Let's start building "Food-as-a-Software" model, where foods are engineered by scientists at a molecular level and uploaded to databases that can be accessed by food designers anywhere in the world. This will result in a far more distributed, localized food-production system that is more stable and resilient than the one it replaces. The new production system will be shielded from volume and price volatility due to the vagaries of seasonality, weather, drought, disease and other natural, economic, and political factors. Geography will no longer offer any competitive advantage. We will move from a centralized system dependent on scarce resources to a distributed system based on abundant resources

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Food innovation using data analysis and interpretation
F o o d T e c h n o l o g i s t – S e p t e m b e r 2 0 1 8

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DESIGN OF EXPERIMENTS, PILOT PLANT AND STAGE-GATE PROCESSES ARE NO LONGER SUFFICIENT
FOR INNOVATION . NEW MINDSET AND CULTURE ARE REQUIRED FOR SUCCESSFUL R&D TEAMS: BIG
DATA TO STAY AHEAD OF THE GAME & SELL IT AS A SERVICE FOR VALUE ADDED CREATION.
New Challenge becomes: how do you bridge the transparency & maintain the trust?
Sensory
Health
Benefit
Mood
Microbiome
DNA
PERSONALISATION
CONSUMER
Insights
Tensions
Needs
Pains
Occasions
Trends
Body

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NEW FOOD ENVIRONMENT FOR DEVELOPERS AND FOOD TECHNOLOGISTS:
Validated opportunity: differentiated value creation and proposition
Consumer journey: unarticulated pain and needs with brain and body-storming sessions
Technology:
• Smart Home
• Connected kitchen
• Digital content & services
• Solutions leveraging technologies
R&D:
• Unify and use data to test hypothesis & draw conclusions
• Discriminant analysis
• Risk-Opportunity plot
• Artificial intelligence
• Test hypothesis
• Introduce modelling
• Machine learning algorithm (platform for discovery
through multi-dimensional mapping.
• Global economy
• Open innovation
• Forever changing regulatory landscape and compliance
Know and understand your customer and consumer deeply
Functional benefit?
Emotional benefit?
Nutritional benefit?

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Value for consumers
• Uniqueness
• Convenience & Easy to use
• Experience -Freshly made at home
• Exclusivity
• Instantaneity
• Personalization
• Easy to understand, use and time saving
• Reassurance via coaching /DNA /Science
• Reassurance doing the right thing
• Transparency where the food is coming from

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Value for ingredient companies
• Machine learning methods and systems for ingredients discovery.
• AI is speeding up the R&D pace by cross-linking data
• Commercialization of science

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Value for manufacturing food &
beverages companies
• Leveraging technology to ensure product transparency and safety as well as food waste
management.
• New business models / Additional sources of revenue
• Addresses «processed barrier»
• User Generated content
• Reduce processing costs
• Premiumization
• Transforming brands into experiences
• Brand building - Be leader in Health & Wellness
• Innovation management (NPD, product reformulation, portfolio management, product adap
tation)
• Deep market insights (competitive market mapping, emergent preference prediction)
• Cognitive marketing (flavor profile preference priming, consumer value chain psychology pr
ediction)

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Value for tech companies
• Additional sources of revenue
• Data capture & machine learning
• User generated content
• Data collection
• Data monetization
• Brand building
• Trust building & story telling – emotional connection

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Pilot blockchain for supply chains
From complex, inefficient, imprecise
Connect IoT tags to shipments with
each shipment assigned a unique
identification number. These IDs will
be tied to products origins,
processing dates, and other
information. At each stage ,
employees can just “check-in” the
product using its ID number to see
real-time data or its history
To transparent & accurate

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• Calorie
• Usage
• Product
• Certificate
• Expiry
• Commerce
• Package
• Nutrient
• Ingredient
Others
• Preservatives
• Organic
• Allergens
• Contaminants
• Scoop
• Sensory
• Texture
• Flavour
• Portion size
• Additives
• Origin of ingredients
• Cooking instructions
• Cooking time
• Weight of the product
• Serving size
• Sustainability
• Storage instructions
• Shelf life
• Brand
• Company
Product features & categories
Nutrition
• Sugar
• Fat
• Saturated fat
• Trans-fat
• Proteins
• Vitamins
• Minerals
• Salt
• Calories
• Fibre
• Carbohydrates
Claims
• Organic
• Health claims
• Nutrition claims
• Free-from
• Low-fat
• No artificial….
Current
Proposed

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Use of Global Sensory Mapping (GSM)
GSM is an integrated tool to build knowledge on consumer for a specific product category & drive innovation. For a given product
category, a global sensory mapping (GSM) is based upon principal component analysis (PCA) with products clustered by sensory
similarities.

