@article{tittlemier_whitaker_2023, title={Current sampling plans can introduce high variance in mycotoxin testing results as demonstrated by the online FAO Mycotoxin Sampling Tool}, volume={16}, ISSN={["1875-0796"]}, DOI={10.3920/WMJ2022.2804}, abstractNote={The free-to-use online FAO Mycotoxin Sampling Tool ( http://tools.fstools.org/mycotoxins/ ) provides users an opportunity to easily estimate impacts of adjusting sampling plan parameters on the risk of misclassifying consignments relative to a defined maximum level, as well as the contributions from sampling, sample preparation, and analytical test stages to the total variance of mycotoxin sampling plan designs, without performing resource-intensive sampling and laboratory analyses. The Tool was used to assess variance in the analysis of aflatoxins, deoxynivalenol, fumonisins, and ochratoxin A in maize, wheat, and powdered ginger for various sampling plans, including those specified in the Codex Alimentarius Commission General Standard on Contaminants and Toxins in Food and Feed. Results indicated that the current Codex sampling plans for maize and wheat could result in total measurement error equivalent or greater than 90% of the current and proposed maximum levels for ochratoxin A in wheat and aflatoxins in maize, respectively.}, number={2}, journal={WORLD MYCOTOXIN JOURNAL}, author={Tittlemier, S. A. and Whitaker, T. B.}, year={2023}, pages={115–126} }
 @misc{xu_baker_whitaker_luo_zhao_stevenson_boesch_zhang_2022, title={Review of good agricultural practices for smallholder maize farmers to minimise aflatoxin contamination}, volume={15}, ISSN={["1875-0796"]}, DOI={10.3920/WMJ2021.2685}, abstractNote={Maize is consumed world-wide as staple food, livestock feed, and industrial raw material. However, it is susceptible to fungal attack and at risk of aflatoxin contamination under certain conditions. Such contamination is a serious threat to human and animal health. Ensuring that the maize used by food industry meets standards for aflatoxin levels requires significant investment across the supply chain. Good Agricultural Practices (GAP) form a critical part of a broader, integrated strategy for reduction of aflatoxin contamination. We reviewed and summarised the GAP of maize that would be effective and practicable for aflatoxin control within high-risk regions for smallholder farmers. The suggested practicable GAP for smallholder farmers were: use of drought-tolerant varieties; timely harvesting before physiological maturity; sorting to remove damaged ears and those having poor husk covering; drying properly to 13% moisture content; storage in suitable conditions to keep the crop clean and under condition with minimally proper aeration, or ideally under hermetic conditions. This information is intended to provide guidance for maize growers that will help reduce aflatoxin in high-risk regions, with a specific focus on smallholder farmers. Following the proposed guidelines would contribute to the reduction of aflatoxin contamination during pre-harvest, harvest, and post-harvest stages of the maize value chain.}, number={2}, journal={WORLD MYCOTOXIN JOURNAL}, author={Xu, F. and Baker, R. C. and Whitaker, T. B. and Luo, H. and Zhao, Y. and Stevenson, A. and Boesch, C. J. and Zhang, G.}, year={2022}, pages={171–186} }
 @article{kumphanda_matumba_monjerezi_whitaker_de saeger_makun_2021, title={Are sample size and sample preparation for mycotoxin quantitation in grain products getting trivialized?}, volume={130}, ISSN={["1873-7129"]}, DOI={10.1016/j.foodcont.2021.108400}, abstractNote={Sampling and sample preparation (grinding and subsampling) are largest sources of variability that negate precision and accuracy of mycotoxin quantitation, particularly in grains. In general, halving sample or sub-sample (e.g., ground test portion) size doubles variance. Therefore, this paper reports on trends in sample and test portion masses used for the quantification of mycotoxin in maize between 1991 and 2020 by reviewing articles on mycotoxin quantitation in maize (grain and flour) published during this period. About 50% of the articles did not explicitly state the sample mass that was ground. Sample and test portion masses show a significant (p < 0.05) decline over the study period. In addition, over two-thirds of the articles did not specify the type of grinder and sieve sizes used in their analysis. Therefore, our findings suggest that standardized sampling plans with emphasis on laboratory sample size and sample preparation methods for maize are increasingly being overlooked during mycotoxin quantitation and increasing the uncertainty associated with estimating the true mycotoxin concentration in grain lots.}, journal={FOOD CONTROL}, author={Kumphanda, Joseph and Matumba, Limbikani and Monjerezi, Maurice and Whitaker, Thomas B. and De Saeger, Sarah and Makun, Hussaini Anthony}, year={2021}, month={Dec} }
 @article{kibugu_mdachi_munga_mburu_whitaker_huynh_grace_lindahl_2021, title={Improved Sample Selection and Preparation Methods for Sampling Plans Used to Facilitate Rapid and Reliable Estimation of Aflatoxin in Chicken Feed}, volume={13}, ISSN={["2072-6651"]}, DOI={10.3390/toxins13030216}, abstractNote={Aflatoxin B1 (AFB1), a toxic fungal metabolite associated with human and animal diseases, is a natural contaminant encountered in agricultural commodities, food and feed. Heterogeneity of AFB1 makes risk estimation a challenge. To overcome this, novel sample selection, preparation and extraction steps were designed for representative sampling of chicken feed. Accuracy, precision, limits of detection and quantification, linearity, robustness and ruggedness were used as performance criteria to validate this modification and Horwitz function for evaluating precision. A modified sampling protocol that ensured representativeness is documented, including sample selection, sampling tools, random procedures, minimum size of field-collected aggregate samples (primary sampling), procedures for mass reduction to 2 kg laboratory (secondary sampling), 25 g test portion (tertiary sampling) and 1.3 g analytical samples (quaternary sampling). The improved coning and quartering procedure described herein (for secondary and tertiary sampling) has acceptable precision, with a Horwitz ratio (HorRat = 0.3) suitable for splitting of 25 g feed aliquots from laboratory samples (tertiary sampling). The water slurring innovation (quaternary sampling) increased aflatoxin extraction efficiency to 95.1% through reduction of both bias (−4.95) and variability of recovery (1.2–1.4) and improved both intra-laboratory precision (HorRat = 1.2–1.5) and within-laboratory reproducibility (HorRat = 0.9–1.3). Optimal extraction conditions are documented. The improved procedure showed satisfactory performance, good field applicability and reduced sample analysis turnaround time.}, number={3}, journal={TOXINS}, author={Kibugu, James and Mdachi, Raymond and Munga, Leonard and Mburu, David and Whitaker, Thomas and Huynh, Thu P. and Grace, Delia and Lindahl, Johanna F.}, year={2021}, month={Mar} }
