NMR AND CHEMOMETRICS: A POWERFUL COMBINATION FOR FOOD ANALYSIS Yulia B. Monakhova, Hartmut Schäfer,...
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Transcript of NMR AND CHEMOMETRICS: A POWERFUL COMBINATION FOR FOOD ANALYSIS Yulia B. Monakhova, Hartmut Schäfer,...
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: [email protected]
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