Sweetpotato residue, a by-product generated after starch extraction in the food industry, has not been effectively utilized. In this study, fermentation with Rhizopus oligosporus RT-3 was carried out ...to improve the nutritional properties and flavor profile of sweetpotato residue. The contents of soluble dietary fiber, protein, phenolics and flavonoids and the antioxidant activities of sweetpotato residue significantly increased during fermentation. The analysis of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and sensory evaluation indicated that fermentation enhanced the aroma of sweetpotato residue due to the increased levels of alcohols, esters, ketones, and alkenes. Metabolite analysis further revealed that fermentation remarkably improved the nutritional composition of the sweetpotato residue. The potential metabolic pathways triggered during fermentation demonstrated that the improved nutritional composition of sweetpotato residue was attributed to the biochemical reactions driven by microbial enzymes.
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•Fermentation improved the flavor profile of sweetpotato residue.•Fermentation increased the antioxidant activity of sweetpotato residue.•R. oligosporus RT-3 produced abundant enzymes during fermentation.•Fungal enzyme-induced metabolic behavior improved nutrition of sweetpotato residue.
Sweetpotato black spot disease caused by Ceratocystis fimbriata is a major sweetpotato disease that not only affects yield and storage but also damages human or animal health. Herein, a four-element ...quartz crystal microbalance (QCM) gas sensor array based on molecularly imprinted polymers (MIPs) and zeolitic imidazolate frameworks (ZIFs) materials were reported to differentiate healthy sweetpotatoes and sick sweetpotatoes. Several volatile organic compounds, namely citronellol, heptanal, benzaldehyde, and 2-pentylfuran, were selected for detection based on the results of gas chromatography-mass spectrometry (GC-MS). The MIPs and ZIFs were characterized by X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, and nitrogen adsorption-desorption, and the results show that materials were successfully obtained. The four sensors based on the as-prepared materials exhibited excellent sensitivity and selectivity toward target gases. Finally, the sensor array was applied to identify sick sweetpotatoes. Frequency shift was selected as the eigenvalue and quadratic support vector machine (QSVM) and weighted k-nearest neighbor (WKNN) models were employed for discrimination. QSVM and WKNN exhibited 100% accuracy in classification, proving that the sensor array can be used for the identification of Ceratocystis-fimbriata-infested sweetpotatoes. This study may contribute to the development of gas sensor arrays for use in agri-food quality control and protection.
•A QCM gas sensor array based on MIPs and ZIFs was reported to identify sweet potato black spot disease.•Citronellol, heptanal, benzaldehyde and 2-pentofuran as specific VOCs based on the results of GC-MS analysis.•Both QSVM and WKNN exhibited strong classification performance with a 100% classification accuracy rate.
Summary
Visual simultaneous localization and mapping (visual SLAM) has been well developed in recent decades. To facilitate tasks such as path planning and exploration, traditional visual SLAM ...systems usually provide mobile robots with the geometric map, which overlooks the semantic information. To address this problem, inspired by the recent success of the deep neural network, we combine it with the visual SLAM system to conduct semantic mapping. Both the geometric and semantic information will be projected into the 3D space for generating a 3D semantic map. We also use an optical-flow-based method to deal with the moving objects such that our method is capable of working robustly in dynamic environments. We have performed our experiments in the public TUM dataset and our recorded office dataset. Experimental results demonstrate the feasibility and impressive performance of the proposed method.
The quality deterioration of sweetpotatoes easily during postharvest storage owing to their high respiration rate and accumulation of reactive oxygen species (ROS). In this study, an edible coating ...(MCW) assembled using montmorillonite, chitosan, and whey protein isolate was fabricated to improve the postharvest quality of sweetpotatoes, and the regulatory mechanism of the edible coating treatment on sweetpotato quality has investigated. The results confirmed that edible coating treatment had a positive effect on maintaining the appearance, firmness, colour, and sensory quality of sweetpotatoes, especially in maintaining free water, starch and soluble protein content, inhibiting respiration rate and delaying weight loss of sweetpotatoes stored at 25 °C for 30 d. Meanwhile, this study illuminated the MCW coating regulates ROS metabolism by upregulating superoxide dismutase, ascorbate peroxidase, and glutathione reductase gene expression, resulting in lower O2−. content in postharvest sweetpotato. Furthermore, the relative expression of the genes encoding lipoxygenase, phospholipase C, and phospholipase D was downregulated by the edible coating treatment and contributed to lower enzyme activity, electrolyte leakage and malondialdehyde content, which further reduced the degree of membrane lipid peroxidation and effectively maintained the structure of plasma membrane and then protected the cell integrity of sweetpotato. Collectively, this study provides useful information at the transcriptional level that MCW coating can enhance the post-harvest quality of sweetpotatoes by regulating ROS and membrane lipid metabolism.
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•An edible coating was developed to preserve the quality of sweetpotatoes.•MCW edible coating was relatively stable and capable of dispersion.•MCW coating improved postharvest quality of sweetpotatoes stored.•MCW coating has effectively regulated ROS and membrane lipid metabolism.
