scholarly journals Estimating Deoxynivalenol in Shelled Corn Barge Lots by Measuring Deoxynivalenol in Corn Screenings

2003 ◽  
Vol 86 (6) ◽  
pp. 1187-1192 ◽  
Author(s):  
Thomas B Whitaker ◽  
John L Richard ◽  
Francis G Giesbrecht ◽  
Andrew B Slate ◽  
Nelson Ruiz
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Test Sample ◽  
Regression Equations

Abstract 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%.

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.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

Notes: Determining Sample Size in Throughfall Studies

1979 ◽  
Vol 25 (4) ◽  
pp. 582-584 ◽  
Author(s):  
D. L. Peterson ◽  
G. L. Rolfe
Keyword(s):  
Nutrient Cycling ◽  
Sample Size ◽  
Predictive Value ◽  
Regression Equations ◽  
Volume Data ◽  
Southern Illinois ◽  
Specific Level ◽  
Collection Data ◽  
Seasonal Basis

Abstract Variation in throughfall collection data is a major concern in nutrient cycling studies. In order to determine the magnitude of this variability in throughfall volume data collected in an oak-hickory stand in southern Illinois, regression equations were developed which indicate the sample size necessary for a specific level of variability. In addition to having predictive value, these equations indicate differences in variability on a seasonal basis. Forest Sci. 25:582-584.

scholarly journals Sample size for estimating mean and coefficient of variation in species of crotalarias

2018 ◽  
Vol 90 (2) ◽  
pp. 1705-1715 ◽  
Author(s):  
MARCOS TOEBE ◽  
LETÍCIA N. MACHADO ◽  
FRANCIELI L. TARTAGLIA ◽  
JULIANA O. DE CARVALHO ◽  
CIRINEU T. BANDEIRA ◽  
...  
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation

A variable sample size run sum coefficient of variation chart

2022 ◽  
Author(s):  
Wai Chung Yeong ◽  
Yen Yoon Tan ◽  
Sok Li Lim ◽  
Khai Wah Khaw ◽  
Michael Boon Chong Khoo
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Variable Sample Size

Modified Automated Determination of 2,3-Diphosphoglycerate in Whole Blood

1972 ◽  
Vol 18 (9) ◽  
pp. 1001-1004 ◽  
Author(s):  
Kenneth F Atkinson
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Whole Blood ◽  
Normal Range ◽  
Blood Samples ◽  
Automated Determination

Abstract A modification is described of the automated determination of 2,3-diphosphoglycerate (DPG) in blood [Grisolia, S., et al., Anal. Biochem. 31, 235 (1969)]. Modifications in the manifold result in a sensitive, noise-free, rapid system and the modifications in the preparations of the reagents ensure stability of the diluted standards and blood samples for at least three weeks. Samples are run at the rate of 60/h and sample size can be as small as 5 µl of whole blood. The coefficient of variation of the overall determination of automated DPG and manual hemoglobin is 3.6% and the SD is ±0.77 µmol/g Hb. The normal range is 14.6 ± 2.2 (SD) µmol/g hemoglobin.

scholarly journals Inspection Strategy under Indeterminacy Based on Neutrosophic Coefficient of Variation

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 193 ◽  
Author(s):  
Muhammad Aslam ◽  
Mansour Sattam Aldosari
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Operating Characteristic ◽  
Uncertain Data ◽  
Sampling Plan ◽  
Sampling Plans ◽  
Interval Method ◽  
Inspection Strategy ◽  
Classical Statistics

The existing sampling plans which use the coefficient of variation (CV) are designed under classical statistics. These available sampling plans cannot be used for sentencing if the sample or the population has indeterminate, imprecise, unknown, incomplete or uncertain data. In this paper, we introduce the neutrosophic coefficient of variation (NCV) first. We design a sampling plan based on the NCV. The neutrosophic operating characteristic (NOC) function is then given and used to determine the neutrosophic plan parameters under some constraints. The neutrosophic plan parameters such as neutrosophic sample size and neutrosophic acceptance number are determined through the neutrosophic optimization solution. We compare the efficiency of the proposed plan under the neutrosophic statistical interval method with the sampling plan under classical statistics. A real example which has indeterminate data is given to illustrate the proposed plan.

A Method for the Analysis of Total Nitrogen in Natural Waters

1984 ◽  
Vol 41 (5) ◽  
pp. 815-819 ◽  
Author(s):  
J. Kalff ◽  
E. Bentzen
Keyword(s):  
Sample Size ◽  
Total Nitrogen ◽  
Coefficient Of Variation ◽  
Natural Waters ◽  
Sodium Salicylate ◽  
Persulfate Oxidation ◽  
Dissolved Nitrogen

This method for analyzing total nitrogen (TN) in freshwaters is based on the persulfate oxidation of nitrogen to nitrate, followed by the analysis of this nitrate by a modified version of the sodium salicylate method. The method is simpler than other reported techniques for TN in oligotrophy and mesotrophic waters and requires equipment readily available in most laboratories. The method is linear to 1000 μg N/L, with the range extendable by changing the sample size. The variability is lowest (coefficient of variation 6.6%) between 100 and 1000 μg N/L. We have successfully used the method for the determination of TN, as well as dissolved nitrogen (DN) on filtered samples and nitrate on nonoxidized samples.

Monitoring the coefficient of variation using a variable sample size control chart

2015 ◽  
Vol 80 (9-12) ◽  
pp. 1561-1576 ◽  
Author(s):  
Philippe Castagliola ◽  
Ali Achouri ◽  
Hassen Taleb ◽  
Giovanni Celano ◽  
Stelios Psarakis
Keyword(s):  
Sample Size ◽  
Control Chart ◽  
Coefficient Of Variation ◽  
Size Control ◽  
Variable Sample Size
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