Transcript presentation - Winter symposium on Chemometrics

Eighth Winter Symposium on Chemometrics 2012
NMR AND
CHEMOMETRICS: A
POWERFUL COMBINATION
FOR FOOD ANALYSIS
Yulia B. Monakhova, Hartmut Schäfer, Eberhard Humpfer,
Manfred Spraul, Thomas Kuballa, Dirk W. Lachenmeier
Baden-Württemberg
Chemische und Veterinäruntersuchungsämter
Bruker Biospin GmbH, Germany
State University, Saratov, Russia
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
pH adjustment
(soft drinks, wine)
Additional
steps
Solvent extraction
(pine nuts)
Hydrolysis/fat extraction
(fish, cheese, meat)
Sucrose without water
suppression
Sucrose with water
suppression
Alcohol: Eightfold suppression
Ethanol
400
Intensity [A.U.]
400
Suppression
200
200
0
0
4
3
2
ppm
1
0
4
3
2
1
ppm
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
0
Performance of the 8-fold suppression:
methanol
1200
Intensity [A.U.]
900
600
300
0
3.374
3.372
3.370
ppm
3.368
3.366
Data preparation for chemometrics
Fourier
transformation (FT)
Baseline and phase correction
and referencing
Peak to peak variations
homemade spirit (72 vol% ethanol)
homemade spirit (70 vol% ethanol)
Intensity [A.U.]
4500
3000
1500
0
1.24
1.20
1.16
ppm
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:
PC3
PLS - Partial Least Squares;
PC2
PCR – Principal Component Regression
- resolution of overlaped signals:
PC1
MCR – Multivariate Curve Resolution
ICA – Independent Component Analysis
Applications: unrecorded alcohol
Scores
8000
7500
Denatured
Denatured alcohol(Russia)
alcohol(Russia)
7000
alcohol(Russia)
6500 Medicinal
Medicinal alcohol(Russia)
6000
5500
5000
PC-2 (24%)
4500
4000
3500
3000
2500
2000
1500
1000
500
0
-500
-1000
-1500
-4000
Romania
Poland
Poland
Romania
Brazil
Poland
Poland
BrazilBrazil
Poland
Samogon(Russia)
Poland
Brazil
Samogon(Russia)
Brazil Brazil
Russia(Essen)
BrazilSamogon(Russia) Poland
Brazil
Poland
Brazil
Russia(Essen)
Poland
Poland
Samogon(Russia)
Russia(Essen)
Russia(Essen)
Brazil
Poland
Russia(Essen)
Russia(Essen)
Russia(Essen)
Brazil
Brazil Poland
Brazil
Russia(Essen)
Russia(Essen)
Brazil
Russia(Essen)
Vodka(Russia)
Brazil
Vodka(Russia)
Russia(Essen)
Vodka(Russia)
Brazil
Poland
Russia(Essen)
Brazil
-2000
0
2000
4000
PC-1 (28%)
6000
8000
10000
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
12000
Applications: quantification of
ethyl carbamate in spirits
1 - Spirit with 9.0 mg/l ethyl carbamate
2 - Spirit with 1.0 mg/l ethyl carbamate
3 - Spirit with n.d. ethyl carbamate
100
Intensity [A.U.]
80
60
1
2
3
40
20
0
4.14
4.11
4.08
ppm
4.05
4.02
PLS models for ethyl carbamate (10 - 6.0 ppm)
Reference
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
0 - 5.0
0.14
0.89
n
Validation set
43
Y. 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 pages
doi: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
Method
Percent of inaccurate
classifications
PLS-DA
0
SIMCA
0
PLS correlation between labeling parameters and
NMR spectra
Reference
range
NMR
range
(ppm)
Energy, (kJ/100 mg)
79-296
Carbohydrate, (g/100ml)
Parameter
Validation
RMSE
R2
3-0
17
0.86
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
China-Normal
China-PNS
Unknown-Normal
Unknown-PNS
Pakistan
Mediterranean
1 H NMR scores
4000
PC2 (5%)
2000
0
-2000
-4000
-10000
-5000
0
5000
10000
PC1 (89%)
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
Premium Brand 1
Premium Brand 2
Discount Brand 3
Discount Brand 4
Discount Brand 5
Discount Brand 6
Discount Brand 7
6
4
PC7
2
0
-2
-4
-80
-60
-40
-20
0
20
40
PC2
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
1
2
3
60000
glucose (R=0.99)
lactose (R=0.98)
galactose (R=1.0)
1,0
50000
40000
Intensity [A.U.]
Intensity [A.U.]
0,8
30000
20000
0,6
0,4
0,2
10000
0,0
0
5,0
4,8
4,6
ppm
4,4
4,2
5,0
4,9
4,8
4,7
4,6
4,5
4,4
ppm
MILCA - Mutual Information Least
Dependent Component Analysis
4,3
4,2
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
a
PLS
factors
Parameter
Reference
range
Methanol, g/hL pa
0-1552
4
Acetaldehyde,
g/hL pa
0-91
7
5
NMR
range
(ppm)
Calibration
Test set validation
RMSE
R2
RMSE
R2
6-3
47.0
0.99
52.9
0.98
3-0
4.28
0.91
9.40
0.61
3-0
37.9
0.98
45.6
0.97
Sum of higher alcohols,
g/hL pa a
0-1416
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
overlapped signal, not possible to quantify with integration