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 (rval2), 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 rval2, 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.

The aim of this research was to determine the rice protein content utilizing a NIR imaging system. The developed imaging system utilized a NIR camera which installed automatically exchanged filters with the wavelength range from 870 nm to 1014 nm. Multiple liner regression (MLR), partial least square regression (PLSR), and artificial neural network (ANN) models were employed as data analysis methods for 6.18%-9.43% rice protein detections within both the NIR imaging system and commercial NIRS. A total of 180 rice samples were used in this study, of which 120 random samples were selected as a calibration set for the MLR and PLSR models. Moreover, for establishing the back-propagation ANN model, the same 120 samples were divided into two parts, 80 samples were used for network training and the other 40 were established as the monitoring set. To compare with the results of MLR, PLSR, and ANN models, the remaining 60 of the total 180 samples were established as the validation set. Applying an MLR linear regression model composed of five wavelengths; the NIR imaging system successfully detected rice protein content. The predicting results of r val 2 and SEP were 0.769 and 0.294%, respectively. In PLSR model, utilizing the imaging system obtained the results of r val 2 = 0.782, and SEP = 0.274% within the wavelength range from 870 nm to 1014 nm. Five significant wavelengths selected by the MLR model were the same as the input data of the ANN model, and the prediction results were r val 2 = 0.806, and SEP = 0.266%. The prediction results indicated that the developed NIR imaging system has the advantages of simple, convenient operation, and high detection accuracy as well as it presents commercial potential in non-destructive detection of rice protein content.

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This study investigated the effects of temperature on the shoot growth and flowering of potted kumquat [Fortunella margarita (Lour.) Swingle] trees grown in subtropical conditions of I-Lan County in Taiwan. Temperature treatments included T 25-32, T 17-25, T 22, and T 18. The T 25-32 treatment trees were to the day/night temperatures of 25/18°C for 2 weeks, followed by 28 weeks at 32/25°C. T 17-25 was exposed for 4 weeks to 17/10°C followed by 26 weeks at 25/18°C. T 22 and T 18 were exposed at 22/18°C and 18/13°C, respectively, for the entire duration of the experiment. Control trees were placed in a plastic greenhouse under conditions similar to the natural environment. The kumquat trees exposed to high-temperature environment of 32/25°C showed more frequent and speedy sprouting of new buds, but induced the earlier termination of shoot elongation growth, resulting in decreased vegetative growth. The temperature treatments lower than 22°C suppressed the new shoot production but increased the shoot growth period, resulting in increased shoot length and diameter. Temperatures higher than 25/18°C readily induced flowering, with flowering being advanced under the higher temperature conditions such as 32/25°C. However, flowering was substantially inhibited under temperature conditions lower than 22/18°C, indicating the negative role of relatively lower temperatures on flowering of kumquat trees.