Meat adulteration, mainly for the purpose of economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat adulteration. Considering the importance and rapid advances in meat adulteration detection technologies, a comprehensive review to summarize the recent progress in this area and to suggest directions for future progress is beneficial. In this review, destructive meat adulteration technologies based on DNA, protein, and metabolite analyses and nondestructive technologies based on spectroscopy were comparatively analyzed. The advantages and disadvantages, application situations of these technologies were discussed. In the future, determining suitable indicators or markers is particularly important for destructive methods. To improve sensitivity and save time, new interdisciplinary technologies, such as biochips and biosensors, are promising for application in the future. For nondestructive techniques, convenient and effective chemometric models are crucial, and the development of portable devices based on these technologies for onsite monitoring is a future trend. Moreover, omics technologies, especially proteomics, are important methods in laboratory detection because they enable multispecies detection and unknown target screening by using mass spectrometry databases.

In contrast with the general trend of producing wine from the most famous grapevine varieties, associated with the French paradigm, such as Cabernet‐Sauvignon, Merlot, Pinot Noir, Syrah, Sauvignon Blanc, and Chardonnay, there is a tendency to revalorize and preserve minority or autochthonous grapevine varieties worldwide. The South American wine region, where most of the varieties derived from varieties brought after European colonization, is not exempt from this. This has allowed new wines to be provided with distinctive identities that are markedly different from the current homogeneous wine production. Moreover, varietal homogenization increases vineyard genetic vulnerability in relation to the emergence of grapevine diseases, to which the commonly cultivated varieties are not resistant. This review summarizes the oenological potential of minority or autochthonous grapevine varieties cultivated within the South American wine region, focusing on Argentina, Chile, and Bolivia. © 2019 Society of Chemical Industry

It is important to select an appropriate emulsifier to overcome the poor stability and dispersibility of the vegetable oils in food system. Previous studies suggest that OSA‐modified konjac glucomannan (KGOS) has potential to be used as a food emulsifier. In this study, in vitro fermentation suggested that KGOS could promote the growth of the important intestinal probiotics Lactobacillus and Bifidobacterium and then promote intestinal fermentation to produce gas and short chain fatty acids. The emulsification experiments indicated that KGOS had good emulsification ability and stability for camellia oil. Under 40 MPa for 90 s homogenization, 0.2% (w/w) KGOS could encapsulate 20% (w/w) camellia oil. The nanoemulsion was stable at a low pH and high concentration of NaCl and ethanol. Konjac glucomannan octenyl succinate encapsulation could prevent the oxidation of camellia oil at 25°C and storage for 30 days.

Hyperspectral imaging technology can obtain the spatial information and spectral information of the simulated operational background and its camouflage materials at the same time and identify and classify them according to their differences. In this paper, we collected the hyperspectral images (400–1000 nm) of the desert background, jungle background, desert camouflage netting, jungle camouflage netting, and jungle camouflage clothing through the hyperspectral imaging system, and the samples were preprocessed by denoising and black-and-white correction. Then, we analysed the region of interest (ROI) of the training samples by principal component analysis (PCA). After the pixels in the region of interest and their surrounding areas were averaged, 60% of the data was used as the training samples, and the remaining 40% was used as the test samples. According to their similarities and differences between them and referenced spectrum, the models of classification were established by combining the Naive Bayes (NB) algorithm, K-nearest neighbour (KNN) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm. The results show that among the four models, SVM model has the highest accuracy of classification and the recognition rate of jungle camouflage clothing is the highest. This study verifies the scientific and feasibility of hyperspectral imaging technology for camouflage identification and classification in a simulated operational environment, which has some practical significance.

Remote sensing cross-modal text-image retrieval (RSCTIR) has recently attracted extensive attention due to its advantages of fast extraction of remote sensing image information and flexible human–computer interaction. Traditional RSCTIR methods mainly focus on improving the performance of uni-modal feature extraction separately, and most rely on pre-trained object detectors to obtain better local feature representation, which not only lack multi-modal interaction information, but also cause the training gap between the pre-trained object detector and the retrieval task. In this paper, we propose an end-to-end RSCTIR framework based on vision-language fusion (EnVLF) consisting of two uni-modal (vision and language) encoders and a muti-modal encoder which can be optimized by multitask training. Specifically, to achieve an end-to-end training process, we introduce a vision transformer module for image local features instead of a pre-trained object detector. By semantic alignment of visual and text features, the vision transformer module achieves the same performance as pre-trained object detectors for image local features. In addition, the trained multi-modal encoder can improve the top-one and top-five ranking performances after retrieval processing. Experiments on common RSICD and RSITMD datasets demonstrate that our EnVLF can obtain state-of-the-art retrieval performance.