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. 2022 Feb 20;31(4):399–406. doi: 10.1007/s10068-022-01047-6

Impact and prospect of the fourth industrial revolution in food safety: Mini-review

Sang-Soon Kim 1, Sangoh Kim 2,✉
PMCID: PMC8994800  PMID: 35464250

Abstract

The fourth industrial revolution represented by big data and artificial intelligence (AI), already had a significant impact on the food industry. In this review, the impacts and prospects of the 4th industrial revolution in food safety were discussed. First, the general process and characteristics of AI application from data collection to visualization are covered. Additionally, various data collection and analysis methods are discussed, with emphasis on the collection of high variety, volume, and velocity data and visualization. Available literature presents examples of machine learning applications in food samples that are mostly associated with the classification of agricultural food items through convolutional neural networks. Based on these examples, the prospects of the 4th industrial revolution in food safety are categorized as follows: prediction of food safety risk, detection of foodborne pathogens, and food safety management. This mini-review will help understand the relationship between the 4th industrial revolution and food safety.

Keywords: Big data, Artificial intelligence, Machine learning, Food industry, Food safety

Introduction

The first industrial revolution changed the world from laborious to efficient with the use of machinery. Electricity made the second significant change (2nd industrial revolution), resulting in higher productivity. Subsequently, private computers and the internet derived the 3rd industrial revolution. Recently, big data and artificial intelligence (AI) have significantly affected our lives and contribute to 4th industrial revolution. For example, AI can suggest movie and clothing options based on our preferences, classify spam e-mails, and be used in navigation and autonomous cars (Kim and Kim, 2017). How does AI perform such tasks? Although we do not realize it, big data are collected from our lives and can be used to produce meaningful results. Cloud systems and machine learning can be used to save and analyze these data. Historically, the word “AI” was suggested in 1956 by John McCarthy at a Dartmouth conference (McCarthy et al. 2006). The Turing test has been used to determine that it is difficult to distinguish between humans and machines (Saygin et al. 2000). The application of AI in real-life has been speculated for a long time. However, interest in machine learning and AI increased significantly after Google’s deep mind technology, AlphaGo, defeated Sedol Lee in 2016 (Chouard, 2016). Thereafter, deep learning-based AI has been applied to various fields, including financial, medical, transportation, and manufacturing industries (Zdravković et al. 2021). Interesting attempts have been reported in the food industry; however, discussion about the relationship between the 4th industrial revolution and food safety is limited. In this mini review, the impact and prospect of the 4th industrial revolution in food safety area were discussed.

General process of AI application

Data collection is the first step in AI application. Various data are produced from our lives and recorded directly or indirectly using sensors and wireless networks. These data are saved on the cloud using the internet and analyzed using various types of machine learning. Thereafter, a predicted value is presented by AI, and the values are verified using conventional methods. From these steps, additional data can be collected and re-used for re-learning, which makes the AI more accurate (Fig. 1). Technology is developing at a fast pace for each step in the process, as discussed in the subsequent sections.

Fig. 1.

Fig. 1

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Schematic diagram of artificial intelligence (AI) application process

Data collection

Data collection is the most important part in the development of AI, because data quality and quantity of data determine the efficiency and accuracy of the prediction. Data can be collected directly through surveys and interviews or indirectly by using previously reported data. Surveys have been conventionally used to collect data, but the amount of data is limited and could also be biased (Klein and Sherman, 1997). Fortunately, large amounts of data can be collected from the internet. For example, some platforms, such as NAVER, provide an application programming interface (API) to users (Lim and Park, 2011). Moreover, data can be collected from social network services (SNS) such as Facebook and Instagram (Han et al., 2017). Similarly, the web scraping method can be applied to collect meaningful data from online resources; however, fake news may affect the quality of the data. In this regard, collecting data using various methods will be helpful to ensure a variety of data. In a previous study, Park et al. (2015) collected real-time or non-realtime information for context awareness, prediction, and tracking of harmful materials in agri-food. API was used to collect information about on-site detection, digital tacho-graph (including temperature, humidity, GPS), weather, storehouse, and accumulated detection information. Additionally, web crawling was used to collect data from SNS, weather, and safety news information. Moreover, an off-line method was used to collect data for harmful material characteristics, profiles, and distribution information. Previously, we collected data from NAVER API to identify the real-time analysis and predictability of the health functional food market in South Korea (Kim, et al., 2021). When the actual market index was compared with shopping search data, the correlation was high indicating that data collected from online can be used to predict the actual market trend. Likewise, various methods can be used for data collection depending on the type of information obtained. It is important to collect data with high volume, variety, and velocity (known as 3 V) while preventing an invasion of privacy (Lu et al., 2014).

