Distribution among sample test results when testing shelled corn lots for fumonisin.

The statistical distribution known as the compound gamma function was studied for suitability in describing the distribution of sample test results associated with testing lots of shelled corn for fumonisin. Thirty-two 1.1 kg test samples were taken from each of 16 contaminated lots of shelled corn. An observed distribution consisted of 32 sample fumonisin test results for each lot. The mean fumonisin concentration, c, and the variance, s2, among the 32 sample fumonisin test results along with the parameters for the compound gamma function were determined for each of the 16 observed distributions. The 16 observed distributions of sample fumonisin test results were compared with the compound gamma function using the Power Divergence test. The null hypothesis that the observed distribution could have resulted from sampling a family of compound gamma distributions was not rejected at the 5% significance level for 15 of the 16 lots studied. Parameters of the compound gamma distribution were calculated from the 32-fumonisin sample test results using the method of moments. Using regression analysis, equations were developed that related the parameters of the compound gamma distribution to fumonisin concentration and the variance associated with a fumonisin test procedure. An operating characteristic curve was developed for a fumonisin sampling plan to demonstrate the use of the compound gamma function.

Novel quantitative assays for estimating the antimicrobial activity of fresh garlic juice.

Novel agar diffusion and broth dilution assays were developed for quantitatively estimating the antimicrobial activity of fresh garlic juice. Bacteria found to be inhibited by garlic juice in agar diffusion assay included two gram-positive and five gram-negative species. Leuconostoc mesenteroides was not inhibited. Escherichia coli B-103 (HB101, with pJH101, ampicillin resistant, 100 microg ml(-1)) was inhibited and chosen as the standard culture for quantitative assays. The agar diffusion assay was based on the slope ratio method, where the slope of dose response for garlic juice was divided by the slope of dose response for methylmethane thiosulfonate (MMTSO2). Juice from fresh garlic varied in activity between 1.76 and 2.31 microg of MMTSO2 per mg of garlic juice. The activity of juice decreased during 11 months of storage of garlic cloves at 5 degrees C from 2.31 to less than 0.1 microg of MMTSO2 per mg of juice. The broth dilution assay also used the E. coli B-103 culture, which permitted selective enumeration of this bacterium when 100 microg ml(-1) of ampicillin was incorporated into the enumerating agar. Selective enumeration was essential since the garlic juice was not sterile and, thus, contained natural flora. Growth of E. coli was unaffected by 0.1%, delayed by 0.25%, and completely inhibited at 0.5 and 2% garlic juice in broth during 24 h of incubation at 37 micro C. The minimum inhibition concentration of garlic juice by broth dilution assay was, thus, estimated to be 0.5%, which is equivalent to 3.46 microg of MMTSO2 per mg of garlic juice by the agar diffusion assay.

Sampling grain shipments to detect genetically modified seed.

Using the binomial distribution, the effect of sample size on the variability among sample test results when sampling a lot with 1.0% genetically modified (GM) or biotech seed was evaluated. The coefficient of variation, cv, among 500-seed sample test results taken from a lot with truly 1.0% was computed to be 44.5%. Increasing sample size to 1000 seeds reduced the cv among sample test results to 31.5%. The effects of sample size and accept/reject limits on the buyer's risk (bad lots accepted) and the seller's risk (good lots rejected) was also evaluated assuming a tolerance of 1.0% GM seed. Increasing sample size decreases both the buyer's and seller's risks at the same time. Using an accept/reject limit below the regulatory tolerance decreases the buyer's risk, but increases the seller's risk. Using an accept/reject limit above the regulatory tolerance decreases the seller's risk but increases the buyer's risk.

Performance of three pneumatic probe samplers and four analytical methods used to estimate aflatoxins in bulk cottonseed.

The requirement by the U.S. Food and Drug Administration that agricultural products susceptible to aflatoxin contamination contain aflatoxin at levels < or =20 parts per billion for consumer-ready products has led to the establishment of inspection programs by various industries. In Arizona, cottonseed samples from 100 ton piles are collected by an accumulation of 3 or more probings with a pneumatic probe. When sampling compacted cottonseed piles, the large official pneumatic probe (7.6 x 127 cm) decreases in efficiency. Two smaller probes (1.9 x 127 cm and 1.9 x 254 cm ) were therefore developed and tested for their suitability for sampling cottonseed piles. Three rapid analytical methods (one thin-layer chromatographic and 2 immunochemical) were tested for suitability as on-site assay systems. An analysis of variance of the analytical test results showed no differences between the various probes tested. Of the rapid methods, however, only the AflaTest-P immunoaffinity column gave results similar to those of the official AOAC thin-layer chromatography method. In terms of safety, however, all methods prevent material contaminated above regulatory limits from reaching the consumer.

Testing shelled corn for aflatoxin, Part II: modeling the observed distribution of aflatoxin test results.

The suitability of several theoretical distributions to predict the observed distribution of aflatoxin test results in shelled corn was investigated. Fifteen positively skewed theoretical distributions were each fitted to 18 empirical distributions of aflatoxin test results for shelled corn. The compound gamma distribution was selected to model aflatoxin test results for shelled corn. The method of moments technique was chosen to estimate the parameters of the compound gamma distribution. Mathematical expressions were developed to calculate the parameters of the compound gamma distribution for any lot aflatoxin concentration and test procedure. Observed acceptance probabilities were compared to operating characteristic curves predicted from the compound gamma distribution, and all 18 observed acceptance probabilities were found to lie within a 95% confidence band. The parameters of compound gamma were used to calculate the fraction of aflatoxin-contaminated kernels in contaminated lots. At 20 ppb, it was estimated that about 6 in 10,000 kernels are contaminated.

Testing shelled corn for aflatoxin, Part III: evaluating the performance of aflatoxin sampling plans.

The effects of changes in sample size and/or sample acceptance level on the performance of aflatoxin sampling plans for shelled corn were investigated. Six sampling plans were evaluated for a range of sample sizes and sample acceptance levels. For a given sample size, decreasing the sample acceptance level decreases the percentage of lots accepted while increasing the percentage of lots rejected at all aflatoxin concentrations, and decreases the average aflatoxin concentration in lots accepted and lots rejected. For a given sample size where the sample acceptance level decreases relative to a fixed regulatory guideline, the number of false positives increases and the number of false negatives decreases. For a given sample size where the sample acceptance level increases relative to a fixed regulatory guideline, the number of false positives decreases and the number of false negatives increases. For a given sample acceptance level, increasing the sample size increases the percentage of lots accepted at concentrations below the regulatory guideline while increasing the percentage of lots rejected at concentrations above the regulatory guideline, and decreases the average aflatoxin concentration in the lots accepted while increasing the average aflatoxin concentration in the rejected lots. For a given sample acceptance level that equals the regulatory guideline, increasing the sample size decreases misclassification of lots, both false positives and false negatives.

Testing shelled corn for aflatoxin, Part I: estimation of variance components.

The variability associated with testing lots of shelled corn for aflatoxin was investigated. Eighteen lots of shelled corn were tested for aflatoxin contamination. The total variance associated with testing shelled corn was estimated and partitioned into sampling, sample preparation, and analytical variances. All variances increased as aflatoxin concentration increased. With the use of regression analysis, mathematical expressions were developed to model the relationship between aflatoxin concentration and the total, sampling, sample preparation, and analytical variances. The expressions for these relationships were used to estimate the variance for any sample size, subsample size, and number of analyses for a specific aflatoxin concentration. Test results on a lot with 20 parts per billion aflatoxin using a 1.13 kg sample, a Romer mill, 50 g subsamples, and liquid chromatographic analysis showed that the total, sampling, sample preparation, and analytical variances were 274.9 (CV = 82.9%), 214.0 (CV = 73.1 %), 56.3 (CV = 37.5%), and 4.6 (CV = 10.7%), respectively. The percentage of the total variance for sampling, sample preparation, and analytical was 77.8, 20.5, and 1.7, respectively.

Sampling, sample preparation, and analytical variability associated with testing wheat for deoxynivalenol.

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

Investigations into genotypic variations of peanut carbohydrates.

Carbohydrates are known to be important precursors in the development of roasted peanut quality. However, little is known about their genotypic variation. A better understanding of the role of carbohydrates in roasted peanut quality requires first an understanding of the genotypic variation in the soluble carbohydrate components. Ion exchange chromatography was used to isolate 20 different carbohydrate components in 52 genotypes grown in replicated trials at two locations. Inositol, glucose, fructose, sucrose, raffinose, and stachyose were quantitated, and 12 unknown peaks were evaluated on the basis of the peak height of the unknown relative to the cellobiose internal standard peak height. Peaks tentatively identified as verbascose and ajugose could not be properly integrated because of tailing. Of the 18 carbohydrates that were estimated, 9 exhibited significant variation between test environments, 5 among market types, 14 among genotypes within market types, and 11 exhibited some significant form of genotype x environment interaction. Genotypes accounted for 38-78% of the total variation for the known components, suggesting that broad-sense heritability for these components is high. The observed high genotypic variation in carbohydrate components is similar to the high genotypic variation observed for the sweetness attribute in roasted peanuts, which raises the question regarding possible interrelationships. The establishment of such interrelationships could be most beneficial to peanut breeding programs to ensure the maintenance of flavor quality in future peanut varieties.

Relationships of sweet, bitter, and roasted peanut sensory attributes with carbohydrate components in peanuts.

Certain roasted peanut quality sensory attributes have been shown to be heritable. Currently the only means of measuring these traits is the use of a trained sensory panel. This is a costly and time-consuming process. It is desirable, from a cost, time, and sample size perspective, to find other methodologies for estimating these traits. Because sweetness is the most heritable trait and it has a significant positive relationship to the roasted peanut trait, the possible relationships between heritable sensory traits and 18 carbohydrate components (inositol, glucose, fructose, sucrose, raffinose, stachyose, and 12 unknown peaks) in raw peanuts from 52 genotypes have been investigated. Previously reported correlations among sweet, bitter, and roasted peanut attributes were evident in this study as well. Where there was positive correlation of total sugars with sweetness, there also was positive correlation of total sugars with roasted peanut attribute and negative correlation of total sugars with bitterness and astringency. The expected generalized relationship of total sugars or sucrose to sweetness could not be established because the relationship was not the same across all market-types. Further work is needed to determine the nature of the chemical components related to the bitter principle, which appear to modify the sweet response and interfere with the sensory perception of sweetness, particularly in the Virginia market-type. Also, certain carbohydrate components showed significant relationships with sensory attributes in one market-type and not another. These differential associations demonstrate the complexity of the interrelationships among sweet, bitter, and roasted peanut sensory attributes. Within two market-types it is possible to improve the efficiency of selection for sweetness and roasted peanut quality by assaying for total carbohydrates. On the basis of the regression values the greatest efficiency would occur in the fastigiate market-type and then the runner.

Performance of sampling plans to determine aflatoxin in farmers' stock peanut lots by measuring aflatoxin in high-risk-grade components.

Five 2 kg test samples were taken from each of 120 farmers' stock peanut lots contaminated with aflatoxin. Kernels from each 2 kg sample were divided into the following U.S. Department of Agriculture grade components: sound mature kernels plus sound splits (SMKSS), other kernels (OK), loose shelled kernels (LSK), and damaged kernels (DAM). The kernel mass (g), aflatoxin mass (ng), and aflatoxin concentration (ng of aflatoxin/g of peanuts) were measured for each of the 2400 component samples. The variabilities associated with measuring aflatoxin mass (ng) in OK + LSK + DAM, or A(OLD)ng, and in LSK + DAM, or A(LD)ng, and aflatoxin concentration (ng/g) in OK + LSK + DAM, or A(OLD)ng/g, and in LSK + DAM, or A(LD)ng/g, were determined. The variance associated with measuring aflatoxin in each of the 4 combinations of components increased with aflatoxin, and functional relationships were developed from regression analysis. The variability associated with estimating the lot concentration from each of the 4 combinations of components was also determined. The coefficients of variation (CV) associated with estimating the aflatoxin for a lot with aflatoxin at 100 ng/g were 90, 86, 94 and 96% for aflatoxin masses A(OLD)ng and A(LD)ng and aflatoxin concentrations A(OLD)ng/g and A(LD)ng/g, respectively. The performance of aflatoxin sampling plans using the combination of aflatoxin masses in OK + LD + DAM and LD + DAM components was evaluated with a 2 kg test sample and a 50 ng/g accept/reject limit.

Sensory attribute variation in low-temperature-stored roasted peanut paste.

Length of sample storage can become significant in sensory studies due to panel fatigue limitations and samples needed for a reasonable expectation of finding significant differences. In roasted peanut sensory studies samples are stored between -10 and -23 degrees C to prevent or retard changes. Studies of up to 13 months' duration have examined stability and slow-rate sensory changes. Sweet taste was relatively stable, whereas bitter and tongue burn attributes increased slightly. Stale taste increased, suggesting lipid oxidation was taking place even at -23 degrees C. Painty attribute did not increase until stale was >3. An increase in fruity attribute was unexpected. With increases in fruity and stale attributes a decrease in roasted peanut was expected. However, storage at -23 degrees C seems to stabilize the roasted peanut lability when compared to storage at -10 degrees C. Fruity and stale interactions with roasted peanut and lability of roasted peanut were shown to be three separate and identifiable effects on roasted peanut.

