, where water and disulfide bonds are absorbing.…”

Section: Spectramentioning

confidence: 98%

“…The different absorption bands observed are consistent with different components expected in fibres. Moisture at 1940 nm and protein-related compounds at 2055, 2170 and 2280 nm (Murray, 1986;Cozzolino et al, 2005;Gishen and Cozzolino, 2007) are expected, as fibres are highly hygroscopic and mostly made from a complex set of proteins. Thus, high absorption bands of amines and sulphur compounds are not unusual, as sheep wool contains more than 170 proteins, comprising 18 of the 22 naturally occurring amino acids; the same is the case for alpaca fibres.…”

Section: Spectramentioning

confidence: 99%

“…This technique has been studied in the wool textile industry mainly for measurements of residual grease, moisture content, wool contamination (Slack-Smith et al, 1979;Hammersley et al, 1995;Church and O'Neill, 1999) and mean fibre diameter (MFD) in both clean and greasy Merino wool (Cozzolino et al, 2005). In a preliminary study with alpacas in Australia (Gishen and Cozzolino, 2007), NIRS was evaluated to predict the fibre diameter and other characteristics of the fibre of this South American camelid. Calibration equations of moderately predictive quality were obtained and the authors concluded that NIRS is a potential analytical method that can be used as a rapid semi-quantitative ranking tool for shorn fleeces and to assist in the selection of superior animals.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy

Canaza-Cayo

Alomar

Quispe

2013

Animal

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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 5 291) 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 (R 2 CV), 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 R 2 CV 5 0.90 and 0.87; RMSECV 5 1.01 and 1.08 mm and RPD 5 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R 2 CV , 0.65 and RPD , 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R 2 of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 mm, 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: alpaca, fibre quality, diameter, near-infrared spectroscopy, NIRS Implications Near-infrared reflectance spectroscopy (NIRS) was evaluated as a technique to assess alpaca fibre diameter, staple length and other related traits, measured in greasy samples. The best calibration models, developed by a multivariate regression method, were able to obtain useful predictions for fibre diameter and spinning fineness. Other variables were poorly predicted. As diameter of fibre is one of the most important variables related to textile quality, NIRS can be employed as an effective technique to evaluate the product and to assist alpaca breeders in the identification of superior animals.

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Section: Spectrasupporting

confidence: 85%