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iForest - Biogeosciences and Forestry

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Chemometric technique performances in predicting forest soil chemical and biological properties from UV-Vis-NIR reflectance spectra with small, high dimensional datasets

Alessandro Bellino (1), Claudio Colombo (2), Paola Iovieno (3), Anna Alfani (1), Giuseppe Palumbo (2), Daniela Baldantoni (1)   

iForest - Biogeosciences and Forestry, Volume 9, Issue 1, Pages 101-108 (2015)
doi: https://doi.org/10.3832/ifor1495-008
Published: Jul 15, 2015 - Copyright © 2015 SISEF

Research Articles


Chemometric analysis applied to diffuse reflectance spectroscopy is increasingly proposed as an effective and accurate methodology to predict soil physical, chemical and biological properties. Its effectiveness, however, largely varies in relation to the calibration techniques and the specific soil properties. In addition, the calibration of UV-Vis-NIR spectra usually requires large datasets, and the identification of techniques suitable to deal with small sample sizes and high dimensionality problems is a primary challenge. In order to investigate the predictability of many soil chemical and biological properties from a small dataset and to identify the most suitable techniques to deal with this type of problems, we analysed 20 top soil samples of three different forests (Fagus sylvatica, Quercus cerris and Quercus ilex) in southern Apennines (Italy). Diffuse reflectance spectra were recorded in the UV-Vis-NIR range (200-2500 nm) and 22 chemical and biological properties were analysed. Three different calibration techniques were tested, namely the Partial Least Square Regression (PLSR), the combinations wavelet transformation/Elastic net and wavelet transformation/Supervised Principal Component (SPC) regression/ Least Absolute Shrinkage and Selection Operator (LASSO), a kind of preconditioned LASSO. Calibration techniques were applied to both raw spectra and spectra subjected to wavelet shrinkage filtering, in order to evaluate the influence on predictions of spectra denoising. Overall, SPC/LASSO outperformed the other techniques with both raw and denoised spectra. Elastic net produced heterogeneous results, but outperformed SPC/LASSO for total organic carbon, whereas PLSR produced the worst results. Spectra denoising improved the prediction accuracy of many parameters, but worsen the predictions in some cases. Our approach highlighted that: (i) SPC/LASSO (and Elastic net in the case of total organic carbon) is especially suitable to calibrate spectra in the case of small, high dimensional datasets; and (ii) spectra denoising could be an effective technique to improve calibration results.

  Keywords


Elastic Net, PLSR, SPC/LASSO, Wavelets, Diffuse Reflectance Spectroscopy, Sample Size

Authors’ address

(1)
Alessandro Bellino
Anna Alfani
Daniela Baldantoni
Dipartimento di Chimica e Biologia, Università degli Studi di Salerno, v. Giovanni Paolo II 132, I-84084 Fisciano, Salerno (Italy)
(2)
Claudio Colombo
Giuseppe Palumbo
Dipartimento di Agricoltura Ambiente Alimenti, Università degli Studi del Molise, v. De Sanctis, I-86100 ampobasso (Italy)
(3)
Paola Iovieno
Consiglio per la Ricerca e la Sperimentazione in Agricoltura (CRA), Centro di ricerca per l’Orticoltura, v. Cavalleggeri 25, I-84098 Pontecagnano, Salerno (Italy)

Corresponding author

 
Daniela Baldantoni
dbaldantoni@unisa.it

Citation

Bellino A, Colombo C, Iovieno P, Alfani A, Palumbo G, Baldantoni D (2015). Chemometric technique performances in predicting forest soil chemical and biological properties from UV-Vis-NIR reflectance spectra with small, high dimensional datasets. iForest 9: 101-108. - doi: 10.3832/ifor1495-008

Academic Editor

Arthur Gessler

Paper history

Received: Nov 06, 2014
Accepted: Mar 10, 2015

First online: Jul 15, 2015
Publication Date: Feb 21, 2016
Publication Time: 4.23 months

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List of the papers citing this article based on CrossRef Cited-by.

