Anal. Chem. 2004, 76, 4790-4798
Quantification of Organic and Amino Acids in Beer by 1H NMR Spectroscopy Lars I. Nord,†,‡ Pia Vaag,§ and Jens Ø. Duus*,†
Carlsberg Laboratory, Gamle Carlsberg Vej 10, DK-2500 Valby Copenhagen, Denmark, and Carlsberg Research Laboratory, Gamle Carlsberg Vej 10, DK-2500 Valby Copenhagen, Denmark
The quantification of organic and amino acids in beer using 1H NMR spectroscopy is demonstrated. Quantification was made both by integration of signals in the spectra together with use of calibration references and by use of partial least-squares (PLS) regression. Results from the NMR quantifications were compared with those obtained from determinations by amino acid analysis on HPLC and organic acid analysis by capillary electrophoresis. The described NMR-based methods could satisfactorily be used for quantification of several of the investigated metabolites in beer down to ∼10 mg/L and for most with a good to high accuracy compared to results obtained by HPLC and capillary electrophoresis (R2 0.90-0.99). This was achieved with a simple sample preparation and onedimensional 1H NMR spectra obtained in a few minutes. The use of PLS clearly improves the accuracy of the quantifications, based on comparison to results obtained by HPLC and capillary electrophoresis, and furthermore permits the determination of components with partially overlapped signals in the spectrum. NMR spectroscopy in combination with PLS will be a useful tool for the quantification of metabolites, not only in beer but also in other beverages and biofluids. Modern nuclear magnetic resonance (NMR) spectroscopy permits analysis of low concentrations of analytes, even in complex matrixes such as beer, and it is a nondestructive technique that selectively can detect a large number of compounds simultaneously.1 This makes 1H NMR spectroscopy an attractive tool for the analysis of an array of components in beer as well as in other beverages and foodstuffs. Some recent examples are in the analysis of wine,2,3 coffee,4 edible oils,5 and tomato.6 NMR has recently been used for the identification of a large number of * Corresponding author. E-mail:
[email protected]. Fax: +45 3327 4708. † Carlsberg Laboratory. ‡ Current address: Department of Chemistry, Swedish University of Agricultural Sciences, P.O. Box 7015, SE-750 07 Uppsala, Sweden. § Carlsberg Research Laboratory. (1) Nicholson, J. K.; Foxall, P. J. D.; Spraul, M.; Farrant, R. D.; Lindon, J. C. Anal. Chem. 1995, 67, 793-811. (2) Gil, A. M.; Duarte, I. F.; Godejohann, M.; Braumann, U.; Maraschin, M.; Spraul, M. Anal. Chim. Acta 2003, 488, 35-51. (3) Brescia, M. A.; Kosˇir, I. J.; Caldarola, V.; Kidricˇ, J.; Sacco, A. J. Agric. Food Chem. 2003, 51, 21-26. (4) Charlton, A. J.; Farrington, W. H. H.; Brereton, P. J. Agric. Food Chem. 2002, 50, 3098-3103. (5) Vigli, G.; Philippidis, A.; Spyros, A.; Dais, P. J. Agric. Food Chem. 2003, 51, 5715-5722.
4790 Analytical Chemistry, Vol. 76, No. 16, August 15, 2004
organic compounds in beer.7 Due to spectroscopic overlap, liquid chromatography in combination with NMR and mass spectrometry (LC-NMR/MS) has also proven to be useful for further characterization.2,8 NMR can also be used for the quantification of minor components, but only a few examples for beverages are found in the literature. Kosˇir et al.9 and Berregi et al.10 used intensities from 1H NMR spectra for the quantification of succinic and acetic acid in wine and for the quantification of chlorogenic acid in cider apple juices. Aromatic beer components were determined by use of integrals of signals in 1H NMR spectra from ethyl acetate extracts of beer.11 Beer consists of more than 100 components present in a very wide range of concentrations, from ppt levels or less up to several percent.12 Besides water and ethanol, carbohydrates are the major components. Other important compounds include proteins, organic acids, amino acids, hop components, and salts. The concentration of amino acids and organic acids in beer may be used as indicators for fermentation performance. Free amino acids present in wort are metabolized by yeast during fermentation and are involved in biosynthetic pathways that lead to formation of important flavor components such as higher alcohols, esters, and sulfur compounds. Wort also contains organic acids originating from the malt, and some of these impacts beer flavor. The profile of amino acids and organic acids in beer is a reflection of wort composition combined with yeast metabolism. Monitoring the levels in beer may help to ensure a uniform production process. Figure 1 shows an example of a 1H NMR spectrum obtained from a lager beer (Grøn Tuborg, Carlsberg Breweries). In the spectrum, major components such as water and ethanol can be detected, but also several minor amino and organic acids. The sample was analyzed after minimal pretreatment, and the acquisition time was less than 5 min. The use of 1H NMR for the quantification of amino acids and organic acids in beer is reported. The method is demonstrated (6) Le Gall, G.; Colquhoun, I. J.; Davis, A. L.; Collins, G. J.; Verhoeyen, M. E. J. Agric. Food Chem. 2003, 51, 2447-2456. (7) Duarte, I.; Barros, A.; Belton, P. S.; Righelato, R.; Spraul, M.; Humpfer, E.; Gil, A. M. J. Agric. Food Chem. 2002, 50, 2475-2481. (8) Duarte, I. F.; Godejohann, M.; Braumann, U.; Spraul, M.; Gil, A. M. J. Agric. Food Chem. 2003, 51, 4847-4852. (9) Kosˇir, I.; Kocjancˇicˇ, M.; Kidricˇ, J. Analusis 1998, 26, 97-101. (10) Berregi, I.; Santos, J. I.; del Campo, G.; Miranda, J. I.; Aizpurua, J. M. Anal. Chim. Acta 2003, 486, 269-274. (11) Belleau, G.; Dadic, M. J. Am. Soc. Brew. Chem. 1977, 35, 191-196. (12) Meilgaard, M. C. Ph.D. Thesis, The Technical University of Denmark, Lyngby, 1981. 10.1021/ac0496852 CCC: $27.50
© 2004 American Chemical Society Published on Web 06/24/2004
Figure 1. 1H NMR spectrum of beer (Grøn Tuborg, Carlsberg Breweries). Some of the investigated metabolites are indicated as well as the intense water and ethanol signals. The inset shows an expansion of the δ 3.5-4.1 ppm region, dominated by carbohydrate ring protons.
