Open AccessNear infrared spectroscopy and machine learning classifier of crosslink density level of prevulcanized natural rubber latex
Author(s) -
Panmanas Sirisomboon,
Chin Hock Lim,
J Posom
Publication year - 2022
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1234/1/012011
Subject(s) - vulcanization , swell , natural rubber , toluene , materials science , fourier transform infrared spectroscopy , composite material , chemical engineering , chemistry , physics , organic chemistry , engineering , thermodynamics
By toluene swell index for cross link density level of prevulcanized (PV) rubber latex knowledge, toluene swell of PV latex is measured for trading and production management. Therefore, aim of this research is to use the Fourier transform near infrared (FT-NIR) spectroscopy with machine learning to classify different cross link density levels by toluene swell index including, Unvulcanized (U) (> 160%swell), Lightly vulcanized (L) (100-160%swell), Moderately vulcanized (M) (80-100%swell), Fully vulcanized (F) (< 80%swell) of prevulcanized (PV) natural rubber latex of raw PV latex and 50% solids content PV latex (PV50). The result shows that toluene swell index of rubber prevulcanized latex could be 91.8% correct classified into L group and M group using PV50 MSC pretreated spectra with PLS-DA classifier. Unfortunately, sample obtained for this experiment were loss of U and F groups. In future, to develop the robust model, the sample of all crosslink density levels should be collected.