Limits...
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

HQISS averaged MIR spectra per subspace.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4834557&req=5

f0025: HQISS averaged MIR spectra per subspace.

Mentions: Finally, the averaged spectrum representing the soils found to be spectrally close to quartz pure mineral spectrum as shown in Fig. 5 (subplot d) shows intense peaks in the regions 2000–1650 and 1080–700 cm− 1[24]. The fundamental O–Si–O stretching and bending frequencies at 1080, 800–780 and 700 cm− 1 were found to be the most dominant bands in the infrared spectra of quartz-rich soils. In our study, we observed other two prominent peaks outside these regions at 1350 and 1220 cm− 1, which are dominated by C–H bending vibrations from organic materials.


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

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

HQISS averaged MIR spectra per subspace.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

f0025: HQISS averaged MIR spectra per subspace.
Mentions: Finally, the averaged spectrum representing the soils found to be spectrally close to quartz pure mineral spectrum as shown in Fig. 5 (subplot d) shows intense peaks in the regions 2000–1650 and 1080–700 cm− 1[24]. The fundamental O–Si–O stretching and bending frequencies at 1080, 800–780 and 700 cm− 1 were found to be the most dominant bands in the infrared spectra of quartz-rich soils. In our study, we observed other two prominent peaks outside these regions at 1350 and 1220 cm− 1, which are dominated by C–H bending vibrations from organic materials.

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