 
(1)
Amato U, Antoniadis A, De Feis I (2006)
Dimension reduction in functional regression with applications. Computational Statistics and Data Analysis 50: 2422-2446.
CrossRef | Gscholar
(2)
Ananyeva ND, Susyan EA, Chernova OV, Wirth SA (2008)
Microbial respiration activities of soils from different climatic regions of European Russia. European Journal of Soil Biology 44: 147-157.
CrossRef | Gscholar
(3)
Anderson JPE, Domsch KH (1978)
A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biology and Biochemistry 10: 215-221.
CrossRef | Gscholar
(4)
Bååth E, Anderson T-H (2003)
Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biology and Biochemistry 35: 955-963.
CrossRef | Gscholar
(5)
Bair E, Tibshirani R (2012)
“superpc”: Supervised principal components. R package version 1.09, web site.
Online | Gscholar
(6)
Baldantoni D, Ligrone R, Alfani A (2009)
Macro- and trace-element concentrations in leaves and roots of Phragmites australis in a volcanic lake in Southern Italy. Journal of Geochemical Exploration 101: 166-174.
CrossRef | Gscholar
(7)
Barber S, Nason GP (2004)
Real non parametric regression using complex wavelets. Journal of the Royal Statistical Society Series B 66: 927-939.
CrossRef | Gscholar
(8)
Baumgardner MF, Silva LF, Biehl LL, Stoner ER (1985)
Reflectance properties of soils. In: “Advances in Agronomy, vol. 38” (Brady NC ed). Academic Press, London, UK, pp. 1-44.
Gscholar
(9)
Bellon-Maurel V, McBratney A (2011)
Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils - Critical review and research perspectives. Soil Biology and Biochemistry 43: 1398-1410.
CrossRef | Gscholar
(10)
Ben-Dor E (2002)
Quantitative remote sensing of soil properties. Advances in Agronomy 75: 173-243.
CrossRef | Gscholar
(11)
Brown PJ, Fearn T, Vannucci M (2001)
Bayesian wavelet regression on curves with application to a spectroscopic calibration problem. Journal of the American Statistical Association 96: 398-408.
CrossRef | Gscholar
(12)
Chang C-W, Laird D, Mausbach MJ, Hurburgh CRJ (2001)
Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Science Society of America Journal 65 (2): 480-490.
CrossRef | Gscholar
(13)
Cohen MJ, Prenger JP, DeBusk WF (2005)
Visible-near infrared reflectance spectroscopy for rapid, non-destructive assessment of wetland soil quality. Journal of Environmental Quality 34: 1422-1434.
CrossRef | Gscholar
(14)
Conforti M, Froio R, Matteucci G, Buttafuoco G (2015)
Visibile and near infrared spectroscopy for predicting texture in forest soil: an application in southern Italy. iForest 8 (3): 339-347.
CrossRef | Gscholar
(15)
Crainiceanu C, Reiss P, Goldsmith J, Huang L, Huo L, Scheipl F (2013)
“refund”: regression with functional data. R package version 0.1-8, web site.
Online | Gscholar
(16)
De Jong S (1993)
SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems 18: 251-263.
CrossRef | Gscholar
(17)
Efron B, Hastie T, Johnston I, Tibshirani R (2004)
Least angle regression (with discussion). Annals of Statistics 32: 407-499.
CrossRef | Gscholar
(18)
Frostegård A, Tunlid A, Bååth E (1993)
Shift in the structure of soil microbial communities in limed forests as revealed by phospholipids fatty acids analysis. Soil Biology and Biochemistry 25: 723-730.
CrossRef | Gscholar
(19)
Hastie T, Efron B (2013)
“lars”: least angle regression, lasso and forward stagewise. R package version 1.2, web site.
Online | Gscholar
(20)
Hastie T, Tibshirani R, Friedman J (2008)
The elements of statistical learning. Springer, New York, USA, pp. 745.
Gscholar
(21)
Heinze S, Vohland M, Joergensen RG, Ludwig B (2013)
Usefulness of near-infrared spectroscopy for the prediction of chemical and biological soil properties in different long-term experiments. Journal of Plant Nutrition and Soil Science 176: 520-528.
CrossRef | Gscholar
(22)
Henderson TL, Baumgardner MF, Franzmeier DP, Stott DE, Coster DC (1992)
High dimensional reflectance analysis of soil organic matter. Soil Science Society of America Journal 56: 865-872.
CrossRef | Gscholar
(23)
Lark RM, Webster R (1999)
Analysis and elucidation of soil variation using wavelets. European Journal of Soil Science 50: 185-206.
CrossRef | Gscholar
(24)
Lina J-M, Mayrand M (1995)
Complex Daubechies wavelets. Applied and Computational Harmonic Analysis 2 (3): 219-229.
CrossRef | Gscholar
(25)
