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Keywords:

  • cuticle;
  • identification of plants;
  • chemistry of leaf surface;
  • thermal infrared;
  • Fourier transform infrared (FTIR) spectroscopy;
  • attenuated total reflectance (ATR)

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information
  • • 
    Attenuated total reflectance (ATR) spectra of plant leaves display complex absorption features related to organic constituents of leaf surfaces. The spectra can be recorded rapidly, both in the field and in the laboratory, without special sample preparation.
  • • 
    This paper explores sources of ATR spectral variation in leaves, including compositional, positional and temporal variations. Interspecific variations are also examined, including the use of ATR spectra as a tool for species identification.
  • • 
    Positional spectral variations generally reflected the abundance of cutin and the epicuticular wax thickness and composition. For example, leaves exposed to full sunlight commonly showed more prominent cutin- and wax-related absorption features compared with shaded leaves. Adaxial vs. abaxial leaf surfaces displayed spectral variations reflecting differences in trichome abundance and wax composition. Mature vs. young leaves showed changes in absorption band position and intensity related to cutin, polysaccharide, and possibly amorphous silica development on and near the leaf surfaces.
  • • 
    Provided that similar samples are compared (e.g. adaxial surfaces of mature, sun-exposed leaves) same-species individuals display practically identical ATR spectra. Using spectral matching procedures to analyze an ATR database containing 117 individuals, including 32 different tree species, 83% of the individuals were correctly identified.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

The need to characterize floristic composition is a key aspect of many ecological studies; however, the identification of plant species is typically a slow process that depends on morphological and anatomical observations of plant structures. For example, many plant species are distinguished by their floral structures and thus the flowers must be present at the time that botanical surveys are being performed. In some areas with high species diversity determining floristic composition is further complicated by the fact that many species remain undescribed. This lack of knowledge, coupled with the rapid destruction of natural vegetation in some areas, points to the need for more efficient techniques for identifying species and for recognizing distinctive physical and chemical attributes.

Leaves are complex assemblages of organic compounds and it might be expected that they would display distinctive spectral features in the thermal infrared energy range (TIR; 4000–400 cm−1). Fundamental vibration modes of various molecular functional groups produce characteristic spectral absorption features that can serve to ‘fingerprint’ many compounds (Silverstein & Webster, 1998). Such functional groups and related spectral features include hydroxyl (OH) in alcohols and acids, carbonyl (C=O) in esters, ketones, aldehydes and acids, and methyl (CH3) and methylene (CH2) in alkanes. Libraries of TIR spectra currently have a wide range of applications in such diverse fields as chemistry, geology, industrial process control and forensics.

In a preliminary exploration of leaf TIR spectral properties, Salisbury (1986) and Salisbury & Milton (1987, 1988) determined that 13 deciduous tree species displayed reflectance features that were unique for each species. Such spectral variability between different plant species parallels the findings of Holloway (1982a) who determined that cuticular structures observed for particular species also were generally unique. The cuticle is the most superficial layer of the aerial parts of terrestrial plants, and consists of a matrix of polymerized lipid, cutin, and/or polymethylene chains, cutan, permeated by intracuticular waxes and covered by epicuticular waxes (Holloway, 1982b; Jeffree, 1996; Heredia, 2003). Cutin is composed mainly of esterified monomers of hydroxyl- and epoxy-fatty acids (Holloway, 1982b; Kolattukudy, 1996). The epicuticular and intracuticular waxes are composed mainly of long-chain aliphatic hydrocarbons, esters, primary and secondary alcohols, ketones, aldehydes and fatty acids. Aromatic and cyclic compounds such as flavonoids and terpenoids may also be present in smaller amounts (Tulloch, 1976; Baker, 1982b; Bianchi, 1995). The unique characteristics of plant cuticles derive both from the numerous organic compounds involved, and the diverse structural arrangements of the components (Holloway, 1982a).

Thermal infrared transmission spectra previously have been used to help understand the composition and the structure of leaf surfaces (Hallam & Chambers, 1970; Holloway, 1982b; Villena et al., 2000), but traditional transmission methods involving the preparation of KBr sample pellets are not well-suited for the study of fresh, water-bearing, plant materials. A relatively recent technique, attenuated total reflectance (ATR), enables the rapid collection of a transmission-like spectrum of a leaf-surface simply by placing a sample in contact with a special, high index of refraction, crystal (Merk et al., 1998; Dubis et al., 1999; Dubis et al., 2001). This paper discusses the general origins of spectral features seen in ATR spectra of leaves, examines sources of variability between samples, and explores the potential use of ATR measurements in the laboratory and the field as a tool for species identification.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

Sample collection

The study was conducted in the Washington, DC, area, USA, between July 2001 and September 2004. Leaves, mostly from native trees, were collected at the US National Arboretum, Washington, DC, and in areas surrounding the US Geological Survey, Oatlands plantation and the town of Lovettsville in northern Virginia. Samples of tropical species were also obtained from the US Botanic Garden, which has greenhouse facilities in the Washington, DC, area. Lycopersicon esculentum (tomato) and Beta vulgaris (red beet) were purchased from the organic grocery ‘Whole Foods’ (Reston, VA, USA). In total there were samples from 268 individuals belonging to 133 species, 89 genera and 59 families.

Leaves from deciduous trees were collected every month between May and September, 2002, to analyse the spectral differences at different stages of the growing season, and in July 2003 and September 2004 to determine if there were any interannual spectral differences. The tree species used for this analysis of temporal spectral variations were Acer rubrum, Aesculus hippocastanum, Aesculus octandra, Carpinus caroliniana, Carya ovata, Cornus florida, Fagus grandifolia, Ginkgo biloba, Liquidambar styraciflua, Liriodendron tulipifera, Maclura pomifera, Magnolia grandiflora, Prunus serotina, Quercus alba, Quercus rubra and Tilia cordata.

