Sampling almonds for aflatoxin, part II: estimating risks associated with various sampling plan designs.

About 100 nations have established regulatory limits for aflatoxin in food and feeds. Because these limits vary widely from one country to another, the Codex Alimentarius Commission, working through the Codex Committee on Food Additives and Contaminants, has initiated work to harmonize aflatoxin limits and sampling plans for almonds, pistachios, hazelnuts, and Brazil nuts. Studies were developed to measure the uncertainty and distribution among test results for replicate samples taken from aflatoxin-contaminated almond shipments. The uncertainty and distribution information was used to develop a model to evaluate the performance of aflatoxin sampling plans so that harmonized sampling plans can be developed for almonds that reduce the misclassifying of lots in the export trade. Twenty lots of shelled almonds were sampled according to an experimental protocol in which sixteen 10 kg samples were taken from each lot. The observed aflatoxin distribution among the 16 sample test results was compared with 3 theoretical distributions. The negative binomial distribution was selected to model aflatoxin distribution among sample test results because it gave acceptable fits across all 20 observed sample distributions. By using the variance and distribution information, operating characteristics curves were developed to predict the effect of sample size and accept/reject limits on the probability of rejecting good lots and accepting bad lots.

Sampling hazelnuts for aflatoxin: effect of sample size and accept/reject limit on reducing the risk of misclassifying lots.

About 100 countries have established regulatory limits for aflatoxin in food and feeds. Because these limits vary widely among regulating countries, the Codex Committee on Food Additives and Contaminants began work in 2004 to harmonize aflatoxin limits and sampling plans for aflatoxin in almonds, pistachios, hazelnuts, and Brazil nuts. Studies were developed to measure the uncertainty and distribution among replicated sample aflatoxin test results taken from aflatoxin-contaminated treenut lots. The uncertainty and distribution information is used to develop a model that can evaluate the performance (risk of misclassifying lots) of aflatoxin sampling plan designs for treenuts. Once the performance of aflatoxin sampling plans can be predicted, they can be designed to reduce the risks of misclassifying lots traded in either the domestic or export markets. A method was developed to evaluate the performance of sampling plans designed to detect aflatoxin in hazelnuts lots. Twenty hazelnut lots with varying levels of contamination were sampled according to an experimental protocol where 16 test samples were taken from each lot. The observed aflatoxin distribution among the 16 aflatoxin sample test results was compared to lognormal, compound gamma, and negative binomial distributions. The negative binomial distribution was selected to model aflatoxin distribution among sample test results because it gave acceptable fits to observed distributions among sample test results taken from a wide range of lot concentrations. Using the negative binomial distribution, computer models were developed to calculate operating characteristic curves for specific aflatoxin sampling plan designs. The effect of sample size and accept/reject limits on the chances of rejecting good lots (sellers' risk) and accepting bad lots (buyers' risk) was demonstrated for various sampling plan designs.

Sampling almonds for aflatoxin, part I: estimation of uncertainty associated with sampling, sample preparation, and analysis.

Domestic and international regulatory limits have been established for aflatoxin in almonds and other tree nuts. It is difficult to obtain an accurate and precise estimate of the true aflatoxin concentration in a bulk lot because of the uncertainty associated with the sampling, sample preparation, and analytical steps of the aflatoxin test procedure. To evaluate the performance of aflatoxin sampling plans, the uncertainty associated with sampling lots of shelled almonds for aflatoxin was investigated. Twenty lots of shelled almonds were sampled for aflatoxin contamination. The total variance associated with measuring B1 and total aflatoxins in bulk almond lots was estimated and partitioned into sampling, sample preparation, and analytical variance components. All variances were found to increase with an increase in aflatoxin concentration (both B1 and total). By using regression analysis, mathematical expressions were developed to predict the relationship between each variance component (total, sampling, sample preparation, and analysis variances) and aflatoxin concentration. Variance estimates were the same for B1 and total aflatoxins. The mathematical relationships can be used to estimate each variance for a given sample size, subsample size, and number of analyses other than that measured in the study. When a lot with total aflatoxins at 15 ng/g was tested by using a 10 kg sample, a vertical cutter mixer type of mill, a 100 g subsample, and high-performance liquid chromatography analysis, the sampling, sample preparation, analytical, and total variances (coefficient of variation, CV) were 394.7 (CV, 132.4%), 14.7 (CV, 25.5%), 0.8 (CV, 6.1%), and 410.2 (CV, 135.0%), respectively. The percentages of the total variance associated with sampling, sample preparation, and analytical steps were 96.2, 3.6, and 0.2, respectively.

Oil, sugar, and starch characteristics in peanut breeding lines selected for low and high oil content and their combining ability.

