nmr and chemometrics: a powerful combination for food analysis

NMR AND CHEMOMETRICS: A

POWERFUL COMBINATION FOR

FOOD ANALYSISYulia B. Monakhova, Hartmut Schäfer, Eberhard

Humpfer, Manfred Spraul, Thomas Kuballa, Dirk W. Lachenmeier

Eighth Winter Symposium on Chemometrics 2012

Baden-WürttembergChemische und Veterinäruntersuchungsämter State University, Saratov,

RussiaBruker Biospin GmbH, Germany

NMR for chemometric applications in food analysis

1. The high spectral information of NMR provides ideal conditions for non-targeted analysis and the opportunity for chemometric discrimination

2. Modern NMR has reached sensitivity down to ppm-range

3. High throughput (minimal sample preparation, fast spectra aquasition and processing) is extremely efficient when dealing with a high number of samples to be analyzed using multivariate methods

Sample preparation

Addition of proper solvent and reference compound

Hydrolysis/fat extraction(fish, cheese, meat)

Solvent extraction (pine nuts)

pH adjustment (soft drinks, wine)

Additional steps

Sucrose without water suppression

Sucrose with water suppression

Alcohol: Eightfold suppression

Y. B. Monakhova, H. Schäfer, E. Humpfer, M. Spraul, T. Kuballa, D.W. Lachenmeier. Application of automated eightfold suppression of water and ethanol signals in 1H NMR to provide sensitivity for analyzing alcoholic beverages. Magnetic resonance in chemistry. 2011. 49, 734–739

Ethanol

4 3 2 1 0

0

200

400

ppm

4 3 2 1 0

0

200

400

Inte

nsi

ty [A

.U.]

ppm

Suppression

Performance of the 8-fold suppression: methanol

Data preparation for chemometrics

Fouriertransformation (FT)

Baseline and phase correction and referencing

Peak to peak variations

Bucketing

Chemometric methods- data reduction: PCA - Principal Component Analysis- classification: SIMCA – Soft Independent Modeling of Class

Analogy; PLS-DA – Partial Least Squares -

Discriminant Analysis; LDA - Linear Discriminant Analysis; SVM - Support Vector Machine - quantitative analysis: PLS - Partial Least Squares; PCR – Principal Component Regression- resolution of overlaped signals: MCR – Multivariate Curve Resolution ICA – Independent Component Analysis

PCPC33

PCPC22

PCPC11

Applications: unrecorded alcohol

PC-1 (28%)-4000 -2000 0 2000 4000 6000 8000 10000 12000

PC

-2 (

24%

)

-1500

-1000

-500

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

6000

6500

7000

7500

8000

Scores

Samogon(Russia)Samogon(Russia)

Samogon(Russia)

Samogon(Russia)

Vodka(Russia)Vodka(Russia)Vodka(Russia)

Denatured alcohol(Russia)Denatured alcohol(Russia)

Medicinal alcohol(Russia)Medicinal alcohol(Russia)Medicinal alcohol(Russia)

Poland

Poland

PolandPoland

Poland

PolandPoland

Poland

PolandPoland

Poland

Poland

Poland

Romania Romania

Brazil

BrazilBrazilBrazil

Brazil

Brazil

BrazilBrazil

Brazil

Brazil Brazil

BrazilBrazilBrazil

BrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazil

BrazilRussia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)Russia(Essen)

Russia(Essen)

Russia(Essen)

Y. B. Monakhova, T. Kuballa, D. W. Lachenmeier. (2012) Nontargeted NMR Analysis to Rapidly Detect Hazardous Substances in Alcoholic Beverages. Applied Magnetic Resonance, DOI 10.1007/s00723-011-0309-2

Applications: quantification of ethyl carbamate in spirits

PLS models for ethyl carbamate (10 - 6.0 ppm)

nReference range,

mg/L

RMSE, mg/L R2

Calibration set 1

146

0 - 9.0 0.15 0.96

Calibration set 2

119

0 - 9.0 0.13 0.98

Validation set

43 0 - 5.0 0.14 0.89Y. B. Monakhova, T. Kuballa, D.W. Lachenmeier (2012) Rapid quantification of ethyl carbamate in spirits using NMR spectroscopy and chemometrics. ISRN Analytical Chemistry, Volume 2012, Article ID 989174, 5 pagesdoi:10.5402/2012/989174

Applications: milk

Y. B. Monakhova, T. Kuballa, J. Leitz, C. Andlauer, D.W. Lachenmeier (2012) NMR Screening of milk, lactose-free milk and milk

substitutes based on soy and grains to validate nutrition labeling. Dairy Science and Technology (92):109–120

Classification methods

MethodPercent of inaccurate

classifications

PLS-DA 0

SIMCA 0

PLS correlation between labeling parameters and NMR spectra

Parameter Referencerange

NMRrange(ppm)

Validation

RMSE

R2

Energy, (kJ/100 mg) 79-296 3-0 17 0.86

Carbohydrate, (g/100ml) 0.2-11 6-3 0.48 0.96

Sugars, (g/100 ml) 0.1-7.3 6-3 0.48 0.82

Protein, (g/100 ml) 0.1-3.7 6-3 0.35 0.93

Fat, (g/100 ml) 0.1-4.2 3-0 0.19 0.96

Saturates, (g/100 ml) 0.1-2.8 3-0 0.19 0.95

Fibre, (g/100 ml) 0.0-1.6 3-0 0.21 0.47

Applications: Pine nuts (Pinus Pinea)

• The first case of adverse effects of pine nut consumption has been reported in 2001 in Belgium. Later it is called „Pine Nut Syndrome“ (PNS)

• PNS is characterized as a bitter, metallic taste disturbance, developing 1-3 days after consumption and lasting for days or weeks.

• A mechanism or specific cause has yet to be identified

1H NMR - Origin

-10000 -5000 0 5000 10000

-4000

-2000

0

2000

4000

China-Normal China-PNS Unknown-Normal Unknown-PNS Pakistan Mediterranean

PC

2 (

5%

)

PC1 (89%)

1 H NMR scores

H. Kobler, Y. B. Monakhova, T. Kuballa, C. Tschiersch, J. Vancutsem, G. Thielert, A. Mohring, D. W. Lachenmeier (2011) Nuclear magnetic resonance spectroscopy and chemometrics to identify pine nuts that cause taste disturbance. Journal of agricultural and food chemistry. 59 (13): 6877-6881.

Applications: Cola beverages

P. Maes, Y. B. Monakhova, T. Kuballa, H. Reusch, D. W. Lachenmeier. Qualitative and quantitative control of carbonated cola beverages using 1H NMR Spectroscopy (2012) Journal of agricultural and food chemistry, accepted

Resolution of of overlaped signals

MILCA - Mutual Information Least Dependent Component Analysis

5,0 4,9 4,8 4,7 4,6 4,5 4,4 4,3 4,2

0,0

0,2

0,4

0,6

0,8

1,0

Inte

nsi

ty [

A.U

.]ppm

glucose (R=0.99) lactose (R=0.98) galactose (R=1.0)

5,0 4,8 4,6 4,4 4,2

0

10000

20000

30000

40000

50000

60000 1 2 3

Inte

nsi

ty [

A.U

.]

ppm

Conclusions• NMR and chemometrics represents a robust

method for checking the food authenticity (geographical origin, the species of plant and animal, labeling validation, etc.)

• NMR spectroscopy combined with chemometric methods can be successfully used for quantification of substances whose resonances overlap with signals of other compounds

• NMR spectroscopy and chemometrics is judged as suitable for the rapid routine analysis of food and the application range will be extended to further matrices in the future.  

Thanks for your attention!!!

Contact: yul-monakhova@mail.ru

PLS correlation between data of reference analysis and NMR spectra

Parameter Reference range

PLS factors

NMR rang

e(ppm)

Calibration Test set validation

RMSE R2 RMSE R2

Methanol, g/hL pa 0-1552 4 6-3 47.0 0.99 52.9 0.98

Acetaldehyde, g/hL pa

0-91 7 3-0 4.28 0.91 9.40 0.61

Sum of higher alcohols,

g/hL pa a

0-14165

3-0 37.9 0.98 45.6 0.97

Propanol, g/hL pa a 0-1202 6 3-0 31.5 0.97 38.5 0.95

Isobutanol, g/hL pa a

0-179 7 3-0 7.59 0.96 9.01 0.95

Amyl alcohol, g/hL pa a

0-398 7 3-0 21.03 0.96 32.0 0.91

2-phenyl alcohol, g/hL pa

0-28 4 10-6 1.27 0.94 1.64 0.90

Methyl acetate, g/hL pa

0-24 7 3-0 1.18 0.93 1.76 0.85

Ethyl acetate, g/hL pa

0-753 7 3-0 15.98 0.98 30.4 0.94

Ethyl caprylate, g/hL pa a

0-3.9 5 6-0 0.55 0.66 0.72 0.45

Ethyl benzoate, g/hL pa

0-2.9 4 10-6 0.40 0.75 0.49 0.64

Benzaldehyde, g/hL pa

0-6.9 7 10-6 0.33 0.96 0.70 0.83

a overlapped signal, not possible to quantify with integration