 @article{davis_agraz_kline_gottschall_nolt_whitaker_osborne_tengstrand_ostrowski_teixeira_et al._2021, title={Measurements of High Oleic Purity in Peanut Lots Using Rapid, Single Kernel Near-Infrared Reflectance Spectroscopy}, volume={98}, ISSN={["1558-9331"]}, DOI={10.1002/aocs.12487}, abstractNote={Abstract High oleic peanuts have improved shelf life vs. conventional peanuts. Purity (percentage of high oleic peanuts within a lot) is critical to ingredient performance and final lot value. Contamination can result from unintentional mix‐ups at the breeder/seed level, improper production handling, or due to physiologically immature high oleic kernels. Therefore, industry groups have established unofficial sampling plans to monitor purity. Assuming equivalent measurement performance and simple random sampling, increasing the sample size decreases variance among replicated sample test results and increases the precision of estimated lot purity. A novel instrument (QSorter Explorer by QualySense AG) using near‐infrared reflectance spectroscopy was evaluated for high speed (20 kernels per second) high oleic purity measurements. The study objectives were to assess instrument performance in: (1) measuring oleic acid (%) in runner peanuts and (2) estimating the true high oleic purity of artificially mixed peanut lots. Three grades (Jumbo, Medium, and No 1) of US Runner mini‐lots each at seven different contamination levels (0, 5, 10, 20, 30, 50, and 100%) were prepared. Oleic acid (%) of individual kernels was measured by scanning replicated samples of 10, 50, 100, and 500 kernels using the QSorter Explorer. The variance associated with each sample size and lot contamination level on returned purity values is discussed in the context of binomial sampling. Overall, the demonstrated measurement performance and capacity of the QSorter Explorer to process much larger sample sizes suggest this instrument can better identify true high oleic peanut lot purity vs. other currently available technologies.}, number={6}, journal={JOURNAL OF THE AMERICAN OIL CHEMISTS SOCIETY}, author={Davis, Brittany I and Agraz, Catherine B. and Kline, Mark and Gottschall, Emma and Nolt, Michael and Whitaker, Thomas B. and Osborne, Jason A. and Tengstrand, Erik and Ostrowski, Kamil and Teixeira, Rita and et al.}, year={2021}, month={Jun}, pages={621–632} }
 @article{sharma_wang_pereira_bedford_wehling_arlinghaus_warren_whitaker_jackson_canida_et al._2021, title={Sampling plan designs for gluten estimation in oat flour by discrete and composite sampling}, volume={129}, ISSN={["1873-7129"]}, DOI={10.1016/j.foodcont.2021.107943}, abstractNote={Heterogeneous gluten distribution in certain food commodities, such as oat flour, warrants a sound sampling plan to reduce measurement variability and the risk associated with lot misclassification. Ten lots of oat flour, 45 kg each, with varying gluten content were produced in a pilot-scale hammer mill to evaluate various sampling plans for gluten in oat flour. Thirty-two samples from each lot were collected during milling to study the effect of discrete (individual samples taken from the lot) and composite (samples taken from a bulk sample prepared by compositing several individual samples) sampling methods. A 5 g test sample was manually taken from each of the 32 samples, extracted with a cocktail extraction buffer, and gluten content was estimated using ELISA by analyzing duplicate aliquots of the extract. The total variance (Vt) from gluten measurements was partitioned into variance between samples (Vs) and aliquots analyzed (Va). Regression analysis revealed a linear relationship between the log of all three variances and the log of gluten concentration, which can be explained by the power equation for discrete and composite sampling methods. The Vt and Vs for discrete sampling tended to be higher than those for composite sampling at a given gluten concentration. A log-normal distribution was found suitable to characterize the distribution of measured gluten in oat flour samples from a test procedure. Operating characteristic (OC) curves plotted to evaluate various sampling plans for gluten in oat flour showed reduced risk of misclassification for composite sampling, as compared to the discrete sampling method for a given sampling plan. Examples are shown for the change in OC curves depending on the sampling method, sample size, number of samples analyzed, and accept/reject limit.}, journal={FOOD CONTROL}, author={Sharma, Girdhari M. and Wang, Shizhen S. and Pereira, Marion and Bedford, Binaifer and Wehling, Paul and Arlinghaus, Mark and Warren, Josh and Whitaker, Thomas B. and Jackson, Lauren S. and Canida, Travis and et al.}, year={2021}, month={Nov} }
 @article{sharma_pereira_wang_chirtel_whitaker_wehling_arlinghaus_canida_jackson_williams_2020, title={Evaluation of sampling plans for measurement of gluten in oat groats}, volume={114}, ISSN={["1873-7129"]}, DOI={10.1016/j.foodcont.2020.107241}, abstractNote={The presence of gluten-containing grains in oat groats is not uncommon. Many countries with regulations on “gluten-free” labeling of foods, such as the US, EU and Canada, have a 20 mg/kg (ppm) limit and allow “gluten-free” claims on oat products, provided that the limit is not exceeded. The non-uniform spatial distribution of gluten-containing grains in a bulk lot poses a challenge when selecting a representative sample of oat groats from a lot to determine if the lot is within regulatory compliance limits. A probability-based method for evaluating sampling plan designs was developed. A balanced nested experimental design was used to estimate gluten concentration in 16 laboratory samples from each of 10 mini-lots of oat groats spiked with varying amounts of wheat kernels used as the gluten source. The total variance of the gluten test procedure was partitioned into the variances between laboratory samples (Vs), test portions (Vtp) and aliquots tested (Va). From regression analysis, each variance was found to be a function of gluten concentration (G): Vs=(100/Ns)35.0880G, Vtp=(1/Ntp)20.0078G and Va=((1/Na)0.0264)G1.5167, where Ns is laboratory sample size in g, Ntp is test portion size in g, and Na is number of aliquots analyzed for gluten. The observed gluten distribution among samples of oat groats tended to follow gamma and negative binomial distributions compared to normal and lognormal distributions, especially at low gluten concentrations. An R program was developed using the relation of variance with gluten concentration and negative binomial distribution to compute and plot an operating characteristic (OC) curve for various gluten sampling plan designs. The OC curve was used to predict the acceptance (or rejection) probability of a bulk lot at given gluten concentration by a specific sampling plan design. The effect of change in laboratory sample size, test portion size, number of laboratory samples and manufacturer's accept/reject limit (generally lower than the regulatory limit) on the OC curve was determined.}, journal={FOOD CONTROL}, author={Sharma, Girdhari M. and Pereira, Marion and Wang, Shizhen S. and Chirtel, Stuart J. and Whitaker, Thomas B. and Wehling, Paul and Arlinghaus, Mark and Canida, Travis and Jackson, Lauren S. and Williams, Kristina M.}, year={2020}, month={Aug} }
 @article{kumphanda_matumba_whitaker_kasapila_sandahl_2019, title={Maize meal slurry mixing: an economical recipe for precise aflatoxin quantitation}, volume={12}, ISSN={["1875-0796"]}, DOI={10.3920/WMJ2018.2415}, abstractNote={The laboratory sample preparation for mycotoxin determination in cereals, often overlooked among sampling plans and analytical methods, was further studied. The precision of aflatoxin analysis in comminuted maize samples using 25 g slurry (prepared from 250 g test portion of comminuted maize, water/matrix (1+1, v/w)) and 12.5 g dry grind test portion were compared against the conventional 50 g dry grind test portion through replicated (10) Aflatest® immunoaffinity fluorometric tests of naturally contaminated samples with aflatoxin concentration ranging from 4.9 to 81.7 μg/kg. The overall mean aflatoxin concentration obtained from the 10 different samples tested using 12.5 g and 50.0 g dry grind procedures was 12% significantly (P<0.05) lower (poorer) compared to 25 g slurry. The sample preparation plus analytical variance associated with testing 25.0 g slurry, 50.0 g dry grind and 12.5 g dry grind test portions were in the ratio of 1:5:15, respectively.}, number={3}, journal={WORLD MYCOTOXIN JOURNAL}, author={Kumphanda, J. and Matumba, L. and Whitaker, T. B. and Kasapila, W. and Sandahl, J.}, year={2019}, pages={203–212} }
