How AI is Shaping the Future of Food Science

Shelf life is also a challenge for food products that contain unsaturated fatty acids, which are prone to lipid oxidation and can easily develop a rancid odor and flavor. Traditionally, the solution has been to add antioxidants, but certain combinations can have adverse effects—and empiric research has been unable to account for their multifaceted responses. To address this knowledge gap, a team in the United States and Argentina worked together on an AI deep-learning architecture to predict antioxidant interactions, publishing their findings in the Journal of Agricultural and Food Chemistry.

At first, the results were disappointing, and the AI struggled with predicting how well certain antioxidant mixtures would behave in actual product samples. But after the authors added additional data from prior experiments to the AI model, the algorithm became more reliable in figuring out how well antioxidant mixtures would work in preventing the negative effects of lipid oxidation. This study has tremendous value far beyond antioxidant applications—a key takeaway is that AI is only as helpful as the information it knows, and providing AI tools with comprehensive and chemically relevant experimental data is crucial for the technology to act as a reliable assistant in scientific research.

Detecting Foodborne Contaminants

AI has also demonstrated potential in detecting foodborne contaminants. The Journal of Agricultural and Food Chemistry recently published novel experimental research using ML to detect mycotoxins from phytopathogenic fungi, which often find their way into food during production and storage and can have harmful effects on humans and animals when consumed.

The authors revealed a first-of-its-kind integrated approach using carbon dots and ML algorithms to simultaneously detect three common mycotoxins in samples of coffee, milk, and several grains with 100% accuracy. The authors note that this methodology demonstrates the tremendous potential for AI and ML to help improve the accuracy and efficiency of foodborne contaminant detection.

Controlling Crop Disease

Huanglongbing (HLB), also known as citrus greening disease, is one of the most devastating diseases for citrus plants, causing them to wither and die within a few years and leading to substantial economic losses. There is currently no known cure; however, a team working in Florida, United States, hypothesized that citrus plant microbiomes may serve as disease antagonists, increasing the level of tolerance for HLB. By inhibiting pathogens directly or triggering the plant’s immune response, these microbiomes can act almost as an extended immune system for the plants.

To investigate this, the researchers introduced a novel multiomics approach, combining metabolomics (studying metabolites) and gene sequencing (studying bacteria) using machine learning techniques. Their results demonstrate that regulation of plant pathways could affect the structure of the microbial community, hinting at potential solutions for controlling this economically devastating disease by modulating bacteria in citrus plants—or breeding tolerant cultivars. This study reinforces the utility of ML in multiomics due to its ability to uncover new or hidden information from experimental data and analytical methods.

Gaining Insight Into Flavor Perception

We’ve covered how AI and ML can be used to help improve food composition and freshness, supply chain management, and crop health, but could these tools also uncover a deeper understanding of our sensory response to aroma and taste? Many foods for humans play a role far beyond simple nutrition, but taste is a subjective concept and sensory evaluations are difficult to measure. The five basic tastes are sweet, sour, bitter, salty—and the elusive umami, which in its native Japanese translates as “tasty” or “delicious” and describes deeply savory and satisfying flavors. Many describe foods such as mushrooms, tomatoes, aged cheeses, meats, and soy sauce (among others) as having umami flavors.

Despite the subjectivity of perceived flavor profiles, we do know that the umami taste is mediated by G-protein coupled receptors and associated with specific signals to taste receptor cells. With this information, a team in China used electroencephalography and machine learning to identify umami in foods by recording electrical waves during brain activity. To characterize brain activity when presented with umami taste stimuli, the researchers employed a model that uses 125 brain wave signals across five regions as input data. The model was then published online, making it accessible for other researchers to use for further studies. The authors note that this model could have applications in diagnosing diseases through taste dysfunction and may lead to future generations of brain–computer interfaces for flavor perception.

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