Variability associated with testing shelled corn for fumonisin.

Variances associated with sampling, sample preparation, and analytical steps of a test procedure that measures fumonisin in shelled corn were estimated. The variance associated with each step of the test procedure increases with fumonisin concentration. Functional relationships between variance and fumonisin concentration were estimated by regression analysis. For each variance component, functional relationships were independent of fumonisin type (total, B1, B2, and B3 fumonisins). At 2 ppm, coefficients of variation associated with sampling (1.1 kg sample), sample preparation (Romer mill and 25 g subsample), and analysis are 16.6, 9.1, and 9.7%, respectively. The coefficient of variation associated with the total fumonisin test procedure was 45% and is about the same order of magnitude as that for measuring aflatoxin in shelled corn with a similar test procedure.

Investigations of the problems of assessing aflatoxin levels in peanuts.

In this study, a number of probability distributions that have been used to model the occurrence of aflatoxin in peanuts are compared. Two distributions, the compound gamma and the negative binomial, are shown to have special appeal in that both can be justified by reasoning from the fundamental biological and stochastic processes that generate the aflatoxin. Since method of moments and maximum likelihood give consistent estimates of parameters in both models, practical considerations suggest using the former. One hundred twenty data sets, each consisting of fifty observations, were not sufficient to provide goodness-of-fit tests to establish either as superior to the other as a model. Both models fit the data well, appreciably better than other models examined. An attractive aspect of the compound gamma and the negative binomial distributions is that, as a consequence of their theoretical underpinnings, both involve parameters that have meaningful interpretations. In the compound gamma, the alpha parameter reflects the shape of the kernel-to-kernel aflatoxin content distribution, the lambda parameter reflects the number (or frequency) of contaminated kernels in the sample, and the beta parameter is a scale parameter. In the negative binomial, the two parameters can be used as measures of mean or location and shape.

Estimating aflatoxin in farmers' stock peanut lots by measuring aflatoxin in various peanut-grade components.

Five, 2 kg test samples were taken from each of 120 farmers' stock peanut lots contaminated with aflatoxin. Kernels from each 2 kg sample were divided into the following grade components: sound mature kernels plus sound splits (SMKSS), other kernels (OK), loose shelled kernels (LSK), and damaged kernels (DAM). Kernel mass, aflatoxin mass, and aflatoxin concentration were measured for each of the 2400 component samples. For 120 lots tested, average aflatoxin concentrations in SMKSS, OK, LSK, and DAM components were 235, 2543, 11,775, and 69,775 ng/g, respectively. Aflatoxins in SMKSS, OK, LSK, and DAM components represented 6.9, 7.9, 33.3, and 51.9% of the total aflatoxin mass, respectively. Cumulatively, 3 aflatoxin risk components--OK, LSK, and DAM--accounted for 93.1% of total aflatoxin, but only 18.4% percent of test sample mass. Correlation analysis suggests that the most accurate predictor of aflatoxin concentration in the lot is the cumulative aflatoxin mass in the high 3 risk components OK + LSK + DAM (correlation coefficient, r = 0.996). If the aflatoxin in the combined OK + LSK + DAM components is expressed in concentration units, r decreases to 0.939. Linear regression equations relating aflatoxin in OK + LSK + DAM to aflatoxin concentration in the lot were developed. The cumulative aflatoxin in the OK + LSK + DAM components was not an accurate predictor (r = 0.539) of aflatoxin in the SMKSS component. Statistical analyses of 3 other data sets published previously yielded similar results.

The effects of ultrapasteurization with and without homogenization on the chemical, physical, and functional properties of aseptically packaged liquid whole egg.

The effects of ultrapasteurization with and without homogenization on some chemical, physical, and functional properties of liquid whole egg were observed. Heat treatments of 64, 68, and 72 C each at 30, 60, and 95 s were conducted on liquid whole egg, unhomogenized and homogenized [10.34 mPa (1,500 psi)]. The viscosity of the egg increased with increased processing temperature, and unhomogenized egg was more viscous than homogenized egg. alpha-Amylase was inactivated at 68 C for 30 s. Although cake height was highly correlated to soluble protein content, this relationship did not hold for all time-temperature combinations. Due to homogenization effects on soluble protein results, soluble protein is not a reliable predictor of egg functionality when homogenization is used in the process. Homogenization had no effect on the population reduction of aerobic bacteria in all but three of the nine time-temperature combinations.

