food sampling
Challenges. The sharing and circulation of data among food regulatory departments is conducive to the construction of intelligent supervision of the food supply chain [ 11 ]. However, there are several challenges.
The food industry chain is composed of enterprises from agriculture, fishing, processing and restaurant and is characterized by many links and wide distribution. At present, all agricultural machinery is electronically controlled to improve operational performance [ 14 , 15 ]. Cloud computing, the Internet of Things, big data and blockchain can integrate isolated production lines in the food supply chain into data-driven interconnected intelligent systems. Through semantic active technology, each operation is automatically integrated, improving the efficiency of precision agriculture and enterprise management [ 16 ]. Using sensors and drones to collect data on weather, geography, and animal and crop behavior can help farmers optimize crop planting and animal growth cycles. Intelligent devices capture actionable data and make decisions that reduce equipment downtime [ 17 ].
In recent years, research on the IoT in the food industry has promoted the diversification of the IoT platform to address market needs, [ 18 , 19 ] different monitoring models [ 20 ], and unbalanced energy consumption [ 21 ]. IoT-integrated applications will help food companies create new data sources. Industry 4.0 not only promotes the rapid development of Agriculture 4.0, but also enables enterprises to transmit real-time information to identify and meet the changing demands of stakeholders [ 15 ]. According to the Eurostat report ( https://www.brookings.edu/research/chinas-influence-on-the-global-middle-class/ (accessed on 2 September 2021)), the application of smart agriculture will save 4–6% of agricultural costs and increase market value by 3% by 2026. The application of big data can not only enable businesses to deal with challenges in food production, but also to obtain more affordable raw materials to reduce production costs [ 14 ]. It also promotes the development of smart agriculture, which helps save water [ 22 ], preserve soil, limit carbon emissions [ 23 ] and improveproductivity [ 24 ]. Smart agriculture provides an opportunity for farmers, service providers, government and other stakeholders (such as financial institutions, investors, traders) to share their experiences in optimizing the agricultural supply chain with the production sustainability [ 25 ].
Challenges. The food industry can benefit from big data services, but there are challenges that need to be addressed, including data fairness such as the searchability, accessibility, interoperability, and reusability of shared data, and a lack of information standards and data processing technology.
Social media has become the main way for users to obtain and share food information [ 27 , 28 ]. According to a statistical report on China’s Internet Network Information Center, the number of Internet users in China has reached 854 million, and 88.8% [ 29 ]. Social networks have gradually become the mainstream platform for disseminating information, a constant strem of videos, news, and other types of data [ 30 ]. Purcell et al. [ 31 ] found that two-thirds of Internet users get their news from Facebook and share news through social media. Through research into the generation, and promotion of social events on the Internet, the mode and characteristics of information transmission can be discovered, which provides support for practical application scenarios. In 2009, Google successfully predicted the spread of the H1N1 virus based on query data in its search engine and brought the public valuable time to prevent an outbreak [ 32 ]. Combining the real-time advantage of big data with the conventional and available advantages of traditional data will enable effective response to the transmission of public health events such as COVID-19 [ 33 ]. Singh et al. discovered supply chain management problems by using Twitter data to improve supply chain management in food industry [ 34 ].
The public participates in the discussion of events by different media, and it expresses clear opinions and attitudes in the form of public opinion [ 35 ]. The report ( https://baijiahao.baidu.com/s?id=1617643364060321280&wfr=spider&for=pc (accessed on 2 September 2021)) shows that food safety and food rumors were first among hot food events in 2018, and the topic has become one of the prime targerts for media rumors, and social media’s intensification of rumors can create a widespread crisis [ 35 ]. By analyzing and understanding the trend of food incidents based on social media data, regulators can formulate timely countermeasures like enhancing public awareness through science education and shaping public opinion [ 36 , 37 , 38 ]. However, the field of media data has its challenges that need to be overcome.
The food supply chain is from farm to table, where the main links are planting and breeding, storage, processing, circulation (transportation) and consumption [ 39 ]. The discovery of value information on original data needs to go through a continuous cycle: of “discrete data—integrated data—knowledge understanding—mechanism extraction—application effect analysis”, from which the potential value of data sets can be mined. The processing system of big data application in food industry is shown in Figure 3 . It is composed of five modules: big data collection of food industry, big data processing and fusion, big data mining and analysis, big data view and big data security. Each module is closely connected, and its functions are briefly described as follows.