on a series of lager-type beers, and 58 samples from different brands and manufacturers were included. Quantification was made either by integration of NMR signals together with the use of calibration references or by partial least-squares (PLS) regression. Results obtained by the NMR-based methods were compared with results from quantification by amino acid analysis on HPLC and organic acid analysis by capillary electrophoresis. Prior to quantification, explorative investigation of spectra were made by hierarchical clustering analysis (HCA), analysis of variance (ANOVA), and principal component analysis (PCA). EXPERIMENTAL SECTION Materials. A total of 58 samples of lager beer were collected, representing 20 different brands from manufacturers around the world. Three sample sets (sets 1-3), representing different batches of each brand, were collected (for two brands, only two samples of each). All beer samples were degassed by sonication for 20 min, and after foam collapse, aliquots of beer were stored at -20 °C before analysis by NMR, HPLC, or capillary electrophoresis. Reference samples for the amino and organic acids were obtained from commercial sources. D2O and CD3COOD for NMR were obtained from Aldrich (Milwaukee, WI) and Sigma (St. Louis, MO), respectively. Sample Preparation for NMR Analysis. To 0.4 mL of thawed beer was added 0.4 mL of a solution containing 20% CD3COOD in D2O. The signal from the residual CHD2COOD in the CD3COOD served as an internal concentration and calibration standard. After thorough mixing, 0.7 mL was transferred to a 5-mm-o.d. NMR tube. For the samples in set 1, twice as much beer and standard solution was used and the samples were divided into two different NMR tubes (set 1a and 1b) for test of the reproducibility of the NMR measurements. The beer samples in set 2 and set 3 were analyzed by NMR six weeks and five months after the samples in set 1, respectively. Before addition of CD3COOD in D2O, the average pH of all beer samples was 4.28 (standard deviation 0.13). After addition of the solution containing 20% CD3COOD in D2O, the average pH was 1.93 (standard deviation 0.18, n ) 7) as measured by a pH electrode without correction for the added D2O. The pH at ∼2 ensures that most of the carboxylic acid functional groups are in the protonated state. A sample preparation without exact pH adjustment was attractive to ensure a short total analysis time.
Amino Acid and Organic Acid Analysis. The concentrations of the amino acids listed in Table 1 (except for tryptophan) were determined by HPLC in all beer samples on a Biochrom 20 amino acid analyzer (Amersham Pharmacia Biotech, Uppsala, Sweden). Free amino acids in the beer samples were determined after precipitation with 3% sulfosalicylic acid and centrifugation. The supernatant was neutralized and analyzed by injection of 25 µL. Calibration was done by use of an amino acid analyzer calibration mixture from Sigma (product AA-S-18). Detection was done at 570 nm (440 nm for proline) after reaction with ninhydrin. Organic acids (Table 1) in beer were determined by capillary electrophoresis using a Beckman P/ACE 5510 with a P/ACE UV absorbance detector. Separations were performed with a fusedsilica capillary (i.d. 75 µm, length 77 cm) in 5 mM 2,6-pyridinedicarboxylic acid, 0.5 mM N-cetyl-N,N,N-trimethylammonium bromide, 0.01 mM EDTA, pH 5.6, at a voltage of 25 kV for 7 min. Indirect UV detection at 200 nm was used to monitor the separations. Reference samples of the organic acids were used for calibration. References for NMR. Reference 1H NMR spectra were recorded for the 20 amino acids listed in Table 1 for assignment of signals. The assignments were aided by comparison with chemical shift values in the literature.13 Each amino acid were dissolved in H2O at a concentration of 0.5 mg/mL, and 0.4 mL was mixed with 0.4 mL of a solution containing 20% CD3COOD in D2O. A subset of selected amino acids was mixed at specified concentrations (Table 2) for the quantitative analyses. 1D 1H and 2D NMR spectra were also acquired of this mixture. References of acetic, citric, lactic, malic, pyruvic, and succinic acids were prepared and analyzed in the same way as the amino acid references (Tables 1 and 2). NMR Spectroscopy. NMR spectra were recorded on a Bruker DRX 600 spectrometer (proton frequency 600.13 MHz) equipped with a 5-mm triple-resonance inverse probe. Acquisition and some processing of spectra were made with the XWINNMR software (Bruker Analytische Messtechnik GmbH, Rheinstetten, Germany, version 2.6). The spectrometer was locked on D2O, and all spectra were acquired at 30 °C. (13) Pretsch, E.; Clerc, T.; Seibl, J.; Simon, W. Tables of Spectral Data for Structure Determination of Organic Compounds, 2nd ed.; Springer-Verlag: Berlin, 1989.