 @article{tittlemier_chan_gaba_pleskach_osborne_slate_whitaker_2019, title={Revisiting the sampling, sample preparation, and analytical variability associated with testing wheat for deoxynivalenol}, volume={12}, ISSN={["1875-0796"]}, DOI={10.3920/WMJ2019.2450}, abstractNote={Fifteen lots of wheat were sampled to characterise the total variance and distribution among sample test results associated with measuring deoxynivalenol (DON) in bulk wheat lots. An unbalanced nested experimental design based on past research was used to determine contributions to the total variance from sampling, sample preparation, and analysis. The wheat lots used in the study contained average DON concentrations that ranged from 0.17 to 24.5 mg/kg. Sampling was determined to be the largest contributor to the total variance of measuring DON at low mg/kg concentrations, which are relevant to existing maximum levels. With the experimental design parameters of 1 kg laboratory samples, sub-division of whole and ground grain using rotary sample division, sample comminution using a commercial-grade coffee grinder, extraction of 100 g test portions, and making one measurement of DON in the test portion by gas chromatography-mass spectrometry, the total variance of DON measurement at 2 mg/kg was 0.046 mg 2 /kg 2 (coefficient of variation=10.7%). At this concentration, sampling contributed 67% to the total variance, followed by sample preparation (18%) and analysis (15%). The DON distribution among sample test results was accurately described by the normal distribution. The mathematical model of variance was used with the normal distribution of DON measurement results to construct operating characteristics curves to model the likelihood of mischaracterising a wheat lot as (non) compliant with a certain decision limit. With realistic laboratory sample and test portion sizes, as well as a practicable decision limit of 1.5 mg/kg, the estimated probability of mischaracterising a wheat lot containing 2 mg/kg DON as less than this concentration was reduced to 1%.}, number={4}, journal={WORLD MYCOTOXIN JOURNAL}, author={Tittlemier, S. A. and Chan, J. and Gaba, D. and Pleskach, K. and Osborne, J. and Slate, A. B. and Whitaker, T. B.}, year={2019}, pages={319–332} }
 @article{pitt_boesch_whitaker_clarke_2018, title={A systematic approach to monitoring high preharvest aflatoxin levels in maize and peanuts in Africa and Asia}, volume={11}, ISSN={["1875-0796"]}, DOI={10.3920/WMJ2018.2317}, abstractNote={Aflatoxin in maize and peanuts remains a critical problem in much of Africa and Asia. Many countries in these regions lack a systematic preharvest approach for providing government agencies with warnings of a potential threat to human and animal health resulting from excessive levels of aflatoxin in crops at harvest. This paper sets out an approach to such a system. It is based on the establishment of a surveillance system in each community to monitor aflatoxin contamination resulting from drought stress before harvest and advise on remedial actions. The system should be under the control of a central government coordinator. If severe drought stress occurs, the coordinator would arrange for samples of the affected crop to be provided to a central aflatoxin laboratory established and controlled by the relevant government department. Assays from the central laboratory would be sent via the central coordinator to a government scientific advisory body, which would recommend appropriate remedial action to be taken at government level.}, number={4}, journal={WORLD MYCOTOXIN JOURNAL}, author={Pitt, J. and Boesch, C. and Whitaker, T. B. and Clarke, R.}, year={2018}, pages={485–491} }
 @article{ozer_basegmez_whitaker_slate_giesbrecht_2017, title={Sampling dried figs for aflatoxin - Part 1: variability associated with sampling, sample preparation, and analysis}, volume={10}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2016.2052}, abstractNote={The variability associated with the aflatoxin test procedure used to estimate aflatoxins in bulk shipments of dried figs was investigated. Sixteen 10 kg laboratory samples were taken from each of twenty commercial bulk lots of dried figs suspected of aflatoxin contamination. Two 55 g test portions were taken from each comminuted laboratory sample using water-slurry comminution methods. Finally, two aliquots from the test portion/solvent blend were analysed for both aflatoxin B1 and total aflatoxins. The total variance associated with testing dried figs for aflatoxins was measured and partitioned into sampling, sample preparation and analytical variance components (total variance is equal to the sum of the sampling variance, sample preparation variance, and analytical variance). Each variance component increased as aflatoxin concentration increased. Using regression analysis, mathematical expressions were developed to model the relationship between aflatoxin concentration and the total, sampling, sample preparation and analytical variances when testing dried figs for aflatoxins. The regression equations were modified to estimate the variances for any sample size, test portion size, and number of analyses for a specific lot aflatoxin concentration. When using the above aflatoxin test procedure to sample a fig lot at 10 μg/kg total aflatoxins, the sampling, sample preparation, analytical, and total variances were 47.20, 0.29, 0.13, and 47.62, respectively. The sampling, sample preparation, and analytical steps accounted for 99.1, 0.6, and 0.3% of the total variance, respectively. For the aflatoxin test procedure used in this study, the sampling step is the largest source of variability.}, number={1}, journal={WORLD MYCOTOXIN JOURNAL}, author={Ozer, H. and Basegmez, H. I. Oktay and Whitaker, T. B. and Slate, A. B. and Giesbrecht, F. G.}, year={2017}, pages={31–40} }
 @article{ozer_basegmez_whitaker_slate_giesbrecht_2017, title={Sampling dried figs for aflatoxin - Part II: effect of sampling plan design on reducing the risk of misclassifying lots}, volume={10}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2016.2127}, abstractNote={Because aflatoxin limits vary widely among regulating countries, the Codex Committee on Contaminants in Foods (CCCF) began work in 2006 to harmonise maximum levels (MLs) and sampling plans for aflatoxin in dried figs. Studies were developed to measure the variability and distribution among replicated sample aflatoxin test results taken from the same aflatoxin contaminated lot of dried figs so that a model could be developed to evaluate the risk of misclassifying lots of dried figs by aflatoxin sampling plan designs. The model was then be used by the CCCF electronic working group (eWG) to recommend MLs and aflatoxin sampling plan designs to the full CCCF membership for lots traded in the export market. Sixteen 10 kg samples were taken from each of 20 dried fig lots with varying levels of contamination. The observed aflatoxin distribution among the 16-aflatoxin sample test results was compared to the normal, lognormal, compound gamma, and negative binomial distributions. The negative binomial distribution was selected to model aflatoxin distribution among sample test results because it gave acceptable fits to observed aflatoxin distributions among sample test results taken from the same contaminated lot. Using the negative binomial distribution, a computer model was developed to show the effect of the number and size of samples and the accept/reject limits on the chances of rejecting good lots (seller's risk) and accepting bad lots (buyer's risk). The information was shared with the CCCF eWG and in March 2012, the 6th session of CCCF adopted at step 5/8 an aflatoxin sampling plan where three 10 kg samples must all test less than an ML of 10 µg/kg total aflatoxins to accept a dried fig lot. The 35th Session of the Codex Alimentarius Commission met in July 2012 and adopted the CCCF recommendations for the ML and the sampling plan as an official Codex standard.}, number={2}, journal={WORLD MYCOTOXIN JOURNAL}, author={Ozer, H. and Basegmez, H. I. Oktay and Whitaker, T. B. and Slate, A. B. and Giesbrecht, F. G.}, year={2017}, pages={99–109} }