The processing model of big data in food.
Social co-governance in the food industry provides a feasible solution to the issues of food security and food quality by using public wisdom [ 52 ]. Social co-governance is usually based on crowdsourcing to cooperate with consumers or experts to create value [ 53 ]. The failure rate of new food product development exceeds 40%, and the failure of new products usually affects the continued operation of small and medium-sized enterprises [ 3 ]. Large food companies have tried to collect consumer preference data through crowdsourcing, and have decided the direction of product development based on an analysis of big data. The Danone company encouraged consumers to vote for a creamy dessert flavor, and the 400,000 participants in 2006 more than doubled to 900,000 in 2011. Lay’s used the wisdom of crowds to develop more than 245,825 flavors of potato chip [ 54 ]. Procter & Gamble, Starbucks and Unilever sought better product design based on collective intelligence [ 55 ]. Employees often have a wealth of heterogeneous expertise, and companies can gain insight from their workforce to help improve economic performance. In addition, crowdfunding is another form of social co-governance, sharing business risks and alleviating capital pressure through mutual assistance [ 56 ]. Social co-governance has great potential for food security. Combining the mobile data of consumer groups with food shelf life, the intelligent control of food inventory can be realized to prevent food spoilage and waste [ 57 ]. Social co-governance can also be applied to monitoring foodborne diseases [ 32 ], identfying contaminated products, reducing the risk rate of food rumors and enhancing food safety [ 58 ].
Although social co-governance can enable food enterprises to obtain consumer demand information through diversified channels, it is still difficult to obtain effective information in time due to the limitation of enterprise resources [ 53 ]. In addition, there is a lack of an incentive or fair evaluation method in the food industry to convince consumers to participate.
There is a huge amount of food-related data both inside and outside the food supply chain, and the collection and analysis can promote enterprises to expand their markets [ 59 ]: (1) By collecting commodity and retail information for analysis, they can appraise the market situation, grasp the business dynamics of their competitors, and define the market positioning of products, thereby grasping market opportunity. (2) Collecting consumer information (purchase lists and channels, commodity preferences, usage cycle, family information, working condition, values) will establish a customer database that can give enterprises portraits of their customers that reveal their preferences, consumption tendency, value orientation and commodity reputation. With this information, enterprises can develop efficient marketing strategies and develop trust, so they can continue to compete effectively. (3) Data clustering analysis of consumers’ food evaluations (advantages and disadvantages, quality, nutritional value) from social platforms such as Facebook, Twitter and Sina Weibo, allows enterprises to anticipate potential problems and optimize the quality of goods and services.
By predicting future commercial demand based on historical sales’ data, agricultural and livestock products can be planned to reduce the probability of “cheap vegetables hurting farmers”. In addition, big data analysis can help predict weather more accurately, helping farmers and herdsmen to prepare for natural disasters. Analyses based on consumption and crop growth data help farmers decide which crops varieties to increase and which to reduce, improve crop yield, facilitate rapid sales, and achieve a return on capital. Big data can also optimize grazing area for local herdsmen and improve the usage rate of pasture. Fishermen can scientifically arrange fishing moratoria and locate fishing areas based on the results of big data analysis.
Consumption trends and habits provided by big data, enable governments to provide accurate guidance for agricultural and animal husbandry production, suggest production levels according to demand, and avoid unnecessary waste of resources and social wealth caused by excess capacity. When combined with drones, big data can promote the development of precision agriculture by allowing farmers to collect information on the growth of crops, diseases, and pests, at a much lower cost and with much higher accuracy than by hired aircraft [ 60 ].
In 1992, chefs Heston Blumenthal and Francois Benzi were deciding which ingredients with similar flavors would work well together, when someone created a combination of white chocolate and caviar. Due to the chemical differences, it tasted terrible. Today, because of food sciecne, there is a large amount of information about food chemicals [ 61 ] and how they make food taste. Consequently, Ahn et al. [ 62 , 63 ] developed a flavor network of ingredients connected by shared flavor compounds, in which flavors were limited by the type of raw materials. Garg et al. developed a flavor database with richer food materials [ 64 ]. Simas et al. promoted a flavor network and constructed the food-bridging network [ 65 ]. However, some well-known food combinations such as red wine and beef, do not share chemical compositions or flavor compounds, yet they are still very popular. Therefore, food pairings need to be seen in a broader spctrum, not just based on flavor compounds or chemical composition.
