Location: Market Quality and Handling Research
2005 Annual Report
B. How serious is the problem? Manufacturers of food products are exerting greater pressures on suppliers of raw commodities to provide product with lower levels of mycotoxins, GM proteins, and foreign material. Government regulatory agencies are also continuing to reduce mycotoxin legal limits on imported products. It is estimated that 25% of all crops are contaminated with mycotoxins resulting in large economic losses to producers, processors, manufacturers, exporters, and importers. As a result of animal studies, aflatoxin and other mycotoxins have been shown to be toxic and carcinogenic. About 100 some countries have established legal limits for aflatoxin and other mycotoxins to control the levels of mycotoxins in food and feed products. Europe and Asia are setting limits on the percent GM seed found in imported lots. As a result individual commodity industries invest substantial resources to test raw product so that consumer-ready products meet FDA legal limits or manufacturer specifications. StarLink Logistic Inc. have spent an estimate 3 million dollars over the last three years assisting the grain industry to keep StarLink corn out of the food market by testing corn for GM protein. With such large investments of resources and concerns for public and animal health, commodity industries and regulatory agencies want a method to evaluate the performance of sampling plans so that efficient sampling plans can be designed to minimize misclassification of lots. Methods to predict the effects of sample designs on the costs and risks to processors, manufacturers, consumers, exporters and importers are needed to improve product quality and improve consumer safety.
1--Complete sample collection and send samples to aflatoxin lab for analysis. (objective 1)
2-- Complete sample collection and send samples to Federal State grading offices for FM count. (objective 2)
Year 2 (FY 2004)
1-- Complete analytical testing and send test results to MQHRU to begin statistical analysis. (objective 1)
2-- Complete FM measurements and send test results to MQHRU to begin statistical analysis. (objective 2)
3-- Complete sample collection and send samples to FDA analytical lab for analysis. (objective 3)
Year 3 (FY 2005)
1-- Complete statistical analysis and begin development of a computer model to predict performance of aflatoxin sampling plans for almonds. (objective 1)
2-- Complete statistical analysis and develop computer model to predict performance of FM sampling plans. (objective 2)
3-- Complete analytical testing and send test results to MQHRU to begin statistical analysis. (objective 3)
Year 4 (FY 2006)
1—Complete computer model and work with industry and regulatory agencies to design sampling plans to meet customer requirements of costs and risks. (objective 1)
2-- Work with industry and regulatory agencies to design sampling plans to meet customer requirements of costs and risks. (objective 2)
3-- Complete statistical analysis and develop computer model to predict performance of sampling plans. (objective 3)
Year 5 (FY 2007)
1—Complete documentation of study. (objective 1)
2.—Complete documentation of study. (objective 2)
3-- Work with industry and regulatory agencies to design GM sampling plans to meet customer requirements of costs and risks. (objective 3)
Measured the Cry9C protein distribution among 800 individual StarLink corn kernels that will be used to develop design StarLink sampling plans for FDA, EPA, USDA, and the grain industry. Because StarLink (Cry9C protein) is a genetically modified corn that produces an insecticidal protein, the U.S. Food and Drug Administration (FDA), USDA, and EPA limit the use of StarLink corn to feed-use only and established a zero tolerance for StarLink corn in the human food supply. Since some StarLink corn has been found in the food supply, FDA, EPA, USDA, and corn millers now inspect over one million shelled and milled corn lots each year, destined for human consumption, for the presence of StarLink. It is difficult to determine accurately the levels of StarLink corn in large shipments because of the uncertainty associated with the sampling and analytical methods used in the test procedure, which results in some lots being misclassified. Working with Francis Giesbrecht (Department of Statistics, NCSU) and Mary Trucksess (FDA), the Cry9C distribution among 800 individual kernels has been measured. From the Cr9C distribution, the effect of sample size on the sampling variance can be determined. Knowing the variability and distributional characteristics, StarLink sampling plans can be designed to reduce the number of lots misclassified, which will reduce both health risks to the consumer and economic loss to the processor.
The sampling and analytical errors associated with measuring StarLink in corm meal and flour were determined so that effective StarLink sampling plans can be developed for the grain industry and regulatory agencies. Because StarLink is a genetically modified corn that produces an insecticidal protein, the U.S. Food and Drug Administration (FDA) limits the use of StarLink corn to feed-use only and has established a zero tolerance for StarLink corn in the human food supply. Since some StarLink corn has been found in the food supply, FDA, USDA, and corn millers now inspect shelled and milled corn, destined for human consumption, for the presence of StarLink. It is difficult to determine accurately the levels of StarLink corn in large shipments because of the errors associated with the sampling and analytical methods used in the test procedure, which results in some lots being misclassified. Working with Francis Giesbrecht (Department of Statistics, NCSU) and Mary Trucksess (FDA), the sampling and analytical errors associated with measuring StarLink in corm meal and flour were determined. Once the magnitude of the testing errors were know, the effect of sample size and number of analyses on reducing testing errors and the number of lots misclassified was demonstrated. Knowledge of the measurement errors will reduce both health risks to the consumer and economic loss to the processor.
The percentage of foreign material in 80 samples taken from each of 12 peanuts lots (960 grade samples) was measured and the variability and distributional characteristics among sample test results was determined to provide data to design effective sampling plans to predict the level of foreign material in shelled peanuts. Food manufacturers of peanut products are requiring shellers supply raw peanuts with the lowest possible levels of foreign material. Even though shellers use the latest sorting technology to remove foreign material, it is next to impossible to completely remove all foreign material. Shellers and food manufacturers sample peanut lots to estimate the quantity of foreign material in a lot. Because of the errors in sampling, some lots will be misclassified according to the level of foreign material in the lot. Working with Francis Giesbrecht (Department of Statistics, NCSU) and Gregg Grimsley (Birdsong Peanuts), 80 grade samples were taken from each of 12 peanut lots containing foreign material. The percentage of foreign material in each of the 960 grade samples (12x80) was determined by grading inspectors. The variability among the 80 sample estimates of foreign material per lot will be statistically analyzed so the rate of misclassifications can be predicted as a function of sample size.
Developed theoretical models to predict the effect of sample size on reducing the chances of not detecting lots intentionally contaminated with chemical agents. Because of increased threats of terrorism, the FDA and USDA are concerned with food security or the intentional adulteration of foods with chemical and/or biological agents. The FDA and USDA are developing plans for larger scale inspections or sampling of foods to detect intentional adulteration. Because of errors associated with sampling, it is difficult to accurate estimate the true level of an unwanted agent in foods and as a result some lots will be misclassified. The model was developed in cooperation with Douglas Park (FDA). While these theoretical models are based on previous studies to predict the performance of sampling plans to detect mycotoxins, an unintentional contaminate produced by fungi, they should provide regulatory agencies with guidelines on how to design sampling plans to minimize consumers’ risk associated with not detecting contaminated lots.
Determined the percent reduction that various sorting methods have on reducing aflatoxin in processed almonds to demonstrate to the EU that current aflatoxin limits for almonds are too low for unprocessed almonds. The EU indicated they would consider increasing the maximum aflatoxin limit for imported almonds from 5 B1/10 total ppb to 8 B1/15 total ppb (current peanut limits) if documented evidence could be produced showing that processing methods reduce aflatoxin in almonds. At the request of the Almond Board of California, experiments were designed, data were analyzed, and results were documented in the Market Quality and Handling Research Unit Laboratory at N.C. State University in cooperation with Julie Adams and Merle Jacobs (ABC), and Frans Verstraete (European Commission) that described the percent reduction that various sorting methods have on reducing aflatoxin in processed almonds. Aflatoxin reductions in processed almonds was consistently in excess of 90% when using the basic industry sorting methods such as electronic color sorting, hand sorting, gravity table sorting, and blanching to remove aflatoxin-contaminated almonds during processing. Results have been incorporated into a document developed by the Almond Board of California and sent to the European Commission for review.
