Open AccessThe rapid determination of volatile fatty acid number in para rubber latex using fourier transform-near infrared spectroscopy based on quantification and discrimination model
Author(s) -
Sureeporn Narongwongwattana,
Ronnarit Rittiron,
Chin Hock Lim
Publication year - 2015
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s179354581550042x
Subject(s) - linear discriminant analysis , natural rubber , chromatography , calibration , analytical chemistry (journal) , detection limit , near infrared spectroscopy , discriminant , chemistry , mathematics , statistics , artificial intelligence , computer science , physics , optics , organic chemistry
Volatile Fatty Acid number (VFA no.) is one of the parameters indicating the state of quality of Para rubber latex at that particular time. Most factories analyze this parameter using standard analytical method as in ISO 506:1992(E). Nevertheless, this procedure is complicated, chemical and time consuming, as well as skilled analyst required. Therefore, near infrared (NIR) spectroscopy which is rapid, accurate and nonchemicals method was applied to determine the VFA no. in field latex and concentrated latex based on quantification and discriminant model. The best calibration equation was obtained from standard normal variate (SNV) spectra in the region of 6109.7–5770.3, 4613.1–4242.9 cm-1 with R = 0.832, SECV = 0.036 and no bias. From the performance check, statistically it was found that SECV and bias were low enough for practical acceptance and the predicted VFA no. was not different significantly from actual VFA no. at 95% confidence intervals. In addition, discriminant model was developed to separate good quality latex from the deteriorated latex using VFA no. at 0.06 as standard as in ISO 2004:2010(E). The discriminant model can be used to screen the latex with overall accuracy of 91.86% in validation set