Construction of advanced
spectroscopic techniques to
detect food fraud and adulteration
Presenter: Dr. Xiaonan Lu
Associate Professor
Food Science
University of British Columbia
Date: June 12, 2017
2016 Global Seafood fraud - Oceana
Recent food fraud issues
2
Sudan I dye
During Dec 2016 and Mar 2017
Food fraud incidents (con’t)
Figure 1. Food fraud incidents categorized by food group
(summarized by Food Protection and Defense Institute)
http://www.foodfraudresources.com/ema-incidents/ 2
Oceana Survey of US Seafood:
Definition of food fraud • Food fraud
“the deliberate and intentional substitution,
addition, tampering, or misrepresentation of food,
food ingredients, or food packaging; or false or
misleading statements made for food products for
economic gain” – Spink and Moyer, 2011
3
Definition of food fraud (Con’t)
Figure 2. Food protection risk matrix (Spink & Moyer, 2011)
Food
Quality
Food
Fraud1
Food
Safety
Food
Defense
Motivation
Gain: Economic
Harm:
Public Health,
Economic, or
Terror
Unintentional Action Intentional
1Includes the subcategory of economically motivated
adulteration and food counterfeiting
4
Economic loss of all parties (i.e. food industry,
government, consumers)
Weaken consumers trust in food industry and
government
Potential health risks
allergens incorporated
pathogen contaminated
poisoning
Detriments of food fraud
Food safety
&
Food defense
5
Traditional techniques
• complex
• time consuming
Sample preparation
• Complicated instrumentation
• marker specific methodology
LC/GC • Complicated
instrumentation
• marker specific methodology
UV/DAD/MS
6
Traditional techniques (con’t)
• Fail to achieve:
rapid analysis
high-throughput screening
user-friendly procedures
detection of new types of deceptive behaviors
• Alternative:
7
Vibrational spectroscopies
• Raman and FT-IR spectroscopies
Vibrational signals of functional groups
Scattering or absorption spectra
Figure 3. Vibrational modes of molecules
symmetrical
stretching
Rocking Wagging Twisting Figure 4. Representative
Raman spectra
Asymmetrical
stretching
Scissoring
8
Vibrational spectroscopies (con’t)
• NMR spectroscopy
Vibrational signals of nucleus
Resonance frequency spectra
NMR: nuclear magnetic resonance
Figure 5. Nucleic magnetic moment changes in
NMR spectroscopy Figure 6. Representative 1H
NMR spectrum
9
Vibrational spectroscopy (con’t)
• Advantages
Non/less-destructive
Rapid
Comprehensive chemical composition
Unique fingerprinting features
Able to emerge any extraneous materials
13
Current Research Projects in Lu Lab
1. Authentication of ground beef meat
2. Determination of Sudan I in paprika powder
3. Identification of salmon species
14
Project 1. Authentication of ground
beef meat
15
• 2013 horse meat scandal raised concerns on processed meat
• Low consumption of animal offal (i.e. by-product) in North
America
• High similarity between authentic ground beef and
adulterated ground beef
Background
16 Figure 7. Adulterated ground beef meat
• DNA based method same animal species
• Immunological based methods
• Liquid chromatographic based methods
• Vibrational spectroscopic based methods
Background (cont’d)
17
Target specific
Expensive
Laborious
Experimental design
18
(Hu et al., Submitted to Nature
Scientific Reports, 2017)
Figure 8. Schematic illustration of
experimental design
Results
100%
accuracy
96%
accuracy
19 LDA: linear discriminant analysis
(Hu et al., Submitted to Nature Scientific Reports, 2017)
Results (cont’d)
20
PLSR: partial least squares regression
RMSE: root-mean squares error; LOD: limit of detection
(Hu et al., Submitted to Nature Scientific Reports, 2017)
Conclusion
21
• FT-IR spectroscopy was able to:
1) differentiate authentic beef meat from beef meat
adulterated with one of the six types of offal,
2) identify the specific type of offal adulterant, and
3) quantify five types of offal in an accurate manner.
• An optimized protocol for the analysis of ground beef meat
using FT-IR spectroscopy was developed with a limit of
detection lower than 10%.
• This protocol has a high potential to be applied by
governmental laboratory and food industry for the real
world analysis.