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Use of Global Sensory Mapping (GSM)
• Monitor competitor strategy (taste / flavour signature)
• Build on knowledge and know-how for product differentiation and portfolio sensory profiles
• Link sensory profile with ingredients / process parameters (predictive tool / modelling)
• Visual mapping of current taste portfolio for specific products belonging to same category
• Identify opportunity for development (new sensory territory)
• Identification of clusters of products with similar sensory profiles => harmonisation of recipes
• Accelerate development projects (select suitable cluster/profile) matching local preferences around taste (i.e. Local Sensory Mapping)
On PCA. See:
https://www.sensorysociety.org/knowledge/sspwiki/Pages/PCA.aspx

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Fundamentals knowledge building using data:
-Consumer landscape
-Market structure & category dynamics
-Usage & attitude studies
-Product preference mapping
Review / generate data from
qualitative/quantitative studies:
-Market survey
-Deep market insights
-Predicted optimal targets
Run PCA & cluster using JMP,
XL stat or R
Prototype
development (MVP)
Preference Mapping
(consumer liking +
sensory profiling)
New approach to NPD and R&D

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Simulation followed by focused experimentation leads to rapid product development.
Food Innovation using Data Science:
When design and experiment meet Challenge:
obsolete
R&D

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Input & labelled trained data to build
a predictive model

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Raphaëlle O’Connor
+353876765112
info@inewtrition.com
www.inewtrition.com

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Back-up slides

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JUST US Patent# 9,760,834
https://patents.justia.com/assignee/hampton-creek-inc and https://patents.google.com/patent/US9760834B2/en
Machine learning approach to identify plant based material of a targeted functionality through multi-dimensional mapping.
Blackbird™ – high throughput machine-learning enabled platform for food discovery
Sourcing (background characterisation) – Garden & raw material processing
Screening (material of interest and material of action) – fractionation of proteins & hitpicking using custom algorithm
Characterisation (molecular & functional assays / model system)
Harvest – Communication network
Data storage to capture and preserve data generated by Discovery platform
Data access – algorithm to query and retrieve information from data storage
Custom applications – custom algorithms to select high-potential protein candidates for food applications

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Not-Co
* 4 or 5 different databases made of internal mass spectrometry data of ingredients & foods and Internal mass spectrometry
data of sensorial parameters.
Combines proprietary machine learning algorithm and food science (molecular composition, taste, texture, color and nutritional
profile of different foods / ingredients) to develop common animal-based foods from plant ingredients.
Using AI, NotCo can identify innovative solutions, reduce product development time, and makes the technology overall cost-
effective
Giuseppe – understand / identify the link between molecular components of food & human perception of taste & texture.
Database* contains both public (commercially available ingredients) and self-generated information on animal-
based and plant based foods / ingredients (molecular composition / structure, sensorial information generated by
trained panel, nutritional information, techno-functional properties)
Algorithm uses this information to propose combination of ingredients (i.e. recipe) to formulate a plant-based analogue of a
given animal-based product. Proposed combinations are tested, and sensorial** results are fed back to the algorithm. Cycle
is repeated for further refinement.
** 2 databases of sensorial feedback – numerical and descriptive.
Crosslinking of data by using algorithm helps them to propose new ways to combine ingredients to deliver targeted texture.
Shelf life, scalability and supply chain.

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Gastrograph AI
1- Translate perception across demographics
2- Hierarchical modelling
3- Predicting preferences today
Decomposition of flavour profiles
Preference is contextual
Applied semantic vectors from decomposed flavour profiles
Modelling & predicting consumer preference
4- Predict the evolution of preferences into the future
Initiated by Givaudan (https://mistafood.com/), sensory science + data science on perception & preference:
https://www.gastrograph.com/

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Obtain and scrub Data:
https://powerbi.microsoft.com/en-us/
Explore & Model Data:
https://www.leanmethods.com/
Initiated by Givaudan (https://mistafood.com/), sensory science + data science on perception & preference:
https://www.gastrograph.com/
Other tools for data analysis/visualization