Spectroscopic measurement approaches in evaluation of dry rubber content of cup lump rubber using machine learning techniques

Dry rubber content (DRC) is an important factor to be considered in evaluating the quality of cup lump rubber. The DRC analysis requires prolonged laboratory validation. To develop fast and effective DRC determination methods, this study proposed methods to evaluate the DRC of cup lump rubber using different spectroscopic measurement approaches. This involved a complete fundamental analysis leading to an efficient measurement method based on either point-based measurement using NIR reflectance spectrometer or area-based measurement using hyperspectral imaging. A dataset was prepared that 120 samples were randomly divided into a calibration set of 90 samples and a validation set of 30 samples. To obtain an average spectrum to represent a cup lump rubber sample, the spectral data were collected by locating and scanning for point-based and area-based measurement, respectively. The spectral data were calibrated using partial least squares regression (PLSR) and the least-squares support vector machine (LS-SVM) methods against the reference values. The experiments showed that the area-based measurement approach with both algorithms performed outstandingly in predicting the DRC of cup lump rubber and was clearly better than the point-based measurement approach. The best predictions of PLSR represented by the coefficient of determination (R2), the root mean square error of prediction (RMSEP) and the residual predictive deviation (RPD) were 0.99, 0.72% and 15.17, while the best prediction of LS-SVM were 0.99, 0.64% and 16.83, respectively. In summary, the area-based measurement based on the LS-SVM prediction model provided a highly accurate estimate of the DRC of cup lump rubber.