- Cornell University and IBM will undertake a shared research project relying on genetic sequencing and Big-Data analyses of cow's milk, the Cornell Chronicle reported last week.
- In sequencing cow's milk, researchers believe they can better recognize diversions in food microbiomes. Additional studies thought to benefit the food supply chain and cold chain transport should result.
- Artificial intelligence and machine learning will be applied to the research process in hopes of gaining insight into the appearance and interaction of microorganisms within a food environment.
The cold chain continues to vex logistics experts who strive to maintain it, both for food and pharmaceutical transport. Between an expanding global market and the sheer variety of foods and medications now available, five separate temperature zones are required, ranging from 35 to 77 degrees. Milk must be kept between 32-36 degrees to maintain its best flavor.
Establishing a baseline for the genetic makeup of normal, raw milk will help producers make the milk-production faster and more efficient. Understanding the genetic code of normal milk will also help producers identify bad milk before it gets to the consumer, thus better ensuring product quality.
Cornell and IBM's new project is something all supply chain managers in the food industry can learn from: Understanding the genetic makeup of good food and monitoring it for quality assurance purposes can allow supply chain managers to move products more quickly down the supply chain. This also allows producers to notify consumers that the food has been scientifically verified as safe to eat. This kind of quality assurance may increase the consumer base as well as improve and solidify the producer's reputation in its industry.