Project 2. Determination of paprika
powder adulterated with Sudan I
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Background
23
• Sudan I
Industrial dye
Group III Carcinogen
• Banned in food since 2003
• still found in various red/orange color food recently
Background (cont’d)
24
Conventional method: HPLC-UV
Extensive sample pretreatment
Complicated procedure
NMR spectroscopy to improve quantification accuracy
HPLC: high performance liquid chromatography, UV: ultraviolet
Experimental design
HR MAS: high resolution magic angle spinning
Figure 9. Schematic illustration of the experimental design
25
(Hu and Lu, 2017. Nature
Scientific Reports, 7, 2637)
Figure 10. 1H solution NMR spectra of paprika powder spiked
with Sudan I at various concentrations
Solution NMR
Results
26 (Hu and Lu, 2017. Nature Scientific Reports, 7, 2637)
y = 40014x - 59516 R² = 0.9983
0.E+00
5.E+06
1.E+07
2.E+07
2.E+07
3.E+07
0 100 200 300 400 500
Inte
gra
l of th
e p
ea
k a
t 7.8
8 p
pm
(a.u
.)
Sudan I concentration in paprika powder (mg kg-1)
Figure 11. Linear regression model for Sudan I in paprika
powder by 1H solution NMR spectrometer.
Solution NMR
• R2 = 0.9983
• RSDave = 4.6%
• Accuracyave = 98%
• LOD = 6.4 mg kg-1
• LOQ = 21.4 mg kg-1
• Time: 35 min
Accurate and rapid
determination of low
concentration Sudan I
27
Results (cont’d)
(Hu and Lu, 2017. Nature Scientific Reports, 7, 2637)
SSNMR
Figure 12. HR MAS SSNMR spectra of paprika powder spiked
with Sudan I at various concentrations
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Results (cont’d)
(Hu and Lu, 2017. Nature Scientific Reports, 7, 2637)
y = 282.87x - 13248 R² = 0.9885
0.E+00
1.E+05
2.E+05
3.E+05
4.E+05
5.E+05
6.E+05
7.E+05
0 500 1000 1500 2000 2500
Inte
gra
l of th
e p
ea
k a
t 7.8
8 p
pm
Sudan I concentration in paprika powder (mg kg-1)
SSNMR
• R2 = 0.9885
• RSDave = 3.9%
• Accuracyave = 105%
• LOD = 122.2 mg kg-1
• LOQ = 298.0 mg kg-1
• Time: 32 min
Figure 13. Linear regression model for Sudan I in paprika
powder by HR MAS SSNMR spectrometer.
Accurate and rapid
determination of low
concentration Sudan I
29
Results (cont’d)
(Hu and Lu, 2017. Nature Scientific Reports, 7, 2637)
Accuracy
(average)
Sensitivity (LOD &
LOQ) time & sample prep.
1H solution NMR
spectroscopy 98% 6.4 & 21.4 mg kg-1 35 min &
moderate complexity
HR MAS SSNMR
spectroscopy 105%
122.2 & 298.0 mg
kg-1
32 min & no sample
prep.
• Accurate and rapid detection of Sudan I in paprika powder
• Solution NMR has higher sensitivity, but more labor
intensive
• SSNMR is faster and less labor involved, but less sensitive
• Generalize to analyze other chemicals
Conclusion
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Project 3. Identification of salmon
species (in progress)
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Salmon fraud
• According to a survey in USA in 2013, 43% of salmons
tested were mislabelled; the most common form of
mislabelling was farmed Atlantic salmon sold as wild
Pacific salmon (69%).
• CFIA has identified that the consumers in Vancouver
area are the most at risk.
• No confirmed data regarding fish fraud and mislabelling
in BC yet.
• Compare genomics (DNA barcoding) and
metabolomics (Raman spectroscopy) to detect salmon
fraud in Vancouver and BC.
Figure 14. Prototype of homemade portable Raman spectrometer (top) and the illustration of the waveguide
fibre for Raman spectral collection of fish samples (bottom).
Figure 15. Baseline corrected mean Raman spectra of fish muscles of Pacific salmon and Atlantic salmon
using the homemade portable Raman spectrometer.
Next step…
Comparison and integration of
chemical library (UBC) &
molecular library (Guelph)
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Lu Food Safety Engineering Lab at UBC
PhD student Yaxi Hu
Dean Rickey Yada
Prof. Eunice Li-Chan
Acknowledgements