Prediction of crosslink density of prevulcanised latex using NIR Spectroscopy based on combination of fractional order derivative (FOD) and variable selection methods

Abstract

Abstract Rapid method in measurement of crosslink density is required in factory. The objective of this study was to develop the prediction model of crosslink densities based on near infrared (NIR) spectroscopy method. The prediction models were developed using partial least squares regression (PLSR) with spectral pre-treatment of fractional order derivatives (FOD) and variable selection methods including successive project algorithm (SPA) and genetic algorithm (GA). The result demonstrated that prevulcanised (PV) latex model had higher accuracy than that of PV 50 latex model. Effective model in predicting crosslink densities of PV and PV 50 latices could be pre-processed with FOD=1 and 0.75, respectively. The prediction model generated with full wavelength had the standard error of cross validation (SECV) of 3.21% and 3.52%, respectively. The model performance of PV latex could improve with variable selection method of GA which reduced the SECV from 3.21% to 3.17% and number of wavelengths reduced from 1059 to 937. The model performance of PV 50 could not reduce by using the variable selection method. However, the GA could reduce the number of wavelengths from 1059 to 216.