Peanut seeds contain approximately 50% oil on a dry weight basis, making them a high fat food. Reduction of the oil content would make peanuts a more desirable food to fat conscious consumers. Removal of existing oil by processing is not feasible for in-shell peanuts, the dominant product of the North Carolina-Virginia area. To reduce oil content in in-shell peanuts, a genetic solution must be found. However, while reduced oil content is a desirable objective, changes in oil must not be accompanied by significant decreases in any of the desirable aspects of peanut flavor. Because the impact of selection for low or high oil on flavor is not known, it would be useful to know in what form dry matter is being stored in the seed, particularly if it is not being stored as oil. Screening of 584 accessions identified two lines (PI 269723 and PI 315608) with high and two (Robusto 2 and Robusto 3) with low oil contents, each pair differing in sugar content. The four parents were crossed in diallel fashion to investigate patterns of inheritance. General combining abilities (GCA) for oil content closely followed values of the parental lines. One low oil parent (Robusto 2) had a correspondingly elevated GCA for sugar content, but neither low oil parent had the effect of elevating starch in progeny. Reciprocal cross differences were found for starch and sugar contents, suggesting influences of cytoplasmic genes on those traits. These lines serve as resource material for researchers interested in the genetic and physiological aspects of the oil-sugar-starch relationship in peanuts.

Effect of high-oleic trait and paste storage variables on sensory attribute stability of roasted peanuts.

There has been much interest in the effect of the high-oleic acid trait of peanuts on various quality factors since discovery of high levels of oleic acid in a peanut mutant genotype. The trait provides greater oxidative stability for the high-oleic oil and seed. Several research groups have investigated high-oleic peanut oil and roasted peanut flavor characteristics, which were similar within high-oleic lines compared to Florunner. It was observed that some high-oleic lines derived from the Sunrunner cultivar have consistently higher predicted breeding values for roasted peanut attribute than Sunrunner itself. This study investigated if this apparent effect of the trait was an artifact arising from the handling procedures during processing and storage or from flavor fade. High-oleic lines used were derived by backcrossing the trait into existing cultivars, and the comparison of sensory attribute intensity was with the recurrent parent used in backcrossing. Previous comparisons have been between lines differing in more than just oleate content, that is, with widely different background genotypes that could contribute to the differences observed. Differential rates of change in sensory attributes were found in different background genotypes, suggesting that the comparison of high- and normal-oleic lines should be made in common background genotypes as well as in common production and postharvest environments. There was no measurable change in roasted peanut attribute in samples stored at -20 degrees C over the 63 day duration of this experiment. There were changes in roasted peanut in samples stored at 22 degrees C, confirming that storage at -20 degrees C is sufficient for large studies that require multiple sensory panel sessions over a period of weeks.

Selection of alternative genetic sources of large-seed size in Virginia-type peanut: evaluation of sensory, composition, and agronomic characteristics.

Jenkins Jumbo, the ancestral source of large-seed size in the Virginia market type (Arachis hypogaea L.), has been shown to have a deleterious effect on flavor of peanut. The pervasiveness of Jenkins Jumbo in the ancestry of large-seeded germplasm contributes to the generally less intense roasted peanut flavor of U.S. cultivars of the Virginia market type. As a remedy to this problem, alternative sources of large-seed size were sought. Nine large-seeded selections, with NC 7 and Florunner as checks, were tested in replicated trials in North Carolina and Florida from 1996 to 1998. Pod yield, grade, weight of 100 seeds, and oil, sugar, and starch contents were measured. A descriptive sensory panel evaluated flavor attributes of a roasted sound mature kernel (SMK) sample from each plot. NC 7 scored low for sweet sensory attribute, high for bitter, and median for roasted peanut. UF714021, a multiline incorporating the Altika cultivar with several sister lines, had the best flavor profile of the large-seeded selections, but it did not have particularly large seeds relative to NC 7. The largest seeded selections were X90037 and X90053, both derived from Japan Jumbo. Flavor scores for X90037 were similar to those for NC 7 for roasted peanut (3.0 vs 2.9 flavor intensity units, fiu) and sweet (2.7 vs 2.6 fiu) but worse than NC 7 for bitter (3.3 vs 3.7 fiu) and astringent (3.5 vs 3.7 fiu). X90053 had intermediate values for roasted peanut and astringent, high value for sweet, and low for bitter. Other lines that had or were likely to have Jenkins Jumbo as a recent ancestor were generally poor in roasted flavor, supporting the hypothesis that ancestry from Jenkins Jumbo imparts poor flavor characteristics. With the exception of the unexpected relationship between astringent attribute and extra large kernel (ELK) content (r = 0.82, P < 0.01), there were no significant correlations between sensory attributes and the important agronomic traits: yield, meat, and ELK content. Among the nine large-seeded lines tested in this study, three appear to have greater potential for use as parents: 86x45B-10-1-2-2-b2-B, UF714021, and X90053.

