This study deals with a comparative investigation of the characteristics of ascorbic acid microcapsules prepared by different methods, such as thermal phase separation, melt dispersion, solvent evaporation and spray drying. Scanning electron microscopy (SEM), release tests and size distribution were used for the evaluation of product characteristics. The results show that microencapsulated ascorbic acid could prevent the ascorbic acid colour change, retard its core release rate, and generally mask its acid taste. In the thermal phase separation, molecular weight (Mw) of ethyl cellulose (EC) and the addition of polyisobutylene (PIB) significantly influenced the aggregation and release rate of microcapsules. In the melt dispersion method, spherical particles were prepared by using carnauba. The ascorbic acid release rate was found to be slower in the case of carnauba-encapsulated ascorbic acid than that made by EC using other methods. In the solvent evaporation method, a higher Mw of EC and the addition of plastizer were also found to be important for good encapsulation. In the spray drying method, loss of ascorbic acid was found to be minimum during microencapsulation. Starch and beta-cyclodextrin encapsulated ascorbic acid delayed the degradation of ascorbic acid during storage at 38 degrees C and relative humidity 84.0%.
IntroductionReal-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process.MethodsTo reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occluded Xiaomila objects, this paper adopts YOLOv7-tiny as the transfer learning model for the field detection of Xiaomila, collects images of immature and mature Xiaomila fruits under different lighting conditions, and proposes an effective model called YOLOv7-PD. Firstly, the main feature extraction network is fused with deformable convolution by replacing the traditional convolution module in the YOLOv7-tiny main network and the ELAN module with deformable convolution, which reduces network parameters while improving the detection accuracy of multi-scale Xiaomila targets. Secondly, the SE (Squeeze-and-Excitation) attention mechanism is introduced into the reconstructed main feature extraction network to improve its ability to extract key features of Xiaomila in complex environments, realizing multi-scale Xiaomila fruit detection. The effectiveness of the proposed method is verified through ablation experiments under different lighting conditions and model comparison experiments.ResultsThe experimental results indicate that YOLOv7-PD achieves higher detection performance than other single-stage detection models. Through these improvements, YOLOv7-PD achieves a mAP (mean Average Precision) of 90.3%, which is 2.2%, 3.6%, and 5.5% higher than that of the original YOLOv7-tiny, YOLOv5s, and Mobilenetv3 models, respectively, the model size is reduced from 12.7 MB to 12.1 MB, and the model’s unit time computation is reduced from 13.1 GFlops to 10.3 GFlops.DiscussionThe results shows that compared to existing models, this model is more effective in detecting Xiaomila fruits in images, and the computational complexity of the model is smaller.
Starch and proteins are the vital nutrients in buckwheat, to achieve the fast detection of the buckwheat internal composition has an important theoretical significance and application value for buckwheat breeding, processing and other steps. In the paper, forty buckwheat samples from different origins have been selected. The starch and protein content of buckwheat was determined, and the mid-infrared transmission spectrum of buckwheat has been obtained using Fourier mid-infrared spectroscopy. Forty samples were randomly divided into the prediction set and validation set with 30 and 10 samples respectively. After smoothing preprocessing, the prediction models of buckwheat starch and protein content have been established using the combination method of principal component analysis and artificial neural network, finally the models have been verified. The results showed that the correlation coefficient between the prediction value and measurement value of buckwheat starch content is 0.9029, and the relative error is smaller and its mean value is 2.33%, the method of the buckwheat starch content prediction is feasible. But the prediction for buckwheat protein content is not ideal, need to be further studied.
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