Prediction of cellulose nanofibril (CNF) amount of CNF/polypropylene composite using near infrared spectroscopy

Abstract

The final goal of this study is to establish a classification method of cellulose nanofibril (CNF)/plastic composites such as their CNF amount, CNF types, and resin types, which are expected to progress the commercialization in the future, using near infrared (NIR) spectroscopy. To achieve this goal, NIR spectra of injection and film samples with different types and addition ratios of CNFs in CNF/polypropylene (PP) composites were measured and analyzed in the range of 1000–2200 nm. The results of the principal component analysis using all samples suggest that CNF addition ratio and sample shape could be expressed by principal component (PC) 1 and PC2 scores, which relate to the chemical components of PP and CNF complexly. Furthermore, the partial least-squares (PLS) regression model was able to predict the CNF addition ratio with about 2.0% accuracy, regardless of CNF type and sample shape. To develop an easier model compared to the PLS model, it was calculated to the simple linear regression model, which used the absorbance quotient of optimum wavelengths combination (OWC). Although this model did not have the accuracy to use the quality control, it is able to discriminate CNF addition ratio of CNF/PP composites with almost the same accuracy as the PLS model. However, if it is possible to separate the sample shapes before the analysis, it is suggested that the OWC regression model is able to predict CNF addition ratio of CNF/PP composites with less than 1% accuracy.