ai in food science and epidemiology · • “state-of-the-art” for use of ai in the food...
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Claire Zoellner, PhDFood Safety Scientist
iFoodDecisionSciences, [email protected]
July 17, 2020
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Outline
• Background• “State-of-the-art” for use of AI in the food industry
• Need for innovation in food safety and epidemiology
• Challenges and limitations
• Potential for AI at retail to improve food safety• Supply chain traceability
• Customer feedback and interaction
• Retail environment controls
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Buzz-Worthy AI in Retail and Food Service
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Key Questions
• Where is the greatest immediate utility for AI in food safety?
• How do we gain experience or learn about managing food safety risks from AI?
• Are there any new governance or operational risks to consider when adopting AI at retail and food service?
• Where is the future of AI applications in food safety and what is the process for adoption by food retail and quick service companies?
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Need for Innovations
in Food Safety:
1. Prevention2. Detection3. Response
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4 Core Elements:Tech-Enabled Traceability
Smarter Tools and Approaches for Prevention and
Outbreak Response
New Business Models and Retail
Modernization
Food Safety Culture
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Challenges and Industry Limitations
Friedlander and Zoellner, Food Protect. Trends 2020; Bekker, 2019 7
Data volume, quality, and bias
Trust, transparency, and accessibility in business matters
Security of information
Resources required within organizations
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Potential for AI at retail to improve food safety
Supply chain
traceability
Retail environment
controls
Customer feedback
andinteraction
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• Standard lot-level traceability creates traceability data at each step of the supply chain – resulting in often 5 or more silos
• Standard lot-level traceability makes it difficult for trading partners or regulatory entities to traceback a single consumer item across the supply chain, or to trace forward to all the places where a contaminated item or ingredient went
The food supply chain is complex: “one-
up-one-back” creates data silos
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Supply chain traceability
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• Item-level tracing provides traceability from seed to shelf, including last mile to consumers for transparency and traceback to origin
• Integrated with food safety compliance data, quality inspections, and shipment tracking for full supply chain visibility
• Potential for use with dynamic-pricing models
• Unique encrypted traceability codes provide ability to verify provenance and authenticity of product
• Ability to traceback from consumer package helps expedite and narrow investigations & recalls
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Supply chain traceability Item-Level Traceability
Currently used for high-value crops and export markets
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HarvestMark Item-level Trace Activity Dashboard 11
Supply chain traceability
Can Traceability Precision Allow for ML to
Identify Food Safety Risks?
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Customer feedback and
interactionProduct + Data as a Platform
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Use of consumer data & restaurant reviews in epidemiology: target inspections, identify foodborne illness clusters, adulteration events
Start up company iwaspoisoned.com founded in 2009 to crowd-source food poisoning complaints
New York City partnered with Yelp to prioritize health department investigations (2014)
Chicago Dept. of Innovation and Technology develops algorithm using public data to predict health code violations (2014)
The Food Safety STL Project: using Twitter to identify and respond to food poisoning (2015-2016; Harris et al., 2017)
Deploying nEmesis: preventing foodborne illness by data mining social media (Las Vegas, 2015; Sadilek et al., 2017)
Boston & Yelp fund a Harvard research tournament to mine Yelpreviews to predict food-safety violations (2016)
MIT/FDA collaborate to predict risks for economically motivated food adulteration using public data (Gu, 2016; Huang et al., 2017)
FINDER: machine-learned epidemiology deployed in Las Vegas and Chicago (2016-2017; Sadilek et al., 2018)
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Customer feedback and
interaction
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Product Home Page & Social Promotion
Traceback to Farm& Processing
Consumer Feedback
Food Safety Status &Real Time Compliance
Data with iFood
Recipes & Other Custom Content
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Customer feedback and
interaction
Can Item-level Consumer Traceability
Provide Data for CPG Epi Algorithms?
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Retail environment
controls
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Robotic monitoring of case temperatures
Augmented reality-based trainings and task lists
Vision systems to detect product labeling issuesSimulation-based risk assessments with analytics
AI for Preventing Conditions that Increase
Food Safety Risks at Point of Sale
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Friedlander and Zoellner, Food Protect. Trends 202016
Potential for AI at retail to improve food safety
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Key
take
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Food retailers are preparing for a highly automated future
AI adoption is not currently motivated by food safety; opportunity to improve outbreak response with AI solutions
AI is enabled by software that provides connectivity and transparency of supply chain food safety data
AI must provide access to actionable information to leverage experience and resources for managing food safety risks
AI helps existing food safety practices work more effectively
Perceived liability, data bias, and accessibility are key challenges to widespread adoption
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Claire Zoellner, PhDFood Safety Scientist