Analytical Chemistry, Vol. 76, No. 16, August 15, 2004
4791
Table 1. Characterization of 1H NMR Signals for Reference Amino Acids and Organic Acids compound
position
alanine (Ala)
R-CH β-CH3 R-CH β-CH2 β-CH2 γ-CH2 δ-CH2 N-H R-CH β-CH2 β-CH2 R-CH β-CH2 β-CH2 R-CH β-CH2 β-CH2 R-CH β-CH2 γ-CH2 R-CH β-CH2 γ-CH2 R-CH2 R-CH β-CH2 β-CH2 Ar-H Ar-H R-CH β-CH γ-CH2 γ-CH2 δ-CH3 -CH3 R-CH β-CH2 β-CH2 γ-CH δ-CH3 R-CH β-CH2 γ-CH2 γ-CH2 δ-CH2 -CH2 R-CH β-CH2 β-CH2 γ-CH2 S-CH3
arginine (Arg)
asparagine (Asn) aspartic acid (Asp) cysteine (Cys) glutamic acid (Glu) glutamine (Gln) glycine (Gly) histidine (His)
isoleucine (Ile)
leucine (Leu)
lysine (Lys)
methionine (Met)
1H
δ (ppm) 3.94 1.52 3.90 1.69 1.75 1.95 3.25 7.22 4.19 2.95 3.01 4.20 3.05 3.07 4.13 3.08 3.15 3.98 2.21 2.62 3.95 2.19 2.50 3.76 4.11 3.37 3.39 7.41 8.68 3.90 2.03 1.31 1.50 0.95 1.03 3.94 1.76 1.81 1.73 0.96 3.89 1.73 1.48 1.53 1.94 3.03 4.06 2.17 2.26 2.68 2.13
m,a J (Hz)
charb
compound
position
δ 1H (ppm)
m,a J (Hz)
charb
q, 7.3 d, 7.3 t, 6.2 m m m m bs dd, 4.4; 7.0 dd, 7.0; 17.3 dd, 4.4; 17.3 bt, 5.5 dd, 6.4; 17.9 dd, 4.5; 17.9 bt, 4.7 dd, 3.7; 15.1 dd, 5.7; 15.1 t, 6.6 m m t, 6.2 m m s (13C 41.2)d t, 6.5 dd, 16.1; 6.7 dd, 16.1; 6.3 s (13C 117.9) s (13C 134.2) d, 3.6 m m m t, 7.5 d, 7.0 bt, 7.1 dd dd m bt t m m m m bt t, 6.3 m m t, 7.4 s (13C 13.9)
OL OBS OL POL POL POL POL POL POL POL OL POL POL POL POL POL POL OL POL POL OL POL POL OL POL POL POL OL OBS OL POL POL POL POL POL OL POL POL POL POL OL POL POL POL POL POL OL POL POL POL POL
phenylalanine (Phe)
R-CH β-CH2 β-CH2 Ar-H Ar-H Ar-H R-CH β-CH2 β-CH2 γ-CH2 δ-CH2 δ-CH2 R-CH β-CH2 R-CH β-CH γ-CH3 R-CH β-CH2 β-CH2 Ar-H Ar-H Ar-H Ar-H Ar-H N-H R-CH β-CH2 β-CH2 Ar-H Ar-H R-CH β-CH γ-CH3 γ-CH3 CH3 CH2 CH2 R-CH CH3 CH2 CH2 R-CH CH3 CH3 CH2
4.19 3.33 3.18 7.33 7.38 7.43 4.25 2.39 2.12 2.03 3.36 3.44 4.02c 4.02c 3.81 4.35 1.35 4.26 3.39 3.52 7.32 7.18 7.27 7.52 7.70 7.58 4.14 3.11 3.24 7.19 6.89 3.82 2.33 1.06 1.02 2.09 3.02 2.86 4.38 1.42 2.90 2.85 4.60 2.40 1.58 2.67
dd, 5.4; 7.9 dd, 14.6; 5.4 dd, 14.6; 7.9 d m m dd, 6.9; 8.6 m m m m m m m d, 4.2 m d, 6.5 dd, 5.0; 7.4 dd, 7.5; 15.5 dd, 4.9; 15.5 s (13C 125.0) bt, 7.3 bt, 7.3 bd, 8.3 bd, 8.3 bs dd, 5.3; 7.7 dd, 14.9; 7.7 dd, 14.9; 5.3 d, 8.5 d, 8.5 d, 4.4 m d, 7.1 d, 7.1 s (13C 20.2) d, 15.8 d, 15.8 q, 7.0 d, 7.0 dd, 16.5; 4.6 dd, 6.7; 16.5 dd, 6.7; 4.6 s (13C 26.1) s (13C 25.1) s (13C 28.8)
POL POL POL OBS OBS OBS POL POL POL POL POL OL OL OL OL POL POL POL OL OL POL POL POL OBS OBS NO OL OL OL POL OBS OL POL POL OBS OBS OBS POL POL OBS OBS POL OL OBS OBS OBS
proline (Pro)
serine (Ser) threonine (Thr) tryptophan (Trp)
tyrosine (Tyr)
valine (Val)
acetic acid citric acid lactic acid malic acid pyruvic acid -, hydrate succinic acid
a Multiplicity of signal: s, singlet; d, doublet; t, triplet; q, quartet; dd, doublet of doublets; m, multiplet; bs, broad singlet; bt, broad triplet. Characterization of signals after comparison with spectrum of beer. OBS, observable in beer; POL, partially overlapped by other signals; OL, overlapped; NO, not observed. c Signals overlapped. d Observed 13C chemical shifts (ppm).
b
The 1D 1H NMR spectra were recorded with the standard pulse sequence for presaturation (zgpr, pl9 at 60 dB) of the water signal at δ 4.86 ppm, and the spectral width was 10 ppm. Data were collected into 32k data points after 64 scans plus 4 dummy scans. The acquisition time was 2.7 s, and the relaxation delay was 1.0 s. 2D NMR spectra used for verification of identified components were run on one beer sample. The 2D NMR experiments included were correlation spectroscopy,14 total correlation spectroscopy,15 heteronuclear single quantum coherence,16 and heteronuclear multiple bond correlation.17 (14) Piantini, U.; Sørensen, O. W.; Ernst, R. R. J. Am. Chem. Soc. 1982, 104, 6800-6801.
4792 Analytical Chemistry, Vol. 76, No. 16, August 15, 2004
Processing of Spectra. FIDs were multiplied by an exponential window function (line-broadening factor of 2.0 Hz) before Fourier transformation in the XWINNMR software. The number of data points in the real part of the spectra was set to 32k. Spectra were manually phase corrected and calibrated by use of the signal from the trace CHD2COOD in CD3COOD at δ 2.062 ppm. The spectra were then transferred to the computer program Matlab (The MathWorks, Inc., Natick, MA, version 5.3) for further (15) Bax, A.; Davis, D. G. J. Magn. Reson. 1985, 65, 355-360. (16) Palmer, A. G.; Cavanagh, J.; Wright, P. E.; Rance, M. J. Magn. Reson. 1991, 93, 151-170. (17) Willker, W.; Leibfritz, D.; Kerssebaum, R.; Bermel, W. Magn. Reson. Chem. 1993, 31, 287-292.
Table 2. Chemical Shift Ranges Used for Integration and Results from the Quantifications of Amino Acids and Organic Acids in Beer by 1H NMR compound
δ int rangea (ppm)
Crefb (mg/L)
Aref/AIrefc
sloped
internal standard Ala His Phe Tyr Val acetic acid citric acid lactic acid malic acid pyruvic acid -, hydrate succinic acid
2.044-2.079 1.508-1.541 8.666-8.693 7.310-7.345 6.865-6.904 0.992-1.036 2.080-2.096 2.985-3.006 1.404-1.432 2.891-2.912 2.384-2.404 1.565-1.586 2.650-2.692
714 743 771 729 643 155 138 240 150 100e 100e 100
1.000 5.630 0.786 1.945 1.608 3.646 0.192 0.049 0.537 0.040 0.082 0.049 0.208
1.1 0.9 1.2 1.0 1.2 1.3 0.9 1.0 0.9 0.7 0.9 0.9
interceptd (mg/L)
R2 d
-1.4 0.0 2.8 -0.3 1.2 -15.5 14.7 23.0 -5.0 9.3 -4.6 4.0
0.98 0.97 0.99 0.98 0.95 0.89 0.85 0.96 0.64 0.87 0.86 0.94
a Chemical shift range for integration of signal. b Concentration of compound in reference spectrum for calibration. c Obtained area for reference compound divided by the area for the internal standard. d Results from comparison of quantification by NMR with quantifications by HPLC (amino acids) or capillary electrophoresis (organic acids). Slope, intercept and squared correlation coefficient (R2) from best fit of a regression line to the data. e Total concentration of pyruvic acid and its hydrate form.
processing, integration of signals, and chemometric analyses. The import of data in Matlab was done by reading Bruker formatted binary 1r files with a user-contributed routine. This yielded one 1 × 32756 vector with the intensity values and one 1 × 32756 vector with the corresponding ppm scale values for each spectrum. All spectra were given a common frequency scale by resampling of the data. This was achieved by interpolation by use of the Matlab function interp1 (with the option cubic), and simultaneously the number of variables was reduced to 500 data points/ppm. Baseline corrections were made in Matlab by use of the baseline function in the PLS_Toolbox for use with Matlab (Eigenvector Research, Inc., version 2.1), and then each spectrum was normalized by dividing intensity values of the data points by the largest value (peak height) for the CHD2COOD signal (δ 2.062 ppm) in that spectrum. Normalization using the peak height instead of the area of the CHD2COOD signal was chosen due to overlapping peaks in some samples that could influence the accuracy of the integrals. Signals related to water (δ 4.700-5.000 ppm) and ethanol (δ 1.120-1.240 and 3.600-3.700 ppm) were removed before the chemometric analyses by deleting the corresponding pairs of intensity/frequency values. Also, peripheral areas without peaks in any of the spectra (δ < 0.700, δ > 8.800) were removed, resulting in 3788 data points (variables) in the data set. Quantitative Analysis by Integration. For the identified components, selected signals were integrated in Matlab by manually picking start and stop positions in the spectra. This was done both in the reference spectra for amino and organic acids with known concentrations of the analytes and in the beer spectra. A straight line was drawn between the two points, and then the difference between the intensity values of the spectrum and the line were summed within the start and stop positions. This procedure is illustrated in Figure 2 for the internal standard and acetic acid signals. The area (A) for the compound to be quantified was then divided by the area for the internal standard (AI). In Table 2, the δ ranges for the integration and concentrations of the reference compounds (Cref) for the calibration are given together with the values obtained for Aref/AIref. The area of an analyte in beer (Abeer) together with the area of the internal standard in the beer spectrum (AIbeer) was then used to calculate
Figure 2. Illustration of areas used for integration of signals in 1H NMR spectra. AI, area for internal standard; A, area for analyte. The partly resolved peak for the internal standard is a 1:2:3:2:1 quintet due to the 1H-2H coupling.
