Source: AGRICULTURAL RESEARCH SERVICE submitted to
POST HARVEST MEASUREMENT AND MANAGEMENT SYSTEMS TO IMPROVE PEANUT QUALITY AND US COMPETITIVENESS
Sponsoring Institution
Agricultural Research Service/USDA
Project Status
TERMINATED
Funding Source
Reporting Frequency
Annual
Accession No.
0408565
Grant No.
(N/A)
Project No.
6604-41430-002-00D
Proposal No.
(N/A)
Multistate No.
(N/A)
Program Code
(N/A)
Project Start Date
Sep 26, 2004
Project End Date
Sep 25, 2009
Grant Year
(N/A)
Project Director
BUTTS C L
Recipient Organization
AGRICULTURAL RESEARCH SERVICE
(N/A)
DAWSON,GA 31742
Performing Department
(N/A)
Non Technical Summary
(N/A)
Animal Health Component
60%
Research Effort Categories
Basic
40%
Applied
60%
Developmental
0%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
20418302020100%
Knowledge Area
204 - Plant Product Quality and Utility (Preharvest);

Subject Of Investigation
1830 - Peanut;

Field Of Science
2020 - Engineering;
Goals / Objectives
Objective 1: Develop a portable sensor and measurement systems to measure moisture in-shell and shelled peanuts during various post harvest processes. Objective 2: Develop and maintain a database of peanut quality parameters and processing characteristics for commercial peanut varieties. Objective 3: Develop peanut curing, transportation, and storage systems and management processes that maintain quality and minimize unit costs and quality deterioration during post harvest processing. Objective 4: Expand the peanut curing decision support system to include new drying equipment and to support management of inventory from the field into the warehouse.
Project Methods
This research will be conducted over a five year period and will consist of laboratory and prototype scale testing. During year 1, runner type peanuts will be dried from various initial moisture contents to a final moisture content of approximately 11% using four levels of incident radiant energy in a laboratory scale catalytic infrared dehydrator. In-shell peanuts will be harvested and divided into five samples approximately 20 kg each. The initial moisture content of each of the samples will be determined using the oven method (ASAE S410.1). As a control, one sample will be dried using conventional forced air curing systems that heat the air 8C above ambient but no higher than 35C. The remaining samples will be dried using four different incident energy levels while recording pod surface temperature. Moisture content will be determined during drying by monitoring the sample mass. Drying will be terminated when the desired mass loss has occurred. The test will be repeated throughout the peanut harvest season, using peanuts with initial moisture contents ranging from 20 to 13%. Each sample will be shelled using a model sheller and kernels separated into commercial sizes to determine milling quality. The shelled peanuts will be subdivided into smaller samples to determine the vigor index, single kernel moisture distribution, and the presence/absence of off flavors. The single kernel moisture distribution will be measured using a Shizouka Seiki CTR-160P. Data will be analyzed using incident energy levels as drying treatments and tests replicated throughout the peanut harvest. The effect of the incident radiant energy on peanut drying time, milling quality, seed germination and vigor, and the incidence of off-flavors will be determined using analysis of variance.

Progress 09/26/04 to 09/25/09

Outputs
Progress Report Objectives (from AD-416) Objective 1: Develop a portable sensor and measurement systems to measure moisture in-shell and shelled peanuts during various post harvest processes. Objective 2: Develop and maintain a database of peanut quality parameters and processing characteristics for commercial peanut varieties. Objective 3: Develop peanut curing, transportation, and storage systems and management processes that maintain quality and minimize unit costs and quality deterioration during post harvest processing. Objective 4: Expand the peanut curing decision support system to include new drying equipment and to support management of inventory from the field into the warehouse. Approach (from AD-416) This research will be conducted over a five year period and will consist of laboratory and prototype scale testing. During year 1, runner type peanuts will be dried from various initial moisture contents to a final moisture content of approximately 11% using four levels of incident radiant energy in a laboratory scale catalytic infrared dehydrator. In- shell peanuts will be harvested and divided into five samples approximately 20 kg each. The initial moisture content of each of the samples will be determined using the oven method (ASAE S410.1). As a control, one sample will be dried using conventional forced air curing systems that heat the air 8C above ambient but no higher than 35C. The remaining samples will be dried using four different incident energy levels while recording pod surface temperature. Moisture content will be determined during drying by monitoring the sample mass. Drying will be terminated when the desired mass loss has occurred. The test will be repeated throughout the peanut harvest season, using peanuts with initial moisture contents ranging from 20 to 13%. Each sample will be shelled using a model sheller and kernels separated into commercial sizes to determine milling quality. The shelled peanuts will be subdivided into smaller samples to determine the vigor index, single kernel moisture distribution, and the presence/absence of off flavors. The single kernel moisture distribution will be measured using a Shizouka Seiki CTR-160P. Data will be analyzed using incident energy levels as drying treatments and tests replicated throughout the peanut harvest. The effect of the incident radiant energy on peanut drying time, milling quality, seed germination and vigor, and the incidence of off-flavors will be determined using analysis of variance. Significant Activities that Support Special Target Populations Research was conducted to develop non-destructive methods for measuring peanut quality. A prototype sensor to measure peanut kernel moisture with the shells still intact was tested. In the same study, x-ray images of unshelled peanut samples were used to estimate farmer stock grade factors of percent foreign material, loose shelled kernels, kernels, and hulls. Samples of all four peanut market types (Runner, Spanish, Valencia, and Virginia) were obtained from each of the major peanut producing regions of the U.S. during the 2008 harvest. Grade factors were determined according to conventional methods and compared to estimates using the new sensors. As a result of testing, collaborators continue to develop and improve commercial prototype sensors for further testing. Basic research has been conducted to identify the bands of visible and near infrared light that can be used to measure peanut quality. Reflectance and absorbance of light in the near infrared and visible color spectrum has been measured on intact, unshelled peanut pods then correlated to various peanut kernel characteristics such as oil and moisture content. Preliminary experiments were conducted for matching the peanut pod color with the colors of the maturity profile board using both microscopic imaging (MI) and Near Infrared Red (NIR) analysis. While the MI techniques have to be further refined, the NIR reflectance spectra gave very encouraging results for both peanut kernels and pods. Research was performed to evaluate the use of low oxygen atmospheres for storing bulk farmer stock peanuts. Farmer stock peanuts were stored in 1/10th scale storage structures and flushed with nitrogen to produce and maintain low oxygen atmospheres. Moisture migration within the mass of peanuts caused mold formation at the peak and cost of nitrogen flushing was excessive with no apparent benefit in quality maintenance. In a second test, approximately 5000 lb of farmer stock peanuts were placed in a commercially available, hermetic storage container. Respiration processes within the flexible container rapidly consumed the oxygen (7 days) creating a low oxygen/high carbon dioxide atmosphere within the container. After a 6-mo storage period, peanuts were removed and excessive mold growth was observed on the top layer of peanuts within the container. PECMAN, the decision support system for managing commercial peanut curing operations, required few revisions in response to user feedback. User feedback indicated that approximately 200 ¿ 300,000 tons of farmer stock peanuts are cured during the 2008 harvest using this software tool to reduce labor and improve drying facility management. A collaborating drying facility used PECMAN to record dryer performance data, then provided that data to researchers for analysis. As expected, these data showed that fuel consumption and dryer capacity was closely correlated to dryer airflow. As result of the test, a major peanut dryer manufacturer, modified the size of the drying container. Technology Transfer Number of Invention Disclosures submitted: 1 Number of Web Sites managed: 2

Impacts
(N/A)

Publications

  • Kandala, C., Butts, C.L., Nelson, S.O. 2007. Determination of moisture content of in-shell peanuts by Parallel-Plate impedance measurements in cylindrical sample holder. Sensing and Instrumentation for Food Quality and Safety. 1:72-78. 2007
  • Butts, C.L., Lamb, M.C., Sheppard, H.T., Kandala, C. 2009. Using a Conveyor-Mounted Spout Sampler to Obtain Farmer Stock Grade Samples. Applied Engineering in Agriculture. 25(3):385-390. 2009.
  • Sundaram, J., Kandala, C., Butts, C.L. 2009. Application of Near Infrared (NIR) Spectroscopy to Peanut Grading and Quality Analysis: Overview. Sensing and Instrumentation for Food Quality and Safety. 2009. DOI: 1007/s11694-009-9081-5. URL: http://dx.doi.org/10.1007/s11694-009-9081-5


Progress 10/01/06 to 09/30/07

Outputs
Progress Report Objectives (from AD-416) Objective 1: Develop a portable sensor and measurement systems to measure moisture in-shell and shelled peanuts during various post harvest processes. Objective 2: Develop and maintain a database of peanut quality parameters and processing characteristics for commercial peanut varieties. Objective 3: Develop peanut curing, transportation, and storage systems and management processes that maintain quality and minimize unit costs and quality deterioration during post harvest processing. Objective 4: Expand the peanut curing decision support system to include new drying equipment and to support management of inventory from the field into the warehouse. Approach (from AD-416) This research will be conducted over a five year period and will consist of laboratory and prototype scale testing. During year 1, runner type peanuts will be dried from various initial moisture contents to a final moisture content of approximately 11% using four levels of incident radiant energy in a laboratory scale catalytic infrared dehydrator. In- shell peanuts will be harvested and divided into five samples approximately 20 kg each. The initial moisture content of each of the samples will be determined using the oven method (ASAE S410.1). As a control, one sample will be dried using conventional forced air curing systems that heat the air 8C above ambient but no higher than 35C. The remaining samples will be dried using four different incident energy levels while recording pod surface temperature. Moisture content will be determined during drying by monitoring the sample mass. Drying will be terminated when the desired mass loss has occurred. The test will be repeated throughout the peanut harvest season, using peanuts with initial moisture contents ranging from 20 to 13%. Each sample will be shelled using a model sheller and kernels separated into commercial sizes to determine milling quality. The shelled peanuts will be subdivided into smaller samples to determine the vigor index, single kernel moisture distribution, and the presence/absence of off flavors. The single kernel moisture distribution will be measured using a Shizouka Seiki CTR-160P. Data will be analyzed using incident energy levels as drying treatments and tests replicated throughout the peanut harvest. The effect of the incident radiant energy on peanut drying time, milling quality, seed germination and vigor, and the incidence of off-flavors will be determined using analysis of variance. Significant Activities that Support Special Target Populations Research was continued toward determining the effects and feasibility of storing farmer stock peanuts in low oxygen atmospheres. Efforts continued to develop instrumentation to measure peanut moisture content while still in the shell to reduce labor and cost of monitoring and measuring moisture content during post harvest processing. A project was conducted in collaboration with USDA, AMS to modify peanut grading equipment to reduce the cost and time of official sample evaluation at the first point of sale. Accomplishments Peanut Curing Management Software Completed and Released. Equations to estimate peanut curing (drying) times and real time estimates of peanut moisture content were evaluated in software written at the National Peanut Research Laboratory by commercial drying facilities. The curing of over 100,000 tons of peanuts was managed using the software during each of the 2005 and 2006 crops with no changes to the prediction equations between seasons. The software has been released for commercial use and operator training sessions held. Users have reported saving approximately 20% of peanut curing costs and improved quality and consistency of peanuts presented for marketing after curing. At an estimated curing cost of $20/ton, this correlates to at least $4/ton of peanuts dried. Assuming that 50% of the peanuts produced in the U.S. were cured using this software, an annual savings of $4 million saved annually in curing costs. NP-306 Action Plan Component 1 (Factors and Processes that Affect Quality) NP-306 Action Plan Problem Statement: Determine influence of post- harvest factors on quality, including storage, handling, grading, and processing. Technology Transfer Number of Invention Disclosures submitted: 1 Number of Web Sites managed: 2 Number of Non-Peer Reviewed Presentations and Proceedings: 6 Number of Newspaper Articles,Presentations for NonScience Audiences: 10

Impacts
(N/A)

Publications

  • Butts, C.L., Faircloth, W.H., Lamb, M.C., Nuti, R.C., Rowland, D., Sorensen, R.B., Guerke, W.R. 2007. Effect of bulk handling on peanut seed quality. Peanut Science. 34:22-26


Progress 10/01/05 to 09/30/06

Outputs
Progress Report 1. What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? Why does it matter? The U.S. peanut industry has identified reducing the cost of production, increasing the food safety of peanut products, improving peanut quality, and improving the health benefits of consuming peanut products as their major goals. Peanut quality is generally defined by flavor, kernel size, and the absence of foreign material and aflatoxin. Adequately determining peanut quality requires that appropriate measurements of physical and chemical properties be made on an adequate and representative sample. Currently, the industry measures kernel size distribution and performs a visual inspection for the presence of aflatoxin-causing molds in a representative sample. The peanut industry needs improved systems to extract samples to measure quality, remove foreign material, detect and manage peanut maturity, dry, handle, and store peanuts. Peanut market type, cultivar, and environmental conditions during the growing season affect all aspects of peanut quality. There are over twenty peanut cultivars available for production depending on peanut market type the spectrum of disease resistance required. Some cultivars are more determinant than others, changing the distribution of peanut maturity on each plant. Some varieties tolerate various stresses, such as water and disease, better than others. When delivered to a processor, peanuts must be segregated based on quality standards as well as physical properties. Peanuts of the same market type, but different cultivars may be commingled and processed together if their physical properties and shelling characteristics are similar. However, if the physical properties of a cultivar are different from the norm for a particular market type, then it must be segregated and processed separately. Since 1997, peanut production has moved out of the traditional production areas in some states, the peanut marketing program has changed, and high capacity harvesting equipment has been implemented across the US. However, curing and storage systems have remained relatively unchanged. The majority of peanuts grown are cured in batches ranging from 4-20 t. High capacity drying systems are needed that will match the harvest capacity of farmers yet be economically viable due to the short duration of the harvest each year. Changes in peanut quality occur during all processing, including curing, storage, shelling, blanching and roasting. These changes can adversely affect peanut flavor, shelf life, seed viability, and consumer acceptance. Degradation of crop quality during storage due to improper moisture control, insect damage, and mechanical damage represents an annual monetary loss of $6 million. Sensors and meters to measure moisture content easily while drying and shelling are needed to optimize these processes. Systems for drying and storing peanuts are needed that will minimize excessive moisture loss, improve moisture uniformity and have low initial capital cost are needed. This project falls within Component 1 Quality Characterization, Preservation, and Enhancement of National Program 306, Quality and Utilization of Agricultural Products. This project focuses equally on Components1b, Methods to Evaluate and Predict Quality, and 1d, Preservation and/or Enhancement of Quality and Marketability. It also has a strong emphasis on 1c, Factors and Processes that Affect Quality. The project objectives are: 1. Develop a portable sensor and measurement systems to measure moisture in-shell and shelled peanuts during various post harvest processes. 2. Develop and maintain a database of peanut quality parameters and processing characteristics for commercial peanut varieties. 3. Develop peanut curing, transportation, and storage systems and management processes that maintain quality and minimize unit costs and quality deterioration during post harvest processing. 4. Expand the peanut curing decision support system to include new drying equipment and to support management of inventory from the field into the warehouse. 2. List by year the currently approved milestones (indicators of research progress) Year 1 (FY 2005) 1. Analyze frequency response data. Develop impedance measurement system and sensor. Conduct calibration tests. 2. Initiate agronomic, physical and chemical properties database from existing UPPT data. Collect data from current year samples. 3. Initiate and conduct laboratory-scale infrared (IR) curing studies. 4. Construct, instrument, and load controlled atmosphere (CA) storage units. 5. Develop drying model and conduct simulation studies. Year 2 (FY 2006) 1. Modify impedance measurement system and sensor. 2. Update peanut properties database. Analyze data. Collect current crop year data. 3. Analyze curing data. Continue IR curing research. 4. Analyze CA storage data. Continue CA peanut storage research. 5. Calibrate peanut curing decision support system with commercial drying data. Year 3 (FY 2007) 1. Analyze moisture calibration data. Refine probe and measurement system. Collect data for all market types. 2. Update peanut properties database. Analyze data. Collect data. Develop mining methods. 3. Analyze curing data. Continue IR curing research. 4. Analyze CA storage data. Continue scale model CA storage. Initiate commercial CA storage. 5. Revise decision support system, release to beta testers. Year 4 (FY 2008) 1. Analyze moisture data. Refine probe. Conduct commercial field studies. 2. Update peanut database. Analyze data. Collect data. 3. Analyze curing data. Conduct prototype/full-scale IR curing research. 4. Analyze CA storage data. Continue scale model and commercial CA storage research. 5. Analyze beta peanut curing decision support system, modify underlying models as necessary. Year 5 (FY 2009) 1. Final analysis and transfer moisture measurement technology to industry. 2. Continue mining/analysis of peanut properties database. Make database available on line. 3. Final analysis of feasibility of IR peanut curing and transfer to industry. 4. Analyze CA storage data. Transfer technology to industry via publication of guidelines for design and operation. 5. Revise and release peanut curing decision support software. 4a List the single most significant research accomplishment during FY 2006. Peanut Curing Decision Support System: The decision support system for managing peanut curing operations released in FY 2005 was revised based on user feedback and released for use by the peanut industry. User feedback of previous version stated that using the decision support system reduced labor, and reduced over drying, and minimized reductions in milling quality thereby reducing the cost of drying peanuts significantly. 4b List other significant research accomplishment(s), if any. Completed development of an air volume control valve and procedure to calibrate pneumatic samplers use to obtain official samples for determining peanut value at farmer marketing. Equipment and procedures have been incorporated into Federal-State Inspection Service maintenance procedures in all peanut producing regions of the U.S. 4c List significant activities that support special target populations. No activities to report. 5. Describe the major accomplishments to date and their predicted or actual impact. FY 2005. PECMAN, a decision support system to manage commercial peanut drying facilities was released and meets milestone 4A of the project plan. System users will reduce labor and minimize risk of over drying farmer stock peanuts. Over drying can cost as much as $22/ton. This accomplishment was directed toward Component 1d, Preservation and/or Enhancement of Quality and Marketability of the action plan for NP 306. By improving the management of the peanut curing process, the risk of quality loss during the first and critical post harvest process is minimized. 6. What science and/or technologies have been transferred and to whom? When is the science and/or technology likely to become available to the end- user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? A decision support system to manage commercial peanut curing operations, PECMAN, was released in July 2005. Commercial peanut curing facilities monitor up to 200 dryers simultaneously using seasonal labor. The software incorporates an empirical model published in 2004 to predict drying times for each active peanut dryer and provides sampling schedules. By using the software, commercial facilities can optimize seasonal labor, minimize the risk of over drying, and maximize system use efficiency. The software is usable by most all education levels typically found at the drying facility. As long as batch systems are used for drying peanuts, this software will be applicable. Guidelines for aerating peanut warehouses to minimize quality loss during farmer stock storage were written based on research results. Guidelines have been incorporated into the American Peanut Shellers Associations online handbook, Handling and Storage of Farmer Stock Peanuts (http://www.peanut-shellers.org/).