The future of food flavor design could be traced to 2019 when the company McCormick partnered with IBM to use Artificial Intelligence and big data to generate new flavor combinations by analyzing data from millions of datasets to meet the changing consumer demand.
In China, the number of online takeout users accounts for more than 44% of sales, and the scale has exceeded 398 million people ( https://www.qianzhan.com/analyst/detail/220/200512-65621d53.html (accessed on 2 September 2021)). The take-out markets with its large number of users and rapid growth has generated a huge amount of takeout data. The takeout big data service platform not only helps the government supervise the industry, but also creates huge economic and social value. First, it predicts and informs customers of the delivery time, thereby avoiding disrupting consumers’ daily plans and helping restaurants establish a good reputation. Second, it helps the take-out enterprise understand consumer demand. Third, the take-out big data platform promotes the transparency of the supply chain, which is conducive to establishing and improving customers’ trust. Fourth, the overall running of the city can be clearly understood by analyzing the take-out dataset [ 66 ].
Since take-out data involves sensitive private information (the customer’s location, preference, bank, identity, and communication), ensuring data security in the take-oout big data platform is a serious challenge.
The development of big data provides technical support for the processing of massive data, and scientific guidance for human nutrition and health management. In the past, people usually learned nutrition information from experts, books, and the Internet. However, there was a lack of accurate nutrition and health management for individuals because of the difference in individual health conditions [ 67 ]. In an example of applying big data to the people’s daily diet Teng et al. proposed to use a recipe recommendation algorithm to determine which food ingredients were necessary [ 68 ]. Grace et al. combined case-based reasoning and a deep learning algorithm to generate new recipes [ 69 , 70 ]. However, it may also generate "dark cuisine" due to the the uncertain factor of deep learning. Some scholars, like Freyne et al., focused on a diet therapy. They developed a personalized recipe recommendation system for obese people based on the suggestions from medical professionals and research on obese people [ 71 ]. In anoher instance, Yoshida et al. proposed a personalized recipe recommendation based on users’ food preferences [ 72 ]. Zeevi et al. broke with traditional experience-based nutritional recommendations by using machine-learning algorithms to combine data (e.g., blood parameters, dietary habits, and gut microbiota) to formulate personalized diets that optimize postprandial glucose levels and metabolites [ 73 ]. The combination of big data with Artificial Intelligence will provide a new approach for the research of precision nutrition.
While the food supply chain can benefit from big data, the following challenges need to be addressed.
Analysis of data security protection technology in the whole life cycle of food data.
Life Cycle | Challenges | Protection Technology |
---|---|---|
Data collection | Data corruption, data loss, data leakage and data forgery | Data encryption [ ] |
Data storage | Illegal intrusion and data disclosure | Storage encryption [ ], blockchain [ ] |
Data transmission | Data leakage and data corruption | Data encryption [ ], privacy protection [ ], blockchain [ ] |
Data usage | Information leakage and data abuse | Access control [ ], SMC [ ], data encryption [ ], differential privacy protection [ ] |
Data Destruction | Privacy disclosure, destruct the data media | Data trusted deletion [ ] |
Because big data can provide a large amount of effective business information, the development of data-driven industries has attracted attention from all countries [ 92 ]. In 2012, the United States promoted big data as a national strategy to promote the formation of new economic growth and enhance national competitiveness [ 93 ]. Subsequently, EU member states formulated big data development strategies to transform traditional national governance models [ 94 ]. Big data to promote the development of the food industry has become a main research topic. This paper introduced the application of big data in food industry and showed that the main data sources are regulatory, enterprise, and media data. The results showed the great potential for big data for the food industry. Big data has particularly broad application prospects in social co-governance of the food industry, quantitative production, exploitation of consumption markets, new dishes, take-out services, and precise nutrition and health management. But, to exploit this full potential of big data, technical, social, and health and sustainable development issues require further research.
Q.T.: Conceptualization, Methodology, Software, Validation and Writing—Original Draft & Review & Editing. H.D.: Investigation. H.W.: Validation and Writing—Review. X.C.: Supervision, and Funding acquisition. All authors have read and agreed to the published version of the manuscript.
The authors would like to acknowledge the support provided by the National Key R&D Program of China (No. 2018YFC1604000).
Not applicable.
Data availability statement, conflicts of interest.
The authors have no competing interest to declare.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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