%0 Journal Article
%T A study on prediction performance of the mechanical properties of rubber using Fourier
%A Juggapong Natwichai
%A Philaiwan Pornprasit
%A Pruet Boonma
%A Rattapol Pornprasit
%J Journal of Near Infrared Spectroscopy
%@ 1751-6552
%D 2018
%R 10.1177/0967033518805277
%X Near infrared spectroscopy is a spectroscopic method used for quality and quantity analysis of agriculture products and industry materials. Rubber is a mostly raw material of any products. NIR spectroscopy had been using to analyze the mechanical properties of rubber and polymer materials. Prediction models were built from the correlation between the NIR spectra and mechanical strength values (hardness and tensile strength). Raw data were pretreated to improve the prediction models, where the prediction models were based on partial least squares regression and support vector regression. In the case of hardness prediction, the raw dataset was pretreated with standard normal variate transformation or a combination of Savitzky¨CGolay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. For tensile strength prediction, the pretreatments were multiplicative scatter correction or combination of Savitzky¨CGolay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. From these processes, the r2 values were greater than 0.9, the bias values were among ˇŔ0.5, and the RMSEP values were lower than 5
%K Fourier-transform near infrared
%K rubber
%K mechanical strength
%K hardness
%K tensile strength
%K partial least squares regression
%U https://journals.sagepub.com/doi/full/10.1177/0967033518805277