 @misc{berthiller_brera_crews_iha_krska_lattanzio_macdonald_malone_maragos_solfrizzo_et al._2016, title={Developments in mycotoxin analysis: an update for 2014-2015}, volume={9}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2015.1998}, abstractNote={This review summarises developments in the determination of mycotoxins over a period between mid-2014 and mid-2015. In tradition with previous articles of this series, analytical methods to determine aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxins, patulin, trichothecenes and zearalenone are covered in individual sections. Advances in proper sampling strategies are discussed in a dedicated section, as are new methods used to analyse botanicals and spices and newly developed LC-MS based multi-mycotoxin methods. The critical review aims to briefly discuss the most important developments and trends in mycotoxin determination as well as to address shortcomings of current methodologies.}, number={1}, journal={WORLD MYCOTOXIN JOURNAL}, author={Berthiller, F. and Brera, C. and Crews, C. and Iha, M. H. and Krska, R. and Lattanzio, V. M. T. and MacDonald, S. and Malone, R. J. and Maragos, C. and Solfrizzo, M. and et al.}, year={2016}, pages={5–29} }
 @article{whitaker_slate_nowicki_giesbrecht_2016, title={Variability and distribution among sample test results when sampling unprocessed wheat lots for ochratoxin A}, volume={9}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2015.1970}, abstractNote={In 2008, Health Canada announced it was considering the establishment of maximum levels for ochratoxin A (OTA) in unprocessed wheat, oats, and their products. The Canada Grains Council and Canadian National Millers Association initiated two studies to measure the variability and distribution among sample test results for unprocessed wheat and oats so that scientifically based OTA sampling plans could be designed to meet regulatory and industry requirements. Sampling statistics related to detecting OTA in oats has been published. 54 OTA contaminated wheat lots representing three wheat classes were identified for the sampling study. Each lot was sampled according to a nested experimental protocol where sixteen 2-kg laboratory samples were taken from each lot, multiple 5-g test portions were taken from each comminuted 2-kg laboratory sample, and multiple OTA measurements were made on each test portion using liquid chromatography. The sampling, sample preparation, and analytical variances associated with each step of the OTA test procedure were found to be a function of OTA concentration and regression equations were developed to predict the functional relationships between variance and OTA concentration. When sampling a wheat lot containing 5 µg/kg OTA with an OTA test procedure consisting of a sampling step employing a single 2-kg laboratory sample, sample preparation step employing a single 100-g test portion, and an analytical step that used liquid chromatography to quantify OTA, the sampling step accounted for 95.3% of the total variability. The observed OTA distribution among the 16 OTA sample results was found to be positively skewed and the negative binomial distribution was selected to model the OTA distribution among sample test results. The sampling statistics were incorporated into the FAO Mycotoxin Sampling Tool and the chances of rejecting good lots and accepting bad lots were calculated for various sampling plan designs.}, number={2}, journal={WORLD MYCOTOXIN JOURNAL}, author={Whitaker, T. B. and Slate, A. B. and Nowicki, T. W. and Giesbrecht, F. G.}, year={2016}, pages={163–178} }
 @misc{berthiller_brera_crews_iha_krska_lattanzio_macdonald_malone_maragos_solfrizzo_et al._2015, title={Developments in mycotoxin analysis: an update for 2013-2014}, volume={8}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2014.1840}, abstractNote={This review highlights developments in the determination of mycotoxins over a period between mid-2013 and mid-2014. It continues in the format of the previous articles of this series, emphasising on analytical methods to determine aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxins, patulin, trichothecenes and zearalenone. The importance of proper sampling and sample preparation is briefly addressed in a dedicated section, while another chapter summarises new methods used to analyse botanicals and spices. As LC-MS/MS instruments are becoming more and more widespread in the determination of multiple classes of mycotoxins, another section is focusing on such newly developed multi-mycotoxin methods. While the wealth of published methods during the 12 month time span makes it impossible to cover every single one, this exhaustive review nevertheless aims to address and briefly discuss the most important developments and trends.}, number={1}, journal={WORLD MYCOTOXIN JOURNAL}, author={Berthiller, F. and Brera, C. and Crews, C. and Iha, M. H. and Krska, R. and Lattanzio, V. M. T. and MacDonald, S. and Malone, R. J. and Maragos, C. and Solfrizzo, M. and et al.}, year={2015}, pages={5–35} }
 @article{farkas_slate_whitaker_suszter_ambrus_2015, title={Use of Combined Uncertainty of Pesticide Residue Results for Testing Compliance with Maximum Residue Limits (MRLs)}, volume={63}, ISSN={["1520-5118"]}, DOI={10.1021/jf505512h}, abstractNote={The uncertainty of pesticide residue levels in crops due to sampling, estimated for 106 individual crops and 24 crop groups from residue data obtained from supervised trials, was adjusted with a factor of 1.3 to accommodate the larger variability of residues under normal field conditions. Further adjustment may be necessary in the case of mixed lots. The combined uncertainty of residue data including the contribution of sampling is used for calculation of an action limit, which should not be exceeded when compliance with maximum residue limits is certified as part of premarketing self-control programs. On the contrary, for testing compliance of marketed commodities the residues measured in composite samples should be greater than or equal to the decision limit calculated only from the combined uncertainty of the laboratory phase of the residue determination. The options of minimizing the combined uncertainty of measured residues are discussed. The principles described are also applicable to other chemical contaminants.}, number={18}, journal={JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY}, author={Farkas, Zsuzsa and Slate, Andrew and Whitaker, Thomas B. and Suszter, Gabriella and Ambrus, Arpad}, year={2015}, month={May}, pages={4418–4428} }
 @article{whitaker_slate_nowicki_giesbrecht_2015, title={Variability and distribution among sample test results when sampling unprocessed oat lots for ochratoxin A}, volume={8}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2014.1858}, abstractNote={In 2008, Health Canada announced it was considering the establishment of maximum levels for ochratoxin A (OTA) in a number of foods, including unprocessed wheat and oats and their products. The Canada Grains Council and Canadian National Millers Association initiated a study to measure the variability and distribution among sample test results so that scientifically based sampling plans could be designed to meet regulatory and industry requirements. Twenty lots of oats naturally contaminated with OTA were identified and sampled according to a nested experimental protocol where 16-two kg laboratory samples were taken from each lot, two 100 g test portions were taken from each comminuted laboratory sample, and two aliquots of the extract from each test portion were analysed for OTA by LC. The variance associated with each step of the OTA test procedure were found to be a function of OTA concentration and regression equations were developed to predict the functional relationship. When using the above OTA test procedure on an oat lot at 5 μg/kg, the sampling, sample preparation, analytical, and total variances were 11.26, 0.10, 0.13 and 11.49, respectively. The 2 kg sampling step accounted for 98.0% (11.26/11.49) of the total variability. The observed OTA distribution among the 16 OTA sample results was found to be positively skewed and the negative binomial distribution was selected to model the OTA distribution among sample test results. The sampling statistics were incorporated into the FAO Mycotoxin Sampling Tool where operating characteristic curves were calculated to predict the chances of rejecting good lots (seller’s risk) and accepting bad lots (buyer’s risk) for various sampling plan designs.}, number={4}, journal={WORLD MYCOTOXIN JOURNAL}, author={Whitaker, T. B. and Slate, A. B. and Nowicki, T. W. and Giesbrecht, F. G.}, year={2015}, pages={511–524} }
