Overview. Introduction

Transcription

1 Analysis of Soil Organic Carbon in Soil Samples using an ASD NIR Spectrometer By: Michaela Kastanek, Applications Coordinator and George Greenwood, Senior Market Manager - Remote Sensing ASD Inc., a PANalytical company Boulder, Colorado, 80301, USA October 2013 Introduction Growing concerns regarding the impact of global carbon emissions have led to rigorous studies to find effective methods and processes to sequester atmospheric carbon back into a terrestrial phase of the carbon cycle. A number of innovative mechanical sequestration solutions have been developed including geologic sub surface and deep ocean storage, while research into passive natural systems have led to a better understanding of the potential for certain soils to act as carbon sinks. Soil Organic Carbon (SOC) sequestration, in which carbon dioxide present in the atmosphere is transferred into the soil through crop residues and other organic solids is not only an important source of carbon in the soil but also provides an off-set from fossil-fuel combustion and other carbon emitting activities (OSU, SOC Factsheet). Overview Introduction Experimental Model Development Results Statistics Overview Percent Soil Organic Carbon Conclusion Understanding the mechanics of SOC is important for determining soil health, productivity, and developing land management strategies, as well as carbon dioxide fluxes in the atmosphere. Accurately measuring current soil organic carbon (SOC) concentrations, understanding the contributing factors, and being able to assess a given soil s potential to sequester additional carbon (Iowa EPSCoR) are at the core of understanding these dynamics. SOC sequestration potential is defined as the maximum possible storage of Total Below-Ground Carbon Allocation (TBCA) under a specific soil-climate-land management regime. Traditional lab-based methods for determining TBCA are costly, time-consuming and tend to destroy the sample in which it is measured (Iowa EPSCoR).

2 Near-infrared (NIR) reflectance spectroscopy provides an efficient cost-effective alternative to traditional lab-based SOC analysis. With NIR reflectance analysis, rapid non-destructive measurements can be taken in the field or in a controlled laboratory environment. Quantitative calibration models can be developed for rapid characterization of soil nutrients and other physical properties. Coupling this technology with hyperspectral imagery and improved spatial statistical methodologies breaks the bottleneck of sample collection and lab analysis and facilitates large-area soil characterization assessments. The goal of this evaluation was to create a calibration model for percent SOC using near-infrared spectra of soil samples collected on the ASD LabSpec 2500 by Kenneth Wacha at the University of Iowa. Figure 1, Ph.D. candidate and research associate Kenneth Wacha performs NIR measurements with the help of a NASA undergraduate assistant Experimental Spectra were collected using the LabSpec 2500 near-infrared spectrometer and Muglight sample accessory. The soil samples used to create the SOC calibration model were dried ground materials collected from Iowa County, Iowa and sieved through 2mm sieve. Samples were packed into the small sample holder and scanned using the Muglight sample accessory. The LabSpec 2500 system has a wavelength range of nm and collects spectra at the rate of 0.1 seconds per spectral scan. A 100 spectrum average was used which is approximately 10 seconds of scan collection per sample. Spectra were supplied to ASD for creation of the multivariate model. 2

