Variability Associated with Chemically Testing Screened Farmers Stock Peanuts For Aflatoxin1

1992 ◽  
Vol 19 (2) ◽  
pp. 88-91 ◽  
Author(s):  
T. B. Whitaker ◽  
J. W. Dorner ◽  
F. E. Dowell ◽  
F. G. Giesbrecht
Keyword(s):  
Variance Components ◽  
Functional Relationship ◽  
State Inspection ◽  
Federal State ◽  
Test Results ◽  
Aflatoxin Concentration ◽  
Total Variability ◽  
Regression Techniques ◽  
Inspection Service ◽  
Screening Device

Abstract Forty farmers stock lots of runner peanuts suspected of containing aflatoxin were identified by the Federal State Inspection Service using the visual Aspergillus flavus method. A 227-kg portion was removed from each of the 40 lots. Each 227-kg portion was screened over a belt screening device with 0.953-cm (24/64 inch) spacing to remove loose shelled kernels, foreign material, and small pods. Each screened portion was divided into ten 9.5-kg samples. Each sample was shelled, all kernels in the sample were comminuted in the Federal State subsampling mill, and the aflatoxin in duplicate 356-g subsamples per sample was extracted and quantified using HPLC methods. The total variability among the 10 aflatoxin test results was determined for each lot. The total variability was partitioned into sampling, subsampling, and analytical variability components for each lot. All variance components were shown to be functions of the aflatoxin concentration. Using regression techniques the functional relationship for each variance components and aflatoxin concentration was developed. The total variance associated with a 9.5-kg sample, 356-g subsample, and HPLC quantification when testing a screened farmers stock lot at 20 ppb is 295.2 and the CV is 89.5%.

Variability Associated with Sampling, Sample Preparation, and Chemical Testing for Aflatoxin in Farmers' Stock Peanuts

1994 ◽  
Vol 77 (1) ◽  
pp. 107-116 ◽  
Author(s):  
Thomas B Whitaker ◽  
Floyd E Dowell ◽  
Winston M Hagler ◽  
Francis G Giesbrecht ◽  
Jeremy Wu
Keyword(s):  
Sample Preparation ◽  
Variance Component ◽  
Analytical Techniques ◽  
State Inspection ◽  
Total Variance ◽  
Federal State ◽  
Inspection Method ◽  
Aflatoxin Concentration ◽  
Total Variability ◽  
Inspection Service

Abstract Forty farmers’ stock lots of runner peanuts suspected of containing aflatoxin were identified by the Federal State Inspection Service by using the visual Aspergillus flavus inspection method. A 900 kg portion was removed from each lot and divided into 50 samples each of 2.27 kg (5 lb), 4.54 kg (10 lb), and 6.81 kg (15 lb) weights. For each sample, foreign material was removed, pods were shelled, and all kernels were comminuted for 7 min in a vertical cutter mixer. A100 g subsample was removed from each comminuted sample for aflatoxin analysis by liquid chromatography (LC). The total variance associated with each sample size was estimated. The total variance was also partitioned into sampling, sample preparation, and analytical variance components. Each variance component was shown to be a function of aflatoxin concentration. By using regression techniques, the relationship between variance and aflatoxin concentration was developed for each variance component. The total, sampling, sample preparation, and analytical variances associated with testing a lot at 100 ppb with a 2.27 kg sample, 100 g subsample, and using LC analytical techniques are 25 378,23 533,1830, and 15, respectively. Sampling, sample preparation, and analysis account for 92.7, 7.2, and 0.1% of the total variability, respectively.

Peanut Foliar Fungicides: Relationships Between Leafspot Control and Kernel Quality

1976 ◽  
Vol 3 (2) ◽  
pp. 70-72 ◽  
Author(s):  
J. M. Hammond ◽  
P. A. Backman ◽  
J. A. Lyle
Keyword(s):  
Leaf Spot ◽  
Untreated Control ◽  
State Inspection ◽  
Federal State ◽  
Peanut Kernel ◽  
Cercospora Arachidicola ◽  
Copper Hydroxide ◽  
Fungicide Treatment ◽  
Kernel Quality ◽  
Inspection Service

Abstract The effectiveness of foliar fungicides for control of peanut leafspot caused by Cercospora arachidicola Hori and Cercosporidium personatum (Berk. & Curt.) Deight, was evaluated from 1971–1974. Benomyl, chlorothalonil, triphenyl-tin-hydroxide and copper hydroxide were applied at recommended rates by conventional ground sprayer at 14-day intervals. Leaf-spot severity was rated by determining percent defoliation and infection. All fungicide-treated plots had less defoliation and infection than the untreated control plots. Kernel quality was determined using Federal-State Inspection Service procedures. Plots sprayed with chlorothalonil had better quality kernels than those from any other fungicide treatment. However, kernels harvested from the untreated control plots had significantly better quality than those from the chlorothalonil-treatment. Kernels harvested from the benomyl and copper hydroxide treatments were slightly inferior in quality than the chlorothalonil treatment. Kernels from the triphenyl-tin-hydroxide treated plots were significantly inferior in quality than those from plots treated with other fungicides. These data indicate that while kernel quality is not related to leafspot control, certain foliar fungicides adversely affect peanut kernel quality probably by altering the ecology of the geocarposphere.

