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

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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)

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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

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• 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)

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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

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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