3 Figure 2, ASD LabSpec 2500 and Muglight Sample Accessory Primary reference data for SOC was obtained using a C/N analyzer with an absolute error of +/- 0.3% by weight. The minute detection limits and low error associated with this method make it ideal for producing chemometric models. Model Development GRAMS IQ version 9.1 multivariate chemometrics program from Thermo Fisher Scientific, Woodbridge, NJ, was used to create the soil model. Spectral data files were imported into GRAMS format as absorbance, Log (1/R) spectra. A PLS1 model was developed for percent SOC. The samples had already been divided into calibration and test sets by Kenneth Wacha at the University of Iowa. The samples reserved for use in the independent test set were not used in the creation of the calibration model. The test samples were assigned by rank ordering the data from low to high then placing every fifth sample into the independent test set. Results Statistics Overview The statistics shown in Table 1 describe the calibration model performance. N is the number of samples remaining in the calibration over the number of samples that were initially imported in the calibration set. Typically less than 10% of the samples should be removed as outliers. If more samples than that are removed the model may not validate well using an independent test set. Factors are the number of descriptive vectors that were used in the model. Typically no more than one factor should be used for every samples in the model. If more factors are used this may also cause the model to perform poorly. The RSQ is the correlation coefficient indicating how well the actual sample data points correlated with the predicted data points. An RSQ of 1.0 indicates a perfect correlation whereas an RSQ value closer to 0 indicates no relationship between the data. RSQ values were calculated for both the calibration set and independent test set. The Standard Error of Cross-Validation (SECV) is a measurement estimating the amount of normal error expected in new unknown samples when using the calibration model. The SECV is used in comparison with the Standard Error of Prediction (SEP) which calculates the amount of error of predicted samples in the independent test set. There are no absolute guidelines to follow for the SECV or SEP. However, if the model has been properly fitted, then the calibration 3

4 set RSQ and test set RSQ should be approximately equal, as should be the SECV and SEP values. The final metric used to gauge the usefulness of the model to make quantitative prediction is called the Ratio of Performance Deviation (RPD). A value of less than 1.75 generally indicates that a model will not be useful for quantitation. Values between 1.75 and 4.0 can indicate that a model may be useful for quantitation, and values greater than 4.0 indicate a model is very likely to be useful for making quantitative predictions. Calibration Set Independent Test Set N Factors RSQ SECV N RSQ SEP RPD Total Carbon 245/ Percent Soil Organic Carbon Table 1, Model Statistics Summary A total of 250 spectra were available for creation of the percent SOC model. A total of 5 outliers were removed during the modeling process for a total of 245 samples in the final calibration set. This was an acceptable number of samples to remove during the modeling process. 9 factors were used during the creation of the model, which is an appropriate number of factors considering the large number of samples used in the model. Savitzky-Golay first derivative with 41 point smoothing was applied in GRAMS IQ as a pretreatment of the spectra, and the wavelength range was restricted to 3 regions, specifically , and The model had an RSQ of 0.85 and an SECV of The independent test set had an RSQ of 0.77 and an SEP of The test set validated well compared to the calibration statistics. The independent test set performance demonstrates that this model would also be expected to perform similarly on samples represented by the model. The RPD value for the model was 2.85 indicating that the model may be useful for making quantitative predictions. Figure 3, Soil Organic Carbon Model and Test Set prediction 4

5 Conclusion A model for percent SOC was developed using the ASD LabSpec 2500 with the Muglight sample accessory on specific soil types from Iowa County, Iowa. The model was validated using samples that were withheld as independent test samples. Calibration set and test set statistics indicate that the model validated well and could be expected to perform similarly on samples of a similar type and would be useful for quantitative determinations of percent SOC. The model that was developed using NIR reflectance spectroscopy in combination with samples having quantified SOC values demonstrates the potential for real-time measurement of SOC. Contact ASD Inc., a PANalytical company at (303) or NIR.sales@panalytical.com for further information regarding analysis of soil organic carbon in soil samples. About the LabSpec 4 Line of Lab Analyzers The Labspec line of laboratory instrumentation offers research-grade instrument performance in a rugged package suitable for transport to the sample location. LabSpec analytical instrumentation performs rapid, non-destructive materials analysis for qualitative and quantitative applications. About ASD Inc., a PANalytical Company Established in 1990 in Boulder, Colo., ASD Inc., a PANalytical company, is the global leader in highperformance analytical instrumentation solutions, solving some of the most challenging real-world materials measurement problems. ASD spectrometers unparalleled in providing laboratory-grade results in the field or on-site are the instruments of choice for remote sensing, environmental sciences, agricultural and mining industry applications, where results drive paradigm-changing insights, efficiency and profit. ASD s collaborative culture and world-class customer service put the best, fastest and most accurate spectroscopic instruments to work for industry and science in more than 70 countries around the world. For more information, please visit 5