Avian influenza is caused by avian influenza virus infection; the H5N1 avian influenza virus is a highly pathogenic subtype, affecting poultry and human health. Since the discovery of the highly pathogenic subtype of the H5N1 avian influenza virus, it has caused enormous losses to the poultry farming industry. It was recently found that the H5N1 avian influenza virus tends to spread among mammals. Therefore, early rapid detection methods are highly significant for effectively preventing the spread of H5N1. This paper discusses the detection technologies used in the detection of the H5N1 avian influenza virus, including serological detection technology, immunological detection technology, molecular biology detection technology, genetic detection technology, and biosensors. Comparisons of these detection technologies were analyzed, aiming to provide some recommendations for the detection of the H5N1 avian influenza virus.

Recombinase polymerase amplification (RPA) has emerged as a rapid, efficient, and highly sensitive method for nucleic acid amplification, thus becoming a focal point of research in the field of virus detection. This paper provides an overview of RPA, emphasizing its unique double-stranded DNA synthesis mechanism, rapid amplification efficiency, and capability to operate at room temperature, among other advantages. In addition, strategies and case studies of RPA in combination with other technologies are detailed to explore the advantages and potential of these integrated approaches for virus detection. Finally, the development prospect of RPA technology is prospected.

This work presents a reliable approach to trace teas’ geographical origins despite changes in teas caused by different harvest years. A total of 1447 tea samples collected from various areas in 2014 (660 samples) and 2015 (787 samples) were detected by FT-NIR. Seven classifiers trained on the 2014 dataset all succeeded to trace origins of samples collected in 2014; however, they all failed to predict origins for the 2015 samples due to different data distributions and imbalanced dataset. Three outlier detection based undersampling approaches—one-class SVM (OC-SVM), isolation forest and elliptic envelope—were then proposed; as a result, the highest macro average recall (MAR) for the 2015 dataset was improved from 56.86% to 73.95% (by SVM). A model updating approach was also applied, and the prediction MAR was significantly improved with increase in the updating rate. The best MAR (90.31%) was first achieved by the OC-SVM combined SVM classifier at a 50% rate.

The manufacture, modification, and application of surface-functionalized Fe₃O₄ nanoparticles in the extraction and isolation of antibiotics are reviewed in this paper. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) can be used to characterize the morphological features of Fe₃O₄ nanoparticles, while Fourier Transform Infrared Spectrometer (FTIR) and X-ray diffractometry (XRD) can be used to determine the crystalline shapes and distinctive functional groups of these particles. We gathered a lot of data to investigate the effects of experimental parameters like pH, adsorbent dosage, contact time, and ionic strength on the extraction of antibiotics from Fe₃O₄ magnetic nanoparticles. We additionally talked about the mechanism of action of magnetic nanoparticles for the extraction of separated antibiotics and how they are utilized in practical detection methods. This work concluded with an overview of the potential for environmental friendliness and dispersion of functionalized Fe₃O₄ nanoparticles, as well as potential challenges and directions.

This paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n=99) collected from nine producing areas and non-Wuyi-Rock tea (n=33) from eleven nonproducing areas were analysed for SIR and MEP by established methods. The SVM model based on coupled data produced the best prediction accuracy (0.9773). This prediction shows that instrumental methods combined with a classification model can provide an effective and stable tool for provenance discrimination. Moreover, every feature variable in stable isotope and metallic element data was ranked by its contribution to the model. The results show thatδ²H,δ¹⁸O, Cs, Cu, Ca, and Rb contents are significant indications for provenance discrimination and not all of the metallic elements improve the prediction accuracy of the SVM model.

This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas (‘non-Zhejiang’). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R² reaching 0.9979, while that of the second derivative was the lowest with an R² of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance.