Testing green coffee for ochratoxin A, part I: estimation of variance components
Related Experiment Video
Updated: Aug 23, 2026

Using Capillary Electrophoresis to Quantify Organic Acids from Plant Tissue: A Test Case Examining Coffea arabica Seeds
Published on: November 12, 2016
Eugenia A Vargas1, Thomas B Whitaker, Eliene A Santos
1Ministério da Agricultura, Pecuária e Abastecimento, Laboratório de Controle de Qualidade e Segurança Alimentar/LAV-MG, Av. Raja Gabaglia, Cidade Jardim, Belo Horizonte, Minas Gerais, Brazil. gena@cdlnet.com.br
Abstract:
The variability associated with testing lots of green coffee beans for ochratoxin A (OTA) was investigated. Twenty-five lots of green coffee were tested for OTA contamination. The total variance associated with testing green coffee was estimated and partitioned into sampling, sample preparation, and analytical variances. All variances increased with an increase in OTA concentration. Using regression analysis, mathematical expressions were developed to model the relationship between OTA 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 OTA concentration. Testing a lot with 5 microg/kg OTA using a 1 kg sample, Romer RAS mill, 25 g subsamples, and liquid chromatography analysis, the total, sampling, sample preparation, and analytical variances were 10.75 (coefficient of variation [CV] = 65.6%), 7.80 (CV = 55.8%), 2.84 (CV = 33.7%), and 0.11 (CV = 6.6%), respectively. The total variance for sampling, sample preparation, and analytical were 73, 26, and 1%, respectively.
Related Concept Videos
Quantifying and Rejecting Outliers: The Grubbs Test
Variability: Analysis
The range is a simple measure of variability, indicating the difference between the highest and...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Introduction to Test of Independence
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Significance Testing: Overview
One-Way ANOVA