the concentration (Canalyte) of each individual component in beer by the relationship Canalyte ) Cref(Abeer/AIbeer)/(Aref/AIref). Chemometric Methods. Mean centering of the data was used prior to analysis by chemometric methods. HCA and PCA were used for explorative data analyses. HCA classifies objects in clusters on the basis of interobject distances in high dimensional space.18 The result is shown in a dendrogram where similar objects are grouped together. The cluster function in the PLS_Toolbox was employed with the K-means nearest option on the 36 × 3788 matrix of 1H NMR spectra from beers in set 1. PCA19,20 is a data compression technique, and the method finds orthogonal principal components (PCs) that describe major trends (18) Brereton, R. G. Chemometrics: data analysis for the laboratory and chemical plant; Wiley & Sons: Chichester, U.K., 2003. (19) Jackson, J. E. A User’s Guide to Principal Components; Wiley & Sons: New York, 1991. (20) Geladi, P.; Kowalski, B. R. Anal. Chim. Acta 1986, 185, 1-17.
Analytical Chemistry, Vol. 76, No. 16, August 15, 2004
4793
Table 3. PLS Regression Models Used for Quantification of Amino Acids and Organic Acids in Beer by 1H NMR compound Ala Arg Asn Asp Cys Glu Gln Gly His Ile Leu Lys Met Phe Pro Ser Thr Tyr Val acetic acid citric acid lactic acid malic acid pyruvic acid -, hydrate succinic acid
δ range for PLS X dataa (ppm) 1.50-1.56 1.90-1.98, 3.24-3.25 2.92-2.96, 4.17-4.21 3.03-3.08, 4.18-4.22 3.13-3.17, 4.10-4.15 2.20-2.22, 2.61-2.63 2.18-2.20, 2.49-2.51 3.75-3.77 8.66-8.70 0.92-0.98, 1.02-1.04, 1.30-1.32, 1.49-1.51, 2.02-2.04 0.94-0.98, 1.70-1.82 1.47-1.49, 1.93-1.95 2.12-2.14 7.28-7.46 2.00-2.04, 2.01-2.14, 2.33-2.39, 3.32-3.38 4.00-4.05 1.33-1.36, 4.34-4.36 6.86-6.91, 7.15-7.21 1.00-1.07 2.08-2.10 2.83-2.88, 2.98-3.04 1.40-1.43, 4.36-4.40 2.84-2.85, 2.89-2.94 2.38-2.42 1.56-1.59 2.65-2.69
X varsb
LVsc
Y vard (%)
slopee
intercepte (mg/L)
R2, e
29 47 38 44 43 18 18 9 19
4 5 3 5 5 3 3 7 3
99.1 97.8 92.6 98.5 72.5 95.4 62.9 66.4 99.4
1.0 1.0 0.9 1.2 0.5 1.1 0.8 0.3 1.0
1.1 -2.4 0.6 1.1 1.0 2.0 3.8 8.6 1.7
0.98 0.98 0.86 0.82 0.18 0.81 0.41 0.13 0.99
65 79 18 9 90
4 4 4 2 3
99.0 99.6 88.3 67.5 99.2
1.1 1.0 1.0 0.5 1.0
-0.4 1.1 4.9 2.1 3.3
0.98 0.99 0.77 0.58 0.99
100 24 23 54 32 9 53 34 28 19 14 19
3 5 4 4 3 4 5 3 4 4 4 2
95.4 34.3 94.5 99.1 99.0 92.4 94.8 99.0 89.9 92.7 95.2 96.0
0.9 0.3 1.0 1.0 1.0 0.9 1.1 1.0 0.9 0.9 0.9 0.9
23.1 6.5 0.2 3.4 3.9 12.4 -14.9 3.1 3.2 11.9 6.4 6.0
0.92 0.13 0.79 0.99 0.97 0.92 0.87 0.99 0.89 0.89 0.91 0.92
a Chemical shift ranges for X data in the PLS regression. b Number of variables in the X data matrix. c Number of significant latent variables obtained by cross-validation. d Explained variance of Y in the PLS model. e Result after PLS prediction of analyte concentrations in the independent test set (40 samples). Comparison with quantification by HPLC (amino acids) or capillary electrophoresis (organic acids). Slope, intercept, and squared correlation coefficient (R2) from best fit of a regression line to the data.