Impacts
(N/A)

Publications

  • Butts, C.L., Faircloth, W.H., Nuti, R.C., Rowland, D. 2006. Bulk seed tenders for handling peanut seed. Georgia Peanut Research Extension Report. 9-13.
  • Pomes', A., Butts, C.L., Chapman, M. 2006. Quantification of ara h 1 in peanuts: roasting makes a difference. Journal of Clinical and Experimental Allergy. 36:824-830.
  • Kandala, C., Butts, C.L., Nelson, S.O. 2006. Parallel-plate sensors for nondestructive measurement of moisture content in in-shell peanuts . Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE). Paper number 066041.
  • Harraz, H., Wang, Y., Butts, C.L. 2006. Dehydration of in-shell peanuts using radio frequency energy with intermittent stirrings. ASABE Paper No. 066048. . American Society of Agri Engineers Special Meetings and Conferences Papers.
  • Kandala, C., Butts, C.L., Nelson, S.O. 2006. Parallel-plate sensors for nondestructive measurement of moisture content in in-shell peanuts . Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE).
  • Butts, C.L., Faircloth, W.H., Nuti, R.C., Rowland, D., Lamb, M.C., Guerke, W.R. 2006. The effect of bulk handling on peanut seed quality. American Peanut Research and Education Society Abstracts.


