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Development of a Near-Infrared Imaging System for Determination of Rice Moisture

¹Lecturer, Department of Biomechatronic Engineering, National Ilan University; PhD candidate, Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, 136, Chou-Shan Rd., Taipei, 106, Taiwan. ²Professor, Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, 136, Chou-Shan Rd., Taipei, 106, Taiwan. ³Corresponding author. E-mail: lufuming@ntu.edu.tw ⁴Assistant professor, Department of Horticulture, National Ilan University, 1, Sec. 1, Shen-Lung Rd., I-Lan, 260, Taiwan.

The objective of this study was to develop a near-infrared (NIR) imaging system to determine rice moisture content. The NIR imaging system fitted with 15 band-pass filters (wavelengths of 870–1,014 nm) was used to capture the spectral image. In this work, calibration methods including multiple linear regression (MLR), partial least squares regression (PLSR), and artificial neural network (ANN) were used in both near-infrared spectrometry (NIRS) and the NIR imaging system to determine the moisture content of rice. Comprehensive performance comparison among MLR, PLSR, and ANN approaches has been conducted. To reduce repetition and redundancy in the input data and obtain a more accurate network, six significant wavelengths selected by the MLR model, which had high correlation with the moisture content of rice, were used as the input data of the ANN. The performance of the developed system was evaluated through experimental tests for rice moisture content. This study adopted the coefficient of determination (r_val²), the standard error of prediction (SEP), and the relative performance determinant (RPD) as the performance indices of the NIR imaging system with respect to the tests of rice moisture content. Utilizing these three models, the analysis results of r_val², SEP, and RPD for the validation set were within 0.942–0.952, 0.435–0.479%, and 4.2–4.6, respectively. From experimental results, the performance of NIR imaging system was almost the same as that of NIRS. Using the developed NIR imaging system, all of the three different calibration methods (MLR, PLSR, and ANN) provided a high prediction capacity for the determination of moisture in rice samples. These results indicated that the NIR imaging system developed in this study can be used as a device for the measurement of rice moisture content.