in the data. The PCs are linear combinations of the variables in the original data and ordered by decreasing amount of variation described. Mathematically PCA decomposes the data matrix (X) of rank r to a sum of r rank 1 matrices. These rank 1 matrices are outer products of vectors called scores and loadings. The scores represents the objects (samples) in the new coordinate system defined by the PCs, and major trends or clusters in the data may be discovered by plotting the scores for the first few PCs against each other. The loadings describe how the PCs are obtained from the original variables, and the numerical loading value for a variable on a PC indicates how much the variable has in common with that PC. Hence, by examination of the loadings, it is possible to analyze which variables that are likely to be related to observed clusters in the score plot. PCA was done using the PLS_Toolbox. ANOVA21 was used to investigate whether there are significant differences between signals in the 1H NMR spectra of different beer brands. ANOVA is a univariate statistical technique for testing of the null hypothesis that two or more samples are drawn from the same population. The within-group variance is compared to the between-group variance to decide whether the means for a particular variable are significantly different between the groups. At the analysis, an F-test is done giving F-values for testing if the between- and the within-group variances are different at a probability level p. If the F-value is higher than the F-value given (21) Miller, J. C.; Miller, J. N. Statistics for analytical chemistry, 3rd ed.; Horwood: Chichester, U.K., 1993.
4794 Analytical Chemistry, Vol. 76, No. 16, August 15, 2004
for a certain p and degrees of freedom, this indicates that the null hypothesis can be rejected. ANOVA has previously been used on 1H NMR data, either on selected signals in the spectra 6,22 or on every individual data point in the spectra.23 The latter approach was used in this paper, and the anova1w routine in the PLS_Toolbox was modified to give F-values for each individual data point. PLS regression20,24 was used to obtain multivariate calibration models relating the NMR spectra (X) to the known analyte concentrations (Y). PLS is similar to PCA in that it decomposes the data matrix X. However, this decomposition is done with respect to Y in order to find a model with good predictive power. The components obtained from PLS are called latent variables (LVs), and the proper number of LVs was estimated by crossvalidation in this work. In PLS, weights (w) are obtained that explain the degree to which the variables in the X data contribute to the model. By examination of the weights from PLS it is thus possible to investigate what signals in the NMR spectra had highest impact on a PLS model. The set 1 beer samples were used for the PLS modeling, and the samples in sets 2 and 3 were used as a test set to assess the predictability of the PLS models. The part of the spectra used as X in the PLS modeling differed depending on analyte (Table 3). Only those areas of the NMR (22) Mannina, L.; Patumi, M.; Proietti, N.; Bassi, D.; Segre, A. L. J. Agric. Food Chem. 2001, 49, 2687-2696. (23) Le Gall, G.; Puaud, M.; Colquhoun, I. J. J. Agric. Food Chem. 2001, 49, 580-588. (24) Wold, S.; Ruhe, A.; Wold, H.; Dunn, W. J., III. SIAM J. Sci. Stat. Comput. 1984, 5, 735-743.
spectra with observable or partially overlapped signals from a specific analyte were included in order to minimize the amount of noncorrelated variance in X. PLS models for each analyte were also calculated using the whole spectra for comparison (Supporting Information Table S-2). PLS was done using the PLS_Toolbox. RESULTS AND DISCUSSION A representative 1H NMR spectrum of beer as shown in Figure 1 is dominated by the water (δ 4.86 ppm) and ethanol (δ 1.18 and 3.65 ppm) signals. The presaturation applied was mild and reduced the intensity of the water signal to approximately the same level as the intensities of the ethanol signals. It was not desirable to reduce these three intense signals further by use of other suppression techniques because of the risk of distortion of minor signals in the spectrum. The dominating nonvolatile compounds observed are carbohydrates8,25 resonating mainly in the δ 3.2-4.2 and 4.5-5.5 ppm regions. In the region δ 1.4-3.2 ppm, weaker signals are observed originating from, for example, organic acids.7 The aromatic region at δ 6.5-8.8 ppm shows many weaker signals originating mainly from aromatic amino acids, nucleic acid bases, and phenolic compounds.2 Broad underlying signals are seen in the aromatic region, indicating the presence of large molecular weight components with aromatic moieties. The 1H NMR signals for the reference compounds were assigned, and the chemical shifts are listed in Table 1. After comparison of each reference spectrum with a spectrum from beer, the assigned signals were characterized as observable (OBS), partially overlapped (POL), or overlapped (OL) in beer. OBS indicates that the signal from the compound is resolved in the 1H NMR spectrum from beer and that further examination of the 2D spectra verified the identity of the compound. POL denotes a signal located in a region in the beer spectrum where other signals of comparable intensities also are present, and OL means that the signal is covered by other intense signals. The reference spectrum for pyruvic acid was found to contain a signal at δ 1.58 ppm that was attributed to the methyl group of the hydrate form of pyruvic acid. The formation of the hydrate occurs readily in acidic aqueous solution.26 Interestingly, this signal was also present in spectra from beer and was therefore included as a candidate signal for quantification by NMR. The recycle time of 3.7 s used for 1H NMR was a compromise between the total acquisition time necessary to obtain good spectra and the T1 values of 1H nuclei in the sample. Observed T1 values (Supporting Information Table S-1) of between 0.7 and 4.8 s means that the magnetization is not at equilibrium during the experiment, but the actual error in the quantified concentrations are small as the same acquisition parameters have been used for all samples including the reference samples. That is, the error only stems from variations in T1 of the same signal between different samples, which are generally small. Explorative Analysis. The reproducibility of the NMR measurements can be tested from the replicates run for the beers in set 1. A cluster analysis gave a dendrogram (Figure 3) indicating good reproducibility, as all duplicates are close to each other. To investigate what signals correspond to the difference among the beers, one-way ANOVA was run on every variable in the spectra (25) Vinogradov, E.; Bock, K. Carbohydr. Res. 1998, 309, 57-64. (26) Damitio, J.; Smith, G.; Meany, J. E.; Pocker, Y. J. Am. Chem. Soc. 1992, 114, 3081-3087.
