Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries. The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside, but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading. Herein, we developed a rapid, robust and nondestructive near-infrared spectroscopy (NIRS)-based method for moisture content determination in rubber sheets. A set of 300 rubber sheets were divided into a calibration (200 samples) and prediction groups (100 samples). The calibration set was used to develop NIRS calibration equation using different calibration models, Partial Least Square Regression (PLSR), Least Square Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN). Among the models investigated, the ANN model with the first derivative of spectral preprocessing presented the best prediction with a coefficient of determination ([Formula: see text] of 0.993, root mean square error of calibration (RMSEC) of 0.126% and root mean square error of prediction (RMSEP) of 0.179%. The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.

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Moisture content is one of the factors measured to evaluate the quality of Camellia oleifera seeds. High quality C. oleifera seeds used for trading must have a low moisture content, specifically not more than 15% on a dry basis (db). Moisture content analysis requires a prolonged laboratory investigation so that the development of fast and effective determination methods is helpful. The objective of this paper was to develop a low-cost portable NIR reflectance spectrometer collaborating with an android application for the rapid prediction of the moisture content in C. oleifera seeds. To calibrate the prediction model, an effective chemometric algorithm, based on partial least squares regression was established, and models based on wavelength selection algorithms such as backward interval partial least squares (biPLS) and partial least squares coupled with variable importance projection (VIP-PLS) were implemented as an improved version of PLS. Both algorithms (biPLS and VIP-PLS) improved the predictive performance and accuracy of the model. The experimental results showed that the biPLS model with the 1 st derivative transformation provided the best prediction for measuring the moisture content of C. oleifera seeds with a coefficient of determination (R 2 ) value of 0.927, standard error of prediction (SEP) of 0.848%db, bias of -0.067%db, function slope of 1.005, and ratio of performance deviation (RPD) of 3.696. Finally, the device was tested according to the ISO 12099:2017(E) standard and confirmed the reliability of the device for infield use.INDEX TERMS C. oleifera seed, moisture content, portable spectrometer, backward interval partial least squares, variable importance projection.

Dry rubber content (DRC) is an important factor to be considered in evaluating the quality of cup lump rubber. The DRC analysis requires prolonged laboratory validation. To develop fast and effective DRC determination methods, this study proposed methods to evaluate the DRC of cup lump rubber using different spectroscopic measurement approaches. This involved a complete fundamental analysis leading to an efficient measurement method based on either point-based measurement using NIR reflectance spectrometer or area-based measurement using hyperspectral imaging. A dataset was prepared that 120 samples were randomly divided into a calibration set of 90 samples and a validation set of 30 samples. To obtain an average spectrum to represent a cup lump rubber sample, the spectral data were collected by locating and scanning for point-based and area-based measurement, respectively. The spectral data were calibrated using partial least squares regression (PLSR) and the least-squares support vector machine (LS-SVM) methods against the reference values. The experiments showed that the area-based measurement approach with both algorithms performed outstandingly in predicting the DRC of cup lump rubber and was clearly better than the point-based measurement approach. The best predictions of PLSR represented by the coefficient of determination (R 2 ), the root mean square error of prediction (RMSEP) and the residual predictive deviation (RPD) were 0.99, 0.72% and 15.17, while the best prediction of LS-SVM were 0.99, 0.64% and 16.83, respectively. In summary, the area-based measurement based on the LS-SVM prediction model provided a highly accurate estimate of the DRC of cup lump rubber.