Tea quality is often evaluated by experienced tea tasters; however, their assessments are subjective, being influenced by their individual physiological and psychological condition. Herein, we fabricated a colorimetric sensor arraybased artificial olfactory system for sensing the quality of Chinese green tea. First, the colorimetric sensors array was man-made using printing 12 chemically responsive dyes (9 porphyrins, metalloporphyrins and 3 pH indicators) on silica-gel flat plate. The plate was exposed to volatile organic compounds, and the colour changes in each sample were obtained by distinguishing between the images of sensor array before and after contact with tea sample. The values of colour composition changes were extracted from the dyes' colour sections. Multivariate calibrations were applied through principal component analysis and back propagation artificial neural network (BP-ANN) for modelling. The optimum BP-ANN model was obtained with nine principal components, and the discrimination rate was equal to 85% and 86% in the calibration and prediction sets, respectively. We thus conclude that the low cost colorimetric sensor array-based artificial olfactory technique has great potential for application in intelligent evaluation of the quality of green tea.
<sec>Thermoacoustic imaging (TAI) is an emerging biomedical imaging method in which microwave is used as an excitation source to generate acoustic signals. The TAI possesses the advantages of high contrast of microwave imaging and high resolution of ultrasound imaging, which is also noninvasive. While the signal-to-noise ratio (SNR) of TAI is often very low. It is usually required by averaging the thermoacoustic signal many times to improve the SNR. However, averaging the signal to improve the SNR can significantly reduce the TAI’s time resolution, which hinders the development of rapid TAI. Here in this paper, we propose to reduce the cost and improve the time resolution of TAI based on multi-channel amplifier and additive circuit. The received thermoacoustic signals are divided into 4 channels and then entered into 4 amplifiers simultaneously.</sec><sec>After being amplified, the signals are added and collected by the data acquisition system for reconstructing the image. The phantom results indicate that the time resolution of TAI increases 5 times and the SNR rises from 6 dB to 12 dB, with the multi-channel amplifier and additive circuit adopted. The method proposed in this paper is helpful in promoting the development and clinical application of TAI, especially it has a great significance for developing the ultra-fast TAI.</sec>
Climate change has had a strong impact on grain production in the Lower Lancang–Mekong River Basin (LMB). Studies have explored the response of LMB rice yield to climate change, but most of them were based on climate projection data before CMIP6 (Coupled Model Intercomparison Project Phase 6). Based on the latest CMIP6 climate projection data and considering three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), this study used the crop growth model (AquaCrop) to simulate and project the LMB rice yield and analyzed the correlation between the yield and the temperature and precipitation during the growth period. The results show that the output of rice yield will increase in the future, with greater yield increases in the SSP5-8.5 scenario (about 35%) than in the SSP2-4.5 (about 15.8%) and SSP1-2.6 (about 9.3%) scenarios. The average temperature of the rice growth period will increase by 1.6 °C, 2.4 °C, and 3.7 °C under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The rice yield was predicted to have a significant positive response to the increase in temperature in the near future (2021–2060). In the far future (2061–2100), the rice yield will continue this positive response under the high-emission scenario (SSP5-8.5) with increasing temperature, while the rice yield under the low-emission scenario (SSP1-2.6) would be negatively correlated with the temperature. There will be a small increase in precipitation during the rice growth period of LMB in the future, but the impact of the precipitation on the rice yield is not obvious. The correlation between the two is not high, and the impact of the precipitation on the yield is more uncertain. This result is valuable for the management of the rice cultivation and irrigation system in the LMB, and it will help the government to adapt the impact of climate change on the rice production, which may contribute to the food security of the LMB under climate change.
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