Optimization of Suri alpaca fibre classification using a multivariate pre classification method in the northern zone of Puno, Peru

Luna E.A.F.; García J.I.Q.; Mamani H.V. · Revista de Investigaciones Veterinarias del Peru (2026) · DOI: 10.15381/rivep.v37i1.30766

Resumen

This study developed an objective pre-classification method for Suri alpaca fibre using multivariate statistical analysis. A total of 2792 fibre samples from 698 fleeces originating from nine districts in the northern Puno region were evaluated using the OFDA2000® optical fibre diameter analyser. Principal component analysis (PCA) identified four components that explained more than 80% of the total variance, while K-means clustering analysis revealed three clusters with differences in all evaluated technological characteristics (p<0.05). Cluster I was characterized by afibre diameter FD= 22.91 ± 1.75 μm, diameter standard deviationDSD = 5.95 ± 1.07 μm, comfort factor CF = 90.49 ± 4.6%, curvature index CI = 17.38 ± 3.20 °/mm, and staple length SL = 123 ± 34.65 mm Cluster II grouped finer fibres characterized by FD = 18.81 ± 1.60 μm, DSD = 5.06 ±0.92 μm, CF = 97.16± 1.87%, CI = 21.11 ±4.61 °/mm, and SL = 108.35 ± 32.79 mm, while Cluster III grouped coarser fibres with FD = 28.09 ±3.19 μm, DSD = 8.40 ± 1.82 μm, CF = 69.99 ± 14.97%, CI = 15.55 ± 3.30 °/mm, and SL = 124.85 ± 37.49 mm. Multivariate statistical methodology offers an objective, versatile and economical alternative, allowing for the optimization of the fibre classification process and its textile processes. © Los autores. Este artículo es publicado por la Rev Inv Vet Perú de la Facultad de Medicina Veterinaria, Universidad Nacional Mayor de San Marcos.

Instituciones: Universidad Nacional del Altiplano