Page Banner

United States Department of Agriculture

Agricultural Research Service

Research Project: IMPROVE THE DETECTION OF QUALITY ATTRIBUTES AND CHEMICAL AGENTS IN AGRICULTURAL COMMODITIES Title: Design of sampling plans to detect foreign material in bulk lots of shelled peanuts

Authors
item Whitaker, Thomas
item Slate, A - NC STATE UNIVERSITY
item Giesbrecht, F - NC STATE UNIVERSITY

Submitted to: Journal of Food Additives & Contaminants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 30, 2007
Publication Date: N/A

Interpretive Summary: As a natural process related to harvesting, handling, and curing, peanuts may contain a variety of foreign material (FM) such as dirt, sticks, rocks, grain seeds, metal objects, and glass. The goal of the sheller is to remove as much of the FM as economically and technically possible during the shelling and cleaning process. Because foreign material in the finished or processed nuts can be a hazard to consumers and an economic liability, food manufacturers have put increasing pressure on shellers to approach 100% FM removal in commercial lots. The sheller and food manufacturer estimate the FM contamination in the bulk lot by taking one or more samples from the lot and counting the number of FM pieces in a sample of a given mass. Because the number of FM pieces among samples taken from the same lots will differ, the sheller can never determine the true proportion of FM in the lot with 100% confidence. Because of the variability among sample test result, some lots will be misclassified. There is a chance that samples from a good lot will test bad and a chance that samples from bad lots will test good. It is important for the food manufacturer and sheller to know the effect of sample size on the uncertainty associated with using samples to estimate the true proportion of FM a lot and how to reduce misclassification of lots relative to a limit specified by the food manufacturer. Results indicated that the variability and distribution among sample test results can be predicted by the binomial distribution. A method was developed, based upon the binomial distribution, to evaluate the performance of sampling plan designs used to estimate FM in a bulk lot of shelled peanuts so that effective sampling plans can be designed to reduce FM in consumer-ready products.

Technical Abstract: When food manufacturer specifies a maximum limit for the amount of foreign material (FM) in the lot, handlers estimate the true percent FM in a commercial lot by measuring FM in a small sample taken from the lot before shipment to a food manufacturer. Because of the uncertainty (variability) in FM among samples taken from the same lot, it is difficult to obtain a precise estimate of the true FM in the lot. The objectives of this study were to (1) measure the variability and FM distribution among sample test results when estimating the true lot proportion of FM in a lot of shelled peanuts, (2) compare the measured variability and FM distribution among sample test results to that predicted by the binomial distribution, (3) develop a computer model, based upon the binomial distribution, to evaluate the performance (buyer’s risk and sheller’s risk) of sampling plan designs used to estimate FM in a bulk lot of shelled peanuts, and (4) demonstrate with the model the effect of increasing sample size to reduce misclassification of lots. Eighty-eight samples, 9 kg (20lb) each, were selected at random from each of six commercial lots of shelled medium runner peanuts. The percent FM (PFM), based upon number of kernels was determined for each sample. The mean, variance, and distribution among the 88 sample test results were calculated for each of the six lots. Results indicated that the variance and distribution among the 88 sample test results and very similar to that predicted by the binomial distribution. The performance of various sampling plan designs was demonstrated using the binomial distribution.

Last Modified: 10/20/2014
Footer Content Back to Top of Page