Advertisement

The need for rapid and inexpensive techniques for soil characterization has led to the investigation of modern technologies, and in particular those based on reflectance spectroscopy. While near-infrared has been traditionally used, midinfrared in the 400-4000 cm-1 range is becoming increasingly common due to the specificity of the absorbance bands in this spectral range. The present work discusses two methods based on mid-infrared spectroscopy for soil classification: attenuated total reflectance (ATR) and photoacoustic spectroscopy. The ATR method requires a soil sample close to water saturation, and as a result only the 800-1600 cm-1 interval of the spectrum yields a useful signal. Typical ATR soil spectra consist mostly of several broad bands in the 800-1200 cm-1 region and a calcium carbonate band around 1450 cm-1. By comparison, photoacoustic measurements are conducted with air-dried samples, and the photoacoustic spectra exhibit a larger number of clearly-defined bands. Both methods were tested on data sets containing over 100 samples of various soils commonly used in Israeli agriculture. Data analysis was conducted by wavelet decomposition and neural network classifiers. Very good classification performances were achieved, with correct classification rates of the validation samples typically above 95%.

Keywords

Fourier transforms infrared (FTIR) attenuated total reflectance (ATR) photoacoustic spectroscopy (PAS) wavelets neural networks 

References

  1. Alsberg B. K., Woodward A. M., Winson M. K., Rowland J. J. and Kell D. B., Variable selection in wavelet regression models, Analytica Chimica Acta, Vol. 368, 1998, pp. 29-44.CrossRefGoogle Scholar
  2. Ben-Dor E. and Banin A., Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties, Soil Science Society of America Journal, Vol. 59, 1995, 364-372.CrossRefGoogle Scholar
  3. Changwen D., Linker R. and Shaviv A., Characterization of soils using photoacoustic spectroscopy, Applied Spectroscopy (Accepted)Google Scholar
  4. Changwen D., Linker R. and Shaviv A. Soil identification using mid-infrared photoacoustic spectroscopy. Submitted to GeodermaGoogle Scholar
  5. Daniel K. W., Tripathi N. K. and Honda K., Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand), Australian Journal of Soil Research, Vol. 41, 2003, pp. 47-59.CrossRefGoogle Scholar
  6. Depczynski U., Jetter K., Molt K. and Niemoller, A., Quantitative analysis of near infrared spectra by wavelet coefficient regression using a genetic algorithm, Chemometrics and Intelligent Laboratory Systems, Vol. 47, 1999, pp. 179-187.CrossRefGoogle Scholar
  7. Ehrentreich F., Wavelet transform applications in analytical chemistry, Analytical and Bioanalytical chemistry, Vol. 372, 2002, pp. 115-121.CrossRefPubMedGoogle Scholar
  8. Figueiredo dos Santos R. N., Galvao R. K. H., Araujo M. C. U. and Cirino da Silva E., Improvement of prediction ability of PLS models employing the wavelet packet transform: A case study concerning FT-IR determination of gasoline parameters, Talanta, Vol. 71, 2007, pp. 1136-1143.CrossRefGoogle Scholar
  9. Gerzabek M. H., Antil R. S., Kogel-Knabner I., Knicker H., Kirchmann H. and Haberhauer G., How are soil use and management reflected by soil organic matter characteristics: A spectroscopic approach, European Journal of Soil Science, Vol. 57, 2006, pp. 485-494.CrossRefGoogle Scholar
  10. Haberhauer G. and Gerzabek M. H., DRIFT and transmission FT-IR spectroscopy of forest soils: an approach to determine decomposition process of forest litter, Vibrational Spectroscopy, Vol. 19, 1999, pp. 413-417.CrossRefGoogle Scholar
  11. Haberhauer G., Rafferty B., Strebl F. and Gerzabek M. H., Comparison of the composition of forest soil litter derived from three different sites at various decompositional stages using FTIR spectroscopy, Geoderma, Vol. 83, 1998, pp. 331-342.CrossRefGoogle Scholar
  12. Haykin S., Neural networks: A comprehensive foundation, Prentice-Hall, 1999.Google Scholar
