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Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

Sila AM, Shepherd KD, Pokhariyal GP - Chemometr Intell Lab Syst (2016)

Bottom Line: The root mean square error of prediction was computed using a one-third-holdout validation set.In summary, the results show that global models outperformed the subspace models.We, therefore, conclude that global models are more accurate than the local models except in few cases.

View Article: PubMed Central - PubMed

Affiliation: World Agroforestry Centre (ICRAF), P.O. Box 30677-00100 GPO, Nairobi, Kenya; School of Mathematics, University of Nairobi, P.O Box 30196-00100 GPO, Nairobi, Kenya.

ABSTRACT

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.

No MeSH data available.


Related in: MedlinePlus

1st derivative MIR spectra important wavebands for predicting pH, m3.Ca. m3.Al, Carbon, Clay and Sand. The shaded points highlight all the important variables tried at each split for each model (pH = 182; m3.Al = 388; m3.Ca = 40; Total Carbon = 40; Clay = 19 and Sand = 86).
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f0035: 1st derivative MIR spectra important wavebands for predicting pH, m3.Ca. m3.Al, Carbon, Clay and Sand. The shaded points highlight all the important variables tried at each split for each model (pH = 182; m3.Al = 388; m3.Ca = 40; Total Carbon = 40; Clay = 19 and Sand = 86).

Mentions: Soil pH was well calibrated (r2 = 0.87 and RMSEC = 0.01). The result was as good as obtained by Terhoeven-Urselmans et al. [37] for the prediction of soil properties from a globally distributed soil MIR spectral library of 971 soil samples (r2 = 0.81, RMSEC = 0.63). Similar results were also reported by Shepherd and Walsh [32] for the characterization of soil properties from a spectral library with 758 soils from eastern and southern Africa (r2 = 0.83, RMSEC = 0.34). But, in terms of RMSEC, our results are much better from those previously reported. However, our model seems to overestimate alkaline soil samples, which can be attributed to fewer samples in this range. There were 182 wavebands found to be the most significant in predicting soil pH. These wavebands are 3683–3639; 2580–2306–; 2137–2098; 1709–1689; 1556–1400 cm− 1 (Fig. 7). These bands are associated with hydroxyl stretching vibrations, alumino-silicate lattice vibrations and Al-OH deformation vibrations [45] and very similar to the ones found by Terhoeven-Urselmans et al. [37] using a PLSR model.


Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

Sila AM, Shepherd KD, Pokhariyal GP - Chemometr Intell Lab Syst (2016)

1st derivative MIR spectra important wavebands for predicting pH, m3.Ca. m3.Al, Carbon, Clay and Sand. The shaded points highlight all the important variables tried at each split for each model (pH = 182; m3.Al = 388; m3.Ca = 40; Total Carbon = 40; Clay = 19 and Sand = 86).
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834557&req=5

f0035: 1st derivative MIR spectra important wavebands for predicting pH, m3.Ca. m3.Al, Carbon, Clay and Sand. The shaded points highlight all the important variables tried at each split for each model (pH = 182; m3.Al = 388; m3.Ca = 40; Total Carbon = 40; Clay = 19 and Sand = 86).
Mentions: Soil pH was well calibrated (r2 = 0.87 and RMSEC = 0.01). The result was as good as obtained by Terhoeven-Urselmans et al. [37] for the prediction of soil properties from a globally distributed soil MIR spectral library of 971 soil samples (r2 = 0.81, RMSEC = 0.63). Similar results were also reported by Shepherd and Walsh [32] for the characterization of soil properties from a spectral library with 758 soils from eastern and southern Africa (r2 = 0.83, RMSEC = 0.34). But, in terms of RMSEC, our results are much better from those previously reported. However, our model seems to overestimate alkaline soil samples, which can be attributed to fewer samples in this range. There were 182 wavebands found to be the most significant in predicting soil pH. These wavebands are 3683–3639; 2580–2306–; 2137–2098; 1709–1689; 1556–1400 cm− 1 (Fig. 7). These bands are associated with hydroxyl stretching vibrations, alumino-silicate lattice vibrations and Al-OH deformation vibrations [45] and very similar to the ones found by Terhoeven-Urselmans et al. [37] using a PLSR model.

Bottom Line: The root mean square error of prediction was computed using a one-third-holdout validation set.In summary, the results show that global models outperformed the subspace models.We, therefore, conclude that global models are more accurate than the local models except in few cases.

View Article: PubMed Central - PubMed

Affiliation: World Agroforestry Centre (ICRAF), P.O. Box 30677-00100 GPO, Nairobi, Kenya; School of Mathematics, University of Nairobi, P.O Box 30196-00100 GPO, Nairobi, Kenya.

ABSTRACT

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.

No MeSH data available.


Related in: MedlinePlus