Abstract
The textile industry is one of the most pollutants in the world. It is estimated that only 20% of the textiles that become solid waste are recycled. In this way, to reduce impacts, the textile industry must include the circular economy in its processing, that is, thinking about textile manufacturing in a closed circuit, which minimizes the consumption of virgin raw materials. The characterization and classification of textile wastes in the industry are done manually, resulting in high costs for the classification of large volumes of textiles. Attenuated total reflected in conjunction with Fourier transformed infrared spectroscopy (ATR-FTIR) can differentiate fibers into plant, animal, and synthetic, being eligible for automated industrial classification. However, this method is limited taking into account the differentiation among cellulosic fibers. This study aimed to evaluate ATR-FTIR spectra obtained from plant origin samples (cotton, kapok, hemp, non-bleached flax, bleached flax, jute, tucum, and tururi) without chemical treatments and observe the potential of multivariate principal component analysis (PCA) and linear discriminant analysis (LDA) on these data. The FTIR “fingerprint” data results values, from 400 to 1800 nm, were employed. To improve the precision of multivariate statistics, these data were previously and individually treated with three types of noise reduction normalization (mean, standardization, and logarithm), and their effects in the final results were analyzed. This database has been normalized with multivariate data analysis PCA (principal component analysis) and LDA (linear discriminant analysis). Employing PCA, tucum, tururi, kapok, jute, and hemp fibers were successfully separated into five different groups, except cotton, non-bleached flax, and bleached flax. For LDA, all fibers were successfully separated, except non-bleached flax and bleached flax. Thus, these results suggest that PCA is a powerful tool in studying textiles with a relatively simple structure, while objects with a more complex or very similar composition, for example, LDA statistics, are more advantageous.
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All data generated or analyzed during this study are included in this published article, more information is available with the corresponding author on reasonable request.
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The authors gratefully acknowledge CAPES (Coordination for the Improvement of Higher Education Personnel of Brazilian Education Ministry).
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The authors, ECS, MAR, PHSR, and JBR, carried out laboratory experiments, data validation process, and design of the study. ECS, AABS, RRAF, MAR, PHSR, and JBR carried out analyses of experimental results associated with technical and statistical processes. The manuscript was written by ECS, while AABS, RRAF, MAR, PHSR, and JBR reviewed critically all versions of the manuscript. All authors, ECS, AABS, RRAF, MAR, PHSR, and JBR, have given final approval for the version to be published.
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da Cruz Santos, E., Silva, A.A.B., Faria, R.R.A. et al. Raw Cellulosic Fibers: Characterization and Classification by FTIR-ATR Spectroscopy and Multivariate Analysis (PCA and LDA). Mater Circ Econ 6, 13 (2024). https://doi.org/10.1007/s42824-024-00104-1
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DOI: https://doi.org/10.1007/s42824-024-00104-1