Authentication of duck blood tofu binary and ternary adulterated with cow and pig blood-based gel using Fourier transform near-infrared coupled with fast chemometrics

Multiplex-PCR method application to identify duck blood and its adulterated varieties

This study applied and validated the Multiplex-PCR method to identify the authenticity of duck blood and four common adulterated animal blood varieties. To this end, the genomic DNAs of duck blood and its counterfeit products were extracted using an efficient high-throughput extraction method. Specific primers were designed using the cytochrome b gene. The reaction system and conditions of a multiplex (namely, Five-plex) PCR were optimized, and the proposed methodology was verified, proving its good specificity, repeatability, and sensitivity. The Five-plex PCR system detected nine duck blood samples sold in the local market, revealing the adulteration of duck blood products. The Multiplex-PCR system can accurately and quickly detect adulterated animal blood in duck blood products, effectively finding counterfeits and identifying the authenticity of genuine duck blood products.

Review of visual analytics methods for food safety risks

With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.