Desarrollo y validación de novedosos sistemas para el análisis de fibras textiles basados en inteligencia artificial: aportes tecnológicos para la evaluación de fibras textiles
Authors:   , Quispe Bonilla Max David 
Published: October 2024

Animal-origin fibers exhibit characteristics that determine their quality, price, and comfort, with diameter and medullation type being particularly important. Medullated fibers are considered problematic for the textile industry as they increase the sensation of itchiness on the skin and the heterogeneity of fabrics, leading to a decrease in fiber quality. Thus, having practical instruments to identify them is very essential. On the other hand, some fibers, depending on the species they come from, are considerably cheaper than others. Hence the existence of counterfeit products, which are detrimental to legitimate producers and consumers alike. Fourier-Transform Infrared (FTIR) spectroscopy can extract a characteristic waveform from fibers than can be later used for classification. However, visually inspecting such waveforms is imprecise. In parallel, Artificial Intelligence is an emerging field that could be useful in this area, offering new possibilities and significant advances. Therefore, the first aim of this thesis was to develop and validate a novel automatic system (referred to as S-Fiber Med) for medullation and diameter assessment of animal fibers based on artificial intelligence. The second objective was to develop and validate two machine learning models, namely, Deep Neural Networks (DNN) and Support Vector Machine (SVM) to classify spectra of fibers by species. For the first objective, the medullation of 88 samples of alpaca, llama and mohair fibers (41, 43 and 4, respectively) was evaluated. Additionally, 269 samples of alpacas were considered for average fiber diameter (AFD) and the results were compared with the Portable Fiber Tester (PFT) and Optical Fibre Diameter Analyser (OFDA) methods (72 and 197 samples, respectively). For the second objective, fiber spectra were acquired using an FTIR spectrometer in Attenuated Total Reflectance (ATR) mode (FTIR-ATR). Camelid (alpaca: n = 51, llama: n=50, vicuña: n = 50) and goat (mohair: n = 35 and cashmere: n = 20) samples were evaluated, from which 1,236 FTIR-ATR spectra were obtained. Results of our first objective indicate that there was no significant difference between medullation percentage obtained with the projection microscope and the S-Fiber Med. The Pearson and Concordance correlation analysis shows a strong, high and significant relationship (p-value < 0.001). The AFDs of alpaca and llama fiber samples obtained with the two methods are very similar, because no significant difference was found at the t-test (p-value>0.172), and they have a strong, high and significant relationship between them, given the high Pearson correlation value (r ≥ 0.96 with p-value <0.001), high Concordance coefficient and bias correction factor. Similar results were found when PFT and OFDA100 were compared with S-Fiber Med. While in the results of our second objective, some visual differences were observed between the spectra of the different species. Accuracies up to 96.75% and 95.12% were obtained when evaluating the DNN and SVM models. Furthermore, an accuracy of 97.8% was obtained when evaluating the FTIR-ATR spectra of South American Camelids (SAC) fibers with DNN, and 97.2% when evaluating them with SVM. A 100% accuracy was obtained when evaluating the FTIR-ATR spectra of vicuña fibers with both models. No significant differences were found (p-value=0.368) by comparing the number of hits against the total number of alpaca, llama, vicuña, mohair and cashmere fibers using DNN. As per the results, it seems that DNN is more accurate than SVM. In conclusion, a novel system was developed that provides rapid (40 seconds/sample), precise, accurate, and automatic measurements of medullation and AFD. Additionally, FTIR-ATR spectrometry techniques combined with machine learning models are a reliable alternative for the identification of SAC and goats through the spectrum of their fibers.