Sampling wheat for deoxynivalenol.

The variability associated with testing wheat for deoxynivalenol (DON) was measured using a 0.454 kg sample, a Romer mill, 25 g of comminuted subsample and the Romer Fluoroquant analytical method. The total variability was partitioned into sampling, sample preparation, and analytical variability components. Each variance component was found to be a function of the DON concentration and equations were developed to predict each variance component using regression techniques. The effects of sample size, subsample size, and number of aliquots on reducing the variability of the DON test procedure were also determined. Using the test procedure described above, the coefficient of variation (CV) associated with testing wheat at 5 ppm DON was found to be 13.4%. The CVs associated with sampling, sample preparation, and analysis were 6.3, 10.0, and 6.3%, respectively. The sample variations associated with testing wheat are relatively small when compared to CVs associated with testing other commodities for other mycotoxins such as aflatoxin in peanuts. Even with the use of a small sample size (0.454 kg), the sampling variation was not the largest source of error as found in other mycotoxin test procedures.

Estimating deoxynivalenol in shelled corn barge lots by measuring deoxynivalenol in corn screenings.

To determine if deoxynivalenol (DON) is concentrated in small corn screenings, fourteen to twenty-three 1.1 kg test samples were taken from each of 10 barges of shelled corn. Each of the 181 test samples was divided into 2 components (fines and clean) using a 5 mm screen. The clean component sample rode the 5 mm screen and the fines component sample passed through the 5 mm screen. The DON concentration in fines component sample was about 3 times the DON concentration in the clean component sample. The DON in the 181 fines and clean component samples averaged 689.0 and 206.1 ng/g, respectively. Regression equations were developed to predict the DON in the barge based upon measurements of DON in the fines component sample. The ratio of DON in the lot to DON in the fines component sample was 0.359. The coefficient of variation (CV) associated with predicting the DON concentration in a lot with 359 ng/g using a 1.1 kg test sample was 47.0%. Increasing sample size to 4.4 kg reduced the CV to 23%.

Evaluation of sampling plans to detect Cry9C protein in corn flour and meal.

StarLink is a genetically modified corn that produces an insecticidal protein, Cry9C. Studies were conducted to determine the variability and Cry9C distribution among sample test results when Cry9C protein was estimated in a bulk lot of corn flour and meal. Emphasis was placed on measuring sampling and analytical variances associated with each step of the test procedure used to measure Cry9C in corn flour and meal. Two commercially available enzyme-linked immunosorbent assay kits were used: one for the determination of Cry9C protein concentration and the other for % StarLink seed. The sampling and analytical variances associated with each step of the Cry9C test procedures were determined for flour and meal. Variances were found to be functions of Cry9C concentration, and regression equations were developed to describe the relationships. Because of the larger particle size, sampling variability associated with cornmeal was about double that for corn flour. For cornmeal, the sampling variance accounted for 92.6% of the total testing variability. The observed sampling and analytical distributions were compared with the Normal distribution. In almost all comparisons, the null hypothesis that the Cry9C protein values were sampled from a Normal distribution could not be rejected at 95% confidence limits. The Normal distribution and the variance estimates were used to evaluate the performance of several Cry9C protein sampling plans for corn flour and meal. Operating characteristic curves were developed and used to demonstrate the effect of increasing sample size on reducing false positives (seller's risk) and false negatives (buyer's risk).

Sampling and analytical variability associated with the determination of total aflatoxins and ochratoxin A in powdered ginger sold as a dietary supplement in capsules.

The U.S. Food and Drug Administration is studying the need to monitor dietary supplements for mycotoxins such as total aflatoxins and ochratoxin A. An effective mycotoxin-monitoring program requires knowledge of the sampling and analytical variability associated with the determination of total aflatoxins (AF) and ochratoxin A (OTA) in dietary supplements. Three lots of ginger sold as a powder in capsule form and packaged in individual bottles were analyzed for both AF and OTA. The total variability associated with measuring AF and OTA in powdered ginger was partitioned into bottle-to-bottle, within bottle, and analytical variances. The variances were estimated using a nested design. For AF and OTA, the within-bottle variance associated with the 5 g laboratory sample size was the largest component of variability accounting for about 43% and 85% of the total variance, respectively; the analytical variance accounted for about 34% and 9% of the total variability, respectively; and the bottle-to-bottle variance accounted for about 23% and 7% of the total variance, respectively. When the total variance is converted into the coefficient of variation (CV or standard deviation relative to the mean concentration), the CV is lower for AF (16.9%) than OTA (24.7%).