Data training

Collected data should be pre-processed for training using machine learning that is classified with a supervised, unsupervised, and reinforcement model (Sasakawa et al., 2008). The solution is provided to the supervised learning model, which has been widely used in the classification problem. For example, classification between an apple and an orange can be solved by the supervised learning model using numerous food images (Kakani et al. 2020; Naik and Patel, 2017). In contrast, the solution is not provided to the unsupervised learning model, which has been widely used to cluster new data. In the reinforcement learning model, reward system is used to make the specific action of machine, and is usually used in the robot and navigation system (Zuo et al. 2014). Specifically, neural networks, imitating the decision tree of humans, have been used to analyze the data in machine learning. Various types of powerful neural networks with characteristics of multiple hidden layers and back propagation were suggested after the adoption of deep learning (Deng and Yu, 2014). For example, convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN) have been widely used as artificial neural networks. The CNN model, suggested by LeCun, has been widely used to solve the classification problem (LeCun et al., 2015). In particular, patterns of images are extracted in the CNN model for application. In this way, the CNN model has been widely used in the field related to computer vision and combined with other methods to maximize its efficiency. The RNN model is useful to analyze sequential data such as text, voice, and video while the GAN model can be used for unsupervised learning (Hughes and Mierle, 2013).

Data visualization

Visualization of analyzed data is important to explain the data to non-experts and consumers. Various tools have been suggested for visualization, including keyword expression and clustering. Currently, keyword expression is widely used to represent the importance of the data. For example, font size is often used to highlight the importance in the word-cloud method, which can show the consumer’s preference intuitionally (Cui et al., 2010). Many portals, including K-ICT big data center, provide platforms for keyword expression, where one can type the word to identify the related keywords. In this review, we collected data (2010–2021) from Google scholar by searching the keywords of big data, artificial intelligence (AI), food, and safety to show an example of keyword visualization. The relationship between the word “food” and other words in big data, AI, and safety is shown in Fig. 2. Similarly, the clustering method is widely used to visualize analyzed data. It is difficult to cluster a large amount of data for several groups, and AI can be used effectively for clustering.

Fig. 2.

Fig. 2

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Schematic diagram of real time prediction of microbial growth in food during transportation using machine learning application

Examples of machine learning applications in the food industry

In machine learning, training types can be divided into classification and prediction studies. In a classification study, answers are provided by several groups. In contrast, in a prediction study, answers are provided as specific values. Previous studies provided several examples of application of machine learning in the food industry that were related to the classification of food samples (Table 1). Particularly, classification of agri-food items according to the maturity, diseases, and freshness was reported. For example, Ko et al. (2018) classified Solanum lycopersicum (tomato) ripeness into six stages (green, breaker, turning, pink, light red, and red) by analyzing high-resolution images of tomato (n = 4,182) with CNN and reported an average accuracy of 91.3%. Similarly, Yang et al. (2019) trained images of immature, mature, and over-mature Fragaria ananassa (strawberries, n = 600) with CNN to classify the strawberries depending on their maturity and reported >90% accuracy. These approaches would facilitate the decision making of harvesting time of agricultural products. Classification was also used effectively to match diseases of agri-food items. Na et al. (2020) trained images of tomato with four diseases using a deep learning algorithm combining Faster R-CNN with ResNet-101 model, resulting in 90.8% accuracy. Similarly, Yang et al. (2018) used Faster R-CNN network to classify the diseases/infestations in Capsicum annuum (paprika) cultivation. It is noteworthy to mention that video data of paprika were used in the study by Yang et al. (2018). In another study, strawberry diseases were classified into seven categories using images of infected strawberry (Kim et al. 2020). In their study, Kim et al. (2020) applied a data augmentation method to increase the efficiency of machine learning with a limited dataset in strawberry disease classification. It is certain that these kinds of applications will be expanded for various agri-food items. Finally, freshness of food items was also classified or predicted using machine learning. Jang et al. (2020) classified the freshness of meat samples (beef, meat, and chicken) using data from 10 sensors and verified that machine learning outperforms the conventionally used torrymeter. Classification was widely used in these food applications, in addition to prediction methods. For example, Lam et al. (2020) predicted the quality changes in pork and fish samples from gas composition and humidity data. Different approaches can be used to assess the pork samples, as reported by Lam et al. (2020) and Jang et al. (2020). Thus, data type and the expected output should be determined before applying machine learning in the food industry.

Table 1.

