Evaluation of dry rubber content of natural rubber sheets by near-infrared spectroscopy: benchtop versus portable instruments
DOI:
https://doi.org/10.5433/1679-0359.2026v47n2p357Keywords:
Chemometrics, DRC, Natural rubber, NIR spectroscopy, PLS, Seasonal variation.Abstract
The aim of this study was to develop a rapid and accurate method for determining dry rubber content (DRC) in natural rubber sheets using near-infrared spectroscopy (NIRS), an analytical method that is performed via both benchtop and portable spectrometers. Samples of rubber coagula for benchtop NIRS (n = 936) and portable NIRS (n = 987) were collected from a rubber processing cooperative between January 2024 and July 2025 under edaphoclimatic conditions during the dry, transition, and wet seasons. Three different collection methods were used: Conventional, frame, and turning. The coagulum samples were processed into rubber sheets and analyzed for DRC. NIR spectra were subsequently acquired using the transflectance method and the accuracy and precision of the models were evaluated. The NIR spectra were correlated with reference values to develop chemometric models of calibration using partial least squares (PLS) regression. The calibration equation for benchtop NIRS exhibited R², root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), slope, and offset values of 0.63, 2.04, 2.18, 0.63, and 24.5, respectively. In comparison, portable NIRS presented calibration parameters of 0.51, 2.37, 2.40, 0.51, and 32.0, respectively, for the same coefficients. The precision and accuracy (external validation) of the fitted models were tested using an independent sample set (n = 163) not included in the calibration step and randomly selected by the chemometric software. The external validation results revealed that both benchtop and portable NIRS exhibited similar performance in predicting the DRC in natural rubber sheets, with very close values of R², root mean square error of prediction (RMSEP), SEP, Pearson’s correlation coefficient (r), and bias. The R² values of 0.68 for benchtop NIRS and 0.61 for portable NIRS, as well as the other performance metrics [residual prediction deviation (RPD) and range error ratio (RER)], reveal that the models should be applied with caution in direct operational use. Overall, both the benchtop and portable NIRS models exhibited potential for predicting DRC in natural rubber sheets. However, before routine analytical application, the models should be further improved through the inclusion of new samples and continuous recalibration. Although the benchtop model exhibited slightly higher accuracy, analyses must be performed under laboratory conditions. Conversely, the portable model offers the advantage of real-time and in situ analysis.
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Copyright (c) 2026 Regina Kitagawa Grizotto, Adriana Novais Martins , Gilberto Batista de Souza , Marli Dias Mascarenhas Oliveira , Antônio Lucio Mello Martins , Elaine Cristine Piffer Gonçalves
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