Positional variations of leaf samples were also studied, including spectral variations between the adaxial (upper) and abaxial (lower) leaf surfaces, and spectral variations related to the degree of sun exposure of the leaf on the tree. ‘Sun’ leaves were collected from the south aspect and upper external parts of tree canopies, and ‘shade’ leaves from the north aspect and lower internal parts. The tree species used for the sun and shade analysis were A. hippocastanum, F. grandifolia, G. biloba, M. grandiflora, L. tulipifera, Q. robur and Q. rubra.

In all cases, leaves were collected in batches of 10–20 samples, bagged in plastic, and placed in an ice chest for transport to the laboratory. Damp cotton balls were placed in the bags to avoid desiccation of the leaves, and the spectral measurements were completed within 1–5 d from collection.

Laboratory and field attenuated total reflectance measurements

The ATR measurements were made with Fourier transform infrared spectrometers equipped with accessory optics that include flat crystalline plates having a high refractive index (in this study the crystalline plates were composed of ZnSe). An ATR accessory is designed so that the infrared beam impinges on the plate at an angle greater than the critical angle causing total internal reflection. Under these conditions, the beam intensity is attenuated by a surface ‘evanescent’ wave that penetrates a short distance into any absorbing sample placed in contact with the crystalline plate. The depth of penetration varies with the angle of incidence, the wavelength, and the indices of refraction of both the plate and the sample (see formula in Spragg, 2000). Plant cuticles have a refractive index of c. 1.5 (Holloway, 1982a, p. 7) and ZnSe crystals have an index of 2.43; this establishes the ATR penetration depth in leaves from c. 0.3 µm at 4000 cm−1 to c. 1.7 µm at 700 cm−1. The resulting spectrum is similar to a transmission spectrum, with some differences in peak intensities because of the variable penetration.

The laboratory measurements were made with a Thermo-Electron Corp. Nexus 670 FTIR (Fourier transform infrared; Thermo Electron Corp., Waltham, MA, USA) spectrometer that is continuously purged with dry air. A deuterated triglycine sulfate detector was used to cover the energy range from 4000 cm−1 and 650 cm−1. Leaves were placed in direct contact with the ZnSe crystal, and the average of 100 scans was recorded for each leaf surface.

A field spectrometer equipped with a prototype field ATR accessory was used to simulate an ecological study requiring in situ species identification. The simulated study was made in the State Tree Grove at the National Arboretum, Washington, DC, where there are multiple individuals of numerous tree species, all of which have been identified and labeled. The spectrometer was a Model 102F µFTIR manufactured by Designs and Prototypes Ltd, (Simsbury, CT, USA). This spectrometer uses a ‘sandwich’ detector to cover the full spectral range: an InSb detector spanned the range from 4000 cm−1 to 1818 cm−1, and an HgCdTe detector covered the range from 1818 cm−1 to 714 cm−1. The detector assembly was cooled with liquid nitrogen, and a low-power (< 2 W) energy source was used to supply the infrared beam for the measurements. The field spectrometer is readily transportable and is normally powered by a rechargeable 12 V battery.

Spectral searches

The OMNIC 6.0 software supplied with the Thermo-Electron laboratory spectrometer includes a spectral search–match algorithm that calculates correlation values between unknown samples and known reference spectra contained in a spectral library. The search algorithm produces a list of spectral ‘matches’ ranked from high to low according to the correlation values. In this study two kinds of searches were performed: (1) searches to detect particular chemical components – oleanolic acid and carnauba wax spectra were compared against the plant library in order to find species having these materials on their leaves; (2) searches to identify particular species – when two or more individuals of the same species were represented in the spectral library, the spectrum of one of the individuals was extracted and run as an unknown against the rest of the spectral library. The unknown was considered to be identified if the spectrum of another same-species individual occurred in first place on the ranked correlation list.

Additional analyses

Epicuticular waxes were extracted from fresh leaf surfaces of A. rubrum, M. grandiflora and Q. rubra using chloroform (Salatino et al., 1985). The extracted waxes were deposited and measured directly on the ATR crystal. In addition, a leaf of Q. rubra was immersed in chloroform for 1 h, and the surface gently brushed with chloroform to remove as much wax as possible. Selected leaf samples were prepared for analysis by scanning electron microscopy (SEM). Small pieces of sample were air-dried, mounted on aluminum stubs, and covered with vaporized carbon in a vacuum chamber. The microscope used was a JEOL 840 (JEOL USA Inc., Peabody, MA, USA) at a beam intensity of 15 kV.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

ATR spectral features of leaves

Spectral mixture model  Fresh leaf surfaces are composed of many organic compounds and produce complex spectra showing many absorption bands. At the present level of understanding it is not possible to identify all of the specific compounds responsible for every spectral feature. However, it is possible to identify major classes of compounds and to recognize whether a particular class is especially abundant in a particular leaf sample. Figure 1 is a mixture simulation of Olea europea, a well-studied species (Frega & Lercker, 1985; Bianchi et al., 1992, 1993). As detailed in the figure caption, the simulation was made by digitally summing ATR spectra of cellulose, water, nonacosane (C29H60), carnauba wax and oleanolic acid (C30H48O3). Although carnauba wax is not found in O. europea, the wax has high ester content (Vandenburg & Wilder, 1970; Tulloch, 1973) and provides a convenient ester standard. The simulation illustrates many of the common ATR spectral features of leaves and provides a framework for understanding details of leaf spectra. Note, however, that the mixture proportions used in the simulation do not necessarily represent the mass proportions for reasons discussed later.

image

Figure 1. Simulation of Olea europea leaf. (1a) Water: (a) 3288 cm−1; (b) 2125 cm−1; (c) 1633 cm−1. (1b) Cellulose: (a) 1055 cm−1; (b) 1032 cm−1. (1c) Nonacosane (C29H60): (a) 2914 cm−1; (b) 2846 cm−1; (c) 1472 cm−1; (d) 1462 cm−1; (e) 729 cm−1; (f) 719 cm−1. (1d) Oleanolic acid (C30H48O3) (University of Sao Paulo, Phytochemistry laboratory): (a) 1688 cm−1; (b) 1029 cm−1; (c) 996 cm−1. (1e) Carnauba wax (see composition in Vandenburg & Wilder, 1970): (a) 1735 cm−1; (b) 1166 cm−1. (1f) Simulation of the composition of Olea europea adaxial surface. Sum: 0.2 water + 10 cellulose +1 nonacosane + 0.5 oleanolic acid + 7 carnauba wax. (1g) Adaxial surface of fresh leaf of Olea europea. Dotted line indicates x-axis scale change at 2000 cm−1. Unless otherwise noted, all reagents were obtained from Aldrich Corp. (Sigma Aldrich).

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The water spectrum in Fig. 1a, shows an intense and broad OH stretching band near 3288 cm−1 (arrow ‘a’), a broad shallow band at 2125 cm−1 (arrow ‘b’), and an HOH bending feature at 1633 cm−1(arrow ‘c’). This last feature is not evident in the Olea europea spectrum, but is common in many of the other leaves studied. The steep downward slope on the right side of the water spectrum is the shoulder of a strong absorption band at 686 cm−1 that is outside of the measurement range. This shoulder is seen in most of the spectra studied. Cellulose (Fig. 1b) is mainly characterized by two strong bands at 1055 cm−1 and 1032 cm−1 (arrows ‘a’ and ‘b’), which were assigned by Maréchal and Chanzy (2000) to the C–O stretching of primary and secondary alcohols, respectively. Nonacosane, a compound found in many plant waxes, including O. europea (Fig. 1c), shows the main features of long chain aliphatic compounds: the methylene (CH2) stretching features at 2914 cm−1 and 2846 cm−1 (arrows ‘a’ and ‘b’), methylene bending features at 1472 cm−1 and 1462 cm−1 (arrows ‘c’ and ‘d’) and the rocking doublet of methylene at 729 cm−1 and 719 cm−1 (arrows ‘e’ and ‘f’) (Silverstein & Webster, 1998). Oleanolic acid (Fig. 1d), which is abundant in O. europea, displays a strong band at 1688 cm−1 assigned to the carbonyl (C=O) stretching vibration mode in acids (arrow ‘a’) (Silverstein & Webster, 1998), and two characteristic unassigned bands at 1029 cm−1 (arrow ‘b’) and 997 cm−1 (arrow ‘c’). Carnauba wax (Fig. 1e) illustrates the main spectral features of esters: carbonyl (C=O) stretching at 1735 cm−1 (arrow ‘a’), and C-C(= O)-O stretching at 1166 cm−1 (arrow ‘b’) (Silverstein & Webster, 1998), which from now on will be called simply the carboxyl or C–O stretching feature. Both of these ester features are strongly expressed in the leaf spectrum.

Cutin vs cutan Lycopersicon esculentum and B. vulgaris are well-studied species that illustrate ATR spectral differences between cutin and cutan (Fig. 2). Whereas cutin is composed mainly of esterified monomers of hydroxyl- and epoxy-fatty acids (Holloway, 1982b; Kolattukudy, 1996), cutan is an unsaponifiable polymer made of polymethylene chains linked by ether bonds (Heredia, 2003). The polyester material cutin comprises more than 80% (by weight) of the tomato cuticle (Baker, 1982a) and the ATR spectrum shows strong features at 1727 cm−1 (Fig. 2, arrow ‘a’), 1165 cm−1 (Fig. 2, arrow ‘b’) and 1103 cm−1 (Fig. 2, arrow ‘c’), which were assigned to the ester carbonyl stretching, the carboxyl asymmetrical stretching and the carboxyl symmetrical stretching vibrations, respectively, by Ramirez et al. (1992). According to Baker (1982a), the epicuticular waxes of the tomato fruit comprise only 3% of the cuticle's total weight and are mainly composed of fatty acids, flavonoids, triterpenoids, and hydrocarbon homologues. Consistent with this small wax amount, the bands observed in the tomato fruit spectrum (Fig. 2) do not appear to be characteristic of these wax-forming compounds.

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Figure 2. Cutin vs cutan spectra. Comparison between cutin-rich Lycopersicon esculentum (tomato) fruit and cutan-rich Beta vulgaris (sugar beet) leaf. Lycopersicon esculentum: (a) 1727 cm−1 (νC=O) (b) 1165 cm−1asC–O–C) and (c) 1103 cm−1sC-O-C). Beta vulgaris: (d) 1735 cm−1 (ν–COOH or ν–C=O) (e) 1606 cm−1as–COO). Dotted line indicates x-axis scale change at 2000 cm−1.

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Beet leaves lack cutin (Jeffree, 1996) and therefore the ATR spectrum of a beet leaf (Fig. 2) should not show ester bands. The major band at 1606 cm−1 (Fig. 2, arrow ‘e’) is similar to a band assigned to the stretching vibration of carboxylate anion (–COO) by Villena et al. (2000) in a study of a nondegradable fraction of Clivia miniata cuticle, presumably an analog for cutan. These same authors assigned a band at 1730 cm−1 to the carbonyl stretching vibration of fatty acids, rather than to esters, inasmuch as esters had already been extracted from their sample. A similar feature occurs near 1735 cm−1 in beet (Fig. 2, arrow ‘d’).

Figure 3 shows spectra of a fresh leaf of Quercus rubra (red oak) (Fig. 3b) compared with its own epicuticular wax (Fig. 3a) and a treated leaf that was immersed in chloroform to remove the surface waxes (Fig. 3c). The epicuticular wax of the red oak displays a double carbonyl band at 1732 cm−1 and 1720 cm−1 (Fig. 3a, arrows ‘a’ and ‘b’), and the fresh leaf similarly displays bands at 1730 cm−1 and 1714 cm−1 (Fig. 3a, arrows ‘d’ and ‘e’). By contrast, the leaf washed with chloroform has only a single carbonyl feature at 1729 cm−1 (Fig. 3c, arrow ‘h’) that is likely analogous to the band seen in tomato at 1727 cm−1 (Fig. 3d, arrow ‘k’). Therefore the band at 1729 cm−1 is inferred to be caused by cutin and not by wax. The wax spectrum displays a relatively weak C–O band at 1171 cm−1 (Fig. 3a, arrow ‘c’) compared to its C=O feature (Fig. 3a, arrows ‘a’ and ‘b’), whereas the leaf washed with chloroform has a C–O band at 1169 cm−1 (Fig. 3c, arrow ‘i’) that is stronger than the corresponding C=O band (Fig. 3c, arrow ‘h’). The fresh leaf shows the C–O band at 1168 cm−1 (Fig. 3b, arrow ‘f’) as having an intermediate strength. Therefore, the 1169 cm−1 band is inferred to be produced by a constituent situated beneath the surface wax layer, and by analogy with the tomato spectrum (Fig. 3d, arrow ‘l’), is probably caused by cutin. The band near 831 cm−1 (Fig. 3, arrows ‘g’, ‘j’ and ‘m’) has not been assigned, but is seen in all the spectra except for the wax. The band may be related to phenolic compounds present in cutin (Holloway, 1982b).

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Figure 3. Wax vs cutin features. Comparison between (a) the epicuticular wax of Quercus rubra, (b) the fresh leaf of Q. rubra (adaxial surface, baseline corrected), (c) the leaf washed with chloroform (adaxial surface, baseline corrected) and (d) Lycopersicon esculentum as a standard for cutin. Arrows within parts (a–d): (a) 1732 cm−1; (b) 1720 cm−1; (c) 1171 cm−1; (d) 1730 cm−1; (e) 1714 cm−1; (f) 1168 cm−1; (g) 829 cm−1; (h) 1729 cm−1; (i) 1169 cm−1; (j) 831 cm−1; (k) 1727 cm−1; (l) 1165 cm−1; (m) 832 cm−1. Spectra are offset for clarity.

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Sources of spectral variability (1) Leaves from different times of the growing season: Young leaves collected in May, and mature leaves collected from the same individuals during July through September, show complex spectral differences involving changes in the intensities of features at 1008, 1032 and 1050 cm−1 associated with cellulose, and possibly other polysaccharide-rich substances such as hemicelluloses and pectins (Fig. 4a–c). The band at 1008 cm−1 showed little change in A. hippocastanum, and A. octandra, but became more developed in mature leaves of C. ovata, C. florida, L. tulipifera, and M. pomifera (Fig. 4a). Wilson et al. (2000) assigned the 1008 cm−1 band to polygalacturonic acid, which is a variety of pectin (Fry, 2004). The band at 1032 cm−1 became more intense in mature leaves of A. rubrum, Q. alba, Q. rubra and T. cordata (Fig. 4b), whereas the band at 1050 cm−1 became stronger in F. grandifolia and M. grandiflora (Fig. 4c). The 1050 cm−1 band in F. grandifolia and M. grandiflora may be related to the abundance of amorphous silica on the leaf surfaces (Postek, 1981). As shown in Fig. 4c, the mature leaves display a broadening and/or displacement of the spectral feature near 1050 cm−1 compared with the immature leaves that could be consistent with increased amounts of amorphous silica. Several other species exhibited only minor variations in all of these bands between their young and the mature leaf spectra, including C. caroliniana, L. styraciflua and P. serotina (Fig. 4d). However, a band near 840 cm−1 in P. serotina, which is probably caused by aromatic compounds, shows a marked decrease in intensity in the mature leaf. An opposite change in the 840 cm−1 feature intensity can be seen in G. biloba (Fig. 4e). Unlike the other species examined, G. biloba did not display bands at 1008, 1032, and 1050 cm−1, but did show band variations between immature and mature leaves at 957, 981 and 1020 cm−1 (Fig. 4e). Causes of these band variations in G. biloba are not presently known.

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Figure 4. Spectral comparisons between young leaves (continuous lines) and mature leaves (dotted lines). The data are organized into three main groups reflecting changes in particular spectral bands. (a) Species showing band changes at 1008 cm−1 (from top to bottom) Aesculus hippocastanum, Aesculus octandra, Carya ovata, Cornus florida, Liriodendron tulipifera and Maclura pomifera; (b) species showing band changes at 1032 cm−1 (from top to bottom) Acer rubrum, Quercus alba, Quercus rubra and Tilia cordata; (c) species showing band changes at 1050 cm−1 (from top to bottom) silica gel (SiO2), Fagus grandifolia and Magnolia grandiflora; (d) species showing little variation (from top to bottom) Carpinus caroliniana, Liquidambar styraciflua and Prunus serotina (with arrow marking possible phenolic feature); (e) Ginkgo biloba shows stronger bands (marked with arrows) in the mature leaves at 1104, 1020, 981, 957 and 840 cm−1. Both spectra in each pair are shown on the same y-axis.

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Spectral differences in the C=O bands near 1727 cm−1 and the C–O features near 1165 cm−1 (Fig. 4) were also seen in young and mature leaves. These differences did not follow a simple pattern of behavior. Both bands were stronger in young leaves of A. rubrum (4.0% and 4.5% greater intensity for the C=O and C–O features, respectively), C. ovata (7.2 and 4.5%), C. florida (4.4 and 3.5%), M. pomifera (7.6% and 1.9%), and T. cordata (8.0 and 4.0%). Conversely, the same features were stronger in the mature leaves of L. tulipifera (6.2% and 5.2% greater intensity for the C=O and C–O features, respectively), M. grandiflora (4.0% and 4.2%) and Q. alba (3.9% and 6.8%). A number of other species displayed almost no variation in these band intensities for young and mature leaves, including A. hippocastanum (0.7% and 0.9%), A. octandra (1.0% and 3.2%), C. caroliniana (1.2% and 0.1%), F. grandifolia (2.1% and 0.6%), G. biloba (0.3% and 0.0%), L. stiraciflua (1.6% and 0.9%), P. serotina (1.0% and 2.2%) and Q. rubra (2.6% and 0.6%). The C=O absorption features showed additional variations – in some species a single feature occurring near 1730 cm−1 or 1727 cm−1 in young leaves became a doublet feature in the mature leaves with band centers near 1730 cm−1 or 1727 cm−1 and 1716 cm−1. This was observed for A. rubrum, A. hippocastanum, C. ovata, F. grandifolia, M. pomifera, P. serotina, Q. alba, and Q. rubra. In other cases the opposite spectral behavior was observed, for example, in A. octandra and L. tulipifera two carbonyl features merged into one as the leaves matured. Finally, there were cases where no changes in the C=O band(s) occurred in the course of leaf development, including C. caroliniana, C. florida, L. styraciflua and M. grandiflora. Possible factors involved in these band changes are discussed later.

(2) Adaxial and abaxial leaf surfaces: The adaxial and abaxial variations were of two main types reflecting differences in wax composition and structure (Fig. 5), or in the abundance of trichomes on the abaxial surface (Fig. 6). The spectra of wax extracts from the two surfaces of A. rubrum show absorption bands in the same positions, but with some bands having different relative intensities (Fig. 5a). The wax spectra point to an overall similarity between the adaxial and abaxial wax compositions, with some differences in the concentrations of the wax constituents.

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Figure 5. Acer rubrum. (a) Spectra of the adaxial and abaxial waxes; (b) spectra of the adaxial (top) and abaxial surfaces (bottom). The wax of the adaxial surface (scanning electron microscopy image, top right) is much thinner than the abaxial wax and permits the infrared to penetrate more deeply into the cuticle and cell wall, resulting in relatively stronger cutin (C=O and C–O) and cellulose/hemicellulose features with a maximum at 1032 cm−1. Bars, 14 µm. Thicker wax on the abaxial surface results in relatively stronger aliphatic CH2 features.

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Figure 6. Comparison of attenuated total reflectance (ATR) spectra and scanning electron microscopy images of adaxial and abaxial leaf surfaces of Fagus grandifolia. Abundant trichomes on the abaxial surface may be responsible for the pronounced polysaccharide feature at 1031 cm−1.

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The whole leaf spectra of A. rubrum display significant spectral differences (Fig. 5b). The abaxial surface has stronger aliphatic features (CH2 bands at 2914 cm−1 and 2847 cm−1, 1472 cm−1 and 1462 cm−1, and 729 cm−1 and 719 cm−1) likely owing to greater wax thickness. The adaxial surface shows stronger contrast in the principal ester bands (C=O at 1725 cm−1 and C–O at 1165 cm−1) and in the major cellulose/polysaccharides feature at 1032 cm−1. This may be explained as a result of a thinner wax layer, possibly coupled with differences in cell structure that allow more ATR interaction with cutin and cellulose on and near the adaxial surface.

Scanning electron microscopy images of the Acer rubrum abaxial and adaxial leaf surfaces (Fig. 5b) show the structure of the epicuticular waxes. The abaxial surface has a porous meshwork of thin wax plates, while the adaxial surface has a striated wax, forming subparallel rodlets. The chemical origins of these marked structural differences are not presently known.

Figure 6 compares the adaxial and abaxial spectra of F. grandifolia. The main difference is that the adaxial surface shows a broad band at 1050 cm−1 while the abaxial feature displays a narrower band at 1031 cm−1. The 1050 cm−1 feature resembles the feature attributed to silica in Magnolia grandiflora (Fig. 4c) and the 1031 cm−1 feature is similar to features seen in a variety of species and attributed to cellulose or other polysaccharides (Fig. 4b). A possible interpretation is that the 1031 cm−1 band is related to a chemical constituent present in trichomes, as many abaxial leaf surfaces with abundant trichomes display this absorption band (Fig. 6, bottom right).

(3) Sun and shade leaves: Figure 7 shows a spectrum of extracted cuticular wax of M. grandiflora (Fig. 7a) compared with the spectra of the adaxial surfaces of sun and shade leaves (Fig. 7b). The main spectral differences between the sun and shade leaves are the presence of stronger C=O and C–O stretching bands in the sun leaf spectrum. Both bands are absent in the wax spectrum (Fig. 7a), and probably originate from cutin, indicating a thicker cuticle that is typically characteristic of sun leaves (Osborn & Taylor, 1990). The sun leaf also has a more intense CH2 stretching feature (c. 11% stronger than the shade leaf), indicating a thicker wax layer. Conversely, the shade leaf has a stronger feature at 1046 cm−1 compared with the sun leaf, probably reflecting greater ATR interaction with cell wall materials, including cellulose, which are partly obscured by the thicker wax and cutin layers of the sun leaf surface.

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Figure 7. Spectral comparison between (a) Magnolia grandiflora wax and (b) M. grandiflora sun (continuous line) and shade (dotted line) leaves (adaxial surfaces). The CH2, C=O and C–O features produced by waxes and cutin are stronger in the sun leaf, whereas the 1046 cm−1 feature related to cell wall materials is stronger in the shade leaf.

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Some other species for which sun and shade leaves were compared, including A. hippocastanum, F. grandifolia and L. tulipifera also showed stronger ester (cutin) bands in the sun leaves. Conversely, G. biloba, Q. robur and Q.rubra showed only minor variations in the spectra for both types of leaves.

Searches for specific components in leaves

All of the ATR laboratory data were compiled into a spectral library allowing searches to be made for specific components in leaves and to identify species having chemical similarities. For example, a search for the triterpene oleanolic acid, using the correlation algorithm, found several plants that appeared to have this compound on their leaf surfaces (Fig. 8). One of the species identified, Eucalyptus globulus, is known to have oleanolic acid in its epicuticular wax (Baker, 1982b). Also, the genera Prunus and Ilex are known to include some species with oleanolic acid in their leaves (Baker et al., 1979; Ouyang et al., 1997; Jetter et al., 2000; Taketa et al., 2000; Jetter & Schäffer, 2001) but no specific references were found for P. serotina, Ilex verticillata or Diospiros virginiana (Fig. 8).

image

Figure 8. A spectral library search using oleanolic acid in the search definition found a number of species with similar spectral features: Eucalyptus globulus, Diospyros virginiana, Ilex verticilata and Prunus serotina. Spectra are offset for clarity.

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A search using a carnauba wax spectrum in the search definition found Copernicia prunifera, the source of carnauba wax, and another Arecacea species, Bismarckia nobilis, to be highly correlated (90% and 84%, respectively) (Fig. 9). Several other unrelated species were also found to be highly correlated, probably because of similarities in their wax compositions. However, if the search was made using a C. prunifera leaf in the search definition instead of the carnauba wax, the correlation of B. nobilis improved to 93% (the highest among all the other species in the library). Very strong ester and methylene bands are present in both C. prunifera and B. nobilis, and the high contrast of these bands in relation to those of cellulose suggests a very thick wax cover. This was verified by SEM imagery, which shows a similar thick wax composed of aggregated rodlets in both species (Fig. 9). The high correlation between the spectra of these species therefore reflects their similar wax compositions, as well as the similar structure and thickness of wax on the leaf surfaces.

image

Figure 9. Spectral comparison between carnauba wax (top), Copernicia prunifera (middle) and Bismarckia nobilis (bottom) (2000–650 cm−1), which had a high spectral correlation. Right, scanning electron microscopy images of adaxial surfaces: top, C. prunifera (bar, 15 µm); bottom, Bismarckia nobilis (bar, 10 µm). Spectra are offset for clarity.

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Species identification

To test the use of ATR spectra for species identification two experiments were performed. In the first experiment, a subset of the laboratory ATR data was compiled into a spectral library. Because of temporal and positional variability discussed above, the library spectral searches were restricted to use only adaxial surfaces of mature, sun-exposed leaves. This experiment included 117 individual spectra belonging to 32 species. In searches using the entire energy range of the ATR measurements (i.e. between 4000 cm−1 and 650 cm−1), 92 individuals (79%) were correctly identified and 25 individuals (21%) were not correctly identified. Using a narrower interval between 1850 cm−1 and 650 cm−1, which eliminated the OH stretching region and the relatively invariant methylene stretching bands, gave slightly better results with 97 individuals (83%) correctly identified (Table 1).

Table 1.  Laboratory attenuated total reflectance spectral database and species identification results
 Number of individualsCorrectly identified
  1. Interval used in search was 1850–650 cm−1.

Acer rubrum  7 6
Acer saccharum  5 4
Aesculus glabra  3 3
Aesculus hippocastanum  2 1
Betula papyrifera  3 2
Cercis canadensis  7 6
Cornus florida  4 4
Dyospiros virginiana  4 3
Fagus grandifolia  3 3
Ginkgo biloba  3 3
Hedera helix  3 3
Ilex opaca  2 2
Liquidambar styraciflua  6 6
Liriodendron tulipifera  4 4
Magnolia grandiflora 1010
Papaver somniferum  3 2
Picea glauca  2 0
Picea pungens  2 1
Pinus monticola  2 0
Pinus strobus  2 1
Populus deltoides  5 5
Prunus serotina  2 2
Pseudotsuga mangiesii  2 2
Quercus alba  6 4
Quercus coccinea  2 0
Quercus macrocarpa  6 6
Quercus rubra  3 3
Salvia fruticosa  3 3
Sequoiadendron giganteum  2 2
Tilia cordata  2 0
Ulmus americana  5 4
Zea mays  2 2
Total11797

Table 2 gives details of the search–match results obtained for five species that are listed in Table 1. (For an extended version of Table 2 showing search-match details for all the species, see Table S1 in the Supplementary Material online). The first entry in Table 2, shows the search result for individual number 116 of M. grandiflora, which was correctly identified with a match of 96.14%. Seven other individuals of M. grandiflora followed in the correlation list ahead of Magnolia ×soulangiana, the first incorrect match with a correlation value of 92.82%. Papaver somniferum 141 was considered to be incorrectly identified because Escholtzia californica appeared in first rank position on its search list. A number of individuals of Picea and Pinus also were incorrectly identified and typically showed relatively low correlation values with same species individuals.

Table 2.  Details of search-match results for five species (interval 1850–650 cm−1)
Species on searchRank position of correct matchCorrelation of correct match (%)Correlation of best incorrect match (%)Rank of best incorrect matchSpecies of incorrect match
Magnolia grandiflora 116 196.1492.828Magnolia x soulangiana 170
Magnolia grandiflora 126 197.1292.674Acer rubrum 69
Magnolia grandiflora 176 197.7293.445Acer rubrum 69
Magnolia grandiflora 235 195.9095.092Acer rubrum 69
Magnolia grandiflora 257 197.6294.174Acer saccharum 259
Magnolia grandiflora 285 197.2492.796Cercis canadensis 120
Magnolia grandiflora 286 197.2494.033Cercis canadensis 120
Magnolia grandiflora 287 196.2193.302Ulmus americana 268
Magnolia grandiflora 288 196.2190.805Acer rubrum 251
Magnolia grandiflora 289 197.6292.393Acer saccharum 259
Papaver somniferum 141 292.9894.981Escholtzia californica
Papaver somniferum 167 197.0593.592Escholtzia californica
Papaver somniferum 186 197.0596.072Papaver rhoeas
Picea glauca 304 375.8387.121Picea pungens 303
Picea glauca 3051075.8387.741Pinus strobus 295
Picea pungens 302 185.9487.122Picea glauca 304
Picea pungens 303 385.9487.741Picea glauca 304
Pinus monticola 2993074.5790.491Pinus strobus 313
Pinus monticola 3002174.5786.161Philodendrum undulatum
Pinus strobus 295 189.8788.612Pinus monticola 299
Pinus strobus 313 289.8790.491Pinus monticola 299

Table 3 lists the correlation values between all the individuals of M. grandiflora based on the full spectral range between 4000 cm−1 and 650 cm−1 (see Table S2 in the Supplementary Material for additional species examples). The bottom row shows the average correlation value between each M. grandiflora individual and nine others. The variability in correlation values between each pair of individuals shows that there is intraspecific spectral variation, although for M. grandiflora the variation is quite low. Factors affecting spectral search-match results are discussed below.

Table 3.  Correlation values between different individuals of Magnolia grandiflora
Sample number116126176235257285286287288289
  1. First column and row are the sample numbers of various individuals. Bottom row is the average correlation of each individual with nine others.

116100.00 97.15 97.26 98.20 98.53 97.84 97.05 92.04 93.79 96.98
126 97.15100.00 98.55 96.22 96.19 97.72 96.89 95.37 95.61 94.58
176 97.26 98.55100.00 96.52 96.39 98.09 97.31 94.99 95.55 95.60
235 98.20 96.22 96.52100.00 97.23 97.18 97.00 89.61 91.20 94.14
257 98.53 96.19 96.39 97.23100.00 96.89 97.21 89.97 91.35 98.48
285 97.84 97.72 98.09 97.18 96.89100.00 98.87 93.42 94.34 96.54
286 97.05 96.89 97.31 97.00 97.21 98.87100.00 90.56 91.52 96.60
287 92.04 95.37 94.99 89.61 89.97 93.42 90.56100.00 97.66 90.74
288 93.79 95.61 95.55 91.20 91.35 94.34 91.52 97.66100.00 91.86
289 96.98 94.58 95.60 94.14 98.48 96.54 96.60 90.74 91.86100.00
Average 96.54 96.48 96.70 95.26 95.80 96.77 95.89 92.71 93.65 95.06

The laboratory search experiment involved a wide variety of species from many different environments, and therefore included species that do not occur together in nature. To test the use of ATR spectral measurements under more realistic field conditions, 116 individuals from a single study area, representing 28 different species, were measured by using the field spectrometer, and these data were compiled into a spectral library. In this case, only the data range between 1800 cm−1 and 705 cm−1 was used in the search with the result that 95 individuals (82%) were correctly identified. In an actual field study the spectrometer data could be integrated with other methods of species identification, which could further improve the speed and accuracy of vegetation species mapping.

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

The simulation of the O. europea ATR spectrum (Fig. 1) shows that it is possible to visually approximate the contribution of various leaf constituents to a leaf spectrum, and suggests the potential for using spectral information to analyse leaf surface composition. However, there are several problems that will need to be addressed for this potential to be realized. First, additional materials and compounds not included in the simulation are likely to be present in the leaf. A more complete ATR spectral database of pure compounds will help to ensure that appropriate material spectra are available for spectral modeling. Another consideration is that the simulation was performed by linear mixing of the spectral components, which is not a valid assumption for spectral mixtures spanning the entire ATR energy range. Because the ATR penetration depth is energy-range dependent, a mixture model that yields a good fit in one part of a leaf spectrum may not work as well in another part. Linear modeling, therefore, will be more accurate when applied to narrower intervals of the available energy range. Another potential problem is the limited ATR penetration depth (c. 0.30–0.55 µm) in the upper wave-number part of the spectrum (c. 2000–4000 cm−1), which can limit the detection of certain compounds having diagnostic features in this energy range. For example, aliphatic fatty acids, which are common in plant waxes (Tulloch, 1976; Baker, 1982b; Bianchi, 1995) and display characteristic broad OH bands in the 2500–3500 cm−1 range, were rarely detected in any of the ATR spectra. Discerning such materials may still be possible by using other parts of the spectral range where the ATR penetration depth and sensitivity to key absorption features is greater.

Wax thickness and distribution is an important factor contributing to ATR spectral differences between species. When the surface waxes are relatively thin (or removed as in Fig. 3c), leaf spectra are more strongly influenced by materials from the inner tissue layers, such as cellulose, and especially, cutin. Conversely, leaf surfaces that have thick wax layers (e.g. Copernicia prunifera) display weaker cutin and cellulose features (Fig. 9), reflecting the interplay between ATR penetration depth and the near-surface leaf composition.

Wax thickness and trichome abundance appear to be important factors causing spectral differences between adaxial and abaxial leaf surfaces. In some species, thick wax layers on the abaxial surfaces are the dominant spectral component (Fig. 5). However, species whose leaves have many trichomes, characteristically show pronounced ATR spectral features near 1031 cm−1 (Fig. 6). Further study is needed on compositional aspects of trichomes that may be responsible for these features.

Comparisons of the ATR spectra of sun and shade leaves for a number of species also show intraspecific variations. For example, a common variation, illustrated by M. grandiflora (Fig. 7) is to show stronger ester bands in the sun leaf spectrum interpreted to be associated with higher amounts of cutin. Detailed structural studies of the cuticular membrane of Quercus velutina made by Osborn & Taylor (1990) showed similar elevated cutin amounts in sun leaves of that species. Methylene features are also commonly stronger in sun leaves, suggesting greater amounts of surface wax. An increase in the wax production of Brassica oleracea paralleling an increase in radiant energy was reported by Bianchi (1995). However, in view of the limited number of sun and shade samples examined here, more studies are needed to explore these aspects of positional spectral variability.

Changes in absorption features attributed to cellulose and other polysaccharides in young vs mature leaves (e.g. Fig. 4) are likely to reflect the development of the epidermal cell wall and cuticular layer over time. The structural ontogeny of the cuticle is well described by Jeffree (1996) and briefly outlined here: the cuticle proper, mainly composed of cutin, begins to develop soon after the epidermis expansion is completed and forms a membrane over the external surfaces of the primary cell walls. As development continues a nonester fraction, such as cutan, may become more abundant, epicuticular waxes may form, and polysaccharide fibers may become more densely packed within the cuticular layer and as part of the secondary cell wall. In the spectra of many immature leaves strong absorption features occur at 1165 cm−1 related to C–O bonds in the ester linkages of cutin. In some cases the 1165 cm−1 band weakens in mature leaves, concurrent with other spectral changes in peak position and intensity of features at 1008 cm−1 and 1032 cm−1 (Fig. 4). Changes in the intensity of the 1008 cm−1 and 1032 cm−1 polysaccharide absorption bands could be related to a variety of factors, none of which is currently well-understood. The possible factors include the formation of new compounds, changes in crystallinity (Higgins et al., 1961; van Soest et al., 1995), differences in hydrogen bonding, anomeric or positional linkage differences (Higgins et al., 1961; Kačuráková & Wilson, 2001) and differences in microfibril orientation caused by cell elongation (McCann et al., 1993; Wilson et al., 2000). Similarly, changes in the C=O features from double to single bands, or vice versa, are possibly related to the development of epicuticular waxes and/or cutin over time, or to other changes in the molecular environment. The 1730, 1728 and 1716 cm−1 features are typically related to the C=O stretching vibration of aliphatic aldehydes, esters and ketones, respectively (Silverstein & Webster, 1998), all of which are commonly found in plant waxes (Tulloch, 1976; Baker, 1982b; Bianchi, 1995). Developmental changes in the mix of compounds in the upper few microns of the leaf surface could produce the observed spectral variations. However, detailed explanation of these band changes will require further study.

Although generally good results were obtained in the use of ATR spectra as a species identification tool, several problem areas are noted. One problem that may influence identification success rates is variation in leaf water content. When the OH stretching feature was considered in the laboratory search, only 79% of the individuals were correctly identified, whereas exclusion of this feature and the methylene CH2 stretching features gave an 83% identification rate. Whether this difference results from variable adsorbed water on the leaf surface or from the actual water content within the leaf, is not presently clear. Nonetheless, the results may indicate the need for experimental procedures to minimize moisture and humidity variations.

Chemical similarity between species is another potential source of spectral identification errors. For example, taxonomically close species such as Picea glauca, Picea pungens and Pinus strobus were confused, as were P. somniferum and E. californica, probably owing to chemical similarities (Corrigan et al., 1978; Jetter & Riederer, 1996). However, chemical similarity by itself is not a sufficient reason for identification errors, as similarities in cuticle structure must also be present. For example, M. grandiflora and L. tulipifera have similar wax compositions (Gülz et al., 1992), but their spectra are readily distinguished (Table 1).

Finally, it should be noted that having a small number of individuals to represent a species within the spectral library may impede identification of that species. Ideally, there should be enough spectral measurements of each species to describe intraspecific variations. Measurement of only two individuals does not always appear to be sufficient, as indicated by difficulties in identifying some of the less-well sampled species in this study (Table 1). However, as illustrated in Table 3, the magnitude of intraspecific variability for well-sampled species is generally quite small, and is the key factor enabling the overall positive spectral search results.

Conclusions

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

Attenuated total reflectance spectral measurements can detect a variety of organic constituents present on the surfaces of leaves, and such spectra may have useful ecological and botanical applications. Unusual chemical constituents can be discerned, and structural aspects, such as wax thickness and the presence of trichomes can sometimes be deduced from leaf spectra. Temporal and positional sources of spectral variation in the ATR spectra were explored, and significant spectral variations were found between young and mature leaves, between adaxial and abaxial leaf surfaces and between sun and shade leaves. Further research is needed to better understand the chemical and structural causes of these spectral variations.

If similar leaf samples are compared, for example, the adaxial surfaces of mature, sun-exposed leaves, intraspecific spectral variations are small. This suggests the potential use of ATR spectral measurements as a tool for species identification. The ATR spectra can be recorded rapidly, with minimal sample preparation, in either the laboratory or the field. In several experiments using ATR data libraries to identify unknown leaf samples, over 80% of the samples were correctly identified by species. Other future uses of ATR spectral measurements may include the identification of plants of economic interest and the early identification of seedlings for ecological surveys and conservation management.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

I thank the US Geological Survey, and particularly James K. Crowley, Harvey E. Belkin and Harry E. Lerch for allowing access to equipment, and for contributing many helpful ideas and discussions. I also thank my thesis advisor, Marisa Dantas Bitencourt, and members of my examination committee, Massuo Jorge Kato, Marico Meguro, Antonio Salatino and Carlos Roberto de Souza Filho. Kevin Tunison, from the National Arboretum, Kyle Wallack from the US Botanic Garden and Karin Mazza from Oatlands Farm, provided critical assistance in sample collection and identification. The insightful comments provided by the anonymous reviewers for New Phytologist are also appreciated.

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  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

Table S1 Search match results (1850–650 cm-1). (An extended version of Table 2 showing search-match details for all species) Table S2 Correlation values between all the individuals of species listed based on the full spectral range between 4000 and 650 cm-1.

FilenameFormatSizeDescription
NPH1823sm_TableS1.xls30KSupporting info item
NPH1823sm_TableS2.xls35KSupporting info item