Relationship of sex, strain, and body weight to carcass yield and offal production in turkeys.

Three strains of male and female market turkeys (British United Turkeys, Hybrid, and Nicholas) were grown under commercial contract production conditions and slaughtered at a range of BW (4,200 to 17,640 g) and age (13 to 22 wk) that covers the range of commercial market weights in use at the time of the study. During processing, the weight of blood, feathers, head, neck, feet plus shanks, heart, liver, gizzard, lungs, gastrointestinal tract, water uptake, hot and chilled carcass with fat pad, fat pad alone, tail, wings, drumsticks, thigh meat, Pectoralis major, Pectoralis minor, scapula meat, lower back, upper back with ribs, breast skin, thigh skin, thigh bone, and hind half (legs, thighs, and saddle) were determined. The data were subjected to least squares analyses using models that included strain and sex effects relative to live BW. Significant differences in yield between the sexes but not among strains were found for feet plus shanks, gizzard, hot and chilled carcass with fat pad, and scapula meat. Similarly, significant differences in yield among strains but not between sexes alone were found for blood, feathers, heart, and hind half. The relationships of live BW with all other variables showed both strain and sex differences in yield. Whereas whole carcass yield as a function of BW was affected by sex alone, most other components varied by both sex and strain. Thus, choice of strain, sex, and age at slaughter affect the projected production of edible carcass and offal components.

Predicting the distribution of aflatoxin test results from farmers' stock peanuts.

Suitability of the negative binomial function for use in estimating the distribution of sample aflatoxin test results associated with testing farmers' stock peanuts for aflatoxin was studied. A 900 kg portion of peanut pods was removed from each of 40 contaminated farmers' stock lots. The lots averaged about 4100 kg. Each 900 kg portion was divided into fifty 2.26 kg samples, fifty 4.21 kg samples, and fifty 6.91 kg samples. The aflatoxin in each sample was quantified by liquid chromatography. An observed distribution of sample aflatoxin test results consisted of 50 aflatoxin test results for each lot and each sample size. The mean aflatoxin concentration, m; the variance, S2 mean among the 50 sample aflatoxin test results; and the shape parameter, k, for the negative binomial function were determined for each of the 120 observed distributions (40 lots times 3 sample sizes). Regression analysis indicated the functional relationship between k and m to be k = 0.000006425m0.8047. The 120 observed distributions of sample aflatoxin test results were compared to the negative binomial function by using the Kolmogorov-Smirnov (KS) test. The null hypothesis that the true unknown distribution function was negative binomial was not rejected at the 5% significance level for 114 of the 120 distributions. The negative binomial function failed the KS test at a sample concentration of 0 ng/g in all 6 of the distributions where the negative binomial function was rejected. The negative binomial function always predicted a smaller percentage of samples testing 0 ng/g than was actually observed.(ABSTRACT TRUNCATED AT 250 WORDS)

Analysis of data from incomplete block designs.

This paper presents an organized solution to the problem of computing inter- and intrablock analyses of incomplete block designs, based on the modified maximum likelihood principle proposed by Patterson and Thompson (1971, Biometrika 58, 545-554). The calculations are set out to be easily programmed on a microcomputer. The method is attractive because it is simple, yet sufficiently general to handle a wide class of designs, including partially balanced incomplete block designs, designs with unequal block sizes, designs with missing values, and generally unbalanced split-plot experiments.

Optimum methanol concentration and solvent/peanut ratio for extraction of aflatoxin from raw peanuts by modified AOAC method II.

The amount of aflatoxin extracted from raw peanuts by using the water-slurry modification of AOAC Method II was determined for 49 different combinations of methanol concentrations and solvent/peanut ratio. Results indicate that the amount of aflatoxins B1 and B2 extracted from raw peanuts is a function of both methanol concentration and solvent/peanut ratio, and a cubic equation was developed, using regression techniques, to describe the combined effects. From the functional relationship, the predicted methanol concentration and solvent/peanut ratio that extracts the most aflatoxin B1 was computed to be 60.0% and 10.8 mL solvent/g peanuts, respectively. This combination extracted 12.1% more aflatoxin than did AOAC Method II.