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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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.