Summary The present work was aimed to evaluate the potential of the gel‐like Pickering emulsions (50%, v/v, oil) stabilised by zein/tannic acid (TA) complex colloidal particles (ZTPE) as a new encapsulation system of lipophilic ingredients. Compared with sodium caseinate‐stabilised emulsions (SCE) and bulk oil, the better protective effects of ZTPE on the chemical stability of curcumin were observed when they suffered from UV light, and the lipid oxidation rate remarkably reduced in ZTPE. Also, the zein particle layers loaded with TA around the oil droplets can provide protection against harsh gastric environment, facilitating to slow down the release of free fatty acids (FFA) and curcumin during in vitro simulated digestion. These findings show that ZTPE have a good potential to act as an efficient encapsulating agent to protect functional ingredients from degradation and control their release during digestion, which can further improve the bioavailability of bioactive ingredients.

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Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint air–ground water quality monitoring activity were performed. The hyperspectral imaging remote sensing system of UAV comprised a light and small UAV platform, spectral scanning hyperspectral imager, and data acquisition and control unit. The spectral principle of the hyperspectral imager is based on the new high-performance acousto-optic tunable (AOTF) technology. During laboratory calibration, the spectral calibration of the imaging spectrometer and image preprocessing in data acquisition were completed. In the UAV air–ground joint experiment, combined with the typical water bodies of the Yangtze River mainstream, the Three Gorges demonstration area, and the Poyang Lake demonstration area, the hyperspectral data cubes of the corresponding water areas were obtained, and geometric registration was completed. Thus, a large field-of-view mosaic and water radiation calibration were realized. A chlorophyl-a (Chl-a) sensor was used to test the actual water control points, and 11 traditional Chl-a sensitive spectrum selection algorithms were analyzed and compared. A random forest algorithm was used to establish a prediction model of water surface spectral reflectance and water quality parameter concentration. Compared with the back propagation neural network, partial least squares, and PSO-LSSVM algorithms, the accuracy of the RF algorithm in predicting Chl-a was significantly improved. The determination coefficient of the training samples was 0.84; root mean square error, 3.19 μg/L; and mean absolute percentage error, 5.46%. The established Chl-a inversion model was applied to UAV hyperspectral remote sensing images. The predicted Chl-a distribution agreed with the field observation results, indicating that the UAV-borne hyperspectral remote sensing water quality monitoring system based on AOTF is a promising remote sensing imaging spectral analysis tool for water.

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