Evaluated the performance (accuracy and precision) associated with four commercially available test kits at detecting peanut proteins in four different foods to assist FDA develop an inspection program to detect peanut protein in foods. Unintentional contamination of foods with peanut proteins can cause severe allergenic reactions that can result in shock and death. The determination of peanut proteins in foods at various stages of the manufacturing process by analytical methods can reduce the risk of serious reactions in highly sensitized individual by removing contaminated foods from the market system. The U.S. Food and Drug Administration (FDA) is developing an inspection program to randomly test foods for unintentional peanut contamination using commercially available analytical test kits. The efficiency of detecting and removing contaminated foods depends on the performance of the test kits used to quantify peanut proteins in foods. The study was conducted in cooperation with Francis Giesbrecht (Department of Statistics, NCSU), Mary Trucksess and Kristine Williams (FDA). The performance of the test kits will be used to help field inspectors minimize the errors associated with not detecting contaminated lots. Minimization of false negatives will make the food supply safer for individuals allergic to peanut proteins.
Determined the uncertainty associated with sampling plans used to detect ochratoxin A (OTA) in green coffee so that effective sampling plans can be developed for the export market. Since OTA is a toxic and carcinogenic compound that naturally occurs in several foods such as grains, coffee, and grapes, organizations such as FDA, European Union, and CODEX are considering the establishment of maximum limits and sampling plans to detect OTA in several foods, particularly in coffee. Because of errors associated with sampling plans used to detect OTA in coffee, some lots are misclassified causing an unnecessary expense to the processor and a health risk to the consumer. The study was conducted in cooperation with Francis Giesbrecht (Department of Statistics, NCSU), Eugenia Vargas (Brazil Ministry of Agriculture), Garnett Wood (FDA), and Frans Verstraete (European Commission). Using the uncertainty estimates, a computer model was developed to predict the performance of OTA sampling plans for green coffee. The computer model is being used to demonstrate to exporting and importing countries how to design OTA sampling plans to minimize misclassification of coffee lots.
Determined the moisture distribution among individual peanut kernels for 50 groups of 100 peanut kernels to predict the percentage of high moisture peanuts in bulk lots after the curing process based upon the measurement of moisture in a limited number of kernels. Because a few high moisture peanut kernels in bulk lots going into storage can cause quality deterioration and aflatoxin formation, there is a need to know the distribution of moisture content among individual peanut kernels after the curing process. Working with Christopher Butts, statistically analyzed single seed moisture content data for 50 groups of 100 peanut kernels/group in the Market Quality and Handling Research Unit Laboratory at N.C. State University in cooperation with Christopher Butts (National Peanut Research Lab) and Francis Giesbrecht (Department of Statistics, NCSU). Compared three theoretical distributions to the each of the 50 observed distributions and determined the most suitable theoretical distribution to characterize the actual moisture content distribution among peanut kernels. The theoretical distribution will be used to predict the percentage of high moisture peanuts in bulk lots after the curing process to improve quality.
Compared peanut kernel size and aflatoxin contamination among peanuts in the current US # 1 grade and the new proposed US #1 grade to assist USDA/AMS decide on implementing a new definition of a n US #1 grade peanut. Because peanut shellers requested that the USDA,AMS and the Peanut Standards Board (PSB) change the definition of a US #1 runner peanut from peanuts that rides a 16/64 slotted (16S) screen to one that rides a 17/64 round (17R) screen, the kernel size distribution and aflatoxin levels among peanut kernels that would ride a 17R screen were compared to kernels that ride a 16S screen. The study was conducted by the Market Quality and Handling Research Unit Laboratory at N.C. State University working in cooperation with Tom Tichenor, Frank Bodiford, Bobby Joyner, Jim Wendland (USDA,AMS), and Francis Giesbrecht (Department of Statistics, NCSU). Results indicated that peanut kernels that ride a 17R screen contains 21.9% more aflatoxin and 21.4% more smaller kernels than peanuts that ride a 16S screen. Studies were presented to the American Shellers Association and Peanut Standards Board meeting. Peanut Standards Board recommended to USDA-AMS that the 17R screen define a US #1 runner.
