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Emerging Technologies
Professor Guy Poppy
Chief Scientific Adviser
Food Standards Agency
The Global Food System
The backbone of the IFTN (based on 2007 dataset)
Ercsey-Ravasz M, Toroczkai Z, Lakner Z, Baranyi J (2012) Complexity of the International Agro-Food Trade
Network and Its Impact on Food Safety. PLoS ONE 7(5): e37810. doi:10.1371/journal.pone.0037810
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037810
Where do the ingredients of a Kit Kat come from?
Milk chocolate (66%) (sugar, cocoa butter, cocoa mass, dried
skimmed milk, whey powder, butterfat, vegetable fat, lactose,
emulsifier (soya lecithin), flavouring), wheat flour, sugar,
vegetable fat, cocoa mass, yeast, raising agent (sodium
bicarbonate), salt, calcium sulphate, flavouring
Key facts about the food system
Risk category Primary
producers
Manufacturers
and Packers
Importers/
Exporters
Distributors/
Transporters
Retailers Restaurants and
Caterers
Total
A 15 605 3 14 396 2,569 3,602
B 92 2,592 12 84 2,771 27,358 32,909
C 285 5,181 131 953 29,805 190,922 227,277
D 675 2,596 162 1,728 27,170 68,245 100,576
E 2,489 5,506 507 5,344 66,498 113,654 193,998
Not Yet Rated
(NYR)
1,183 1,418 127 756 6,462 25,797 35,743
Outside * 1,157 387 163 685 2,610 9,036 14,038
Total 5,896 18,285 1,105 9,564 135,712 437,581 608,143
Breakdown by country
England 3,589 13,448 995 7,838 111,687 359,533 497,090
Northern
Ireland
59 994 41 415 4,330 13,946 19,785
Scotland 1,873 2,777 55 876 12,010 39,760 57,351
Wales 375 1,066 14 435 7,685 24,342 33,917
Number, type and distribution of food businesses – LAEMS data 2013
Role of Emerging Technologies
• Big Data
• Internet of Things
• Social/Digital Media
• Gut Microbiome
01:00
In the past minute…
Brands and organisations
on Facebook receive
34,722likes
Flickr users add
3,125new photos
Wordpress users publish
347new blog posts
Foursquare users
perform 2,083check-ins
Instagram users share
3,600new photos
YouTube users upload
48hours of new video
Apple received about
47,000app downloads
Google receives over
2,000,000search queries
Twitter users send over
100,000 tweets
The Big Data Wave
90% of the world’s existing
data has been created in
the last 2 years
By 2020, 35 Zettabytes of data
will be created
35 Zettabytes = 35,000,000,000,000,000,000,000 bytes
Source:
bigdataanalyticstoday.com
Using and analysing data isn’t new…
Paleolithic tribespeople would carve notches into bones or stones as a
way to track trading activity or supplies. Comparison of these “tally sticks”
would allow for rudimentary calculations, which included predicting how
long food supplies would last.
The Ishango Bone, c.18000 BCE. Discovered in modern day Uganda.
How should we use data?
Win Win for everyone
Contested definition of Big Data
Source: Forbes.com
Definitions of Big Data
The basic idea behind the phrase 'Big Data' is that everything we do is
increasingly leaving a digital trace (or data), which we (and others) can
use and analyse. Big Data therefore refers to that data being collected
and our ability to make use of it.
Brendan Marr, Author and Business and Data Expert
Extremely large data sets that may be analysed computationally to reveal
patterns, trends, and associations, especially relating to human behaviour
and interactions.
Oxford Dictionaries
There are now at least 16 V’s of Big Data and counting…
Industry and Big Data
Source: EtQ Inc.