The proven efficacy of safety strategies based on 2-D laser rangefinder (LRF) strongly stimulates their application to mobile robots operating in the home environment. However, it remains a challenge ...for the robot to avoid collisions with all obstacles in the environment. Since LRF can only scan a horizontal slice of the world, some objects cannot be fully observed, such as tables and chairs. In this article, an effective solution based on laser-visual fusion is presented to enhance the safety of the robot. First, a vision sensor is adopted to help detect obstacles that are not fully visible to LRF. Then we propose a method to convert the depth information of the visual image into 2-D pseudo-laser data representation. With this representation, a strategy for 2-D mapping is developed. On this basis, a novel map fusion algorithm is proposed to generate an improved grid map that amends the incorrect representation of obstacles on the traditional 2-D grid map. We further investigate a robot autonomous navigation strategy that considers LRF data and pseudo-laser data to avoid all obstacles. Experimental results show that the improved grid map together with the presented navigation strategy allows the robot not only to plan a ``real'' collision-free path, but also to navigate safely in both static and dynamic scenarios, and the proposed strategies can significantly enhance the performance of robot navigation in terms of safety, reliability and robustness.
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•QCM gas sensors based on the composite of modified CAU-1@ZIF-8 was fabricated.•Adsorption of modified CAU-1 on trans-caryophyllene increased 2.8 times.•The sensor has better ...selectivity and sensitivity to trans-caryophyllene.•The early infestation sweetpotato can be nondestructively detected by the sensor.
Changes in the content of trans-caryophyllene could identify whether sweetpotatoes are infected with black spot disease. Therefore, in this study, a quartz crystal microbalance (QCM) sensor based on modified CAU-1@ZIF-8 was used for non-destructive detection of sweetpotato black spot. The experimental results showed that the constructed sensor performed best when both modified CAU-1 and ZIF-8 were present. The sensor showed a linear trend (R2 = 0.996, limit of detection (LOD) = 0.55 ppm) in response to trans-caryophyllene in the concentration range of 20–340 ppm with a sensitivity of −0.489 Hz (ppm)−1. The developed gas sensor was successfully used for the detection of trans-caryophyllene in real samples of sweetpotato. The experimental data showed a significant correlation with the results of gas chromatography-mass spectrometry (GC–MS) with a correlation coefficient of 0.970 (p < 0.01). Therefore, it could be used for early warning of black spot disease in sweetpotato.
In a goal-conditioned grasping task, a robot is asked to grasp the objects designated by a user. Existing methods for goal-conditioned grasping either can only handle relatively simple scenes or ...require extra user annotations. This letter proposes an autonomous method to enable the grasping of target object in a challenging yet general scene that contains multiple objects of different classes. It can effectively learn a dense descriptor and integrate it with a newly designed grasp affordance model. The proposed method is a self-supervised pipeline trained without any human supervision or robotic sampling. We validate our method via both simulated and real-world experiments while the training relies only on a variety of synthetic data, demonstrating a good generalization capability. Supplementary video demonstrations and material are available at https://vsislab.github.io/agcg/ .
In recent decades, semantic mapping has become a hot topic benefited from the maturity of visual simultaneous localization and mapping (visual SLAM) and the success of deep learning. Despite the ...impressive performance of the current state-of-the-art systems, semantic mapping in dynamic environments is still a challenging task. To address this problem, we propose a framework that fuses geometric information, semantic information, and human activity into a 3D dense map. The accuracy of the map is guaranteed by the reliable camera trajectory estimation and the static pixels used for 3D reconstruction. With the proposed framework, we achieve two objectives. On the one hand, we accurately reconstruct the environment from both geometric and semantic perspectives. On the other hand, we record human activity by tracking the human trajectory during the mapping period. We conduct both qualitative and quantitative experiments on the public TUM dataset. The experimental results demonstrate the feasibility and effectiveness of the proposed framework.
A novel blood pressure estimation method based on long short-term memory neural network, one of the recurrent neural networks being commonly used nowadays, is proposed in this paper for better ...chronic diseases monitoring. Along with the neural network, a newly proposed ambulatory blood pressure (ABP) processing technique called Two-stage Zero-order Holding (TZH) algorithm has also been presented in the paper. The proposed methodology has the advantages over traditional blood pressure estimation algorithms which are based on Pulse Transit time (PTT). The paper addresses the effectiveness of the algorithm by computing the Root-Mean-Squared Errors (RMSE) between the BP estimated and the ground truth. Our algorithm shows precise systolic blood pressure and diastolic blood pressure estimation with the average RMSE values in 2.751 mmHg and 1.604 mmHg respectively across the sample used. Experimental results suggest that BP estimation based on LSTM has great potential to be embedded into monitoring system for better accuracy and generalization.
Learning from demonstration holds the promise of enabling robots to learn diverse actions from expert experience. In contrast to learning from observation-action pairs, humans learn to imitate in a ...more flexible and efficient manner: learning behaviors by simply "watching." In this article, we propose a "watch-and-act" imitation learning pipeline that endows a robot with the ability of learning diverse manipulations from visual demonstrations. Specifically, we address this problem by intuitively casting it as two subtasks: 1) understanding the demonstration video and 2) learning the demonstrated manipulations. First, a captioning module based on visual change is presented to understand the demonstration by translating the demonstration video into a command sentence. Then, to execute the captioning command, a manipulation module that learns the demonstrated manipulations is built upon an instance segmentation model and a manipulation affordance prediction model. We validate the superiority of the two modules over existing methods separately via extensive experiments and demonstrate the whole robotic imitation system developed based on the two modules in diverse scenarios using a real robotic arm. Supplementary video is available at https://vsislab.github.io/watch-and-act/.
