Evaluation of DRIFTS technique with PLS regression for determination of added mineral nitrogen in soil

Persistent Link:
http://hdl.handle.net/10150/289864
Title:
Evaluation of DRIFTS technique with PLS regression for determination of added mineral nitrogen in soil
Author:
Boonmung, Suwanee
Issue Date:
2003
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) in both near infrared (NIR) and mid infrared (MIR) has been previously shown to be effective in quantifying soil nitrogen (N) concentrations when calibrated using numerous field soil samples. However, such an approach provides samples that likely contain substantial correlations between physical and chemical properties. To address these concerns, the performance of DRIFTS coupled with PLS regression in NIR regions, 5,000-4,000 cm⁻¹ (2,000-2,500 nm) and 6,500-5,500 cm⁻¹ (1,540-1,820 nm), and the M1R region, 3,400-2,400 cm⁻¹ (2,940-4,170 nm), was assessed first through analysis of the concentration of mineral N (ammonium (NH₄⁺) (0-50 ppm) and nitrate (NO₃⁻) (0-200 ppm)) artificially incorporated into a series of silica sand and clay samples with a consistent particle size. The influence of the reduction of sand particles to silt was also analyzed quantitatively. Subsequently, the Pima clay loam soil was evaluated and the concentration ranges of 0-200 ppm NH₄⁺ and 180-1,000 ppm NO₃⁻ were added in soil samples. All three regions provided good measurement of NH₄⁺ but the MIR region was significantly more useful for NO₃⁻ measurement in sand. The detection limits for the measurement of mineral N in sand with particle sizes within 212-300 μm using the MIR region were 9 ppm NH₄⁺ (7 ppm NH₄-N) and 36 ppm NO₃⁻ (8 ppm NO₃-N). For silt (particles less than 53 mum), the most effective model was the MIR region for both NH₄⁺ and NO₃⁻ measurements, yielding the detection limits of 15 ppm NH₄⁺ (12 ppm NH₄-N) and 50 ppm NO₃⁻ (11 ppm NO₃-N). The MIR region also performed reasonably well with soil samples but both NIR regions provided poor results. The detection limits for NH₄⁺ and NO₃⁻ measurements in soil were 100 ppm NH₄⁺ (78 ppm NH₄-N) and 330 ppm NO₃⁻ (75 ppm NO₃-N) with the correlation coefficients (R²) of roughly 80% and 90%, respectively. The spectral range of 2,900-2,400 cm⁻¹ was the effective common range for mineral N measurement in sand, silt, and soil samples.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Agriculture, Soil Science.; Engineering, Agricultural.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Agricultural and Biosystems Engineering
Degree Grantor:
University of Arizona
Advisor:
Riley, Mark R.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleEvaluation of DRIFTS technique with PLS regression for determination of added mineral nitrogen in soilen_US
dc.creatorBoonmung, Suwaneeen_US
dc.contributor.authorBoonmung, Suwaneeen_US
dc.date.issued2003en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractDiffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) in both near infrared (NIR) and mid infrared (MIR) has been previously shown to be effective in quantifying soil nitrogen (N) concentrations when calibrated using numerous field soil samples. However, such an approach provides samples that likely contain substantial correlations between physical and chemical properties. To address these concerns, the performance of DRIFTS coupled with PLS regression in NIR regions, 5,000-4,000 cm⁻¹ (2,000-2,500 nm) and 6,500-5,500 cm⁻¹ (1,540-1,820 nm), and the M1R region, 3,400-2,400 cm⁻¹ (2,940-4,170 nm), was assessed first through analysis of the concentration of mineral N (ammonium (NH₄⁺) (0-50 ppm) and nitrate (NO₃⁻) (0-200 ppm)) artificially incorporated into a series of silica sand and clay samples with a consistent particle size. The influence of the reduction of sand particles to silt was also analyzed quantitatively. Subsequently, the Pima clay loam soil was evaluated and the concentration ranges of 0-200 ppm NH₄⁺ and 180-1,000 ppm NO₃⁻ were added in soil samples. All three regions provided good measurement of NH₄⁺ but the MIR region was significantly more useful for NO₃⁻ measurement in sand. The detection limits for the measurement of mineral N in sand with particle sizes within 212-300 μm using the MIR region were 9 ppm NH₄⁺ (7 ppm NH₄-N) and 36 ppm NO₃⁻ (8 ppm NO₃-N). For silt (particles less than 53 mum), the most effective model was the MIR region for both NH₄⁺ and NO₃⁻ measurements, yielding the detection limits of 15 ppm NH₄⁺ (12 ppm NH₄-N) and 50 ppm NO₃⁻ (11 ppm NO₃-N). The MIR region also performed reasonably well with soil samples but both NIR regions provided poor results. The detection limits for NH₄⁺ and NO₃⁻ measurements in soil were 100 ppm NH₄⁺ (78 ppm NH₄-N) and 330 ppm NO₃⁻ (75 ppm NO₃-N) with the correlation coefficients (R²) of roughly 80% and 90%, respectively. The spectral range of 2,900-2,400 cm⁻¹ was the effective common range for mineral N measurement in sand, silt, and soil samples.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectAgriculture, Soil Science.en_US
dc.subjectEngineering, Agricultural.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineAgricultural and Biosystems Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorRiley, Mark R.en_US
dc.identifier.proquest3089904en_US
dc.identifier.bibrecord.b44417639en_US
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