Comparison of Visual, Enzyme-Linked Immunosorbent Assay Screening, and HPLC Methods in Detecting Aflatoxin in Farmers Stock Peanut Grade Samples

1988 ◽  
Vol 15 (2) ◽  
pp. 61-63 ◽  
Author(s):  
R. J. Cole ◽  
J. W. Dorner ◽  
J. W. Kirksey ◽  
F. E. Dowell
Keyword(s):  
Immunosorbent Assay ◽  
Screening Test ◽  
High Performance ◽  
State Inspection ◽  
Rapid Screening ◽  
Federal State ◽  
Enzyme Linked Immunosorbent Assay ◽  
Inspection Method ◽  
Rapid Screening Test ◽  
Inspection Service

Abstract Grade samples from 152 lots of farmers stock peanuts were analyzed for aflatoxin by both an Enzyme-Linked Immunosorbent Assay (ELISA) rapid screening test and high performance liquid chromatography (HPLC). Results from HPLC and ELISA were compared to the results of the visual inspection method used by the Federal State Inspection Service (FSIS). The results showed 41% of the grade samples with visible Aspergillus flavus (Segregation 3) contained less than 20 ppb aflatoxin when analyzed by both ELISA and HPLC methods; 18.7% of Segregation 1 peanuts actually contained aflatoxin with a range of 26-2542 ppb. The results of ELISA and HPLC agreed in 98.6% of the composite lot analyses with the detection of 20 ppb or greater. However, the ELISA rapid screening test failed to give positive tests 12 of 13 times when the aflatoxin content was between 20-43 ppb in the component samples.

A Laboratory Colorimeter Method to Measure Pod Brightness in Virginia-Type Peanuts

1997 ◽  
Vol 24 (2) ◽  
pp. 81-84 ◽  
Author(s):  
T. G. Isleib ◽  
H. E. Pattee ◽  
P. W. Rice
Keyword(s):  
Optical Sensor ◽  
State Inspection ◽  
Federal State ◽  
Strongly Correlated ◽  
Visual Aesthetics ◽  
Sample Area ◽  
Optical Aperture ◽  
Color Measurements ◽  
Highly Correlated ◽  
Inspection Service

Abstract Pod brightness is an important characteristic that influences consumers to purchase in-shell peanuts. A method is needed to quantitate pod brightness. Studies were conducted to determine whether pod color measurements were related to visual aesthetics rated by a panel representing seven virginia peanut shelling companies and to determine the effect of the optical aperture of the colorimeter on the measurements obtained. Forty-eight virginia-type pod lots were separated into fancy and jumbo fractions using a standard Federal-State Inspection Service grading peanut sizer. Pod color was measured for three subsamples of each fraction using a Hunterlab D25-PC2 colorimeter equipped with the D25-2RAL Reduced Area Viewing for L optical sensor (51-mm diameter sample area). The 96 samples also were rated by 11 Virginia-Carolina area shellers for pod color and size. Sheller ratings for the two traits were highly correlated (r > 0.6, P ≤ 0.01). Hunter L and b scores were strongly correlated with sheller color ratings. The colorimeter is a useful tool for measuring pod brightness as an adjunct to breeding for improved pod brightness. Use of a 95 mm aperture resulted in greater average Hunter L, a, and b scores and significantly reduced the variance among subsamples. The larger aperture should be used when the quantity of pods available for measurement permits.

scholarly journals Variability Associated with Determining Grade Factors and Support Price of Farmers Stock Peanuts1

1991 ◽  
Vol 18 (2) ◽  
pp. 122-126
Author(s):  
Thomas B. Whitaker ◽  
James W. Dickens ◽  
Francis G. Giesbrecht
Keyword(s):  
State Inspection ◽  
Federal State ◽  
Foreign Material ◽  
True Value ◽  
Coefficients Of Variation ◽  
Support Price ◽  
Inspection Service

Abstract Replicated grade samples were taken from runner, Spanish, and virginia-type farmers stock peanut lots. Each sample was graded according to the procedures of the Federal State Inspection Service. The variability of % foreign material (%FM) and % loose shelled kernels (%LSK) associated with a 1800-g sample was measured. The variability of % sound mature kernels (%SMK), % sound splits (%SS), % other kernels (%OK), % damage (%DAM), and % extra large kernels (%ELK) associated with a 500-g sample was also measured. The variance was shown to be a function of the magnitude of the grade determination and was described by a relationship derived from binomial theory. From the measured grade factors, the support price per gross ton was calculated for each grade sample using the 1988 USDA loan schedule. The variance of the price per gross ton was also estimated and appeared to be independent of the price per gross ton. The coefficients of variation averaged across all lots tested were 21.1, 18.7, 2.6, 21.2, 14.0, 55.3, 7.8, and 2.4% for %FM, %LSK, %SMK, %SS, %OK, %DAM, %ELK, and price per gross ton, respectively. The computed price per gross ton of a farmers lot that has a true value of $600 was estimated to vary from $573 to $627 95% of the time when using the 500 and 1800 g grade sample to measure each grade factor.