 @misc{berthiller_burdaspal_crews_iha_krska_lattanzio_macdonald_malone_maragos_solfrizzo_et al._2014, title={Developments in mycotoxin analysis: an update for 2012-2013}, volume={7}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2013.1637}, abstractNote={This review highlights developments in mycotoxin analysis and sampling over a period between mid-2012 and mid-2013. It covers the major mycotoxins: aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxins, patulin, trichothecenes and zearalenone. A wide range of analytical methods for mycotoxin determination in food and feed were developed last year, in particular immunochemical methods and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS)-based methods. After a section on sampling and sample preparation, due to the rapid spread and developments in the field of LC-MS/MS multimycotoxin methods, a separate section has been devoted to this area of research. It is followed by a section on mycotoxins in botanicals and spices, before continuing with the format of previous reviews in this series with dedicated sections on method developments for the individual mycotoxins.}, number={1}, journal={WORLD MYCOTOXIN JOURNAL}, author={Berthiller, F. and Burdaspal, P. A. and Crews, C. and Iha, M. H. and Krska, R. and Lattanzio, V. M. T. and MacDonald, S. and Malone, R. J. and Maragos, C. and Solfrizzo, M. and et al.}, year={2014}, month={Feb}, pages={3–33} }
 @article{sharma_khuda_pereira_slate_jackson_pardo_williams_whitaker_2013, title={Development of an Incurred Cornbread Model for Gluten Detection by Immunoassays}, volume={61}, ISSN={["1520-5118"]}, DOI={10.1021/jf404072x}, abstractNote={Gluten that is present in food as a result of cross-contact or misbranding can cause severe health concerns to wheat-allergic and celiac patients. Immunoassays, such as enzyme-linked immunosorbent assay (ELISA) and lateral flow device (LFD), are commonly used to detect gluten traces in foods. However, the performance of immunoassays can be affected by non-assay-related factors, such as food matrix and processing conditions. Gluten (0-500 ppm) and wheat flour (20-1000 ppm) incurred cornbread was prepared at different incurred levels and baking conditions (204.4 °C for 20, 27, and 34 min) to study the accuracy and precision of gluten measurement by seven immunoassay kits (three LFD and four ELISA kits). The stability and immunoreactivity of gluten proteins, as measured by western blot using three different antibodies, were not adversely affected by the baking conditions. However, the gluten recovery varied depending upon the ELISA kit and the gluten source used to make the incurred cornbread, affecting the accuracy of gluten quantification (BioKits, 9-77%; Morinaga, 91-137%; R-Biopharm, 61-108%; and Romer Labs, 113-190%). Gluten recovery was reduced with increased baking time for most ELISA kits analyzed. Both the sampling and analytical variance increased with an increase in the gluten incurred level. The predicted analytical coefficient of variation associated with all ELISA kits was below 12% for all incurred levels, indicative of good analytical precision.}, number={49}, journal={JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY}, author={Sharma, Girdhari M. and Khuda, Sefat E. and Pereira, Marion and Slate, Andrew and Jackson, Lauren S. and Pardo, Christopher and Williams, Kristina M. and Whitaker, Thomas B.}, year={2013}, month={Dec}, pages={12146–12154} }
 @article{shephard_berthiller_burdaspal_crews_jonker_krska_macdonald_malone_maragos_sabino_et al._2012, title={Developments in mycotoxin analysis: an update for 2010-2011}, volume={5}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2011.1338}, abstractNote={This review highlights developments in mycotoxin analysis and sampling over a period between mid-2010 and mid-2011.
It covers the major mycotoxins: aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxin, patulin,
trichothecenes, and zearalenone. Analytical methods for mycotoxins continue to be developed and published. Despite
much interest in immunochemical methods and in the rapid development of LC-MS methodology, more conventional
methods, sometimes linked to novel clean-up protocols, have also been the subject of research publications over
the above period. Occurrence of mycotoxins falls outside the main focus of this review; however, where relevant to
analytical method development, this has been mentioned.}, number={1}, journal={WORLD MYCOTOXIN JOURNAL}, author={Shephard, G. S. and Berthiller, E. and Burdaspal, P. A. and Crews, C. and Jonker, M. A. and Krska, R. and MacDonald, S. and Malone, R. J. and Maragos, C. and Sabino, M. and et al.}, year={2012}, month={Feb}, pages={3–30} }
 @article{vargas_santos_whitaker_slate_2011, title={Determination of aflatoxin risk components for in-shell Brazil nuts}, volume={28}, ISSN={["1944-0057"]}, DOI={10.1080/19440049.2011.596488}, abstractNote={A study was conducted on the risk from aflatoxins associated with the kernels and shells of Brazil nuts. Samples were collected from processing plants in Amazonia, Brazil. A total of 54 test samples (40 kg) were taken from 13 in-shell Brazil nut lots ready for market. Each in-shell sample was shelled and the kernels and shells were sorted in five fractions: good kernels, rotten kernels, good shells with kernel residue, good shells without kernel residue, and rotten shells, and analysed for aflatoxins. The kernel : shell ratio mass (w/w) was 50.2/49.8%. The Brazil nut shell was found to be contaminated with aflatoxin. Rotten nuts were found to be a high-risk fraction for aflatoxin in in-shell Brazil nut lots. Rotten nuts contributed only 4.2% of the sample mass (kg), but contributed 76.6% of the total aflatoxin mass (µg) in the in-shell test sample. The highest correlations were found between the aflatoxin concentration in in-shell Brazil nuts samples and the aflatoxin concentration in all defective fractions (R 2 = 0.97). The aflatoxin mass of all defective fractions (R 2 = 0.90) as well as that of the rotten nut (R 2 = 0.88) were also strongly correlated with the aflatoxin concentration of the in-shell test samples. Process factors of 0.17, 0.16 and 0.24 were respectively calculated to estimate the aflatoxin concentration in the good kernels (edible) and good nuts by measuring the aflatoxin concentration in the in-shell test sample and in all kernels, respectively.}, number={9}, journal={FOOD ADDITIVES AND CONTAMINANTS PART A-CHEMISTRY ANALYSIS CONTROL EXPOSURE & RISK ASSESSMENT}, author={Vargas, E. A. and Santos, E. A. and Whitaker, T. B. and Slate, A. B.}, year={2011}, pages={1242–1260} }
 @article{shephard_berthiller_burdaspal_crews_jonker_krska_macdonald_malone_maragos_sabino_et al._2011, title={Developments in mycotoxin analysis: an update for 2009-2010}, volume={4}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2010.1249}, abstractNote={This review highlights developments in mycotoxin analysis and sampling over a period between mid-2009 and mid-2010. It covers the major mycotoxins aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxin, patulin, trichothecenes, and zearalenone. New and improved methods for mycotoxins continue to be published. Immunological-based method developments continue to be of wide interest in a broad range of formats. Multimycotoxin determination by LC-MS/MS is now being targeted at the specific ranges of mycotoxins and matrices of interest or concern to the individual laboratory. Although falling outside the main emphasis of the review, some aspects of natural occurrence have been mentioned, especially if linked to novel method developments.}, number={1}, journal={WORLD MYCOTOXIN JOURNAL}, author={Shephard, G. S. and Berthiller, F. and Burdaspal, P. and Crews, C. and Jonker, M. A. and Krska, R. and MacDonald, S. and Malone, B. and Maragos, C. and Sabino, M. and et al.}, year={2011}, month={Feb}, pages={3–28} }
