Food and Beverage Tech Review : News

Like any other sector, the baking industry can also use artificial intelligence while making bread to maintain its consistency. Many companies still consider the term artificial intelligence as an idea of the future. But the most intriguing part about AI is when it is utilized in a sector, it quickly becomes accepted among people. This effect has been witnessed in almost every industry. The AI-based software helps the companies to increase their efficiency of speech, facial recognition, and vision processing in photography. AI is also used in robotics and to provide special effects in movies. In recent times, many big companies have made AI technology cost-effective and more accessible to software developers. However, in the food and beverage industry AI can be utilized to make bread. The technology can be used to make the model of the process make sure about the consistency of baking. AI can be beneficial in the baking industry because, for them, it is necessary to maintain the quality of the bread and make it consistent. However, to affect the crust and crumb quality, several inputs, and variables need to communicate. According to most of the bakers, the primary processing method that can impact the quality of the final product is dough mixing. Therefore, the bakery companies can use artificial intelligence to measure the consistency of the dough and enhance the bread dough mixing procedure. There are also other factors which help the bakers to achieve the desired dough quality. Getting accurate dough mixture is essential for operational success, but it can be challenging to accomplish. The primary reason behind such complications is the unpredictable reaction among biology (yeast), physics (water movement), and chemistry (enzyme reactions, oxidation) while bread processing. The AI solution needs numerous data and offering the AI model with such bread processing data is essential. Therefore, there are two types of data which is necessary to make the AI work, and those are the present project data and the new ones. The bakers can provide appropriate sets of flour and baking data so that the team which is managing the format for the data inputs to the AI software can use it. The project of intelligent dough mixing is at a nascent stage. There can be many hurdles in the way to use AI in mixing dough, but there can also be several methods in which it can be used for baking in the future. ...Read more

The Brazilian start-up Fazenda Futuro raisesUS$21 million to launch into the United States with its meat-free meat. Brazilian start-up Fazenda Futuro has announced that it raised R$115 million (~US$21 million) .  The leading companies in it were BTG Pactual and ENFINI Investments. They were joined by returning investors like Monashees and Go4it Capital. With the money, the start-up has planned to make its first appearance in the United States with its meat-free meat. This investment will also help in developing more products like plant-based chicken and fish. With the second round, it is estimated that the start-up’s market value has risen to almost R$715 million (~US$133 million). Fazenda Futuro and food tech with animal-free diets  Recently, the start-ups who are working with plant-based alternatives attracting several investments. Swiss Blue Horizon led a Series A for US$2 million in Mexican Heartbest for its vegan products, in June. In the previous month, Chilean The Live Green Co. closed around US$1 million, and the NotCo was believed to be raising around US$85 million to hit a US$250 million valuation.  Similarly, in recent times IDB Lab and Syngenta Ventures channeled money into AgVentures II, which is a fund for start-ups in agriculture and food tech. Since the entire world is considering a net-zero carbon environment, a transformation towards animal-free diets is becoming tastier for some consumers and investors. However, it will take a while for Latin America to adopt this new type of diet because it is not known for its vast vegetarian or vegan markets the way Europe is. Moreover, meat is still just too cheap there, and alternatives can be expensive. Another big challenge that the food tech companies are witnessing is making the substitutes tasty and affordable.  However, start-ups are strategically helping consumers to embrace animal-free food. In NotCo’s native Chile, its NotMeat product is available at Burger King and on Papa John’s vegan pizza. In Brazil, it has partnered with iFood to provide its restaurant-prepared vegan burgers. Fazenda Futuro has also collaborated with Brazilian supermarkets Pão de Açúcar and restaurant chain Spoleto to position its meat-free meatballs. ...Read more

Defining the utilization of AL in increasing the efficiency and sustainability of supply chain in industries. The sustainable scheduling and management of logistics in many enterprises is not up to the mark; even today, supply chains have not been environmentally friendly. The fine line of control over factors like speed, flexibility, cost, and carbon footprint is blurred when looked in terms of shipping or transportation of the goods. It is highly unlikely for the enterprises to encourage sustainable practices with the current trends for shipping. Newer technologies like big data analytics and AI have enforced a positive push over the companies, enabling the supply chains to function both efficiently and sustainably as possible. AI adoption has always resulted in causing dramatic effects on the supply chains, assisting the companies in benefiting from the most cost-effective and sustainable routes for shipping. To implement these technologies, firms have focused on jumping to a sharing economy in the case of supply chain management. By utilizing AI, data, and unique algorithms, a paradigm shift in the handling and operations of supply chains can be enabled along with the encouragement to work together. There are some recognized specific areas where these solutions can be applied to build a smart, efficient supply chain. • The Shipping Co-Operative: With the use of technology, AI-based algorithms can be utilized among organizations to incorporate a shared use of shipping and other transport methods. The algorithms developed specifically for this purpose will identify opportunities to share the commuting resources by collaborating with several transporting firms. The algorithm also uses the data from GPS to schedule and manage the logs from collections and drop-off points of the shipping firms providing enhanced coordination. With real-time updates recorded as well, the entire system and the participants of the system are aware of the status of the shipment at all times with specific updates about the routes, stocks, and costs. The state of economic co-operation among the organizations has also opened up gates for mutual benefitting from various supply chains with other firms as well. • Synchromodality: As different packages need to reach at different time intervals, and most times the package delivery date may vary at the last minute. With the use of the physical internet, the syncromodalilty or the synchronization in the scheduling of shipping routes can be aptly adjusted depending on the methods of transport. This method can take into considerations the emergencies and can make the deliveries without compromising on quality or brand integrity. With the use of real-time data system, the transportation method of the ongoing transaction can be adapted suitably while the shipment is en-route. Even during transportation, the algorithm has the power to select the most efficient and sustainable option in real-time. • Deep Reinforcement Learning: Deep reinforcement learning is an element of machine learning that involves the training of algorithms to enable better decision-making skills. The training method is a trial and error process where the technology is taught and diverted towards making the ideal solution at that point. With positive reinforcement, the robot is trained to streamline the random activities to repeat only those actions which show a good outcome for the institution. Enterprises who use deep reinforcement learning can train its own AI module to conduct complex and positive supply chain decisions which include numerous variables. By doing the industry-specific learning program, the AI can accurately determine the details of the shipment to be sent, when to ship the products and the best mode of transportation. The same program can be used to create algorithms, which encourage and efficiently carry out collaborative shipping, and use syncromodality approach to rejuvenate the company’s inventory. The interlinking of all the aspects mentioned above will result in creating the most efficient supply chain possible for an organization. As the integration of newer technologies like AI to the supply chain occurs, the company converts into an environmentally supportive one with the reduction in the pollution and pressure on the atmosphere. The company’s carbon footprint will also reduce allowing the company to make a positive impression on the world. ...Read more