Search

Abstract While many have advocated for widespread closure of Chinese wet and wholesale markets due to numerous zoonotic disease outbreaks (e.g., SARS) and food safety risks, this is impractical due to their central role in China’s food system. This first-of-its-kind work offers a data science enabled approach to identify market-level risks. Using a massive, self-constructed dataset of food safety tests, market-level adulteration risk scores are created through machine learning techniques. Analysis shows that provinces with more high-risk markets also have more human cases of zoonotic flu, and specific markets associated with zoonotic disease have higher risk scores. Furthermore, it is shown that high-risk markets have management deficiencies (e.g., illegal wild animal sales), potentially indicating that increased and integrated regulation targeting high-risk markets could mitigate these risks.

Abstract Nanoparticle corona phase (CP) design offers a unique approach toward molecular recognition (MR) for sensing applications. Single-walled carbon nanotube (SWCNT) CPs can additionally transduce MR through its band-gap photoluminescence (PL). While DNA oligonucleotides have been used as SWCNT CPs, no generalized scheme exists for MR prediction de novo due to their sequence-dependent three-dimensional complexity. This work generated the largest DNA-SWCNT PL response library of 1408 elements and leveraged machine learning (ML) techniques to understand MR and DNA sequence dependence through local (LFs) and high-level features (HLFs). Out-of-sample analysis of our ML model showed significant correlations between model predictions and actual sensor responses for 6 out of 8 experimental conditions. Different HLF combinations were found to be uniquely correlated with different analytes. Furthermore, models utilizing both LFs and HLFs show improvement over that with HLFs alone, demonstrating that DNA-SWCNT CP engineering is more complex than simply specifying molecular properties.

Supply chain traceability is key to reduce food safety risks, since it allows problems to be traced to their sources. Moreover, it allows regulatory agencies to understand where risk is introduced into the supply chain, and offers a major disincentive for upstream agricultural businesses engaging in economically motivated adulteration. This paper focuses on the aquatic supply chain in China, and seeks to understand the adoption of traceability both through an analytical model, and empirical analysis based on data collected through an extensive (largest ever) field survey of Chinese aquatic wholesale markets. The field survey includes 76 managers and 753 vendors, covering all aquatic wholesale markets in Zhejiang and Hunan provinces. The analytical and empirical results suggest that the adoption of traceability among wholesale market vendors is significantly associated with inspection intensity, their individual history of food safety problems, and their risk awareness. The effect of inspection intensity on traceability adoption is stronger in markets which are privately owned than in markets with state/collective ownership. The analysis offers insights into the current state of traceability in China. More importantly, it suggests several hypothesized factors that might affect the adoption of traceability and could be leveraged by regulatory organizations to improve it.