 @article{whitaker_slate_adams_birmingham_giesbrecht_2010, title={Comparing the performance of sampling plans that use a single regulatory limit based upon total aflatoxins to sampling plans that use dual limits based upon B-1 and total aflatoxins}, volume={3}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2009.1169}, abstractNote={The European Commission (EC) aflatoxin sampling plan for ready-to-eat tree nuts such as almonds requires that
each of the three 10 kg laboratory samples must all test less than 2 ng/g aflatoxin B1 (AFB1) and 4 ng/g total aflatoxins
(AFT) for the lot to be accepted. Exporters have observed that the AFB1/AFT ratio varied greatly from sample to
sample and the ratio appeared to average more than 50%. Because of the concern that dual limits associated with
the EC aflatoxin sampling plans may reject more lots than similar sampling plans that use a single limit based upon
total aflatoxins, studies were designed with the objectives to (a) measure the distribution of AFB1/AFT ratio values
using sample test results associated with testing U.S. almond lots exported to the European Union; (b) use Monte
Carlo methods to develop a model to compute the effects of using dual limits based upon AFB1 and AFT on the
probability of accepting almond lots; and (c) compare the probability of accepting almond lots using the current
Codex aflatoxin sampling plans for tree nuts when using single limits versus the use of dual limits. The study
results showed that the mean and median among 3,257 AFB1/AFT ratio values was 87.6% and 91.9%, respectively,
indicating that the distribution among the ratio values was negatively skewed. Only 31% of the 3,257 AFB1/AFT
ratio values are less than the mean ratio of 87.6%. Codex aflatoxin sampling plans for tree nuts using a single limit
based upon total aflatoxins had the highest probability of accepting lots at all lot concentrations when compared to
the probability of accepting lots with dual limits. As the AFB1 limit decreased from 90 to 50% of the total limit, the
probability of rejecting lots at all concentrations increased when compared to the Codex aflatoxin sampling plans
with a single limit based upon total aflatoxins.}, number={1}, journal={WORLD MYCOTOXIN JOURNAL}, author={Whitaker, T. B. and Slate, A. B. and Adams, J. G. and Birmingham, T. and Giesbrecht, F. G.}, year={2010}, month={Feb}, pages={35–44} }
 @article{whitaker_slate_birmingham_adams_jacobs_gray_2010, title={Correlation between aflatoxin contamination and various USDA grade categories of shelled almonds}, volume={93}, number={3}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Slate, A. and Birmingham, T. and Adams, J. and Jacobs, M. and Gray, G.}, year={2010}, pages={943–947} }
 @article{shephard_berthiller_dorner_krska_lombaert_malone_maragos_sabino_solfrizzo_trucksess_et al._2010, title={Developments in mycotoxin analysis: an update for 2008-2009}, volume={3}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2009.1172}, abstractNote={This review highlights developments in mycotoxin analysis and sampling over a period between mid-2008 and
mid-2009. It covers the major mycotoxins: aflatoxins, alternaria toxins, cyclopiazonic acid, fumonisins, ochratoxin,
patulin, trichothecenes and zearalenone. Developments in mycotoxin analysis continue, with emphasis on novel
immunological methods and further description of LC-MS and LC-MS/MS, particularly as multimycotoxin
applications for different ranges of mycotoxins. Although falling outside the main emphasis of the review, some
aspects of natural occurrence have been mentioned, especially if linked to novel method developments.}, number={1}, journal={WORLD MYCOTOXIN JOURNAL}, author={Shephard, G. S. and Berthiller, F. and Dorner, J. and Krska, R. and Lombaert, G. A. and Malone, B. and Maragos, C. and Sabino, M. and Solfrizzo, M. and Trucksess, M. W. and et al.}, year={2010}, month={Feb}, pages={3–23} }
 @article{brera_de santis_prantera_debegnach_pannunzi_fasano_berdini_slate_miraglia_whitaker_2010, title={Effect of Sample Size in the Evaluation of "In-Field" Sampling Plans for Aflatoxin B-1 Determination in Corn}, volume={58}, ISSN={["1520-5118"]}, DOI={10.1021/jf1018356}, abstractNote={Use of proper sampling methods throughout the agri-food chain is crucial when it comes to effectively detecting contaminants in foods and feeds. The objective of the study was to estimate the performance of sampling plan designs to determine aflatoxin B(1) (AFB(1)) contamination in corn fields. A total of 840 ears were selected from a corn field suspected of being contaminated with aflatoxin. The mean and variance among the aflatoxin values for each ear were 10.6 mug/kg and 2233.3, respectively. The variability and confidence intervals associated with sample means of a given size could be predicted using an equation associated with the normal distribution. Sample sizes of 248 and 674 ears would be required to estimate the true field concentration of 10.6 mug/kg within +/-50 and +/-30%, respectively. Using the distribution information from the study, operating characteristic curves were developed to show the performance of various sampling plan designs.}, number={15}, journal={JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY}, author={Brera, Carlo and De Santis, Barbara and Prantera, Elisabetta and Debegnach, Francesca and Pannunzi, Elena and Fasano, Eloriana and Berdini, Clara and Slate, Andrew B. and Miraglia, Marina and Whitaker, Thomas B.}, year={2010}, month={Aug}, pages={8481–8489} }
 @article{peterson_whitaker_stefanski_podleckis_phillips_wu_martinez_2009, title={A Risk Assessment Model for Importation of United States Milling Wheat Containing Tilletia contraversa}, volume={93}, ISSN={["0191-2917"]}, DOI={10.1094/PDIS-93-6-0560}, abstractNote={ Dwarf bunt of wheat, caused by the fungus Tilletia contraversa, is a pathogen historically limited in distribution by its very specific climatic requirements for establishment. In an effort to both address the need for adequate protection and eliminate unwarranted trade barriers, a number of countries have examined restrictions on importing milling wheat containing teliospores of T. contraversa. Pest risk analysis (PRA), under the guidelines of the World Trade Organization and Food and Agriculture Organization, has become an internationally accepted process for evaluating such issues. As a component of a dwarf bunt PRA, our objective was to develop a quantitative mathematical model to evaluate and communicate the potential risk of dwarf bunt establishment from the importation of U.S. milling wheat containing teliospores of T. contraversa. A T. contraversa–risk model (TCK-risk model) was developed using new data, historic literature, and industry statistics to estimate teliospore diversion from transport and milling processes, spore contamination levels, grain handling, and end-product usage. A climatic model was developed to identify potential regions where the environment was favorable for disease development. The likelihood of disease establishment and wheat yield loss was determined using the model to conduct Monte Carlo simulations of 100,000 wheat shipping-years. The model is dynamic in that individual components can be easily updated or modified in order to determine the effect of numerous scenarios (e.g., climate changes, shipping tonnage, contamination levels, mitigation strategies) on the probability of dwarf bunt establishment. TCK-risk model evaluation scenarios previously conducted for the People's Republic of China, Brazil, Mexico, and Peru are presented as examples. }, number={6}, journal={PLANT DISEASE}, author={Peterson, G. L. and Whitaker, T. B. and Stefanski, R. J. and Podleckis, E. V. and Phillips, J. G. and Wu, J. S. and Martinez, W. H.}, year={2009}, month={Jun}, pages={560–573} }