Previous studies related to the application of machine learning in the food industry

Food samples Type of training Type of data Type of neural network Main results Reported accuracy References
Tomato Classification High resolution tomato images (n = 4,182) Convolutional neural network (CNN) Ripeness of tomato can be exactly classified for 6 stage (green, breaker, turning, pink, light red, and red) by deep learning Average accuracy of 91.3% Ko et al. (2018)
Strawberry Classification

Images of immature, mature, and over mature strawberry

(n = 600)

 Convolutional neural network (CNN) Maturities of strawberry were classified with an accuracy of over 90%. > 90% with 200 training images and 8,000 training steps Yang et al. (2019)

Meat samples

(Beef, meat, chicken)

 Classification

Data from ten sensors for freshness measurement

(n = 7000)

 Neural network with 1–3 hidden layer Machine learning using selected three sensors by principle component analysis outperforms the conventionally used torrymeter for prediction of meat freshness

Approximately 100% when k = 1 in Normal Distribution

(µ, kσ 2)

 Jang et al. (2020)
Tomato Classification

Images of tomato with four diseases

(n = 850)

 Combination of Faster R-CNN with the pre-trained CNN Deep learning algorithm combining Faster R-CNN with ResNet101 model showed the highest accuracy

90.8%

(Faster R-CNN with ResNet 101)

 Na et al. (2020)
Strawberry Classification Images of strawberry with diseases MobileNetV2 High accuracy (95.6%) was achieved in the classification of strawberry diseases of seven categories with data augmentation method 95.6% in the classification problem Kim et al. (2020)
Paprika Classification

Video data of paprika

(n = 1649)

 Faster R-CNN Diseases/infestations in paprika cultivation can be detected automatically by using a Faster R-CNN network with high precision 96.76% of mean average precision Yang et al. (2018)
Pork and fish Prediction Gas composition and humidity data Convolutional neural network (CNN) Quality changes of pork and fish samples were predicted with high accuracy from collected data

Pork sample : 97.9%

Fish sample : 90.6%

 Lam et al. (2020)
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Impact and prospects of the 4th industrial revolution in food safety

Foodborne outbreaks are continuously reported worldwide. In South Korea, 250-400 foodborne outbreak cases are reported annually, resulting in 5,000–7500 cases of foodborne diseases (Kim and Kim, 2021). More than 80% of these cases result from microbiological hazards, such as foodborne bacteria and viruses. Bacterial pathogen outbreaks occur during the summer season, whereas viral outbreaks usually occur during the winter season. In South Korea, pathogenic Escherichia coli is the most notorious, followed by Salmonella. Therefore, researchers have been focused on how to detect and control these bacterial pathogens in food products to ensure food safety. For example, Park et al. (2015) presented a framework for the rapid management against harmful materials of agri-products. In this frame work, agri-food-related information from production to consumer supply was collected and saved in the Hadoop Ecosystem. Collected data is analyzed using tools such as R and Map/Reduce. In this way, the results of the harmful material prediction and conditions are visualized and used for managing the agri-food. The impact and prospects of the 4th industrial revolution in food safety are discussed in three parts: prediction of food quality and safety, detection of foodborne pathogens, and food safety management.

Prediction of food quality and safety

Prediction of food quality and safety is very important to ensure food safety. Conventionally, mathematical modeling of quality indicators such as total aerobic cell count, lipid oxidation, and color values have been used for prediction. Recently, several attempts based on machine learning have been reported. Geng et al. (2017) applied analytic hierarchy process integrated extreme learning machine (AHP-ELM) to develop an early warning system. They collected massive food inspection data (18,657, period: 2010–2014) and processed the data to obtain meaningful results. The data were classified as pathogenic bacteria, heavy metals, and chemical contaminants, which represent the biological, physical, and chemical hazards, respectively. The risk level of food was calculated from the levels of each category, and it was verified that the predicted risk level was very similar to real values. Lam et al. (2020) developed a deep learning-based food quality estimation method using radio frequency-powered sensor mote to predict change in food quality. In this study, 915 MHz radio frequency power was used to provide energy and sense the gas and humidity condition. Collected data on gas composition and humidity were used in a deep learning model to predict the food quality. It was verified that quality changes of pork and fish were predicted with 97.9% and 90.6% accuracy, respectively. As related technology develop, it is certain that the accuracy will be increased to more than 99%. These examples suggest that the growth of pathogens can be predicted precisely from the environmental data, such as temperature, humidity, and gas composition (Fig. 3). Food products are usually transported from the farm to table through wholesale and retail stores. It is not difficult to collect environmental data such as temperature, humidity, and gas composition conditions surrounding food products, which significantly influence pathogen growth. By analyzing these data using machine learning, it is possible to accurately predict pathogen growth. Moreover, food safety risk can be visualized as danger, warning, caution, and notice. When we find a danger or warning signal at any point, the number of pathogens can be verified with conventionally used selective media and polymerase chain reaction (PCR) analysis. By using this approach, the time and labor needed to ensure food safety during transportation will be reduced significantly.

Fig. 3.

