The usefulness of classifying the Alpaca wool samples according to their color, sex and location is associated with their economic value in the market, hence adequate methods for rapid classification are needed to assess the of wool value. This study evaluated the potential of the visible and near infrared (vis-NIR) spectroscopy combined with multivariate statistical analysis to classify Alpaca (Lama Pacos) fiber samples according to age (1 and 2-3-year-old), sex (Male and Female) and color (Black, Brown, LF and White). Samples (n=291) were scanned in reflectance mode in the wavelength range of 400-2500nm using a monochromator instrument (FOSS NIRSystems6500, Inc., Silver Spring, MD, USA). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify fiber samples. Cross-validation was used for validation of classification models developed. Results showed that PLS-DA correctly classified 100% of fiber samples into ages, intermediate classification rates were obtained for color, while lower classification rates were obtained for the discrimination of wool samples according to sex. The results from this study suggested that vis-NIR spectroscopy in combination with multivariate data analysis can be used as a rapid method to classify Alpaca fiber samples according to age, sex and color.
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