The food supply chain is a complex system that involves a multitude of “stakeholders” such as farmers, production factories, distributors, retailers and consumers. “Information asymmetry” between stakeholders is one of the major factors that lead to food fraud. Some current researches have shown that applying blockchain can help ensure food safety. However, they tend to study the traceability of food but not its supervision. This paper provides a blockchain-based credit evaluation system to strengthen the effectiveness of supervision and management in the food supply chain. The system gathers credit evaluation text from traders by smart contracts on the blockchain. Then the gathered text is analyzed directly by a deep learning network named Long Short Term Memory (LSTM). Finally traders’ credit results are used as a reference for the supervision and management of regulators. By applying blockchain, traders can be held accountable for their actions in the process of transaction and credit evaluation. Regulators can gather more reliable, authentic and sufficient information about traders. The results of experiments show that adopting LSTM results in better performance than traditional machine learning methods such as Support Vector Machine (SVM) and Navie Bayes (NB) to analyze the credit evaluation text. The system provides a friendly interface for the convenience of users.

Agri-food trade has a profound impact on social stability and sustainable economic development. However, there are several technological problems in current agricultural product transactions. For example, it is almost impossible to improve the efficiency of transactions and maintain market stability. This paper designs a novel Food Trading System with COnsortium blockchaiN (FTSCON) to eliminate information asymmetry in the food trade, in order to establish a sustainable and credible trading environment, the system uses consortium blockchain technology to meet the challenge of different authentications and permissions for different roles in food trade. Meanwhile, we have used the online double auction mechanism to eliminate competition. We also have designed a improved Practical Byzantine Fault Tolerance (iPBFT) algorithm to improve efficiency. In addition, a case study based on a series of data from Shandong Province, China indicate that the FTSCON can achieve profit improvement of merchants. Therefore, the proposed system proved to have high commercial value.

Current food traceability systems have a number of problems, such as data being easily tampered with and a lack of effective methods to intuitively analyze the causes of risks. Therefore, a novel method has been proposed that combines blockchain technology with visualization technology, which uses Hyperledger to build an information storage platform. Features such as distribution and tamper-resistance can guarantee the authenticity and validity of data. A data structure model is designed to implement the data storage of the blockchain. The food safety risks of unqualified detection data can be quantitatively analyzed, and a food safety risk assessment model is established according to failure rate and qualification deviation. Risk analysis used visual techniques, such as heat maps, to show the areas where unqualified products appeared, with a migration map and a force-directed graph used to trace these products. Moreover, the food sampling data were used as the experimental data set to test the validity of the method. Instead of difficult-to-understand and highly specialized food data sets, such as elements in food, food sampling data for the entire year of 2016 was used to analyze the risks of food incidents. A case study using aquatic products as an example was explored, where the results showed the risks intuitively. Furthermore, by analyzing the reasons and traceability processes effectively, it can be proven that the proposed method provides a basis to formulate a regulatory strategy for regions with risks.

This review explores the transformative role of artificial intelligence (AI) in enhancing our understanding of olfaction and gustation, two senses that significantly influence human behavior and decision-making. AI methodologies, including machine learning and neural networks, are revolutionizing sensory experiences in industries such as food, fragrance, and healthcare. In the food industry, AI is enhancing flavor profiling, ensuring product safety, and aligning offerings with consumer preferences, all while preserving nutritional value. In fragrance, AI is enabling personalized scent creation, allowing for bespoke products tailored to diverse consumer needs. In healthcare, AI is advancing the diagnosis and treatment of sensory disorders, ultimately improving the quality of life for individuals with sensory impairments. Despite these advancements, challenges persist, such as the need for diverse and representative datasets, the subjective nature of sensory perception, and ethical concerns surrounding privacy. Addressing these issues requires interdisciplinary collaboration across neuroscience, computer science, and food technology. Future research should focus on developing adaptive AI systems that can dynamically interpret and predict sensory preferences, reshaping interactions with food, fragrances, and health products. This review underscores AI’s critical role in driving sensory innovation and lays the groundwork for future studies aimed at enhancing consumer experiences and improving quality of life.

E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users’ emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions.