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Rapid phytochemical analysis of birch (Betula) and poplar (Populus) foliage by near-infrared reflectance spectroscopy

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Abstract

Poplar (Populus) and birch (Betula) species are widely distributed throughout the northern hemisphere, where they are foundation species in forest ecosystems and serve as important sources of pulpwood. The ecology of these species is strongly linked to their foliar chemistry, creating demand for a rapid, inexpensive method to analyze phytochemistry. Our study demonstrates the feasibility of using near-infrared reflectance spectroscopy (NIRS) as an inexpensive, high-throughput tool for determining primary (e.g., nitrogen, sugars, starch) and secondary (e.g., tannins, phenolic glycosides) foliar chemistry of Populus and Betula species, and identifies conditions necessary for obtaining reliable quantitative data. We developed calibrations with high predictive power (residual predictive deviations ≤ 7.4) by relating phytochemical concentrations determined with classical analytical methods (e.g., spectrophotometric assays, liquid chromatography) to NIR spectra, using modified partial least squares regression. We determine that NIRS, although less sensitive and precise than classical methods for some compounds, provides useful predictions in a much faster, less expensive manner than do classical methods.

Near-infrared reflectance spectroscopy with calibrations based on modified partial least squares regression can provide quantitative measurements of foliar nitrogen, carbohydrate, tannin, and phenolic glycoside content in poplar and birch

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References

  1. Sibley DA (2009) The Sibley guide to trees. Knopf, New York

    Google Scholar 

  2. Slavov TT, Zhelev P (2010) Salient biological features, systematics, and genetic variation of Populus. In: Jansson S, Bhalerao RP, Groover AT (eds) Genetics and genomics of Populus. Springer, New York, pp 15–38

    Google Scholar 

  3. Stettler RF, Bradshaw HD Jr, Heilman PE, Hinckley TM (1996) Preface. In: Stettler RF, Bradshaw HD Jr, Heilman PE, Hinckley TM (eds) Biology of Populus. NRC, Ottawa, pp ix–xi

    Google Scholar 

  4. Index of species information: Betula papyrifera (2011) U.S.D.A. Forest Service. http://www.fs.fed.us/database/feis/plants/tree/betpap/all.html. Accessed 22 Aug 2011

  5. Yang X, Kalluri UC, DiFazio SP, Wullschleger SD, Tschaplinski TJ, Cheng ZM, Tuskan GA (2009) Poplar genomics: state of the science. Crit Rev Plant Sci 28:285–308

    Article  CAS  Google Scholar 

  6. Peltonen PA, Vapaavuori E, Heinonen J, Julkunen-Tiitto R, Holopainen JK (2010) Do elevated atmospheric CO2 and O3 affect food quality and performance of folivorous insects on silver birch? Glob Change Biol 16:918–935

    Article  Google Scholar 

  7. Lindroth RL (2010) Impacts of elevated atmospheric CO2 and O3 on forests: phytochemistry, trophic interactions, and ecosystem dynamics. J Chem Ecol 36:2–21

    Article  CAS  Google Scholar 

  8. Constabel CP, Lindroth RL (2010) The impact of genomics on advances in herbivore defense and secondary metabolism in Populus. In: Jansson S (ed) Genetics and genomics of Populus, vol 8. Springer, Dordrecht, p 387

    Google Scholar 

  9. Schweitzer JA, Madritch MD, Bailey JK, LeRoy CJ, Fischer DG, Rehill BJ, Lindroth RL, Hagerman AE, Wooley SC, Hart SC, Whitham TG (2008) From genes to ecosystems: the genetic basis of condensed tannins and their role in nutrient regulation in a Populus model system. Ecosystems 11:1005–1020

    Article  CAS  Google Scholar 

  10. Haukioja E (2003) Putting the insect into the birch–insect interaction. Oecologia 136:161–168

    Article  Google Scholar 

  11. Sollins P, Glassman C, Paul EA, Swanston C, Lajtha K, Heil JW, Elliott ET (1999) Soil carbon and nitrogen: pools and fractions. In: Robertson GP, Coleman DC, Bledsoe CS, Sollins P (eds) Standard soil methods for long-term ecological research. Oxford University Press, New York, pp 89–105

    Google Scholar 

  12. Lindroth RL, Hsia MTS, Scriber JM (1987) Seasonal patterns in the phytochemistry of 3 Populus species. Biochem Syst Ecol 15:681–686

    Article  CAS  Google Scholar 

  13. Porter LJ, Hrstich LN, Chan BG (1986) The conversion of procyanidins and prodelphinidins to cyanidin and delphinidin. Phytochemistry 25:223–230

    Article  CAS  Google Scholar 

  14. Dickson RE, Lewin KF, Isebrands JG, Coleman MD, Heilman WE, Riemenschneider DE, Sober J, Host GE, Hendrey GR, Pregitzer KS, Karnosky DF, Zak DR (2000) Forest atmosphere carbon transfer and storage (FACTS-II)—the aspen free-air CO2 and O3 enrichment (FACE) project: an overview (General Technical Report NC-214). USDA Forest Service, St. Paul

    Google Scholar 

  15. Foley WJ, McIlwee A, Lawler I, Aragones L, Woolnough AP, Berding N (1998) Ecological applications of near-infrared reflectance spectroscopy—a tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance. Oecologia 116:293–305

    Google Scholar 

  16. Shenk JS, Workman JJ Jr, Westerhaus MO (1992) Application of NIR spectroscopy to agricultural products. In: Burns DA, Ciuczak EW (eds) Handbook of near-infrared analysis, vol 13. Marcel-Dekker, New York, pp 383–431

    Google Scholar 

  17. Workman JJ Jr (1992) NIR spectroscopy calibration basics. In: Burns DA, Ciuczak EW (eds) Handbook of near-infrared analysis, vol 13. Marcel-Dekker, New York, p 681

    Google Scholar 

  18. Petisco C, Garcia-Criado B, Mediavilla S, de Aldana BRV, Zabalgogeazcoa I, Garcia-Ciudad A (2006) Near-infrared reflectance spectroscopy as a fast and non-destructive tool to predict foliar organic constituents of several woody species. Anal Bioanal Chem 386:1823–1833

    Article  CAS  Google Scholar 

  19. McIlwee AM, Lawler IR, Cork SJ, Foley WJ (2001) Coping with chemical complexity in mammal-plant interactions: near-infrared spectroscopy as a predictor of Eucalyptus foliar nutrients and of the feeding rates of folivorous marsupials. Oecologia 128:539–548

    Article  Google Scholar 

  20. Schulz H, Engelhardt UH, Wegent A, Drews HH, Lapczynski S (1999) Application of near-infrared reflectance spectroscopy to the simultaneous prediction of alkaloids and phenolic substances in green tea leaves. J Agric Food Chem 47:5064–5067

    Article  CAS  Google Scholar 

  21. Fontaine J, Horr J, Schirmer B (2001) Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal. J Agric Food Chem 49:57–66

    Article  CAS  Google Scholar 

  22. Williams PC (2001) Implementation of near-infrared technology. In: Williams PC, Norris KH (eds) Near-infrared technology in the agricultural and food industries, 2nd edn. American Association of Cereal Chemists, St. Paul, pp 145–169

    Google Scholar 

  23. Shenk JS, Westerhaus MO (1991) Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Sci 31:469–474

    Article  Google Scholar 

  24. Bjørsvik HR, Martens HA (2001) Data analysis: calibration of NIR instruments by PLS regression. In: Butler LG, Ciuczak EW (eds) Handbook of near-infrared analysis, vol 27, Practical spectroscopy series. Marcel Dekker, New York, pp 185–207

    Google Scholar 

  25. Martens H, Naes T (2001) Multivariate calibration by data compression. In: Williams P, Norris K (eds) Near-infrared technology in the agricultural and food industries, 2nd edn. American Association of Cereal Chemists, St. Paul, pp 59–100

    Google Scholar 

  26. Martens H, Naes T (1989) Methods for calibration. In: multivariate calibration. Wiley, New York, pp 73–236

    Google Scholar 

  27. NIRSystems (1995) Routine operation, calibration development and network system management manual—NIRS3 Version 3.10. Infrasoft, Silver Spring, Maryland

    Google Scholar 

  28. Zhang CY, Shen Y, Chen J, Xiao P, Bao JS (2008) Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared spectroscopy. J Agric Food Chem 56:8268–8272

    Article  CAS  Google Scholar 

  29. Shenk JS, Westerhaus MO (1991) Population structuring of near-infrared spectra and modified partial least squares regression. Crop Sci 31:1548–1555

    Article  CAS  Google Scholar 

  30. Hruschka WR (2001) Data analysis: wavelength selection methods. In: Williams PC, Norris K (eds) Near-infrared technology in the agricultural and food industries, 2nd edn. American Association of Cereal Chemists, St. Paul, pp 39–58

    Google Scholar 

  31. Mahalanobis PC (1936) On the generalized distance in statistics. Proc Natl Inst Sci India 2:49–55

    Google Scholar 

  32. Lindroth RL, Kinney KK, Platz CL (1993) Responses of deciduous trees to elevated atmospheric CO2: productivity, phytochemistry, and insect performance. Ecology 74:763–777