 @article{shephard_berthiller_dorner_krska_lombaert_malone_maragos_sabino_solfrizzo_trucksess_et al._2009, title={Developments in mycotoxin analysis: an update for 2007-2008}, volume={2}, ISSN={["1875-0796"]}, DOI={10.3920/wmj2008.1095}, abstractNote={This review highlights developments in mycotoxin analysis and sampling over a period between mid-2007 and mid-2008. It covers the major mycotoxins: aflatoxins,Alternariatoxins, cyclopiazonic acid, fumonisins, ochratoxin, patulin, trichothecenes, and zearalenone. Some aspects of natural occurrence, particularly if linked to novel aspects of analytical methods, are also included. The review demonstrates the rise of LC-MS methods, the continuing interest in developing alternative and rapid methods and the modification of well-established mycotoxin analytical methods by individual laboratories to meet their own requirements.}, number={1}, journal={WORLD MYCOTOXIN JOURNAL}, author={Shephard, G. S. and Berthiller, F. and Dorner, J. and Krska, R. and Lombaert, G. A. and Malone, B. and Maragos, C. and Sabino, M. and Solfrizzo, M. and Trucksess, M. W. and et al.}, year={2009}, month={Feb}, pages={3–21} }
 @article{trucksess_whitaker_weaver_slate_giesbrecht_rader_betz_2009, title={Sampling and Analytical Variability Associated with the Determination of Total Aflatoxins and Ochratoxin A in Powdered Ginger Sold As a Dietary Supplement in Capsules}, volume={57}, ISSN={["0021-8561"]}, DOI={10.1021/jf8017854}, abstractNote={The U.S. Food and Drug Administration is studying the need to monitor dietary supplements for mycotoxins such as total aflatoxins and ochratoxin A. An effective mycotoxin-monitoring program requires knowledge of the sampling and analytical variability associated with the determination of total aflatoxins (AF) and ochratoxin A (OTA) in dietary supplements. Three lots of ginger sold as a powder in capsule form and packaged in individual bottles were analyzed for both AF and OTA. The total variability associated with measuring AF and OTA in powdered ginger was partitioned into bottle-to-bottle, within bottle, and analytical variances. The variances were estimated using a nested design. For AF and OTA, the within-bottle variance associated with the 5 g laboratory sample size was the largest component of variability accounting for about 43% and 85% of the total variance, respectively; the analytical variance accounted for about 34% and 9% of the total variability, respectively; and the bottle-to-bottle variance accounted for about 23% and 7% of the total variance, respectively. When the total variance is converted into the coefficient of variation (CV or standard deviation relative to the mean concentration), the CV is lower for AF (16.9%) than OTA (24.7%).}, number={2}, journal={JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY}, author={Trucksess, Mary W. and Whitaker, Thomas B. and Weaver, Carol M. and Slate, Andrew and Giesbrecht, Francis G. and Rader, Jeanne I. and Betz, Joseph M.}, year={2009}, month={Jan}, pages={321–325} }
 @article{whitaker_trucksess_weaver_slate_2009, title={Sampling and analytical variability associated with the determination of aflatoxins and ochratoxin A in bulk lots of powdered ginger marketed in 1-lb bags}, volume={395}, ISSN={["1618-2650"]}, DOI={10.1007/s00216-009-2880-z}, abstractNote={Ginger has been used as a food, dietary supplement, and condiment for centuries. Mycotoxins such as the aflatoxins (AF) and ochratoxin A (OTA) have been reported in ginger roots in several studies. It is important to design effective sampling methods that will accurately and precisely predict the true mycotoxin level in a bulk lot. The objective of this study was to measure the sampling and analytical variability associated with the test procedure used to measure AF and OTA in a bulk lot of powdered ginger using a 5-g laboratory sample and HPLC analytical methods. Twelve 5-g laboratory samples were taken from each of two lots. Duplicate aliquots were removed from each 5-g laboratory sample/solvent blend, and each aliquot was simultaneously analyzed for AF and OTA by HPLC analytical methods. Using a balanced nested design, the total variance associated with the above AF and OTA test procedures was partitioned into sampling and analytical variance components for each lot. Averaged across both lots, the sampling and analytical variances accounted for 87% and 13% of the total variance, respectively, for AF and 97% and 3%, respectively, for OTA. The sampling and analytical coefficients of variation were 9.5% and 3.6%, respectively, for AF, and 16.6% and 2.9%, respectively, for OTA when using a single 5-g laboratory sample and HPLC analytical methods. Equations are derived to show the effect of increasing laboratory sample size and/or number of aliquots on reducing the variability of the test procedures used to estimate OTA and AF in powdered ginger.}, number={5}, journal={ANALYTICAL AND BIOANALYTICAL CHEMISTRY}, author={Whitaker, Thomas B. and Trucksess, Mary W. and Weaver, Carol M. and Slate, Andrew}, year={2009}, month={Nov}, pages={1291–1299} }
 @article{park_whitaker_simonson_morris_durr_njapau_2007, title={Determining the variability associated with testing shelled corn for aflatoxin using different analytical procedures in Louisiana in 1998}, volume={90}, number={4}, journal={Journal of AOAC International}, author={Park, D. L. and Whitaker, T. B. and Simonson, J. and Morris, H. F. and Durr, B. and Njapau, H.}, year={2007}, pages={1036–1041} }
 @article{whitaker_doko_maestroni_slate_ogunbanwo_2007, title={Evaluating the performance of sampling plans to detect fumonisin B-1 in maize lots marketed in Nigeria}, volume={90}, number={4}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Doko, M. B. and Maestroni, B. M. and Slate, A. B. and Ogunbanwo, B. F.}, year={2007}, pages={1050–1059} }
 @article{whitaker_saltsman_ware_slate_2007, title={Evaluating the performance of sampling plans to detect hypoglycin A in ackee fruit shipments imported into the United States}, volume={90}, number={4}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Saltsman, J. J. and Ware, G. M. and Slate, A. B.}, year={2007}, pages={1060–1072} }
 @article{greene_whitaker_hendrix_sanders_2007, title={Fruity fermented off-flavor distribution in samples from large peanut lots}, volume={22}, ISSN={["0887-8250"]}, DOI={10.1111/j.1745-459X.2007.00119.x}, abstractNote={ABSTRACT Fruity fermented (FF) off‐flavor develops when immature peanuts are cured at excessive temperatures (>35C). The objective of this study was to characterize FF distributions and determine the variability among samples from large peanut lots. Twenty peanut lots identified as having a range of FF off‐flavor were sampled. Twenty samples from each lot were roasted and processed into paste for descriptive sensory analysis. Differences in FF intensity were noted within and among lots. The FF intensity mean of the lots was either greater or less than the median value for the samples, indicating that the distributions were skewed. The skewed distributions and the variation among samples from a single lot demonstrated the need to develop a sampling plan for FF off‐flavor.}, number={4}, journal={JOURNAL OF SENSORY STUDIES}, author={Greene, J. L. and Whitaker, T. B. and Hendrix, K. W. and Sanders, T. H.}, year={2007}, month={Aug}, pages={453–461} }
 @article{whitaker_slate_hurley_giesbrecht_2007, title={Sampling almonds for aflatoxin, Part II: Estimating risks associated with various sampling plan designs}, volume={90}, number={3}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Slate, A. B. and Hurley, J. M. and Giesbrecht, F. G.}, year={2007}, pages={778–785} }
 @article{ozay_seyhan_yilmaz_whitaker_slate_giesbrecht_2007, title={Sampling hazelnuts for aflatoxin: Effect of sample size and accept/reject limit on reducing the risk of misclassifying lots}, volume={90}, number={4}, journal={Journal of AOAC International}, author={Ozay, G. and Seyhan, F. and Yilmaz, A. and Whitaker, T. B. and Slate, A. B. and Giesbrecht, F. C.}, year={2007}, pages={1028–1035} }
