Determination of soil properties with visible to near- and mid-infrared spectroscopy: Effects of spectral variable selection

Geoderma

Volumn 223-225, Issue 1, 2014, Pages 88-96

  Vohland, M ^a,b   Ludwig, M ^b   Thiele Bruhn, S ^b   Ludwig, B ^c  

^a UNIVERSITY OF LEIPZIG   (Germany)

^b UNIVERSITY OF TRIER   (Germany)

^c UNIVERSITY OF KASSEL   (Germany)

Author keywords

Carbon pools; DRIFT spectroscopy; Partial least squares regression; Soil organic matter; Spectral variable selection; VIS NIR spectroscopy

Indexed keywords

BIOGEOCHEMISTRY; BIOLOGICAL MATERIALS; C (PROGRAMMING LANGUAGE); CALIBRATION; COHERENT SCATTERING; INFRARED SPECTROSCOPY; LEAST SQUARES APPROXIMATIONS; NEAR INFRARED SPECTROSCOPY; ORGANIC COMPOUNDS; PH; REFLECTION; SOIL SURVEYS; SOILS;

CARBON POOL; DRIFT SPECTROSCOPY; PARTIAL LEAST SQUARES REGRESSION; SOIL ORGANIC MATTERS; SPECTRAL VARIABLES; VIS-NIR SPECTROSCOPY;

INFRARED DEVICES;

INFRARED SPECTROSCOPY; MULTIVARIATE ANALYSIS; REFLECTANCE; SOIL CARBON; SOIL ORGANIC MATTER; SOIL TEXTURE; VISIBLE SPECTRUM;

EID: 84897443551     PISSN: 00167061     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.geoderma.2014.01.013     Document Type: Article

References (61)

