Abstract

The objective of this research was to apply shortwave near infrared (NIR) (700–1100nm) spectroscopy to the evaluation of dry rubber content (DRC, %) and total solids content (TSC, %) in Para rubber field and concentrated latex. The spectra were obtained in transmission mode. There were 282 samples including field latex, concentrated latex and concentration adjusted concentrated latex of 171, 86 and 25 samples, respectively. The field latex had around 30% DRC and the concentrated latex had around 60% DRC. The concentration adjusted concentrated latex was in five levels which were 55%, 50%, 45%, 40% and 35% DRC. The calibration models were developed by original and pre-treated absorbance spectra using partial least square regression The pre-treatment methods were smoothing, normalisation, derivative, baseline offset, standard normal variate or multiple scatter correction. By statistical analysis, the model developed from smoothing and range normalisation pre-treated spectra in the wavelength range of 700–950 nm provided the best accuracy of prediction for DRC and the models using raw spectra in the same wavelength range gave the best results for TSC. The slope, offset, coefficient of determination (r²), standard error of prediction (SEP) and bias were 1.0154, −0.6286, 0.992, 1.1092% and 0.0321%, respectively, for the prediction of DRC and 1.0084, −0.2332, 0.991, 1.3611% and 0.1456%, respectively for the prediction of TSC. The best models were validated using new sets of 50 and 35 unknown samples for DRC and TSC, respectively. The models achieved an r² of 0.988 and 0.974, SEP of 1.4296 and 2.1255, RPD of 10.0 and 6.2 and bias of −0.6191% and 0.5476% for DRC and TSC, respectively. This work showed the high accuracy of SW-NIR spectroscopy in the evaluation of TSC, the essential parameter for quality assurance and process control in the concentrated rubber latex factory. However, the accuracy for prediction of DRC was still not acceptable for the latex factory because a bias of more than 0.01% is not acceptable for it would affect the amount of money which the factory may lose or gain.

© 2013 IM Publications LLP

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