Biomedical subjects

F G Giesbrecht

Publications and source records attributed to F G Giesbrecht.

25 records · Page 2Linked to original sources

PubMedDate not supplied

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.

Carboxylic Acids↗

PubMedDate not supplied

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.

Analysis of Variance↗

PubMedDate not supplied

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.

Aflatoxins↗

PubMedDate not supplied

Effects of methanol concentration and solvent:peanut ratio on extraction of aflatoxin from raw peanuts.

Aflatoxin B1 was extracted by a water slurry process using methanol concentrations of 55, 60, 65, and 70% in water and solvent:peanut ratios of 3, 4, 5, and 6 mL/g. Results failed to show that methanol concentration had an effect on amount of B1 extracted; however, the amount of B1 extracted increased with an increase in solvent:peanut ratio. Aflatoxin B1 was also extracted by the official AOAC method II, using methanol concentrations of 55, 60, 65, and 70% in water and solvent:peanut ratios of 2, 3, 4, and 5 mL/g. Results showed that the amount of B1 extracted increased with percent methanol at low solvent:peanut ratios but not at high ratios. Also, the amount of B1 extracted increased with solvent:peanut ratios at all methanol concentrations.

Aflatoxins↗

PubMedDate not supplied

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)

Aflatoxins↗

PubMedDate not supplied

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.

Aflatoxins↗

PubMedDate not supplied

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.

Carboxylic Acids↗