1. Araújo M.C.U., Saldanha T.C.B., Galvão R.K.H., Yoneyama T., Chame H.C., Visani V. The Successive Projections Algorithm for variable selection in spectroscopic multicomponent. Chemom. Intell. Lab. Syst. 2001, 57:65-73.
2. Barton F.E., Himmelsbach D.S., Duckworth J.H., Smith M.J. Two-dimensional vibration spectroscopy: correlation of mid- and near-infrared regions. Appl. Spectrosc. 1992, 46:420-429.
3. Bellon-Maurel V., McBratney A. Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils - critical review and research perspectives. Soil Biol. Biochem. 2011, 43:1398-1410.
4. Blume H.-P., Brümmer G.W., Horn R., Kandeler E., Kögel-Knabner I., Kretzschmar R., Stahr K., Wilke B.-M. Scheffer/Schachtschabel: Lehrbuch der Bodenkunde 2010, Spektrum Akademischer Verlag, Heidelberg, Germany.
5. Capriel P. Hydrophobicity of organic matter in arable soils: influence of management. Eur. J. Soil Sci. 1997, 48:457-462.
6. Cécillon L., Barthès B.G., Gomez C., Ertlen D., Genot V., Hedde M., Stevens A., Brun J.J. Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS). Eur. J. Soil Sci. 2009, 60:770-784.
7. Cécillon L., Cassagne N., Czarnes S., Gros R., Brun J.-J. Variable selection in near infrared spectra for the biological characterization of soil and earthworm casts. Soil Biol. Biochem. 2008, 40:1975-1979.
8. Celi L., Schnitzer M., Nègre M. Analysis of carboxyl groups in soil humic acids by wet chemical method, Fourier-transform infrared spectrophotometry and solution-state carbon-13 nuclear magnetic resonance. A comparative study. Soil Sci. 1997, 162:189-197.
9. Centner V., Massart D.-L., De Noord O.E., De Jong S., Vandeginste B.M., Sterna C. Elimination of uninformative variables for multivariate calibration. Anal. Chem. 1996, 68:3851-3858.
10. Chodak M. Near-infrared spectroscopy for rapid estimation of microbial properties in reclaimed mine soils. J. Plant Nutr. Soil Sci. 2011, 174:702-709.
11. Chong I.G., Jun C.H. Performance of some variable selection methods when multicollinearity is present. Chemom. Intell. Lab. Syst. 2005, 78:103-112.
12. Confalonieri M., Fornasier F., Ursino A., Boccardi F., Pintus B., Odoardi M. The potential of near infrared reflectance spectroscopy as a tool for the chemical characterisation of agricultural soils. J. Near Infrared Spectrosc. 2001, 9:123-141.
13. Cozzolino D., Morón A. Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions. Soil Tillage Res. 2006, 85:78-85.
14. 13C and FTIR signatures. Geoderma 2005, 128:28-37.
15. Estienne F., Massart D.L., Zanier-Szydlowski N., Marteau P. Multivariate calibration with Raman spectroscopic data: a case study. Anal. Chim. Acta 2000, 424:185-201.
16. Fan W., Shan Y., Li G., Lv H., Li H., Liang Y. Application of competitive adaptive reweighted sampling method to determine effective wavelengths for prediction of total acid of vinegar. Food Anal. Methods 2012, 5:585-590.
17. Ge Y., Morgan C.L.S., Thomasson J.A., Waiser T. A new perspective to near-infrared reflectance spectroscopy: a wavelet approach. Trans. ASABE 2007, 50:303-311.
18. Gerzabek M.H., Antil R.S., Kögel-Knabner I., Knicker H., Kirchmann H., Haberhauer G. How are soil use and management reflected by soil organic matter characteristics: a spectroscopic approach. Eur. J. Soil Sci. 2006, 57:485-494.
19. Günzler H., Böck H. IR-Spektroskopie. Eine Einführung 1990, VCH, Weinheim, Germany.
20. Haney R.L., Haney E.B. Simple and rapid laboratory method for rewetting dry soil for incubations. Commun. Soil Sci. Plant Anal. 2010, 41:1493-1501.
21. IUSS Working Group WRB World reference base for soil resources 2006. World Soil Resources Reports No. 103 2006, FAO, Rome.
22. 2. Agrobiol. Res. 1995, 48:319-324.
23. Körschens M., Weigel A., Schulz E. Turnover of soil organic matter (SOM) and long-term balances - tools for evaluating sustainable productivity of soils. Z. Pflanz. Bodenkunde 1998, 161:409-424.
24. Kuang B., Mahmood H.S., Quraishi M.Z., Hoogmoed W.B., Mouazen A.M., van Hentent E.J. Sensing soil properties in the laboratory, in situ, and on-line: a review. Adv. Agron. 2012, 114:155-223.
25. Leardi R., Boggia R., Terrile M. Genetic algorithms as a strategy for feature selection. J. Chemom. 1992, 6:267-281.
26. Leinweber P., Schulten H.R., Körschens M. Hot water extracted organic matter: chemical composition and temporal variations in a long-term field experiment. Biol. Fertil. Soils 1995, 20:17-23.
27. Li H., Liang Y., Xu Q., Cao D. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal. Chim. Acta 2009, 648:77-84.
28. Ludwig B., Nitschke R., Terhoeven-Urselmans T., Michel K., Flessa H. Use of mid-infrared spectroscopy in the diffuse-reflectance mode for the prediction of the composition of organic matter in soil and litter. J. Plant Nutr. Soil Sci. 2008, 171:384-391.
29. Lundquist E.J., Scow K.M., Jackson L.E., Uesugi S.L., Johnson C.R. Rapid response of soil microbial communities from conventional, low input, and organic farming systems to a wet/dry cycle. Soil Biol. Biochem. 1999, 31:1661-1675.
30. Madari B.E., Reeves J.B., Machado P.L.O.A., Guimarães C.M., Torres E., McCarty G.W. Mid- and near-infrared spectroscopic assessment of soil compositional parameters and structural indices in two Ferralsols. Geoderma 2006, 136:245-259.
