The world is moving at a very fast pace in terms of digitization and improvement in novel analytical techniques and as such there have been several developments in facets such as agriculture, architecture, etc (Bendre et al., 2022). The food industry is no exception when these kinds of growth are being discussed. In areas of food safety and food processing where microorganisms play a significant role in the realm of food, impacting both its safety and quality. The presence and absence of pathogenic and beneficial bacteria, viruses, or fungi in food can lead to food-borne illnesses or have a positive impact, making food microbiology an important discipline in ensuring public health. Standard culture-based detection methods of microbiological analysis have always been the backbone but these methods involved are time-consuming and labor-intensive processes(Bendre et al., 2022; CHEN and YU, 2022; Liu et al., 2023). Although there has been development of a variety of molecular techniques such as nucleic acid-based methods, immunoassays, and metabolic fingerprinting. However, these approaches require sophisticated equipment and specialized personnel, which are less accessible to food industries.
Despite these advancements, there are still gaps in the early detection of even contaminated food products. However, the rise of artificial intelligence (AI) has ushered in a new era in food microbiology, offering innovative solutions to enhance efficiency and accuracy in microbial analysis. Which is why its relevance cannot be overemphasized.
Artificial intelligence has found extensive applications in various fields due to its ability to process enormous amounts of data and identify complex patterns. Early detection of microbial contamination in food products is significantly important for consumer safety and outbreak prevention(Qu et al., 2019; Jiang et al., 2022). Artificial intelligence provides several advantages within the context of food microbiology. With the help of Machine learning algorithms and techniques, which are a subset of artificial intelligence, artificial intelligence can analyze microbiological data faster and more accurately than standard culture-based detection methods. This includes the detection of pathogenic microorganisms, prediction of microbial growth, and identification of spoilage patterns (Jiang et al., 2022).
Several studies have explored the application of artificial intelligence in food microbiology. For example, machine learning techniques have been employed to predict the shelf life of perishable foods by analyzing microbial growth patterns and environmental conditions. A recent study by Ma et al (2023) employed a method that combines artificial intelligence and optical imaging. This method was quite reliable and swift enough to identify bacteria in food, suggesting that it may be an essential tool for stopping food-borne diseases and outbreaks. The study utilized the use of an algorithm called You Only Look Once (YOLO, version 4) to detect bacteria. This machine-learning technique was simple, cost-effective, and rapid as compared to conventional pathogen detection methods. The team also took digital images of romaine lettuce with a conventional microscope which is equipped with white light and used an artificial intelligence-enabled software to detect and identify bacterial micro-colonies. When put to the test, researchers said the real-time object detection algorithm was able to accurately identify 11 out of 12 lettuce samples that were contaminated with E. coli. (Ma et al., 2023). The technique utilized in this study is broadly applicable to the identification of diverse bacterial species. In addition, this approach can be implemented in resource-limited areas since it does not require expensive instruments and significantly trained human resources. This artificial intelligence-assisted detection not only achieves high accuracy in bacterial classification but also ensures the potential for automated bacterial detection, reducing labor workloads in food industries, environmental monitoring, and clinical settings.
In a study by Liu et al (2023), a predictive model using artificial intelligence successfully predicted microbial contamination in food products, permitting timely interventions and prevention procedures. Additionally, artificial intelligence has been used in the development of rapid detection methods for food-borne pathogens, accelerating the identification process and minimizing the risk of food-borne outbreaks.
Artificial intelligence, as a far-reaching emerging technology, has experienced birth, ups, and downs, and the harvest, not only impacting our personal lives but also essentially transforming how firms make decisions.
References
1. Bendre, S. et al. (2022) ‘Artificial Intelligence in Food Industry: A Current Panorama’, Asian Journal of Pharmacy and Technology, pp. 242–250. Available at: https://doi.org/10.52711/2231-5713.2022.00040.
2. CHEN, T.-C. and YU, S.-Y. (2022) ‘The review of food safety inspection system based on artificial intelligence, image processing, and robotic’, Food Science and Technology, 42. Available at: https://doi.org/10.1590/fst.35421.
3. GOWEN, A. et al. (2007) ‘Hyperspectral imaging – an emerging process analytical tool for food quality and safety control’, Trends in Food Science & Technology, 18(12), pp. 590–598. Available at: https://doi.org/10.1016/j.tifs.2007.06.001.
4. Goyache, F. et al. (2001) ‘The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry’, Trends in Food Science & Technology, 12(10), pp. 370–381. Available at: https://doi.org/10.1016/s0924-2244(02)00010-9.
5. Jiang, Y. et al. (2022) ‘Machine Learning Advances in Microbiology: A Review of Methods and Applications’, Frontiers in Microbiology, 13(May). Available at: https://doi.org/10.3389/fmicb.2022.925454.
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