Fig. 3

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Example of keyword expression representing the relationship of food (or food safety) and big data/artificial intelligence (AI) in the previously reported papers during 2010–2021

Detection of foodborne pathogens

Conventionally, enumeration on selective or nonselective media is used to detect foodborne pathogens in food products (Park et al. 2012). Since PCR was suggested, detection based on genomic sequencing has been widely used (Kwon et al., 2021). Currently, massive genomic data can be analyzed rapidly and affordably using next generation sequencing. Moreover, a variety of data from genomic to proteomic levels can be collected and analyzed by omic profiling, which has a huge impact in the field of food safety (Marvin et al. 2017). Metagenomics is a branch of omics profiling that analyes genomic data in the environment, food, and gut. Machine learning is being used in metagenomics to analyze the massive data. For example, machine learning technology was applied to classify the microflora when the data from amplicon sequencing was analyzed using a 16 S rRNA database called Silva.

Several attempts of application of machine learning or metagenomics to detect foodborne pathogens in food samples have been reported. Im et al. (2021) applied a machine learning model to identify the pathogenic potential of the Shiga toxin-producing E. coli (STEC). In this study, support vector machine algorithm was suggested as the most effective model to identify STEC. In another study, Bağcıoğlu et al. (2019) used machine learning-based FTIR spectroscopy to detect and identify Bacillus cereus, B. cytotoxicus, B. thuringiensis, B. mycoides, and B. weihenstephanensis. It has been difficult to differentiate B. cereus from closely related species such as B. thuringiensis and B. mycoides (Hong et al. 2020). Bağcıoğlu et al. (2019) reported that machine learning-based FTIR can be effectively used to differentiate these species as the FTIR spectra showed different patterns for each microorganism. When artificial neural network based machine learning was applied, the results reported more than 99.5% accuracy. Walsh et al. (2017) applied strain-level metagenomic analysis of the fermented dairy beverage Nunu to detect foodborne pathogens. Consequently, it was verified that short-read-alignment-based bioinformatics approaches are suitable tools to detect pathogenic E. coli and Klebsiella pneumoniae strains. Currently, amplicon sequencing and shotgun sequencing are widely being used for metagenomics and enable identification of bacterial pathogens at the genus and species levels, respectively. In the future, it is certain that machine learning will aid identification of pathogens, particularly at the strain level. In conclusion, the previous examples indicate that machine learning can be applied effectively to overcome the limitations of conventionally used detection methods.

Food safety management

Government agencies such as the FDA regulate the food manufacturing and processing businesses. Inspections are conducted based on the food hygiene surveillance system as it is impossible to examine all companies and all items individually. Machine learning-based approaches are helpful to decide which company should be inspected. For example, Cho and Cho (2020) applied machine learning (supervised training) to improve the efficiency of the food hygiene surveillance system in South Korea and indicated that a prediction system based on machine learning could be used to maximize inspection efficiency when various data, such as revenues, operation duration, number of employees, and inspection track record, were used for training. In addition, evaluation of the imported food items is necessary since their number has tremendously. Similarly, all imported food items are not inspected, owing to limitations of budget and manpower. Cho and Choi (2018) suggested predictive models based on the big data of imported food items to reduce the burden of inspection of imported foods. In this way, machine learning will be widely used in food safety management, maximizing the effectiveness of inspection.

Recently suggested technologies such as machine learning and block chain will be applied in the management program such as Hazard Analysis and Critical Control Point (HACCP) and Good Manufacturing Practices (GMP). For example, conventionally during the HACCP plan, inspectors had to check the temperature humidity, pH, and record them periodically. These approaches are not only laborious and time consuming but also had risk of miss interpretation and falsification. Moreover, foodborne outbreaks from the HACCP certificated products have been reported. These risk can be minimized by applying the sensing and recording technology in the HACCP plan, recently called as smart HACCP (Moran et al., 2017). In the smart HACCP, data such as temperature, pH, humidity, etc. will be collected real time and recorded automatically which will the minimize the risk of miss interpretation. The collected data will be used to predict the possibility of foodborne pathogens as the method describe in the machine learning part. The analyzed microbiological growth will be used to warn the contamination like virtual sensor. Furthermore, the risk of falsification will be prevented by applying the block chain technology. The block chain technology was already adopted in the food distribution area by Walmart and IBM Food Trust (Kim, 2021). It is certain that the application of sensing and recording technologies would prevent the foodborne illness and improve the consumer’s trust. Therefore, governmental and research agencies should deliberate how to apply these technologies in the relatively small food related businesses.

Acknowledgements

None.

Declarations

Conflict of Interest

The authors declare that there are no conflicts of interest regarding the publication of this article.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Sang-Soon Kim, Email: ssk@dankook.ac.kr.

Sangoh Kim, Email: samkim@smu.ac.kr.

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