Figure 3. Dendrogram from HCA on 1H NMR spectra from duplicate analyses of 18 beer samples.
Figure 4. F from ANOVA calculations on every variable (data point) in the 1H NMR spectra. Some of the investigated metabolites are indicated.
from the beers in set 1. Thus, 3788 ANOVA calculations were made, and the outcome of these calculations are presented as F-values for each variable (Figure 4). Interestingly, variables corresponding to real NMR signals have the highest F-values and there is no correlation to the intensity of the signals in the NMR spectrum; i.e., signals from the carbohydrates get comparably small F-values. This is especially valid for signals in the region δ 3.2-4.2 ppm with many overlapping signals from both carbohydrates and other molecules. The discriminative ability of the carbohydrate region of the NMR spectrum for different beer samples is thus low for this set of lager beers. This suggests that the areas containing signals with high F-values (δ 1.4-3.2 and 6.8-8.8 ppm) are “fingerprint” areas of NMR beer spectra with the potential to differentiate between different lager beer, but this could be different if different beer types had been included in the same set. However, it is expected that beers of different types would be easier to distinguish. PCA was run on the 58 × 3788 matrix of 1H NMR spectra from the three beer sample sets. Three PCs explained 93% of the Analytical Chemistry, Vol. 76, No. 16, August 15, 2004
4795
Figure 6. Loadings from PCA on 1H NMR spectra from 58 beer samples: (a) PC 1; (b) PC 2; (c) PC 3. Some of the investigated metabolites are indicated.
Figure 5. Score plots from PCA on 1H NMR spectra from 58 beer samples. Lines join samples of the same beer brand: (a) PC 1 and PC 2; (b) PC 2 and PC 3.
variance in the data. The result is presented in score plots (Figure 5) where lines join the three samples from each beer brand. In the score plot for PC 1 and 2 (Figure 5a), the three samples are spread along the first PC. This spread is mainly due to differences in the intensities of signals from carbohydrates as shown by examination of the corresponding loadings for PC 1 (Figure 6a). PC 1 is thus associated with differences in the concentration of carbohydrates in different batches, errors introduced by the normalization procedure employed, or both. In the score plot for PC 2 and 3 (Figure 5b), samples from the same beer brand generally are situated close to each other in the score plot. The corresponding loadings for PC 2 and 3 (Figure 6b and c) show that the spread according to beer brand is caused by differences in the composition of the carbohydrates as well as in the concentration of amino acids and organic acids. PCA may therefore be useful for the monitoring of batches of beer and comparison of beer produced at different sites as several manufacturers produce beer that gives similar 1H NMR spectra from batch to batch. Quantification by Integration of Signals. Alanine, histidine, phenylalanine, tyrosine, and valine and acetic, citric, lactic, malic, 4796 Analytical Chemistry, Vol. 76, No. 16, August 15, 2004
pyruvic, and succinic acids were found to give signals in regions in the beer spectrum relatively free from other signals, and these acids were selected for quantification in beer by NMR. Integration of these signals and the use of reference spectra for calibration permitted the quantitative determination of the corresponding compounds in the 58 beer samples. The quantifications by NMR are compared with the quantifications obtained by HPLC (amino acids) and capillary electrophoresis (organic acids), and the results are presented as the slope, intercept, and squared correlation coefficient (R2) from the best fit of a regression line to the data for each analyte (Table 2, Figures 7 and 8). Generally very good correlations (R2 0.95-0.99) were obtained for the amino acids whereas for the organic acids the correlations ranged from R2 0.64 (malic acid) to 0.96 (lactic acid). The poor correlation for malic acid can be explained by the fact that the signal used for integration is rather weak and split into a doublet of doublets as well as due to baseline artifacts. The two forms of pyruvic acid (ketone and hydrate) gave similar correlations (R2 0.87 and 0.86). However, it was found that the ratio of concentrations obtained from the quantifications of the ketone and hydrate forms of pyruvic acid by NMR varied from 0.74 to 1.40, which indicates that the ketone/hydrate ratio is not constant at the experimental conditions. This would lower the accuracy in the quantifications pyruvic acid by NMR. Quantification by integration of signals relies on correct start and stop positions for the integration. When there are small shifts in chemical shift for a signal between spectra, due to, for example, pH variations, there may be large integration errors if the same
Figure 7. Comparison of quantification of amino acids by integration of NMR signals with quantification by HPLC: (a) alanine, (b) histidine, (c) phenylalanine, (d) tyrosine, and (e) valine. The solid line represents the best fit to the data.