Cross Calibration by FFT Equalization

1997 ◽  
Vol 119 (2) ◽  
pp. 236-242 ◽  
Author(s):  
K. Peleg
Keyword(s):  
Measurement Method ◽  
Functional Relationship ◽  
Measurement Methods ◽  
New Method ◽  
Measurement Systems ◽  
True Value ◽  
Calibration Problem ◽  
Regression Techniques ◽  
The Cross ◽  
Cross Calibration

The classical calibration problem is primarily concerned with comparing an approximate measurement method with a very precise one. Frequently, both measurement methods are very noisy, so we cannot regard either method as giving the true value of the quantity being measured. Sometimes, it is desired to replace a destructive or slow measurement method, by a noninvasive, faster or less expensive one. The simplest solution is to cross calibrate one measurement method in terms of the other. The common practice is to use regression models, as cross calibration formulas. However, such models do not attempt to discriminate between the clutter and the true functional relationship between the cross calibrated measurement methods. A new approach is proposed, based on minimizing the sum of squares of the differences between the absolute values of the Fast Fourier Transform (FFT) series, derived from the readings of the cross calibrated measurement methods. The line taken is illustrated by cross calibration examples of simulated linear and nonlinear measurement systems, with various levels of additive noise, wherein the new method is compared to the classical regression techniques. It is shown, that the new method can discover better the true functional relationship between two measurement systems, which is occluded by the noise.

Soft Computing Tools to Predict Progression of Percent Embedment of Aggregates in Chip Seals

2018 ◽  
Vol 2672 (12) ◽  
pp. 32-39 ◽  
Author(s):  
Aksel Seitllari ◽  
M. Emin Kutay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Embedment Depth ◽  
Test Results ◽  
Multilinear Regression ◽  
Neuro Fuzzy ◽  
Chip Seal ◽  
Regression Techniques ◽  
Artificial Neural

In this study, soft computing and multilinear regression techniques were employed to develop models for prediction of progression of chip seal percent embedment depth ( Pe). The model uses inputs such as cumulative equivalent traffic volume, Vialit test results, dust content of aggregates, and initial embedment depth. Multilinear regression, adaptive neuro-fuzzy system, and artificial neural network techniques were used to estimate the Pe. The contribution of the variables affecting Pe was evaluated through a sensitivity analysis. The results indicate that while most of the proposed models were able to predict the Pe reasonably, the artificial neural network model performed the best.

scholarly journals Predicting Aflatoxin and Fumonisin in Shelled Corn Lots Using Poor-Quality Grade Components

2006 ◽  
Vol 89 (2) ◽  
pp. 433-440 ◽  
Author(s):  
Anders S Johansson ◽  
Thomas B Whitaker ◽  
Winston M Hagler ◽  
Daryl T Bowman ◽  
Andy B Slate ◽  
...  
Keyword(s):  
North Carolina ◽  
Total Concentration ◽  
Screening Method ◽  
Test Sample ◽  
Poor Quality ◽  
Foreign Material ◽  
Total Aflatoxin ◽  
Quality Grade ◽  
Aflatoxin Concentration ◽  
Inspection Service

Abstract A study was conducted to determine if aflatoxin and fumonisin are concentrated in the poor-quality grade components of shelled corn. Four 1.0 kg test samples were each taken from 23 lots of shelled corn marketed in North Carolina. Inspectors from the Federal Grain Inspection Service divided each test sample into 3 grade components: (1) damaged kernels (DM), (2) broken corn and foreign material (BCFM), and )3) whole kernels (WH). The aflatoxin and fumonisin concentration was measured in each component and a mass balance equation was used to calculate the total concentration of each mycotoxin in each test sample. Averaged across all test samples, the aflatoxin concentrations in the DM, BCFM, and WH components were 1300.3, 455.2, and 37.3 ppb, respectively. Averaged across all test samples, the fumonisin concentrations in the DM, BCFM, and WH components were 148.3, 51.3, and 1.8 ppm, respectively. The DM and BCFM components combined accounted for only 5.0% of the test sample mass, but accounted for 59.8 and 77.5% of the total aflatoxin and fumonisin mass in the test sample, respectively. Both aflatoxin mass (ng) and aflatoxin concentration (ng/g) in the combined DM and BCFM components had high correlations with aflatoxin concentration in the lot. The highest correlation occurred when aflatoxin mass (ng) in the combined DM and BCFM components was related to aflatoxin concentration in the lot (0.964). Similar results were obtained for fumonisin. This study indicated that measuring either aflatoxin or fumonisin in the combined DM and BCFM grade components could be used as a screening method to predict either aflatoxin or fumonisin in a bulk lot of shelled corn.