Big Data: Food Safety & Integrity Applications- Nestle work in this space – John O’Brien
• Food safety early warning systems
• Search engine queries to detect disease outbreaks
• Whole genome sequencing data from environmental, food and clinical pathogen
isolates
• Metagenomics data from food and environmental samples
• Non-target fingerprint data sets for food authenticity and adulteration
• Satellite imaging data to detect illegal fishing
• Meteorological data to predict mycotoxin risks in crops
• GIS data to detect food fraud
• Social media analysis to understand consumer concerns and preferences
• Traceability and RM/ingredient data
• Image analysis and automated processes
• Computational microbiology, chemistry and toxicology
• Discussions with AgriMetrics, Digital Catapult and Open data Institutes
• Discussions with Nestle, Mars, Sainsburys
Big Data: Turing Institute – fellow with UCL
How it works
1. Early-warning tool on Norovirus spikes – helps decide when to intervene
2. Set of words relating to Norovirus symptoms generated
3. Weekly use volumes of words collected using social media listening
software
4. Fortnightly changes in word use/lab reports calculated
5. Word use volumes lagged by four weeks to allow model to be predictive
6. Correlations between lagged word use volumes and lab reports
calculated
7. Words with significant correlation used in logistic regression model
8. Logistic model predicts if there will be a significant rise or not in next
fortnight
Twitter – Norovirus Model
The Maths
1. FACTOR ANALYSIS: Used to group approximately 70 different
keywords into 10 groups of similarly correlated words (often with similar
subject too)
2. BIVARIATE LOGISTIC REGRESSION: Uses changes in keyword
groups to predict whether or not there is to be a significant change
3. RECEIVER OPERATOR CHARACTERISTIC CURVE: Uses variation of
true/false positives/negatives depending on cut off threshold in log.
regression model to decide on most accurate model:
• Initial model had a cut off of 0.5 – approx. 45% accurate
• Revised model had a cut off of 0.35 – approx. 70% accurate
4. PROPOSED NEW TECHNIQUES:
• MULTIVARIATE LOGISTIC REGRESSION: Will predict size of
change on predefined scale
• MACHINE LEARNING: More accurate method of identifying at what
point a ‘significant change’ is occurring
Twitter – Norovirus Model
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eport
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Lab Reports Sickness Tweets
LAB REPORTS vs. TWEET VOLUMES –
Prize winning FSA work – Predicting outbreaks weeks before labs
*Includes: sick bug, sickness bug and #sicknessbug
Twitter – Norovirus Model
White Rose Projects
• Part of an ESRC White Rose Network on food safety and big
data
• Collaboration between FSA and Leeds, Sheffield and York, with
the FSA and Pulsar as external partners.
• Pulsar is a private sector social media platform.
• PhD studentships across a number of disciplines with the aim of
harnessing big data to produce new insights into food safety.
• Project 1: “Using Visual Social Media Data to Better Understand
Safety Cultures”.
• Project 2: “Spatial Data Analytics for Food Safety”.
• Project 3: “Food Fraud and Big Social Data”.
Internet of Things – work with ITaaU on new pilot projects looking at sensors through the delivery chain
BY
2020
4 Billion
Connected
People
$4 Trillion
Revenue
Opportunity
$25+ Million
Apps
25+ Billion
Embedded
and
Intelligent
Systems
50 Trillion GBs of
Data
IoT pilot projects through EPSRC’s ITaaU network (and a review)
1. University of Birmingham with EHOs in the city tagging pre-packed
sandwiches and collecting sensor data using IoT to look at controls.
2. University of Lincoln with Tesco as a partner looking at temperature
profiles of food between stores, the home and storage in the domestic
setting. They plan to use the Tesco panel to deliver the research and we
have asked for a comparison with evidence about temperature control
within the supply chain.
IoT pilot projects through ITaaU network
3. University of Aberdeen working with food premises supplying food to
consumers in restaurants. Sensors will track different parameters of food
including some elements of traceability.
4. University of Nottingham with Kew and a number of other partners
looking at grow your own and local food production collecting information
about growth and other aspects of food production in a collaborative way
engaging with participants throughout the research.
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