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Multi-pharmaceuticals analysis in fish by continuous series solid-phase extraction coupled with UPLC-MS/MS

Gao Lei, Wang Peng, Chen Zhongxiang, Tang Shizhan, Wu Song, Li Gang, Qin Dongli

Acta Chromatographica

2022

Vol. 34, no. 1

61--70

Pharmaceuticals which are widely used in aquatic can easily migrate into the environment and aquatic animals, and can increase the risk of drug resistance and allergic symptoms if consumed by humans. In order to achieve high-throughput analysis of pharmaceuticals with different physical and chemical properties from complex matrices, we developed a new method for various types pharmaceuticals in fish and shrimp tissue. Series solid-phase extraction (s-SPE) with different adsorbents was selected for extracting and purifying analytes with different paddings. s-SPE were combined with ultra performance liquid chromatography triple quadruple tandem mass spectrometry (UPLC-MS/MS) for the detection of 30 pharmaceuticals antibiotics in fish samples. This method was stabilized and reliable to determinate the pharmaceuticals in fish and shrimp samples. As the method combined multiple Chinese national standards method, it could be easily treat the multi-pharmaceuticals from the fish and shrimp samples once time. It provided for both quantitative and qualitative methods and they could be applied to single- or multi-residue methods.

Identification of Miao Embroidery in Southeast Guizhou Province of China Based on Convolution Neural Network

Zhang Chune, Wu Song, Chen Jianhui

Autex Research Journal

2021

Vol. 21, no. 2

198--206

Miao embroidery of the southeast area of Guizhou province in China is a kind of precious intangible cultural heritage, as well as national costume handcrafts and textiles, with delicate patterns that require exquisite workmanship. There are various skills to make Miao embroidery; therefore, it is difficult to distinguish the categories of Miao embroidery if there is a lack of sufficient knowledge about it. Furthermore, the identification of Miao embroidery based on existing manual methods is relatively low and inefficient. Thus, in this work, a novel method is proposed to identify different categories of Miao embroidery by using deep convolutional neural networks (CNNs). Firstly, we established a Miao embroidery image database and manually assigned an accurate category label of Miao embroidery to each image. Then, a pre-trained deep CNN model is fine-tuned based on the established database to learning a more robust deep model to identify the types of Miao embroidery. To evaluate the performance of the proposed deep model for the application of Miao embroidery categories recognition, three traditional non-deep methods, that is, bag-of-words (BoW), Fisher vector (FV), and vector of locally aggregated descriptors (VLAD) are employed and compared in the experiment. The experimental results demonstrate that the proposed deep CNN model outperforms the compared three non-deep methods and achieved a recognition accuracy of 98.88%. To our best knowledge, this is the first one to apply CNNs on the application of Miao embroidery categories recognition. Moreover, the effectiveness of our proposed method illustrates that the CNN-based approach might be a promising strategy for the discrimination and identification of different other embroidery and national costume patterns.