Development of low-cost portable spectrometer equipped with 18-band spectral sensors using deep learning model for evaluating moisture content of rubber sheets

Abstract

While the choice of spectrometer can vary depending on its intended use, the increased cost of high-performance spectrometers may not be justified in certain applications. Therefore, this research developed an affordable and portable device using 18-band spectral sensors incorporating a deep learning model for accurately determining the moisture content in rubber sheets. A set of 286 rubber sheets was randomly separated into two categories: 200 for model calibration and 86 for model validation. In the calibration process, the spectral data were calibrated using a one-dimensional convolutional neural network (1D-CNN) and then were compared with a recognized linear model using partial least squares regression (PLSR). The experiments revealed the exceptional performance of the 1D-CNN model in predicting the moisture content of rubber sheets, outperforming the PLSR model. The 1D-CNN model had a better prediction accuracy, with a coefficient of determination (R²) of 0.962, a root mean squared error of prediction (RMSEP) of 0.410 %, a prediction-to-deviation ratio (RPD) of 5.2, and an error range ratio (RER) of 18.0. A portable device was constructed by incorporating the 1D-CNN model into a 32-bit microcontroller, which was embedded within the measurement device. During testing of the instrument, the results indicated that its predictive performance did not differ significantly from that of the primary calibration model. Therefore, it could be concluded that the designed instrument was capable of accurately measuring the moisture content of rubber sheets and is suitable for field use due to its portability and cost-effectiveness.