Progress 10/01/04 to 09/30/05

Outputs
1. What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? What does it matter? The U.S. peanut industry has identified reducing the cost of production, increasing the food safety of peanut products, improving peanut quality, and improving the health benefits of consuming peanut products as their major goals. Peanut quality is generally defined by flavor, kernel size, and the absence of foreign material and aflatoxin. Adequately determining peanut quality requires that appropriate measurements of physical and chemical properties be made on an adequate and representative sample. Currently, the industry measures kernel size distribution and performs a visual inspection for the presence of aflatoxin-causing molds in a representative sample. The peanut industry needs improved systems to extract samples to measure quality, remove foreign material, detect and manage peanut maturity, dry, handle, and store peanuts. Peanut market type, cultivar, and environmental conditions during the growing season affect all aspects of peanut quality. There are over twenty peanut cultivars available for production depending on peanut market type the spectrum of disease resistance required. Some cultivars are more determinant than others, changing the distribution of peanut maturity on each plant. Some varieties tolerate various stresses, such as water and disease, better than others. When delivered to a processor, peanuts must be segregated based on quality standards as well as physical properties. Peanuts of the same market type, but different cultivars may be commingled and processed together if their physical properties and shelling characteristics are similar. However, if the physical properties of a cultivar are different from the norm for a particular market type, then it must be segregated and processed separately. Since 1997, peanut production has moved out of the traditional production areas in some states, the peanut marketing program has changed, and high capacity harvesting equipment has been implemented across the US. However, curing and storage systems have remained relatively unchanged. The majority of peanuts grown are cured in batches ranging from 4-20 t. High capacity drying systems are needed that will match the harvest capacity of farmers yet be economically viable due to the short duration of the harvest each year. Changes in peanut quality occur during all processing, including curing, storage, shelling, blanching and roasting. These changes can adversely affect peanut flavor, shelf life, seed viability, and consumer acceptance. Degradation of crop quality during storage due to improper moisture control, insect damage, and mechanical damage represents an annual monetary loss of $6 million. Sensors and meters to measure moisture content easily while drying and shelling are needed to optimize these processes. Systems for drying and storing peanuts are needed that will minimize excessive moisture loss, improve moisture uniformity and have low initial capital cost are needed. This project falls within Component 1 Quality Characterization, Preservation, and Enhancement of National Program 306, Quality and Utilization of Agricultural Products. This project focuses equally on Components1b, Methods to Evaluate and Predict Quality, and 1d, Preservation and/or Enhancement of Quality and Marketability. It also has a strong emphasis on 1c, Factors and Processes that Affect Quality. The project objectives are: 1. Develop a portable sensor and measurement systems to measure moisture in-shell and shelled peanuts during various post harvest processes. 2. Develop and maintain a database of peanut quality parameters and processing characteristics for commercial peanut varieties. 3. Develop peanut curing, transportation, and storage systems and management processes that maintain quality and minimize unit costs and quality deterioration during post harvest processing. 4. Expand the peanut curing decision support system to include new drying equipment and to support management of inventory from the field into the warehouse. 2. List the milestones (indicators of progress) from your Project Plan. Year 1 (FY 2005) 1. Analyze frequency response data. Develop impedance measurement system and sensor. Conduct calibration tests. 2. Initiate agronomic, physical and chemical properties database from existing UPPT data. Collect data from current year samples. 3. Initiate and conduct laboratory-scale infrared (IR) curing studies. 4. Construct, instrument, and load controlled atmosphere (CA) storage units. 5. Develop drying model and conduct simulation studies. Year 2 (FY 2006) 1. Modify impedance measurement system and sensor. 2. Update peanut properties database. Analyze data. Collect current crop year data. 3. Analyze curing data. Continue IR curing research. 