With increased intervention of technology in agriculture, finance, education, healthcare and many others, food industry is no different. The use of applications, robots, data, and processing methods, however, has altered how we create and obtain our food over time.
A recent ING analysis claims that technology aids agricultural producers in producing food more effectively to meet the needs of an expanding global population. The current population of the globe is 8 billion, which results in a yearly increase in the demand for food. Tech advancements in processing and packaging can extend the shelf life and increase food safety.
1. Alternative sources of protein
One of the most important changes in food technology is the movement in consumer preference toward alternative protein sources because of environmental and health concerns. The main sources of alternative protein are cultured meat, lab-grown food, plant-based nutrition, edible insects, and mycoprotein. Unlike protein from livestock, they are not only nutrient-rich but also require fewer resources from farm to fork. Since alternative protein sources only necessitate minimal dietary changes and health monitoring, they lower overall expenditures. Startups can provide sustainable alternatives to protein synthesis through the use of 3D printing, fermentation, and molecular biology advancements. This helps food companies balance the ethical issues and carbon footprint associated with the industrial production of meat.
2. Personalized nutrition
In the future, “one-size-fits-all” guidance may be replaced by “personalised nutrition,” which is food advice catered to your genetics, tastes, and predispositions. In addition to nutrigenomics-based diets, these also include dietary preferences including sugar- and gluten-free eating plans, vegan diets, and clean label food items. The integration of robotics in food assembly lines and advances in 3D printing enable food producers to offer nutrition customisation at scale. Additionally, consumers can choose eating patterns that best match their genetic profiles thanks to at-home blood and urine testing kits. In order to optimise their diet, individuals can track their diet and health problems using a variety of tracking devices. As a result, customer convenience and sales are increased because it gives consumers control over their dietary preferences.
3. Inhouse Service digitization by restaurants
Restaurant digitization improves customer experience and facilitates efficient administration of operations. Additionally, it enables data-driven decision-making throughout operations by enabling restaurant brands to collect data points at each stage. Moreover, restaurants are shifting to implement digital management systems throughout the supply chain as a result of the COVID-19’s disruption of the food and beverage business. Restaurants incorporate digital menus, self-service kiosks, and cashless payment options to lessen direct human touch. Additionally, emerging solutions for assisting consumers with meal orders and other restaurant-related questions include chatbots and voice bots. AI-enabled solutions provide meal recommendations to customers and create new recipes using data on customer preferences and behaviour.
4. Food safety
Food safety is a major worry as consumers are becoming more cautious about the quality of the food products they purchase. Customers may make informed decisions about food items by using the smart labelling and standalone food grading equipment that are readily available. Food brands may now offer end-to-end traceability thanks to developments in blockchain technology and real-time food monitoring utilising Internet of Things (IoT) sensors. Startups are advancing food safety and openness by creating scalable and affordable monitoring tools. This improves consumer and food producer trust, which benefits brand credibility and sales.
5. Taste habit analysis
Similar to how AI algorithms examine clients’ shopping preferences, they can also research their dietary preferences. Customers themselves or food service firms can utilise this information after that. For instance, if an AI algorithm notices that a user frequently orders fast food, it might warn them about their excessive intake of harmful food. If it discovers that the customer prefers Margherita, it might advise the pizza shop owner to provide a special deal, such as two Margheritas and a drink for free.