31. Marschner P., Kandeler E., Marschner B. Structure and function of the soil microbial community in a long-term fertilizer experiment. Soil Biol. Biochem. 2003, 35:453-461.
32. Matějková Š., Šimon T. Application of FTIR spectroscopy for evaluation of hydrophobic/hydrophilic organic components in arable soils. Plant Soil Environ. 2012, 58:192-195.
33. McCarty G.W., Reeves J.B. Comparison of near infrared and mid infrared diffuse reflectance spectroscopy for field-scale measurement of soil fertility parameters. Soil Sci. 2006, 171:94-102.
34. McCarty G.W., Reeves J.B., Reeves V.B., Follett R.F., Kimble J.M. Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon management. Soil Sci. Soc. Am. J. 2002, 66:640-646.
35. Michel K., Terhoeven-Urselmans T., Nitschke R., Steffan P., Ludwig B. Use of near- and mid-infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest-floor material in soils. J. Plant Nutr. Soil Sci. 2009, 172:63-70.
36. Mouazen A.M., Kuang B., De Baerdemaeker J., Ramon H. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma 2010, 158:23-31.
37. Nørgaard L., Saudland A., Wagner J., Nielsen J.P., Munck L., Engelsen S.B. Interval Partial Least Squares Regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Appl. Spectrosc. 2000, 54:413-419.
38. Post J.L., Noble P.N. The near-infrared combination band frequencies of dioctahedral smectites, micas, and illites. Clays Clay Miner. 1993, 41:639-644.
39. Reeves J.B. Near- versus mid-infrared diffuse reflectance spectroscopy for soil analysis emphasizing carbon and laboratory versus on-site analysis: where are we and what needs to be done?. Geoderma 2010, 158:3-14.
40. Reeves J.B. Mid-infrared spectral interpretation of soils: is it practical or accurate?. Geoderma 2012, 189-190:508-513.
41. Reeves J.B., Follett R.F., McCarty G.W., Kimble J.M. Can near or mid-infrared diffuse reflectance spectroscopy be used to determine soil carbon pools?. Commun. Soil Sci. Plant Anal. 2006, 37:2307-2325.
42. Saeys W., Mouazen A.M., Ramon H. Potential for onsite and online analysis of pig manure using visible and near infrared spectroscopy. Biosyst. Eng. 2005, 91:393-402.
43. Schmidt L., Warnstorff K., Dorfel H., Leinweber P., Lange H., Merbach W. The influence of fertilization and rotation on soil organic matter and plant yields in the long-term Eternal Rye trial in Halle (Saale), Germany. J. Plant Nutr. Soil Sci. 2000, 163:639-648.
44. 3+ oxides and oxide hydroxides in the near IR to near UV. Am. Mineral. 1985, 70:1262-1269.
45. Stevenson F.J. Humus Chemistry. Genesis, Composition, Reactions 1994, Wiley, New York. 2nd ed.
46. Tatzber M., Mutsch F., Mentler A., Leitgeb E., Englisch M., Gerzabek M.H. Determination of organic and inorganic carbon in forest soil samples by mid-infrared spectroscopy and partial least squares regression. Appl. Spectrosc. 2010, 64:1167-1175.
47. Uchida Y., Nishimura S., Akiyama H. The relationship of water-soluble carbon and hot-water-soluble carbon with soil respiration in agricultural fields. Agric. Ecosyst. Environ. 2012, 156:116-122.
48. Vaidyanathan S., Macaloney G., McNeil B. Fundamental investigations on the near-infrared spectra of microbial biomass as applicable to bioprocess monitoring. Analyst 1999, 124:157-162.
49. Vasques G.M., Grunwald S., Sickman J.O. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 2008, 146:14-25.
50. Viscarra Rossel R.A., Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 2010, 158:46-54.
51. Viscarra Rossel R.A., Lark R.M. Improved analysis and modelling of soil diffuse reflectance spectra using wavelets. Eur. J. Soil Sci. 2009, 60:453-464.
52. Viscarra Rossel R.A., Walvoort D.J.J., McBratney A.B., Janik L.J., Skjemstad J.O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 2006, 131:59-75.
53. Vohland M., Emmerling C. Determination of total soil organic C and hot water-extractable C from VIS-NIR soil reflectance with partial least squares regression and spectral feature selection techniques. Eur. J. Soil Sci. 2011, 62:598-606.
54. Vohland M., Besold J., Hill J., Fründ H.-C. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma 2011, 166:198-205.
55. Wold S., Sjöström M., Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. 2001, 58:109-130.
56. Workman J., Weyer L. Practical Guide to Interpretive Near-infrared Spectroscopy 2008, CRC Press, Taylor & Francis Group, Boca Raton, Florida.
57. Wu J., Joergensen R.G., Pommerening B., Chaussod R., Brookes P.C. Measurement of soil microbial biomass C by fumigation-extraction - an automated procedure. Soil Biol. Biochem. 1990, 22:1167-1169.
58. Xiaobo Z., Jiewen Z., Povey M.J.W., Holmes M., Hanpin M. Variables selection methods in near-infrared spectroscopy. Anal. Chim. Acta 2010, 667:14-32.
59. Yang H., Kuang B., Mouazen A.M. Quantitative analysis of soil nitrogen and carbon at a farm scale using visible and near infrared spectroscopy coupled with wavelength reduction. Eur. J. Soil Sci. 2012, 63:410-420.
60. Ye S., Wang D., Min S. Successive projections algorithm combined with uninformative variable elimination for spectral variable selection. Chemom. Intell. Lab. 2008, 91:194-199.
61. Yoshida H., Leardi R., Funatsu K., Varmuza K. Feature selection by genetic algorithms for mass spectral classifiers. Anal. Chim. Acta 2001, 446:485-494.