Compared the percent damage, percent foreign material, and aflatoxin levels in imported peanut to domestic peanuts to help make U.S. peanut industry more competitive in the world market with foreign origin peanuts. Using grading records provided by the USDA-AMS and Georgia Federal State Inspection service, the percent damage and foreign material were compared in both domestic and imported peanuts were sorted by grade and type peanut in the Market Quality and Handling Research Unit Laboratory at N.C. State University in cooperation with Frank Bodiford, Nate Tichnor, Bobby Joyner, and Howard Valentine. Results showed that percent damage and foreign material were in much smaller amount in domestic peanuts than in foreign origins. Results were presented to a USDA-FAS and American Peanut Council trade workshop with European buyers.
Assisted a biotech company develop a risk assessment model that will be used to end mandatory sampling and testing of corn for StarLink in the food chain. USDA, FDA, and EPA have reviewed the protocol and have initially indicated the protocol has merit and will be reviewed further.
Collected and stored 40,000 records (one record per shelled peanut lot marketed) of aflatoxin and grade data for the 2002 crop year for the Peanut Standards Board (PSB) formally the Peanut Administrative Committee. Provided data analysis of 40,000 lot records showing PSB the extent of the aflatoxin contamination in that crop year. The data based, started in 1975, contains about 1,200,000 total records of aflatoxin and grade data. PSB and USDA/AMS frequently request analysis of the database to help guide future decisions about the USDA aflatoxin control program for peanuts.
Assisted a major peanut sheller develop a program to effectively detect foreign material in shelled peanuts contracted by food manufacturers. Sampling methods will help reduce false negatives, which is a major concern to both the sheller and food manufacturer.
Assisted both the almond and pistachio industries with information needed to design aflatoxin sampling program for both the domestic and export markets. False negatives are a special concern for product in the export market to the EU since their aflatoxin limits are much lower than the U.S. FDA.
Provided sampling plan designs to the Codex Committee on Food Additives and Contaminates (CCFAC), which are under review as possible harmonized aflatoxin sampling plans for treenuts. The harmonization of sampling plans will improve world trade and provide better consumer protection.
Assisted peanut shellers, FSIS, and USDA/AMS by suggesting methods to reduce costs associated with taking and processing grade samples for farmers’ stock peanuts. All parties are concerned about the costs associated with selecting and processing grade samples for the large semi-trailer loads that are becoming more prevalent. Ways to reduce time and costs without significant sacrifice in accuracy were suggested and will be reviewed by the Peanut Standards Board.
Risks associated with harmonized aflatoxin sampling plans for peanuts in the export market. Harvey W. Wiley Award Symposium, Association of Official Analytical Chemists. St. Louis, Missouri. 2004.
Sampling almonds for aflatoxin. Almond Board of California. Modesto, California, 2005.
Sampling foods for mycotoxins. Symposium on Agricultural Marketing Issues. European Union/Dried Fruits Association. Fresno, California, 2005.
Effect of shelling plant processes on removing aflatoxin from farmers’ stock peanuts. Peanut Foundation, American Peanut Council, Washington, DC, 2005.
Sampling pistachios for aflatoxin in the domestic and export markets. California Pistachio Board. Fresno, California.
Sampling farmers’ stock peanuts for grade. Supervisors Annual Meeting. USDA/AMS and the Federal State Inspection Service, Oklahoma City, Oklahoma, 2005.Whitaker, T.B., Dorner, J.W., Giesbrecht, F.G., Slate, A.B. 2004. Variability among aflatoxin test results on runner peanuts harvested from five-foot field plots. Peanut Science. 31:59-63.
Whitaker, T.B., Johansson, A.S. 2005. Sampling uncertainties for the detection of chemical agents in complex food matrices. Journal of Food Protection. 68:1306-1313.
Vargas, E.A., Whitaker, T.B., Santos, E.A., Slate, A.B., Lima, F.B., Franca, R.C. 2005. Testing green coffee for ochratoxin a, part ii: observed distribution of ochratoxin a test results. Journal of the Association of Official Analytical Chemists. 88:780-787.
Whitaker, T.B., Slate, A.B., Williams, K.M., Trucksess, M.W. 2005. Immunochemical analytical methods for the determination of peanut protein in foods. Journal of the Association of Official Analytical Chemists. 88:161-174.