4. Analyze CA storage data. Continue CA peanut storage research. 5. Calibrate peanut curing decision support system with commercial drying data. Year 3 (FY 2007) 1. Analyze moisture calibration data. Refine probe and measurement system. Collect data for all market types. 2. Update peanut properties database. Analyze data. Collect data. Develop mining methods. 3. Analyze curing data. Continue IR curing research. 4. Analyze CA storage data. Continue scale model CA storage. Initiate commercial CA storage. 5. Revise decision support system, release to beta testers. Year 4 (FY 2008) 1. Analyze moisture data. Refine probe. Conduct commercial field studies. 2. Update peanut database. Analyze data. Collect data. 3. Analyze curing data. Conduct prototype/full-scale IR curing research. 4. Analyze CA storage data. Continue scale model and commercial CA storage research. 5. Analyze beta peanut curing decision support system, modify underlying models as necessary. Year 5 (FY 2009) 1. Final analysis and transfer moisture measurement technology to industry. 2. Continue mining/analysis of peanut properties database. Make database available on line. 3. Final analysis of feasibility of IR peanut curing and transfer to industry. 4. Analyze CA storage data. Transfer technology to industry via publication of guidelines for design and operation. 5. Revise and release peanut curing decision support software. 3a List the milestones that were scheduled to be addressed in FY 2005. For each milestone, indicate the status: fully met, substantially met, or not met. If not met, why. 1. Analyze frequency response data. Develop impedance measurement system and sensor. Conduct calibration tests. Milestone Substantially Met 2. Initiate agronomic, physical and chemical properties database from existing UPPT data. Collect data from current year samples. Milestone Substantially Met 3. Initiate and conduct laboratory-scale infrared (IR) curing studies. Milestone Not Met Critical SY Vacancy 4. Construct, instrument, and load controlled atmosphere (CA) storage units. Milestone Fully Met 5. Develop drying model and conduct simulation studies Milestone Fully Met 3b List the milestones that you expect to address over the next 3 years (FY 2006, 2007, and 2008). What do you expect to accomplish, year by year, over the next 3 years under each milestone? FY 2006. 1. Milestone: Modify impedance measurement system and sensor. Anticipated accomplishment: Develop probe for bulk moisture measurements. Collect runner peanut calibration data. 2. Milestone: Update peanut properties database. Anticipated accomplishment: Agronomic and chemical data from the Uniform Peanut Performance Tests (UPPT) will be added to the current database of physical properties. This will make all of the UPPT data available and searchable in one location. 3. Milestone: Analyze curing data. Continue IR curing research. Anticipated accomplishment: Initiate collaborative studies with Biosystems Engineering faculty at Auburn University to develop radio frequency (RF) dielectric heating to cure farmer stock peanuts. 4. Milestone: Analyze CA storage data. Continue CA peanut storage research. Anticipated accomplishment: Controlled atmosphere storage research will continue at the 1/10th scale to determine the effects on quality and develop management strategies. Strategies developed on 1/10th scale models will be implemented on full scale commercial facilities through the non-funded cooperative agreement. 5. Milestone: Calibrate peanut curing decision support system with commercial drying data. Anticipated Accomplishment: Performance data from peanut curing facilities will be obtained for the 2005 peanut harvest from users of the curing decision support system. Field data will be used to increase the accuracy and dependability of the drying time models. FY2007: 1. Analyze moisture calibration data. Refine probe and measurement system. Collect data for all market types. Anticipated Accomplishment: Refinement of the moisture probe and extending the calibration to Virginia, Spanish, and Valencia peanut market types during FY2007 should increase accuracy and range of applicability of the moisture meter and probe. 2. Update peanut properties database. Analyze data. Collect data. Develop mining methods. Anticipated Accomplishment: Continued addition of physical, chemical, and agronomic data to the UPPT database will increase the usefulness of the data. Developing web based data mining and reporting will make the database accessible by the peanut industry. 