Comparación de tres métodos de clasificación de vellones de alpaca

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