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Rapid classification of White Oak (Quercus alba) and Northern Red Oak (Quercus rubra) by using pyrolysis direct analysis in real time (DART™) and time-of-flight mass spectrometry
Journal of Analytical and Applied Pyrolysis 95 (2012) 134–137
Contents lists available at SciVerse ScienceDirect
Journal of Analytical and Applied Pyrolysis journal homepage: www.elsevier.com/locate/jaap
Rapid classification of White Oak (Quercus alba) and Northern Red Oak (Quercus rubra) by using pyrolysis direct analysis in real time (DARTTM ) and time-of-flight mass spectrometry Robert B. Cody a,∗ , A. John Dane a , Benjamin Dawson-Andoh b , Emmanuel Oluwatosin Adedipe c , Kofi Nkansah b a
JEOL USA, Inc., Peabody, MA 01960, United States Division of Forestry & Natural Resources, West Virginia University, Morgantown, WV 26506, United States c Department of Soil and Crop Sciences, Colorado State University, Ft. Collins, CO 80523, United States b
a r t i c l e
i n f o
Article history: Received 26 October 2011 Accepted 27 January 2012 Available online 5 February 2012 Keywords: Ambient ionization Direct analysis in real time Time-of-flight Red oak White oak
a b s t r a c t Thirty-four samples of Red Oak (Quercus rubra) and fifty samples of White Oak (Quercus alba) were analyzed by pyrolytic direct analysis in real time (DART) ionization coupled with time-of-flight (TOF) mass spectrometry. Although significant differences were not observed in the positive-ion mass spectra, the negative-ion mass spectra showed clear differences. Principal component analysis (PCA) and linear discriminant analysis (LDA) were calculated for the relative abundances of 11 peaks in the negativeion mass spectra including peaks tentatively assigned as representing deprotonated acetic, malic, gallic, dimethoxycinnamic, and ellagic acids. Leave one out cross validation (LOOCV) was 100% successful in classifying the samples for both PCA and LDA. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Northern Red (Quercus rubra) and White Oak (Quercus alba) are two important commercial hardwood species with extensive use in the Railway Ties and furniture industries. In the Railway Ties Industry, they are primarily pressure-treated with oil-borne wood preservatives, notably creosote and copper naphthenate. These two hardwoods respond very differently to pressure-treatment. While Northern Red Oak pressure-treats easily, White Oak does not. Therefore, it is important to separate these wood species because they require different pressure-treatment protocols. White Oak is slightly harder and more resistant to rot than Red Oak, and is thus better suited for furniture intended for outdoor use, whereas untreated Red Oak is recommended for indoor use only. Northern Red and White Oak lumber are difficult to separate because they have similar physical properties. Despite these similarities, White Oak can be distinguished anatomically from Northern Red Oak due to the presence of plastic-like structures called tyloses in their pores [1]. The presence of these tyloses gives White Oak the “water tight” properties that allow their use in the
∗ Corresponding author. Tel.: +1 978 535 5900. E-mail address: [email protected] (R.B. Cody). 0165-2370/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jaap.2012.01.018
cooperage industry. However, a trained anatomist is generally necessary to visually identify their presence in the pores. Several additional methods have been developed in an attempt to improve the process of differentiating these two hardwood species [1]. One such method involves blowing bubbles over the wood, which is difficult to apply in practice. Colorimetric methods have also been evaluated and generally include the application of nitrite solution to the wood. During this test, the White Oak wood turns black. However, this method relies on a subjective judgment about the degree of color change. Overall, both of these physical and chemical methods are not practical and tend to be time consuming. Thus, in practice, separation of these two wood species typically involves the service of a trained wood anatomist. Recently, in an effort to expand wood analysis into laboratory analytical instrumentation through the use of spectroscopy, Adedipe and Dawson-Andoh (2008) used Near Infrared (NIR) spectroscopy coupled with multivariate data analysis (MVDA) to classify and separate White and Northern Red Oak lumber [2]. Mass spectrometry (MS) offers an alternative laboratory approach. MS is a powerful analytical tool that has found application in a wide range of industries because it provides a wealth of chemical information about the components found within a given sample. However, ion sources used in traditional MS systems are operated under vacuum, placing severe constraints on sample introduction. As a result, time-consuming sample
R.B. Cody et al. / Journal of Analytical and Applied Pyrolysis 95 (2012) 134–137
135
163.077
100
193.087
Red Oak 249.113
115.042 97.028
61.030
97.026
131.051 209.080
163.074
50
100
249.110
White Oak
193.083
150
200
250
300
350
400
450
500
m/z Fig. 1. Comparison of averaged positive-ion DART mass spectra for Red Oak and White Oak.
preparation is often required prior to analysis. Consequently, this was generally considered to be a major limitation for this analytical technique. Within the last decade, MS has undergone a very rapid advancement in the area of “ambient” or “open source” atmospheric ionization [3]. One of the earliest and simplest of these new techniques was direct analysis in real time (DARTTM ) which offers a truly open atmospheric pressure ionization source [4]. The user can directly place the sample (liquids, solids or gases) into the ionization region under ambient conditions and, within just a few seconds, see a response for the analytes. Moreover, the samples require minimal to no sample preparation prior to MS analysis. The principles underlying this technique are provided in detail elsewhere [4,5] and will not be described further here. DART ionization has been successfully applied to a wide range of analytical problems [5] including the analysis of hardwood chemithermomechanical pulp (printing and writing papers) [6] and has been combined with chemometric methods for the classification of bacteria [7] and beer [8,9]. The objective of this study was to evaluate the feasibility of separating White and Northern Red Oak lumber by using DART-MS. 2. Experimental 2.1. Material The wood samples consisted of 50 blocks of White Oak and 34 blocks of Northern Red Oak that were previously identified by classical anatomical methods.
temperature of 500 ◦ C. The mass spectrometer resolving power was 6000 (FWHM) measured at m/z 609 for protonated reserpine. The mass spectrometer operating parameters in positive ion mode were: orifice 1 = +20 V, orifice 2 and ring lens = +5 V, RF ion guide = +500 V, ion multiplier = +2400 V. Voltage polarities were reversed for operation in negative-ion mode. The DART-SVP was operated in positive-ion mode with the exit grid at +250 V and in negative-ion mode with the exit grid operated at −530 V. The DART-SVP gas flow and discharge needle voltage were operated at their factory-preset values. Small samples (approximately 1 mm wide × 3 mm long) were cut from each wood block and positioned in the DART gas stream with forceps for several seconds. Positive-ion and negative-ion mass spectra were obtained for samples from each wood block over the m/z range 50–1100 at a spectral acquisition rate of 1 spectrum per second. Mass spectra of polyethylene glycol (“PEG”, average molecular weight 600) (Aldrich, St. Louis, MO) placed on a melting point tube were measured between each sample to serve as a mass calibration reference standard and to act as dividing markers between each wood sample. Mass-calibrated and centroided mass spectra were exported from the data processing software (TSSPro3, Shrader Analytical Labs, Detroit, MI) as text files for entry into the elemental composition and classification software (Mass Spec Tools II, RBC Software, Peabody, MA). Principal components were calculated by using the correlation matrix. Abundances used for classification were selected from each mass spectrum for eleven peaks having m/z values within 0.005 u of the target m/z. 3. Results and discussion
2.2. Methods Mass spectra were obtained by using a JEOL AccuTOF-DART-SVP mass spectrometer (Peabody, MA) operated with helium DART gas under conditions that favored pyrolysis with a gas heater
Typical positive-ion DART mass spectra for Red Oak and White Oak are shown in Fig. 1, and the corresponding negative-ion mass spectra are shown in Fig. 2. The positive-ion mass spectra show several ions with exact masses that are consistent with species
169.012
100
Red Oak
113.025
193.029
85.026
59.015
85.030
133.012
275.018 261.001 177.053
300.997
207.060
White Oak
113.024
50
100
150
200
250
300
350
400
450
m/z Fig. 2. Comparison of averaged negative-ion DART mass spectra for Red Oak and White Oak.
500
136
R.B. Cody et al. / Journal of Analytical and Applied Pyrolysis 95 (2012) 134–137
Rel. Intens.
Table 1 Measured m/z values for negative ions used for classification.
Red Oak
100
50
0 50
100
150
200
250
300
350
400
Rel. Intens.
100
White Oak −H
50
0 50
100
150
200
250
300
350
400
m/z Fig. 3. Tentative assignments for major peaks in the negative-ion mass spectra for Red Oak and White Oak.
m/z
[M−H]− Composition
Assignment
59.0150 85.0312 113.0243 133.0156 139.0041 169.0157 207.0637 247.0394 261.0102 285.0549 300.9947
C2 H3 O2 C4 H6 O2 C5 H6 O3 C4 H5 O5 C6 H4 O4 C7 H5 O5 C11 H11 O4
Acetic acid
Malic acid Gallic acid Dimethoxycinnamic acid
a
C12 H6 O7 a
C14 H5 O8
Ellagic acid
Note: Absolute identifications are difficult to assign based solely on exact-mass data for unknown peaks measured under pyrolysis conditions. Even for the labeled assignments, several isomeric structures are possible for each composition and an unequivocal assignment cannot be made on the basis of elemental composition alone. a Unidentified.
Principal Component Analysis
Red Oak White Oak
5 4 3
PC2
2 1 0 -4
-2
0
2
4
-1 -2 -3 PC1 Fig. 4. First and second principal components for Red Oak and White Oak. (For interpretation of the references to color in the text, the reader is referred to the web version of the article.)
Linear Discriminant Analysis 20
10
0 -50
-40
-30
-20
-10
0
10
20
30
-10
Y
previously identified by Adams [6]. However, the averaged positive-ion mass spectra for Red Oak and White Oak are very similar. The differences between species are clearly more pronounced for the negative-ion mass spectra. Therefore, the negative-ion data were used for classification. Tentative assignments for major peaks in the negative-ion mass spectra are given in Fig. 3. These assignments are based on the elemental compositions for each peak as determined using the Mass Spec Tools II software from exact masses and abundances for the measured isotopic peaks. The most common base peak in the Red Oak mass spectra is assigned as gallic acid with other major peaks being assigned as malic acid and C5 H5 O3 − White Oak shows a more complex spectrum with the base peak typically being assigned as C5 H5 O3 − . Other component assignments in the White Oak spectrum include acetic, malic, gallic, dimethoxycinnamic and ellagic acids. Although the averaged negative-ion mass spectra in Fig. 2 show clear differences between species, there was significant variation between individual wood samples. Neither visual inspection nor computer-assisted spectral matching was satisfactory for unambiguous classification of each sample. Therefore, principal component analysis was carried out selecting the relative abundances for several characteristic m/z values. Relative abundances were extracted from each mass spectrum in the training set for measured m/z values within an error tolerance of 0.005 u of the target m/z. These abundances comprised the feature set used for principal component analysis. The list of selected m/z values is given in Table 1. The first two principal components obtained by using the correlation matrix account for only 56.74% of the variance. Nevertheless, a clear separation between Red Oak (red squares) and White Oak (blue triangles) is evident in Fig. 4. Linear discriminant analysis gave similar results, but with an even wider separation (Fig. 5). Leave one out cross-validation (LOOCV) on the training set gave 100% classification accuracy for both methods. Classification was based on the distance of each unknown from the cluster mean. An alternative validation was carried out by omitting 10 randomly selected samples of Red Oak and 10 samples of White Oak from the training set. These 20 samples were then treated by the linear discriminant analysis program as unknowns for classification. All 20 “unknown” samples were correctly classified. It should be noted that high resolving power and the choice of the m/z error tolerance is very important. Too small a value resulted in missing some peaks and too large a value caused incorrect classifications due to isobaric interferences. Interferences could be problematic if a low-resolution mass spectrometer were
-20
-30
Red oak White oak
-40
-50
X Fig. 5. Linear discriminant analysis applied to the Red Oak and White Oak data.
R.B. Cody et al. / Journal of Analytical and Applied Pyrolysis 95 (2012) 134–137
to be used for the analysis. The value of 0.005 u chosen here gave good results for the TOF data reported herein. 4. Conclusions Negative-ion DART mass spectra obtained under pyrolysis conditions showed significant differences between Red Oak and White Oak. Principal component analysis and linear discriminant analysis based on the abundances of 11 selected peaks in the mass spectra were successful in classifying wood from each oak species. The method only requires a few seconds per sample and only tiny quantities of wood are consumed for the analysis. References [1] E. Meier, Distinguishing Red and White Oak. The Wood Database. By Woodworkers, For Woodworkers. Available from: http://www.wood-database.com/woodarticles/distinguishing-red-oak-from-white-oak/.
137
[2] O.E. Adedipe, et al., Classification of red oak (Quercus rubra) and white oak (Quercus alba) wood using a near infrared spectrometer and soft independent modelling of class analogies, Journal of Near Infrared Spectroscopy 16 (1) (2008) 49–57. [3] G.A. Harris, A.S. Galhena, F.M. Fernandez, Ambient sampling/ionization mass spectrometry: applications and current trends, Analytical Chemistry 83 (12) (2011) 4508–4538. [4] R.B. Cody, J.A. Laramee, H.D. Durst, Versatile new ion source for the analysis of materials in open air under ambient conditions, Anal. Chem. 77 (8) (2005) 2297–2302. [5] R.B. Cody, A.J. Dane, Direct analysis in real time ion source, in: R.A. Meyers (Ed.), Encyclopedia of Analytical Chemistry, John Wiley & Sons, Ltd, 2010 (published online: December 15). [6] J. Adams, Analysis of printing and writing papers by using direct analysis in real time mass spectrometry, International Journal of Mass Spectrometry 301 (1-3) (2011) 109–126. [7] C.Y. Pierce, et al., Ambient generation of fatty acid methyl ester ions from bacterial whole cells by direct analysis in real time (DART) mass spectrometry, Chem. Commun. 8 (2007) 807–809. [8] T. Cajka, et al., Recognition of beer brand based on multivariate analysis of volatile fingerprint, Journal of Chromatography A 1217 (25) (2010) 4195–4203. [9] T. Cajka, et al., Ambient mass spectrometry employing a DART ion source for metabolomic fingerprinting/profiling: a powerful tool for beer origin recognition, Metabolomics (2011) 1–9.
Contents lists available at SciVerse ScienceDirect
Journal of Analytical and Applied Pyrolysis journal homepage: www.elsevier.com/locate/jaap
Rapid classification of White Oak (Quercus alba) and Northern Red Oak (Quercus rubra) by using pyrolysis direct analysis in real time (DARTTM ) and time-of-flight mass spectrometry Robert B. Cody a,∗ , A. John Dane a , Benjamin Dawson-Andoh b , Emmanuel Oluwatosin Adedipe c , Kofi Nkansah b a
JEOL USA, Inc., Peabody, MA 01960, United States Division of Forestry & Natural Resources, West Virginia University, Morgantown, WV 26506, United States c Department of Soil and Crop Sciences, Colorado State University, Ft. Collins, CO 80523, United States b
a r t i c l e
i n f o
Article history: Received 26 October 2011 Accepted 27 January 2012 Available online 5 February 2012 Keywords: Ambient ionization Direct analysis in real time Time-of-flight Red oak White oak
a b s t r a c t Thirty-four samples of Red Oak (Quercus rubra) and fifty samples of White Oak (Quercus alba) were analyzed by pyrolytic direct analysis in real time (DART) ionization coupled with time-of-flight (TOF) mass spectrometry. Although significant differences were not observed in the positive-ion mass spectra, the negative-ion mass spectra showed clear differences. Principal component analysis (PCA) and linear discriminant analysis (LDA) were calculated for the relative abundances of 11 peaks in the negativeion mass spectra including peaks tentatively assigned as representing deprotonated acetic, malic, gallic, dimethoxycinnamic, and ellagic acids. Leave one out cross validation (LOOCV) was 100% successful in classifying the samples for both PCA and LDA. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Northern Red (Quercus rubra) and White Oak (Quercus alba) are two important commercial hardwood species with extensive use in the Railway Ties and furniture industries. In the Railway Ties Industry, they are primarily pressure-treated with oil-borne wood preservatives, notably creosote and copper naphthenate. These two hardwoods respond very differently to pressure-treatment. While Northern Red Oak pressure-treats easily, White Oak does not. Therefore, it is important to separate these wood species because they require different pressure-treatment protocols. White Oak is slightly harder and more resistant to rot than Red Oak, and is thus better suited for furniture intended for outdoor use, whereas untreated Red Oak is recommended for indoor use only. Northern Red and White Oak lumber are difficult to separate because they have similar physical properties. Despite these similarities, White Oak can be distinguished anatomically from Northern Red Oak due to the presence of plastic-like structures called tyloses in their pores [1]. The presence of these tyloses gives White Oak the “water tight” properties that allow their use in the
∗ Corresponding author. Tel.: +1 978 535 5900. E-mail address: [email protected] (R.B. Cody). 0165-2370/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jaap.2012.01.018
cooperage industry. However, a trained anatomist is generally necessary to visually identify their presence in the pores. Several additional methods have been developed in an attempt to improve the process of differentiating these two hardwood species [1]. One such method involves blowing bubbles over the wood, which is difficult to apply in practice. Colorimetric methods have also been evaluated and generally include the application of nitrite solution to the wood. During this test, the White Oak wood turns black. However, this method relies on a subjective judgment about the degree of color change. Overall, both of these physical and chemical methods are not practical and tend to be time consuming. Thus, in practice, separation of these two wood species typically involves the service of a trained wood anatomist. Recently, in an effort to expand wood analysis into laboratory analytical instrumentation through the use of spectroscopy, Adedipe and Dawson-Andoh (2008) used Near Infrared (NIR) spectroscopy coupled with multivariate data analysis (MVDA) to classify and separate White and Northern Red Oak lumber [2]. Mass spectrometry (MS) offers an alternative laboratory approach. MS is a powerful analytical tool that has found application in a wide range of industries because it provides a wealth of chemical information about the components found within a given sample. However, ion sources used in traditional MS systems are operated under vacuum, placing severe constraints on sample introduction. As a result, time-consuming sample
R.B. Cody et al. / Journal of Analytical and Applied Pyrolysis 95 (2012) 134–137
135
163.077
100
193.087
Red Oak 249.113
115.042 97.028
61.030
97.026
131.051 209.080
163.074
50
100
249.110
White Oak
193.083
150
200
250
300
350
400
450
500
m/z Fig. 1. Comparison of averaged positive-ion DART mass spectra for Red Oak and White Oak.
preparation is often required prior to analysis. Consequently, this was generally considered to be a major limitation for this analytical technique. Within the last decade, MS has undergone a very rapid advancement in the area of “ambient” or “open source” atmospheric ionization [3]. One of the earliest and simplest of these new techniques was direct analysis in real time (DARTTM ) which offers a truly open atmospheric pressure ionization source [4]. The user can directly place the sample (liquids, solids or gases) into the ionization region under ambient conditions and, within just a few seconds, see a response for the analytes. Moreover, the samples require minimal to no sample preparation prior to MS analysis. The principles underlying this technique are provided in detail elsewhere [4,5] and will not be described further here. DART ionization has been successfully applied to a wide range of analytical problems [5] including the analysis of hardwood chemithermomechanical pulp (printing and writing papers) [6] and has been combined with chemometric methods for the classification of bacteria [7] and beer [8,9]. The objective of this study was to evaluate the feasibility of separating White and Northern Red Oak lumber by using DART-MS. 2. Experimental 2.1. Material The wood samples consisted of 50 blocks of White Oak and 34 blocks of Northern Red Oak that were previously identified by classical anatomical methods.
temperature of 500 ◦ C. The mass spectrometer resolving power was 6000 (FWHM) measured at m/z 609 for protonated reserpine. The mass spectrometer operating parameters in positive ion mode were: orifice 1 = +20 V, orifice 2 and ring lens = +5 V, RF ion guide = +500 V, ion multiplier = +2400 V. Voltage polarities were reversed for operation in negative-ion mode. The DART-SVP was operated in positive-ion mode with the exit grid at +250 V and in negative-ion mode with the exit grid operated at −530 V. The DART-SVP gas flow and discharge needle voltage were operated at their factory-preset values. Small samples (approximately 1 mm wide × 3 mm long) were cut from each wood block and positioned in the DART gas stream with forceps for several seconds. Positive-ion and negative-ion mass spectra were obtained for samples from each wood block over the m/z range 50–1100 at a spectral acquisition rate of 1 spectrum per second. Mass spectra of polyethylene glycol (“PEG”, average molecular weight 600) (Aldrich, St. Louis, MO) placed on a melting point tube were measured between each sample to serve as a mass calibration reference standard and to act as dividing markers between each wood sample. Mass-calibrated and centroided mass spectra were exported from the data processing software (TSSPro3, Shrader Analytical Labs, Detroit, MI) as text files for entry into the elemental composition and classification software (Mass Spec Tools II, RBC Software, Peabody, MA). Principal components were calculated by using the correlation matrix. Abundances used for classification were selected from each mass spectrum for eleven peaks having m/z values within 0.005 u of the target m/z. 3. Results and discussion
2.2. Methods Mass spectra were obtained by using a JEOL AccuTOF-DART-SVP mass spectrometer (Peabody, MA) operated with helium DART gas under conditions that favored pyrolysis with a gas heater
Typical positive-ion DART mass spectra for Red Oak and White Oak are shown in Fig. 1, and the corresponding negative-ion mass spectra are shown in Fig. 2. The positive-ion mass spectra show several ions with exact masses that are consistent with species
169.012
100
Red Oak
113.025
193.029
85.026
59.015
85.030
133.012
275.018 261.001 177.053
300.997
207.060
White Oak
113.024
50
100
150
200
250
300
350
400
450
m/z Fig. 2. Comparison of averaged negative-ion DART mass spectra for Red Oak and White Oak.
500
136
R.B. Cody et al. / Journal of Analytical and Applied Pyrolysis 95 (2012) 134–137
Rel. Intens.
Table 1 Measured m/z values for negative ions used for classification.
Red Oak
100
50
0 50
100
150
200
250
300
350
400
Rel. Intens.
100
White Oak −H
50
0 50
100
150
200
250
300
350
400
m/z Fig. 3. Tentative assignments for major peaks in the negative-ion mass spectra for Red Oak and White Oak.
m/z
[M−H]− Composition
Assignment
59.0150 85.0312 113.0243 133.0156 139.0041 169.0157 207.0637 247.0394 261.0102 285.0549 300.9947
C2 H3 O2 C4 H6 O2 C5 H6 O3 C4 H5 O5 C6 H4 O4 C7 H5 O5 C11 H11 O4
Acetic acid
Malic acid Gallic acid Dimethoxycinnamic acid
a
C12 H6 O7 a
C14 H5 O8
Ellagic acid
Note: Absolute identifications are difficult to assign based solely on exact-mass data for unknown peaks measured under pyrolysis conditions. Even for the labeled assignments, several isomeric structures are possible for each composition and an unequivocal assignment cannot be made on the basis of elemental composition alone. a Unidentified.
Principal Component Analysis
Red Oak White Oak
5 4 3
PC2
2 1 0 -4
-2
0
2
4
-1 -2 -3 PC1 Fig. 4. First and second principal components for Red Oak and White Oak. (For interpretation of the references to color in the text, the reader is referred to the web version of the article.)
Linear Discriminant Analysis 20
10
0 -50
-40
-30
-20
-10
0
10
20
30
-10
Y
previously identified by Adams [6]. However, the averaged positive-ion mass spectra for Red Oak and White Oak are very similar. The differences between species are clearly more pronounced for the negative-ion mass spectra. Therefore, the negative-ion data were used for classification. Tentative assignments for major peaks in the negative-ion mass spectra are given in Fig. 3. These assignments are based on the elemental compositions for each peak as determined using the Mass Spec Tools II software from exact masses and abundances for the measured isotopic peaks. The most common base peak in the Red Oak mass spectra is assigned as gallic acid with other major peaks being assigned as malic acid and C5 H5 O3 − White Oak shows a more complex spectrum with the base peak typically being assigned as C5 H5 O3 − . Other component assignments in the White Oak spectrum include acetic, malic, gallic, dimethoxycinnamic and ellagic acids. Although the averaged negative-ion mass spectra in Fig. 2 show clear differences between species, there was significant variation between individual wood samples. Neither visual inspection nor computer-assisted spectral matching was satisfactory for unambiguous classification of each sample. Therefore, principal component analysis was carried out selecting the relative abundances for several characteristic m/z values. Relative abundances were extracted from each mass spectrum in the training set for measured m/z values within an error tolerance of 0.005 u of the target m/z. These abundances comprised the feature set used for principal component analysis. The list of selected m/z values is given in Table 1. The first two principal components obtained by using the correlation matrix account for only 56.74% of the variance. Nevertheless, a clear separation between Red Oak (red squares) and White Oak (blue triangles) is evident in Fig. 4. Linear discriminant analysis gave similar results, but with an even wider separation (Fig. 5). Leave one out cross-validation (LOOCV) on the training set gave 100% classification accuracy for both methods. Classification was based on the distance of each unknown from the cluster mean. An alternative validation was carried out by omitting 10 randomly selected samples of Red Oak and 10 samples of White Oak from the training set. These 20 samples were then treated by the linear discriminant analysis program as unknowns for classification. All 20 “unknown” samples were correctly classified. It should be noted that high resolving power and the choice of the m/z error tolerance is very important. Too small a value resulted in missing some peaks and too large a value caused incorrect classifications due to isobaric interferences. Interferences could be problematic if a low-resolution mass spectrometer were
-20
-30
Red oak White oak
-40
-50
X Fig. 5. Linear discriminant analysis applied to the Red Oak and White Oak data.
R.B. Cody et al. / Journal of Analytical and Applied Pyrolysis 95 (2012) 134–137
to be used for the analysis. The value of 0.005 u chosen here gave good results for the TOF data reported herein. 4. Conclusions Negative-ion DART mass spectra obtained under pyrolysis conditions showed significant differences between Red Oak and White Oak. Principal component analysis and linear discriminant analysis based on the abundances of 11 selected peaks in the mass spectra were successful in classifying wood from each oak species. The method only requires a few seconds per sample and only tiny quantities of wood are consumed for the analysis. References [1] E. Meier, Distinguishing Red and White Oak. The Wood Database. By Woodworkers, For Woodworkers. Available from: http://www.wood-database.com/woodarticles/distinguishing-red-oak-from-white-oak/.
137
[2] O.E. Adedipe, et al., Classification of red oak (Quercus rubra) and white oak (Quercus alba) wood using a near infrared spectrometer and soft independent modelling of class analogies, Journal of Near Infrared Spectroscopy 16 (1) (2008) 49–57. [3] G.A. Harris, A.S. Galhena, F.M. Fernandez, Ambient sampling/ionization mass spectrometry: applications and current trends, Analytical Chemistry 83 (12) (2011) 4508–4538. [4] R.B. Cody, J.A. Laramee, H.D. Durst, Versatile new ion source for the analysis of materials in open air under ambient conditions, Anal. Chem. 77 (8) (2005) 2297–2302. [5] R.B. Cody, A.J. Dane, Direct analysis in real time ion source, in: R.A. Meyers (Ed.), Encyclopedia of Analytical Chemistry, John Wiley & Sons, Ltd, 2010 (published online: December 15). [6] J. Adams, Analysis of printing and writing papers by using direct analysis in real time mass spectrometry, International Journal of Mass Spectrometry 301 (1-3) (2011) 109–126. [7] C.Y. Pierce, et al., Ambient generation of fatty acid methyl ester ions from bacterial whole cells by direct analysis in real time (DART) mass spectrometry, Chem. Commun. 8 (2007) 807–809. [8] T. Cajka, et al., Recognition of beer brand based on multivariate analysis of volatile fingerprint, Journal of Chromatography A 1217 (25) (2010) 4195–4203. [9] T. Cajka, et al., Ambient mass spectrometry employing a DART ion source for metabolomic fingerprinting/profiling: a powerful tool for beer origin recognition, Metabolomics (2011) 1–9.