Application of stochastic Bayesian modeling to assess safety status of baby formulas and quantify factors leading to unsafe products in China market

CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network

Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample's contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.

Evidence from a Choice Experiment in Consumer Preference towards Infant Milk Formula (IMF) in the Context of Dairy Revitalization and COVID-19 Pandemic

China is the largest global consumer of infant milk formula (IMF). Chinese consumer preferences towards IMF have evolved over time but have also been rocked in recent years by COVID-19 with major implications for the IMF industry, globally and within China. This study is the first to document parents’ preferences toward IMF since the outbreak. We used novel methods to do so, through an online choice experiment of 804 participants that included risk perceptions and socio-demographic variables. Our study finds that Chinese parents continue to prioritize quality and safety attributes of IMF represented by functional ingredients, organic labelling and traceability information. Notably, it also finds greatly increased confidence in Chinese domestically produced IMF and an underlying preference away from expensive products. This implies that the era of ‘go for foreign’ and ‘go for the most expensive’ in IMF purchasing may be coming to an end. The shift in sentiment is driven by the longer-term revitalization of the Chinese dairy industry, accelerated by COVID-19. Understanding these trends will be of major benefit to both Chinese producers and non-Chinese exporters of IMF.