Towards citizen science approaches in food quality and safety … · 2018. 7. 2. · Medtech sales:...
Transcript of Towards citizen science approaches in food quality and safety … · 2018. 7. 2. · Medtech sales:...
Towards citizen science approaches in
food quality and safety testing
1RIKILT, Institute of Food Safety; 2Wageningen University, Lab. of Organic Chemistry
Michel Nielen1,2
Evolution of the ‘Measuring Man’
1960s: Apollo space program biomedical sensing
• ECG electrodes
• Blood pressure
• Pneumography
• Body temp.
The ‘Fourth Industrial Revolution’Sh
in et a
l., An
alyst, 1
43
(20
18
) 15
15
-15
25
Transition in Worldwide Medtech sales:Portables account for 40 Bn$ in 2022
Health apps, life style apps, food
apps, intelligent packaging, etc:
Increasing demand
for reliable data
Consequences of this evolution for future EU
food quality & safety monitoring practices ?!
Only potentially interesting samples to theofficial lab
• More focus• Less paper work• Less transport• Less storage • More data• Even involve citizens?
Sure!
How to assure lab quality on-site?
Starting points: knowing what you’re measuring!
Awareness of risks of false positive and false negative results
Approaches to reliable quality:1. A chemometric model plus a generic spectral profile (e.g., NIR)
2. More specific spectral profile (e.g., MS)
3. Biorecognition (e.g., a strip test or LFIA)
Current status: ‘consumer spectroscopy’
Modelling
Classification
To which of the defined classes does the sample belong?
Estimation
What is the concentration of x in the sample?
Spectroscopic analysis
Short-wavelength near infrared (SW-NIR)
750-1059 nm
Smartphone
Easy to use
Time: ≈ 5 sec
Yannick Weesepoel
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NIR model for adulteration of distilled spirits
Capabilities:-percent range-relevant for fraud testing-relevant for macro composition food
How to assure lab quality on-site?
Starting points: knowing what you’re measuring!
Awareness of risks of false positive and false negative results
Approaches to reliable quality:1. A chemometric model plus a generic spectral profile (e.g., NIR)
2. More specific spectral profile (e.g., MS)
3. Biorecognition (e.g., a strip test or LFIA)
(Trans)portable mass spectrometry• Crucial for on-site multi-contaminant analyses at low levels.
• Crucial at major incidents
Upcoming but still several technical challenges:
• Heavy weight vacuum systems, power and gas consumption,, high voltages, etc.
• Our research focus on simplified sample introduction and critical evaluation of commercial systems
Scratch your lemon! (Trans)portable mass
spectrometry for fungicides: organic y/n
m/z 298
Or check your Tequila
Marco Blokland
Check your Fipronil eggs! (trans)portable
mass spectrometry initial trials
How to assure lab quality on-site?
Starting points: knowing what you’re measuring!
Awareness of risks of false positive and false negative results
Approaches to reliable quality:1. A chemometric model plus a generic spectral profile (e.g., NIR)
2. More specific spectral profile (e.g., MS)
3. Biorecognition (e.g., a strip test or LFIA or multiplex test, with smartphone
readout) for simplified food contaminant screening at low levels
Like a pregnancy test: neonics strip test
2 strip tests for on-site neonicotinoids testing
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Imidacloprid Acetamiprid Nitenpyram Water
Clothianidin Thiacloprid Thiamethoxam Water
I A I A I A I A
I A I A I A I A
Neonicotinoid Imidacloprid strip Acetamiprid strip
Imidacloprid ++ -
Acetamiprid ++ ++
Clothianidin ++ -
Thiacloprid ++ ++
Nitenpyram ++ -
Thiamethoxam + -
Zhejiang University
CAAS – Tea Research Institute
Jeroen Peters
Added 50 ml of boiling water to 5 grams of homogenized material
Application of 2-strip test neonicotinoids on hot
water extract of cut flowers, herbs and teas
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Number Flower species Strip Results Neonics content in µg/kg
Imidacloprid Acetamiprid acetamiprid clothianidin imidacloprid nitenpyram thiacloprid thiamethoxam
1 Aster - +
2 Phloxes -- ++ 29,64
3 Chrysanthemum -- --
4 Germini -- +
5 Charmelia -- --
6 Carnation ++ -- 4,02 36,95 19,52
7 Lily ++ -
8 Alstromeria -- --
9 Carnation + - 12,54
10 Rose -- --
11 Gladiolus -- --
12 Lily ++ ++ 261,11 7,59 11,85
13 Carnation - ++ 33,43
14 Crysanthemum + --
15 Rose -- -
16 Germini -- --
17 Chrysanthemum ++ - 29,76
18 Fresia + - 11,14
19 Alstromeria -- --
20 Carnations - + 48,73
21 Chrysanthemum -- --
22 Sunflower -- -
23 Roses + ++ 30,35
24 Gladiolus -- -
25 Crysanthemum ++ -- 145,72 150,39
26 Phloxes ++ -- 146,56
27 Lily -- --
28 Chrysanthemum ++ - 46,73 33,65
29 Carnation + --
30 Delphinium ++ + 4,72
31 Tanacium ++ + 1637,60 1264,27
Toward smartphone-based on-site testing
• Wide-spread, battery, computer, graphics, image detector, USB,
time, location, wireless data communication, nice Apps,
Internet of Things......
• 7 billion smartphone users in 2020
• Potential for adding the spatiotemporal
mapping dimension to food testing
World’s cheapest smartphone, costing under £3, begins shipping next week
http://www.telegraph.co.uk/technology/2016/07/01/worlds-cheapest-smartphone-costing-under-3-begins-shipping-next/
At the horizon
• Food quality and safety testing more focused and more effective
• Simplified smartphone tools available for on-site testing
• Anywhere and anybody: in the field, at the farm, at the retailer, in
industry, at the BIP, in a restaurant, even at home
• Intuitive, simple and affordable; DIY, also for citizen science
approaches to food quality and safety monitoring and food fraud
prevention
• FoodSmartphone >34 man yrs EU-funded research programme
Multiplex smartphone food assays: lab status
Ludwig et al., PLOS ONE, 10(8): e0134360 (2015)
Smartphone-based assays: but.......
Major science and innovation gaps versus current status
• Miniaturising
• Flexibility
• Validation, robustness
• Non-expert use
• Costs
• Analysis time
A personalized food allergen testing platform on a cellphoneCoskun at al., Lab Chip, 2013, 13, 636-640
In FoodSmartphone research towards
• Biochemical recognition elements
• High-speed biorecognition
• Optical detection using smartphone camera
• Electrochemical detection via USB-port
• Robustness and ease of use
• Software, server and cloud interfacing, App
development
• Applicability demonstrators for:
antibiotics, allergens, spoilage microorganisms,
pesticides, mycotoxines, marine biotoxines
Follow the progress of the FoodSmartphone network
Web: www.FoodSmartphone.eu
Blog: www.FoodSmartphone.blog
Twitter: @FoodSmartphone
Facebook: FoodSmartphone
YouTube: lXceX3TITzs
The dark side: risks of food testing by non-experts....
• Poor food quality tests
• Food testing performed badly
• Unrepresentative sampling
• Tsunami of fake data...., who is right?
• Communication, privacy, data mgt issues
• Novel QA/QC approaches and adapted
quality performance regulations needed
Fake news caused by fake data!
Summary
• On-site testing of food quality and safety is rapidly emerging and
soon offered to anybody, including consumers
• 3 different technical solutions for on-site testing, having different
scope of applicability and in different stages of development
• Major consequences for food testing laboratories and stakeholders
• Major consequences for communication: fake data, fake news
• Major consequences for validation and (remote) quality control
• Smart, evidence-based sensing, ready for citizen science?!
Many thanks for your kind attention!
Acknowledgements:FoodSmartphone consortium;
WUR colleagues Yannick
Weesepoel, Jeroen Peters, Marco
Blokland, Arjen Gerssen, Michael
van Dam, Paul Zoontjes, Gina
Ross; students Jorrien Hattink,
Josha jager, Hua Easton,
Rachelle Linders
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 720325