  13. Jahn B. R., Linker R., Upadhyaya S. K., Shaviv A., Slaughter D. C. and Shmulevich I., Mid infrared spectroscopic determination of soil nitrate content. Biosystems Engineering, Vol. 94, 2006, pp. 505-515.CrossRefGoogle Scholar
  14. Janik L. J., Merry R. H. and Skjemstand J. O., Can mid infrared diffuse reflectance analysis replace soil extractions, Australian Journal of Soil Research, Vol. 38, 1998, pp. 681-696.Google Scholar
  15. Jolliffe I. T., Principal Component Analysis, Springer-Verlag, New York, 1986.CrossRefGoogle Scholar
  16. Leung A. K., Chau F. T., Gao J. B. and Shih T. M., Application of wavelet transform in infrared spectrometry: spectral compression and library search, Chemometrics and Intelligent Laboratory Systems, Vol. 43, 1998, pp. 69-88CrossRefGoogle Scholar
  17. Linker R., Kenny A., Shaviv A., Singher L. and Shmulevich I. FTIR/ATR nitrate determination of soil pastes using PCR, PLS and cross-correlation, Applied Spectroscopy, Vol. 58, 2004, pp. 516-520.CrossRefPubMedGoogle Scholar
  18. Linker R., Shmulevich I., Kenny A. and Shaviv A., Soil identification and chemometrics for direct determination of nitrate in soils using FTIR-ATR mid-infrared spectroscopy, Chemosphere, Vol. 61, 2005, pp. 652-658.CrossRefPubMedGoogle Scholar
  19. Linker R., Weiner M., Shmulevich I. and Shaviv A., Nitrate determination in soil pastes using FTIR-ATR mid-infrared spectroscopy: Improved accuracy via soil identification, Biosystems Engineering, Vol. 94, 2006, pp. 111-118.CrossRefGoogle Scholar
  20. Liu Y. and Brown S. D., Wavelet multiscale regression from the perspective of data fusion: new conceptual approaches. Analytical and Bioanalytical chemistry, Vol. 380, 2004, pp. 445-452.CrossRefPubMedGoogle Scholar
  21. McBratney A. B., Minasny B., Viscarra Rossel R. A., Spectral soil analysis and inference systems: A powerful combination for solving the soil data crisis, Geoderma, Vol. 136, 2006, pp. 272-278.CrossRefGoogle Scholar
  22. McCarty G. W., Reeves III J. B., Follett R. F. and Kimble J. M., Mid-infrared and nearinfrared diffuse reflectance spectroscopy for soil carbon measurement, Soil Science Society of America Journal, Vol. 66, 2002, pp. 640-646.CrossRefGoogle Scholar
  23. McClelland J. F., Jones R. W. and Bajic S. J., Photoacoustic Spectroscopy, In Handbook of Vibrational Spectroscopy (Volume II), J. M. Chalmers and P. R. Griffiths, Eds., Wiley & Sons, 2001.Google Scholar
  24. Nguyen T. T., Janik L. J. and Raupach M., Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy in soils studies, Australian Journal of Soil Research, Vol. 29, 1991, pp. 49-67.CrossRefGoogle Scholar
  25. Shaviv A., Kenny A., Shmulevich I., Singher L. Reichlin Y. and Katzir A., IR fiberoptic systems in situ and real time monitoring of nitrate in water and environmental systems, Environmental Science & Technology, Vol. 37, 2003, pp. 2807-2812.CrossRefGoogle Scholar
  26. Trygg J. and Wold S., PLS regression on wavelet compressed NIR spectra, Chemometrics and Intelligent Laboratory Systems, Vol. 42, 1998, pp. 209-220.CrossRefGoogle Scholar
  27. Viscarra Rossel R. A. and McBratney A. B., Soil chemical analytical accuracy and costs: Implications from precision agriculture, Australian Journal of Experimental Agriculture, Vol. 38, 1998, pp. 765-775.CrossRefGoogle Scholar
  28. Viscarra Rossel R. A., Walvoort D. J. J., McBratney A. B., Janick L. J. and Skjemstad J. O., Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties, Geoderma, Vol. 131, 2006, pp. 59-75.CrossRefGoogle Scholar
  29. Walczak B. and Massart D. L., Noise suppression and signal compression using the wavelet packet transform, Chemometrics and Intelligent Laboratory Systems, Vol. 36, 1997, pp. 81-94.CrossRefGoogle Scholar
  30. Zhang X., Jin J., Zheng J. and Gao H., Genetic algorithms based on wavelet transform for resolving simulated overlapped spectra, Analytical and Bioanalytical Chemistry, Vol. 377, 2003, pp. 1153-1158.CrossRefPubMedGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Raphael Linker
    • 1
  1. 1.Faculty of Civil and Environmental EngineeringTechnion-Israel Institute of TechnologyIsrael

Personalised recommendations