Comparison of three methods for the grading of alpaca fleece

Huayllani F.; Quispe E.; Quispe C.; Aliaga J.; Portugal E.; Quispe M. · Revista de Investigaciones Veterinarias del Peru (2026) · DOI: 10.15381/rivep.v37i1.32889

Resumen

The classification of alpaca fleeces is a crucial process in the textile value chain, directly impacting the price and final quality of the products. The Peruvian Technical Standard 231.301:2022 establishes quality groups for fibre classification. Traditionally, this task is performed by expert classifiers («master graders») using sight and touch, a laborious method susceptible to variability. Given the need to optimize this process, the use of artificial intelligence(AI)-based technology has emerged. Therefore, the objective of this research was to compare the accuracy, productivity, and economic viability of three methods for classifying alpaca fibre: an automatic AI-based classifier (Fiber Class), objective classification using measurements with an electronic fibre characterizer (considered the «Standard»), and the traditional subjective method («master grader»). Twenty-one fleece samples previously classified by the master graders were used, covering seven quality groups. Each sample classified by the «master grader» was evaluated by the Fiber Class and then reclassified using the «Standard» method, subdividing them and measuring each mini sample with the Fibre EC to determine the actual quality and internal proportions. Productivity was evaluated by measuring the time each method takes to classify alpaca fibre fleece samples (kg/month); an economic analysis was also performed. Fiber Class achieved an accuracy of 85.7 ± 7.6%, with no significant difference from the «Standard» method (p>0.05), while the «master graders» obtained 47.6 ± 7.6% accuracy, with a tendency to overestimate quality, being significantly different (p<0.01) from the «Standard» method. Fiber Class also provided information on the proportion of different qualities within each sample and, in terms of productivity (3463 kg/month), surpassed the «master graders» (2304 kg/month) and the «Standard» method (12 kg/ month). Economically, Fiber Class generated the highest monthly profit due to its high productivity and lower cost per kilogram, despite higher total costs, while the «Standard» method proved unviable due to its high cost. The findings demonstrate the potential of Fiber Class to improve objectivity, accuracy, efficiency, and profitability in alpaca fibre classification. ©Los autores. Este artículo es publicado por la Rev Inv Vet Perú de la Facultad de Medicina Veterinaria, Universidad Nacional Mayor de San Marcos. Este es un artículo de acceso abierto, distribuido bajo los términos de la licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0) [https://creativecommons.org/licenses/by/4.0/deed.es] que permite el uso, distribución y reproducción en cualquier medio, siempre que la obra original sea debidamente citada de su fuente original

Instituciones: Universidad Nacional Agraria La Molina