3. Analyze curing data. Continue IR curing research. Anticipated Accomplishment: Continuing the radio frequency drying research will provide operational data regarding effectiveness, cost, and suitability of the control parameters developed from the previous year. 4. Analyze CA storage data. Continue scale model CA storage. Initiate commercial CA storage. Anticipated Accomplishment: Controlled atmosphere storage research will continue at the 1/10th scale. Aeration and controlled atmosphere management strategies learned in FY2006 being applied in the cooperating commercial scale facilities. 5. Revise decision support system, release to beta testers. Anticipated Accomplishment: Incorporate user suggestions and improve usability of the decision support system. FY2008: 1. Analyze moisture data. Refine probe. Conduct commercial field studies. Anticipated Accomplishment: Data from laboratory tests will be analyzed and used to refine the probe. Tests will be conducted during the peanut harvest at commercial facilities. 2. Update peanut database. Analyze data. Collect data. Anticipated Accomplishment: Data from collaborators in the UPPT will be added to the database. An index to objectively evaluate the peanut cultivars in terms of agronomic, chemical, and manufacturing performance will be developed. 3. Analyze curing data. Conduct prototype/full-scale IR curing research. Anticipated Accomplishment: Continue collaborative research to provide operational parameters for RF dryers for curing farmer stock peanuts. Initiate tests to test the feasibility of RF heating for blanching and roasting. Successful implementation of RF drying may provide a method for fast continuous flow drying for farmer stock peanuts. 4. Analyze CA storage data. Continue scale model and commercial CA storage research. Anticipated Accomplishment: Continue scale model and commercial controlled atmosphere storage research. Implementing controlled atmosphere storage at the commercial level will reduce losses due to mold and insects during storage. 5. Analyze beta peanut curing decision support system, modify underlying models as necessary. Anticipated Accomplishment: Compare commercial dryer performance data to model output. Modify model if necessary. Expand commercial use of the peanut curing decision support system. 4a What was the single most significant accomplishment this past year? Peanut Curing Decision Support System: A decision support system for managing peanut curing operations was released for use by the peanut industry. The software included models to accurately predict peanut drying time in response to specific drying equipment, weather conditions, and dryer control parameters. The software includes tools to manage the flow of peanuts at the drying facility from the time they are delivered by the farmer until they are graded and marketed. Use of the decision support system will reduce labor, minimize over drying, and document drying conditions for all peanuts cured at that facility. 4c: No activities to report. 5. Describe the major accomplishments over the life of the project, including their predicted or actual impact. FY 2005. PECMAN, a decision support system to manage commercial peanut drying facilities was released and meets milestone 4A of the project plan. System users will reduce labor and minimize risk of over drying farmer stock peanuts. Over drying can cost as much as $22/ton. This accomplishment was directed toward Component 1d, Preservation and/or Enhancement of Quality and Marketability of the action plan for NP 306. By improving the management of the peanut curing process, the risk of quality loss during the first and critical post harvest process is minimized. 6. What science and/or technologies have been transferred and to whom? When is the science and/or technology likely to become available to the end- user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? A decision support system to manage commercial peanut curing operations, PECMAN, was released in July 2005. Commercial peanut curing facilities monitor up to 200 dryers simultaneously using seasonal labor. The software incorporates an empirical model published in 2004 to predict drying times for each active peanut dryer and provides sampling schedules. By using the software, commercial facilities can optimize seasonal labor, minimize the risk of over drying, and maximize system use efficiency. The software is usable by most all education levels typically found at the drying facility. As long as batch systems are used for drying peanuts, this software will be applicable

Impacts
(N/A)

Publications

  • Heping, Z, Butts, C.L., Lamb, M.C., Blankenship, P.D.. 2004.An implement to install and retrieve surface drip irrigation laterals. Applied Engineering in Agriculture. 20(1):17-23.
  • Butts, C.L., Davidson, J.I., Lamb, M.C., Kandala, C., Troeger, J.M. 2004. Estimating drying time for a stock peanut curing decision support system. Transactions of the ASAE. Vol 47(3): 925-932.
  • ASABE Paper number 05-6182. ASABE, St. Joseph, MI. p. 13.