Marchetti M, Tognetti R, Lombardi F, Chiavetta U, Palumbo G, Sellitto M, Colombo C, Iovieno P, Alfani A, Baldantoni D, Barbati A, Ferrari B, Bonacquisti S, Capotorti G, Copiz R, Blasi C (2010)
Ecological portrayal of old-growth forests and persistent woodlands in the Cilento and Vallo di Diano National Park (southern Italy). Plant Biosystems 144 (1): 130-147.
CrossRef | Gscholar
(26)
Mevik BH, Cederkvist HR (2004)
Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR). Journal of Chemometrics 18 (9): 422-429.
CrossRef | Gscholar
(27)
Mevik BH, Wehrens R, Liland KH (2013)
“pls”: Partial Least Squares and Principal Component regression. R package version 2.4-3, web site.
Online | Gscholar
(28)
Nason GP (2008)
Wavelet methods in statistics with R. Springer, New York, USA, pp. 259.
Gscholar
(29)
Nason GP (2013)
“wavethresh”: Wavelets statistics and transforms. R package version 4.6.5, web site.
Online | Gscholar
(30)
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H (2013)
“vegan”: Community Ecology Package. R package version 2.0-8, web site.
Online | Gscholar
(31)
Paul D, Bair E, Hastie T, Tibshirani R (2008)
“Preconditioning” for feature selection and regression in high-dimensional problems. Annals of Statistics 36 (4): 1595-1618.
CrossRef | Gscholar
(32)
R Core Team (2013)
R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Online | Gscholar
(33)
Rinnan R, Rinnan A (2007)
Application of near infrared reflectance (NIR) and fluorescence spectroscopy to analysis of microbiological and chemical properties of artic soil. Soil Biology and Biochemistry 39: 1664-1673.
CrossRef | Gscholar
(34)
Rodríguez-Loinaz G, Onaindia M, Amezaga I, Mijangos I, Garbisu C (2008)
Relationship between vegetation diversity and soil functional diversity in native mixed-oak forests. Soil Biology and Biochemistry 40: 49-60.
CrossRef | Gscholar
(35)
Schnürer J, Rosswall T (1982)
Fluorescein diacetate hydrolysis as a measure of total microbial activity in soil and litter. Applied and Environmental Microbiology 43: 1256-1261.
Online | Gscholar
(36)
Terhoeven-Urselmans T, Schmidt H, Joergensen RG, Ludwig B (2008)
Usefulness of near-infrared spectroscopy to determine biological and chemical soil properties: Importance of sample pre-treatment. Soil Biology and Biochemistry 40: 1178-1188.
CrossRef | Gscholar
(37)
Tibshirani R, Saunders M, Rosset S, Zhu J, Knight K (2005)
Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society Series B 67: 91-108.
CrossRef | Gscholar
(38)
Violante P (2000)
Metodi di analisi chimica del suolo [Methods for soil chemical analyses]. FrancoAngeli Edizioni, Milano, Italy, pp. 536.
Gscholar
(39)
Viscarra Rossel RA, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO (2006a)
Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131: 59-75.
CrossRef | Gscholar
(40)
Viscarra Rossel RA, Mc Glynn RN, McBratney AB (2006b)
Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma 137: 70-82.
CrossRef | Gscholar
(41)
Viscarra Rossel RA, Lark RM (2009)
Improved analysis and modelling of soil diffuse reflectance spectra using wavelets. European Journal of Soil Science 60: 453-464.
CrossRef | Gscholar
(42)
WRB-FAO (2014)
World reference base for soil resources. International Soil Classification System for Naming Soils and Creating Legends for Soil Maps, World Soil Resources Reports, FAO, Rome, pp. 106.
Gscholar
(43)
Yang H, Mouazen AM (2012)
Vis/near- and Mid- infrared spectroscopy for predicting soil N and C at a farm scale. In: “Infrared Spectroscopy - Life and Biomedical Sciences” (Theophanides T ed). InTech, Rijeka, Croatia, pp. 185-210.
Online | Gscholar
(44)
Zhao Y, Ogden RT, Reiss PT (2013)
Wavelet-based LASSO in functional linear regression. Journal of Computational and Graphical Statistics 21 (3): 600-617.
CrossRef | Gscholar
(45)
Zimmerman M, Leifeld J, Fuhrer J (2007)
Quantifying soil organic carbon fractions by infrared spectroscopy. Soil Biology and Biochemistry 39: 224-231.
CrossRef | Gscholar
(46)
Zornoza R, Guerrero C, Mataix-Solera J, Scow KM, Arcenegui V, Mataix-Beneyto J (2008)
Near infrared spectroscopy for determination of various physical, chemical and biochemical properties in Mediterranean soils. Soil Biology and Biochemistry 40 (7): 1923-1930.
CrossRef | Gscholar
(47)
Zou H, Hastie T (2005)
Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B 67: 301-320.
CrossRef | Gscholar
 

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