Figure 8. Comparison of quantification of organic acids by integration of NMR signals with quantification by capillary electrophoresis: (a) acetic acid, (b) citric acid, (c) lactic acid, (d) malic acid, (e) pyruvic acid, and (f) succinic acid. The solid line represents the best fit to the data.
integration range is used. Errors can also be introduced for partially overlapped signals. It was found that the best result was obtained when each signal was manually integrated in each individual spectrum. Quantification by PLS. Separate PLS models were calculated, and unique regions in the 1H NMR spectra from the beer samples in set 1 were selected for every investigated analyte, resulting in different X-data matrices (Table 3). The selection of the regions was based on the assignment and characterization of 1H NMR signals (Table 1). Only observable or partially overlapped signals from a specific analyte were included in order to reduce the amount of noncorrelated data in X. The PLS models are summarized in Table 3, and the number of latent variables used varied between 2 and 7. The percent explained variance of the Y-data in the PLS models varied between 34.3 (serine) and 99.6 (lysine). The predictability of the PLS models was assessed by use of the beer samples in sets 2 and 3 as a test set. The PLS models for
respective analytes were thus applied on the data in the test set, and the predicted concentrations were compared with the quantifications obtained by HPLC (amino acids) and capillary electrophoresis (organic acids). Results are presented as the slope, intercept, and squared correlation coefficient (R2) from the best fit of a regression line to the data for each analyte (Table 3). Highquality PLS predictions with R2 > 0.95 were obtained for Ala, Arg, His, Ile, Leu, Phe, Tyr, Val, and lactic acid with a slope of 1.0 (except Ile, slope 1.1). The PLS models for Asn, Asp, Glu, and Pro and acetic, citric, malic, pyruvic, and succinic acid all gave acceptable predictions (0.80 < R2 < 0.95 and slope ∼1.0). For Cys, Gln, Gly, Lys, Met, Ser, and Thr, less useful PLS models were obtained (R2 < 0.80), and this was partly due to the fact that the majority of the beer samples had concentration levels of these amino acids lower than 10 mg/L. Also, for Gly, the only useful 1H NMR signal at δ 3.76 ppm is severely overlapped, which is reflected in the corresponding PLS model (Table 3). Analytical Chemistry, Vol. 76, No. 16, August 15, 2004
4797
Figure 9. (a) Weights for LV 1 in the PLS model for Phe. (b) Part of the phenylalanine reference 1H NMR spectrum. (c) Part of the 1H NMR spectrum of beer (Grøn Tuborg, Carlsberg Breweries).
Figure 9a shows the weights for LV 1 (96.0% Y variance explained) from the PLS model for phenylalanine that was based on spectral data between δ 7.28 and 7.46 ppm (Table 3). High weight values are observed for signals from phenylalanine as can be seen by comparison with the corresponding part of the phenylalanine reference spectrum (Figure 9b). The similarity between weights and the reference spectrum indicates that the PLS model is valid and based on signals associated with phenylalanine. The corresponding part of the spectrum of a beer (Figure 9c) also shows a similar pattern. The extra two LVs (LVs 2 and 3) needed in the PLS model for Phe explain 2.6 and 0.5% Y variance, respectively, and are associated with small chemical shift variations between spectra. This is indicated by a dispersive profile in the loadings for LVs 2 and 3 (not shown) and are generally noted in the PLS models obtained. For comparison, PLS models were also calculated for each analyte using the whole 1H NMR spectra (Supporting Information Table S-2). Generally, the predictive ability was lower and a higher number of latent variables were needed than compared with the corresponding models based on selected regions of the spectra.
4798 Analytical Chemistry, Vol. 76, No. 16, August 15, 2004
CONCLUSION The described NMR-based methods could satisfactory be used for quantification of several of the investigated metabolites in beer. The speed and accuracy of NMR makes it an attractive tool for quantification but also for explorative analyses and monitoring. The concentration limit for quantification of amino acids in beer by the described NMR-based method is estimated to be between 5 and 10 mg/L. However, the detection limit could probably be decreased by at least a factor of 10 by use of higher field strength, cryoprobe technology, or larger number of scans. For organic acids, levels down to ∼30 mg/mL were found in beer and the concentration limit for quantification by NMR is probably somewhat lower. The five-month difference in date of analysis between the beer samples in set 1 and set 3 indicates that the NMR methods for quantification are robust. The PLS models gave predictions with equal or higher R2 values (Table 3) than from the corresponding quantifications by the integration method (Table 2). An advantage of the PLS method over the integration method is that no calibration reference is needed. However, a set of representative samples is needed for the PLS modeling. The main drawback with the integration method is the sensitivity toward overlapping signals. Hence, it was necessary to manually control that the limits for integration were set correctly. This problem is less accentuated for the PLS method, which could thus successfully be used for the quantification of several components with partially overlapped signals in the spectrum. The PLS method has therefore a larger potential for automatic quantification of metabolites than the method based on integration. The use of NMR spectroscopy in combination with PLS regression is an interesting tool for the quantification of metabolites, not only in beer but also in other beverages and biofluids. ACKNOWLEDGMENT We thank Lone Sørensen for the quantification of amino acids by HPLC, Jack Olsen for the quantification of organic acids by capillary electrophoresis, and finally Nils Nyberg for sharing m-files for import of NMR spectra into Matlab. SUPPORTING INFORMATION AVAILABLE Table S-1 gives observed T1 values for OBS signals in a beer sample and Table S-2 summarizes PLS models for each analyte using the whole 1H NMR spectra. This material is available free of charge via the Internet at http://pubs.acs.org.
Received for review February 27, 2004. Accepted May 19, 2004. AC0496852