Comparing the USDA/AMS Subsampling Mill to a Vertical Cutter Mixer Type Mill Used to Comminute Shelled Peanut Samples for Aflatoxin Analysis1

2012 ◽  
Vol 39 (1) ◽  
pp. 69-81 ◽  
Author(s):  
Thomas B. Whitaker ◽  
Andrew B. Slate
Keyword(s):  
Operating Characteristic ◽  
Test Procedure ◽  
Sampling Plan ◽  
Test Results ◽  
Test Portion ◽  
Two Samples ◽  
The Us ◽  
Sample Test ◽  
Total Variability ◽  
Very High

Abstract The US peanut industry can use up to three 21.8 kg samples per lot to determine if shelled peanut lots are acceptable or unacceptable due to aflatoxin content. If a lot is accepted by the first 21.8 kg sample (1AB≤8 ng/g) prepared with the USDA/AMS Subsampling mill (DM), then some peanut buyers request that the sheller prepare the second sample (2AB) and in some cases the 3AB sample (called special samples in the trade) with a vertical cutter mixer (VCM) type mill. These requests to specifically use the VCM instead of the DM to prepare official (1AB) and special (2AB or 3AB) samples is based in part on a perception that analytical results associated with a test portion taken from the 21.8 kg sample comminuted with a DM does not detect the full magnitude of aflatoxin in the 21.8 kg sample and that negative aflatoxin certificates (lot acceptance) are more likely to occur when samples are prepared with a DM than a VCM. Analysis of aflatoxin test results from two shellers along with Monte Carlo simulation indicate that differences between the 1AB and special sample test results are due to the use of a cut-off limit (≤ 8 ng/g associated with the 1AB) requested by the buyer as part of the acceptance criteria and not due to any bias associated with the DM. Operating characteristic curves were used to demonstrate that the performance of the USDA/AMS aflatoxin sampling plan is about the same regardless of the use of a DM or a VCM for sample preparation. The performances are similar because the DM, with an 1100 g test portion, account for only 8% of the total variability of the aflatoxin test procedure (sampling and analysis account for about 92%). A sampling plan that requires two 21.8 kg samples to test less than a limit, regardless of mill used to prepare the two samples, has a very low risk of accepting bad lots above the FDA limit of 20 ng/g, but has a very high risk of rejecting good lots, which makes for an extremely high economic burden on the sheller.

Use of Loose Shelled Kernels to Estimate Aflatoxin in Farmers Stock Peanut Lots1

1999 ◽  
Vol 26 (1) ◽  
pp. 39-44 ◽  
Author(s):  
T. B. Whitaker ◽  
F. G. Giesbrecht ◽  
W. M. Hagler
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Functional Relationship ◽  
Test Sample ◽  
Sampling Plan ◽  
Sample Sizes ◽  
Total Aflatoxin ◽  
Aflatoxin Concentration

Abstract Loose shelled kernels (LSK) are a defined grade component of farmers stock peanuts and represented, on the average, 33.3% of the total aflatoxin mass and 7.7% of the kernel mass among the 120 farmers stock peanut lots studied. The functional relationship between aflatoxin in LSK taken from 2-kg test samples and the aflatoxin in farmers stock peanut lots was determined to be linear with zero intercept and a slope of 0.297. The correlation between aflatoxin in LSK and aflatoxin in the lot was 0.844 which suggests that LSK taken from large test samples can be used to estimate the aflatoxin concentration in a farmer's lot. Using only LSK allows large test samples to be used to estimate the lot concentration since LSK can be easily screened from a large test sample. If LSK accounts for 7.7% of the lot kernel mass, a 50-kg sample will yield about 3.9 kg of LSK which can be easily prepared for aflatoxin analysis. Increasing the test sample size from 2 to 50 kg reduced the coefficient of variation associated with estimating a lot with 100 parts per billion (ppb) aflatoxin from 114 to 23%, respectively. As an example, a farmers stock aflatoxin sampling plan with dual tolerances (10 and 100 ppb) that classified lots into three categories was evaluated for two test sample sizes (2 and 50 kg). The effect of increasing test sample size from 2 to 50 kg on the number of lots classified into each of the three categories was demonstrated when measuring aflatoxin only in LSK.

Sign in / Sign up

Export Citation Format

Share Document