Predictive shelf-life modeling for food products

About 

Kraft Heinz is a global food and beverage company based in the United States, committed to food safety, quality, and innovation. It offers a broad portfolio of well-known brands across condiments, dairy, meals, snacks, and beverages.

The Challenge  

The challenge focuses on advancing predictive shelf-life modeling for packaged food products using data-driven approaches. It aims to explore:

• Use of machine learning and AI techniques for accurate shelf-life estimation
• Integration of modeling with existing shelf-life testing protocols
• Applications in both organoleptic and food safety shelf life across categories like sauces, beverages, powdered mixes, and dairy

The ultimate goal is to enhance product quality, safety, and testing efficiency by leveraging predictive analytics grounded in food science and real-world data.

Reward

Successful applicants may receive competitive rates for a specified timeframe, with potential funding for a pilot project. Also, will gain access to Kraft Heinz subject matter experts and early exposure to Kraft Heinz’s strategic priorities, with priority consideration for future collaboration opportunities.

Timeframe

• Deadline for Submitting Proposals: July 31

Who can apply  

• Applicants with proven experience developing and applying predictive models specifically for shelf-life estimation of packaged foods
• Experts with advanced degrees (MS or Ph.D.) in relevant fields, such as food science, statistics, computer science, or engineering
• Professionals with strong expertise in machine learning algorithms, data modeling, and analytics techniques

How to apply 

Application through HALO Platform.

Find here all the details and the submission page

 The SPIN4EIC consortium is not responsible for the challenges launched by private or public buyers. Each participating entity is solely responsible for its own challenge, including the selection process, communication with applicants, and all information provided to participants.