U.S. flagAn official website of the United States government
                    Here's how you know **Official websites use .gov**
                            
                            A **.gov** website belongs to an official government organization in the United States.
 **Secure .gov websites use HTTPS**
                            
                            A **lock** (                            
                              A locked padlock                                                      
                            
                            ) or **https://** means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.


Menu Agricultural Research Service
						
                    U.S. DEPARTMENT OF AGRICULTURE
 close



Research
                Research Home
                            National Programs
                            Research Projects
                            Scientific Manuscripts
                            International Engagement
                            Scientific Software/Models
                            Databases and Datasets
                            Office of Scientific Quality Review
            Media
                News & Features
                            Multimedia
                            Briefing Room
                            Down on the Farm
                            Press Room
                            Science in Your Shopping Cart
                            Scientific Discoveries
                            Tellus
                            Under the Microscope
            About ARS
                About ARS Home
                            Staff Directory
                            Labs and Research Centers (Map)
                            Headquarter Offices
                            Organizational Chart
                            Employee Services (REE Employees Only)
                            Office of Outreach, Diversity, and Equal Opportunity
            Work With Us
                Work With Us Home
                            ARS Vacancies at USAJOBS
                            Careers at ARS Information
                            Scientific Collaborations
                            Extramural Agreements
                            1890 Faculty Research Sabbatical ProgramSearch small
                    Search
            
            ARS Home
                                About ARS
                                Contact Us




ARS Home Research Publications at this Location





- ARS National Programs
- Search for a scientific manuscript
- Search for a research project
- ARS Software
- Programs and Projects
- Collaborations
- Subjects of Investigation
- Publications

Title:   Author  item
                                            IGNE, BENOIT - University Of Iowaitem
                                            Reeves  Iii, Jamesitem
                                            McCarty, Gregoryitem
                                            Hively, Wells

                                                         - *Dean*item
                                            LUND, ERIC - Veris Technologiesitem
                                            HURBURGH, JR., CHARLES - Iowa State University
  Submitted to: Geoderma
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 6/18/2010
                                    Publication  Date: 7/15/2010
                                    Citation: Igne, B., Reeves, J.B., McCarty, G.W., Hively, W.D., Lund, E., Hurburgh, C.R. 2010. Evaluation of PLS, LS-SVM, and LWR for quantitative spectroscopic analysis of soils. Geoderma. 18(3):167-176.Interpretive Summary: Recently there has been a lot of interest on the potential for soil to remove and sequester carbon from the atmosphere. This would both improve soil quality and potentially help with reduce global warming. If credit or payments are to be made for these efforts, methods are needed to determine how much carbon is being sequestered in the soil over a specific period of time. Traditional methods for determining carbon in soil are very time consuming and expensive and newer methods such as near- and mid-infrared spectroscopy have received a lot of attention as possible methods for measuring soil carbon. Both methods, which utilize the interaction of light, beyond the range of human vision, with the soil are rapid, non-destructive, and generate no chemical wastes, but require complex mathematical methods to relate the resulting spectral data to the composition (carbon content) of the soil. The objective of this research was to investigate several different statistical procedures to determine if any performed better at extracting the information of interest. A comparison of the use of spectral pretreatment as well as the implementation of linear and non-linear regression methods was performed. This study presents an overview of the use of infrared spectroscopy for the prediction of five physical (sand, silt, and clay) and chemical (total carbon and total nitrogen) soil parameters with near and mid infrared units in bench top and field setups. Even though no significant differences existed among pretreatment methods, models using second derivatives performed better. The implementation of partial least squares (PLS), least squares support vector machines (LS-SVM), and locally weighted regression (LWR) for the development of the calibration models showed that the LS-SVM did not outperform linear methods for most components while LWR that creates simpler models performed well. The present results tend to show that soil models are quite sensitive to the complexity of the model. The ability of LWR to select only the appropriate samples did help in the development of robust models. Results also proved that field units performed as well as bench top instruments. This was true for both near-infrared and mid-infrared technology.
                                Technical Abstract: Soil testing requires the analysis of large numbers of samples in laboratory that are often time consuming and expensive. Mid-infrared spectroscopy (mid-IR) and near-infrared spectroscopy (NIRS) are fast, non-destructive, and inexpensive analytical methods that have been used for soil analysis, in laboratory and on the go, to reduce the need for measurements using complex chemical/physical analyses. A comparison of the use of spectral pretreatment as well as the implementation of linear and non-linear regression methods was performed. This study presents an overview of the use of infrared spectroscopy for the prediction of five physical (sand, silt, and clay) and chemical (total carbon and total nitrogen) soil parameters with near and mid infrared units in bench top and field setups. Even though no significant differences existed among pretreatment methods, models using second derivatives performed better. The implementation of partial least squares (PLS), least squares support vector machines (LS-SVM), and locally weighted regression (LWR) for the development of the calibration models showed that the LS-SVM did not outperform linear methods for most components while LWR that creates simpler models performed well. The present results tend to show that soil models are quite sensitive to the complexity of the model. The ability of LWR to select only the appropriate samples did help in the development of robust models. Results also proved that field units performed as well as bench top instruments. This was true for both near-infrared and mid-infrared technology.


Return to top


Connect with ARS Sign up


ARS Home
                            USDA.gov
                            Plain Writing
                            Policies & Links
                            Civil Rights Statements
                            FOIA
                            Accessibility Statement
                            Privacy Policy
                            Non-Discrimination Statement
                            Quality of Information
                            USA.gov
                            WhiteHouse.gov