This paper addresses the precision in factor loadings during partial least squares (PLS) and principal components regression (PCR) of wood chemistry content from near infrared reflectance (NIR) spectra. The precision of the loadings is considered important because these estimates are often utilized to interpret chemometric models or selection of meaningful wavenumbers. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set. PLS and PCR, before and after 1st derivative pretreatment, was utilized for model building and loadings investigation. As demonstrated by others, PLS was found to provide better predictive diagnostics. However, PCR exhibited a more precise estimate of loading peaks which makes PCR better for interpretation. Application of the 1st derivative appeared to assist in improving both PCR and PLS loading precision, but due to the small sample size, the two chemometric methods could not be compared statistically. This work is important because to date most research works have committed to PLS because it yields better predictive performance. But this research suggests there is a tradeoff between better prediction and model interpretation. Future work is needed to compare PLS and PCR for a suite of spectral pretreatment techniques.

Fiber identification is the primary task of waste textile recycling, which plays an important guiding role in the recovery and reuse of waste textiles. In this study, 186 pure spinning textiles with different fiber species were chosen as the raw materials, the near-infrared spectra were collected and the differences among various fibers species were also studied. The fast and accurate classification/identification model of textile fiber was established using the near-infrared spectral modeling technique. The soft independent modeling of class analogy method was used to construct the model. The results show that the model recognition rate can be up to 97% after selecting the wavenumber range of 6800–5300 cm^–1 with the first derivative treatment on the spectra. It was found by external validation that the prediction accuracy of the model was 100% for polyester, polyamide, acrylic, silk and wool. The prediction accuracy of cotton fiber and polyester fabric was higher than 90%. The above result demonstrated that the textile fiber identification model established in this study can be used for fast and accurate identification and sorting of waste textiles.