    Article  CAS  Google Scholar 

  33. Fearn T (2002) Assessing calibrations: SEP, RPD, RER and R 2. NIR News 13:12–14

    Google Scholar 

  34. Gonzalez AG, Herrador AG, Asuero AG (1999) Intra-laboratory testing of method accuracy from recovery assays. Talanta 48:729–736

    Article  CAS  Google Scholar 

  35. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130

    Article  CAS  Google Scholar 

  36. Williams P (1987) Variables affecting near-infrared reflectance spectroscopic analysis. In: Williams P, Norris K (eds) Near infrared technology in the agricultural and food industries. American Association of Cereal Chemists, St. Paul, pp 143–167

    Google Scholar 

  37. Coleman SW, Christiansen S, Shenk JS (1990) Prediction of botanical composition using NIRS calibrations developed from botanically pure samples. Crop Sci 30:202–207

    Article  Google Scholar 

  38. Shenk JS, Westerhaus MO (1993) Near-infrared reflectance analysis with single-product and multiproduct calibrations. Crop Sci 33:582–584

    Article  CAS  Google Scholar 

  39. Scioneaux AN, Schmidt MA, Moore MA, Lindroth RL, Wooley SC, Hagerman AE (2011) Qualitative variation in proanthocyanidin composition of Populus species and hybrids: Genetics is the key. J Chem Ecol 37:57–70

    Article  CAS  Google Scholar 

  40. Flinn PC, Edwards NJ, Oldham CM, McNeil M (1996) Near-infrared analysis of the fodder shrub tagaste (Chamaecytisus proliferus) for nutritive value and anti-nutritive factors. In: Davies AMC, Williams P (eds) Near infrared spectroscopy: the future waves. NIR, Chichester, pp 576–580

    Google Scholar 

  41. Windham WR (1987) Influence of grind and gravimetric technique on dry matter determination of forages intended for analysis by near-infrared reflectance spectroscopy. Crop Sci 27:773–776

    Article  Google Scholar 

  42. Couture JJ (2011) Impacts of elevated carbon dioxide and ozone on community herbivory in a northern temperate forest. University of Wisconsin—Madison, Madison

    Google Scholar 

  43. Couture JJ, Lindroth RL (2012) Atmospheric change alters performance of an invasive forest insect. Glob Change Biol. doi:10.1111/gcb.12014

  44. Lindroth RL, Hwang SY (1996) Clonal variation in foliar chemistry of quaking aspen (Populus tremuloides Michx). Biochem Syst Ecol 24:357–364

    Article  CAS  Google Scholar 

  45. Bangert RK, Lonsdorf EV, Wimp GM, Shuster SM, Fischer D, Schweitzer JA, Allan GJ, Bailey JK, Whitham TG (2008) Genetic structure of a foundation species: scaling community phenotypes from the individual to the region. Heredity 100:121–131

    Article  CAS  Google Scholar 

  46. DOE US (2006) Breaking the biological barriers to cellulosic ethanol: a joint research agenda. Rockville, Maryland

    Google Scholar 

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Acknowledgments

We thank Michael Hillstrom and Peter Ladwig for laboratory assistance, Kevin Silveira for technical assistance in the operation of the FOSS NIR spectrometer, Nicholas Keuler (UW-Madison CALS Statistical Consulting Services) for statistical consultation, and Timothy Meehan for the helpful discussion. Funding for this research was provided by the U.S. Department of Energy (Office of Science, BER) grant DE-FG02-06ER64232 and the National Science Foundation (grants DEB-0425908 and DEB-0841609).

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Authors and Affiliations

  1. Dept. of Entomology, University of Wisconsin, 237 Russell Labs, 1630 Linden Drive, Madison, WI, 53706, USA

    Kennedy F. Rubert-Nason, Liza M. Holeski, John J. Couture, Adam Gusse & Richard L. Lindroth

  2. Dept. of Agronomy, University of Wisconsin, Madison, WI, 53706, USA

    Daniel J. Undersander

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  1. Kennedy F. Rubert-Nason
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Correspondence to Kennedy F. Rubert-Nason.

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Rubert-Nason, K.F., Holeski, L.M., Couture, J.J. et al. Rapid phytochemical analysis of birch (Betula) and poplar (Populus) foliage by near-infrared reflectance spectroscopy. Anal Bioanal Chem 405, 1333–1344 (2013). https://doi.org/10.1007/s00216-012-6513-6

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