 @article{vargas_whitaker_dos santos_slate_lima_franca_2006, title={Design of a sampling plan to detect ochratoxin A in green coffee}, volume={23}, ISSN={["1944-0057"]}, DOI={10.1080/02652030500258656}, abstractNote={The establishment of maximum limits for ochratoxin A (OTA) in coffee by importing countries requires that coffee-producing countries develop scientifically based sampling plans to assess OTA contents in lots of green coffee before coffee enters the market thus reducing consumer exposure to OTA, minimizing the number of lots rejected, and reducing financial loss for producing countries. A study was carried out to design an official sampling plan to determine OTA in green coffee produced in Brazil. Twenty-five lots of green coffee (type 7 – approximately 160 defects) were sampled according to an experimental protocol where 16 test samples were taken from each lot (total of 16 kg) resulting in a total of 800 OTA analyses. The total, sampling, sample preparation, and analytical variances were 10.75 (CV = 65.6%), 7.80 (CV = 55.8%), 2.84 (CV = 33.7%), and 0.11 (CV = 6.6%), respectively, assuming a regulatory limit of 5 µg kg−1 OTA and using a 1 kg sample, Romer RAS mill, 25 g sub-samples, and high performance liquid chromatography. The observed OTA distribution among the 16 OTA sample results was compared to several theoretical distributions. The 2 parameter-log normal distribution was selected to model OTA test results for green coffee as it gave the best fit across all 25 lot distributions. Specific computer software was developed using the variance and distribution information to predict the probability of accepting or rejecting coffee lots at specific OTA concentrations. The acceptation probability was used to compute an operating characteristic (OC) curve specific to a sampling plan design. The OC curve was used to predict the rejection of good lots (sellers’ or exporters’ risk) and the acceptance of bad lots (buyers’ or importers’ risk).}, number={1}, journal={FOOD ADDITIVES AND CONTAMINANTS PART A-CHEMISTRY ANALYSIS CONTROL EXPOSURE & RISK ASSESSMENT}, author={Vargas, EA and Whitaker, TB and Dos Santos, EA and Slate, AB and Lima, FB and Franca, RCA}, year={2006}, month={Jan}, pages={62–72} }
 @article{johansson_whitaker_hagler_bowman_slate_payne_2006, title={Predicting aflatoxin and fumonisin in shelled corn lots sing poor-quality grade components}, volume={89}, number={2}, journal={Journal of AOAC International}, author={Johansson, A. S. and Whitaker, T. B. and Hagler, W. M. and Bowman, D. T. and Slate, A. B. and Payne, G.}, year={2006}, pages={433–440} }
 @article{whitaker_slate_jacobs_hurley_adams_giesbrecht_2006, title={Sampling almonds for aflatoxin, part I: Estimation of uncertainty associated with sampling, sample preparation, and analysis}, volume={89}, number={4}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Slate, A. B. and Jacobs, M. and Hurley, J. M. and Adams, J. C. and Giesbrecht, F. C.}, year={2006}, pages={1027–1034} }
 @article{whitaker_2006, title={Sampling foods for mycotoxins}, volume={23}, ISSN={["0265-203X"]}, DOI={10.1080/02652030500241587}, abstractNote={It is difficult to obtain precise and accurate estimates of the true mycotoxin concentration of a bulk lot when using a mycotoxin-sampling plan that measures the concentration in only a small portion of the bulk lot. A mycotoxin-sampling plan is defined by a mycotoxin test procedure and a defined accept/reject limit. A mycotoxin test procedure is a complicated process and generally consists of several steps: (1) a sample of a given size is taken from the lot, (2) the sample is ground (comminuted) in a mill to reduce its particle size, (3) a subsample is removed from the comminuted sample, and (4) the mycotoxin is extracted from the comminuted subsample and quantified. Even when using accepted test procedures, there is uncertainty associated with each step of the mycotoxin test procedure. Because of this variability, the true mycotoxin concentration in the lot cannot be determined with 100% certainty by measuring the mycotoxin concentration in a sample taken from the lot. The variability for each step of the mycotoxin test procedure, as measured by the variance statistic, is shown to increase with mycotoxin concentration. Sampling is usually the largest source of variability associated with the mycotoxin test procedure. Sampling variability is large because a small percentage of kernels are contaminated and the level of contamination on a single seed can be very large. Methods to reduce sampling, sample preparation and analytical variability are discussed.}, number={1}, journal={FOOD ADDITIVES AND CONTAMINANTS}, author={Whitaker, TB}, year={2006}, month={Jan}, pages={50–61} }
 @article{ozay_seyhan_yilmaz_whitaker_slate_giesbrecht_2006, title={Sampling hazelnuts for aflatoxin: Uncertainty associated with sampling, sample preparation, and analysis}, volume={89}, number={4}, journal={Journal of AOAC International}, author={Ozay, G. and Seyhan, F. and Yilmaz, A. and Whitaker, T. B. and Slate, A. B. and Giesbrecht, F.}, year={2006}, pages={1004–1011} }
 @article{vargas_whitaker_santos_slate_lima_franca_2006, title={Testing green coffee for ochratoxin A, part III: Performance of ochratoxin A sampling plan}, volume={89}, number={4}, journal={Journal of AOAC International}, author={Vargas, E. A. and Whitaker, T. B. and Santos, E. A. and Slate, A. B. and Lima, F. B. and Franca, R. C. A.}, year={2006}, pages={1021–1026} }
 @article{whitaker_williams_trucksess_slate_2005, title={Immunochemical analytical methods for the determination of peanut proteins in foods}, volume={88}, number={1}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Williams, K. M. and Trucksess, M. W. and Slate, A. B.}, year={2005}, pages={161–174} }
 @article{whitaker_johansson_2005, title={Sampling uncertainties for the detection of chemical agents in complex food matrices}, volume={68}, ISSN={["1944-9097"]}, DOI={10.4315/0362-028X-68.6.1306}, abstractNote={Using uncertainty associated with detection of aflatoxin in shelled corn as a model, the uncertainty associated with detecting chemical agents intentionally added to food products was evaluated. Accuracy and precision are two types of uncertainties generally associated with sampling plans. Sources of variability that affect precision were the primary focus of this investigation. Test procedures used to detect chemical agents generally include sampling, sample preparation, and analytical steps. The uncertainty of each step contributes to the total uncertainty of the test procedure. Using variance as a statistical measure of uncertainty, the variance associated with each step of the test procedure used to detect aflatoxin in shelled corn was determined for both low and high levels of contamination. For example, when using a 1-kg sample, Romer mill, 50-g subsample, and high-performance liquid chromatography to test a lot of shelled corn contaminated with aflatoxin at 10 ng/g, the total variance associated with the test procedure was 149.2 (coefficient of variation of 122.1%). The sampling, sample preparation, and analytical steps accounted for 83.0, 15.6, and 1.4% of the total variance, respectively. A variance of 149.2 suggests that repeated test results will vary from 0 to 33.9 ng/g. Using the same test procedure to detect aflatoxin at 10,000 ng/g, the total variance was 264,719 (coefficient of variation of 5.1%). The sampling, sample preparation, and analytical steps accounted for 41, 57, and 2% of the total variance, respectively. A variance of 264,719 suggests that repeated test results will vary from 8,992 to 11,008 ng/g. Foods contaminated at low levels reflect a situation in which a small percentage of particles is contaminated and sampling becomes the largest source of uncertainty. Large samples are required to overcome the "needle-in-the-haystack" problem. Aflatoxin is easier to detect and identify in foods intentionally contaminated at high levels than in foods with low levels of contamination because the relative standard deviation (coefficient of variation) decreases and the percentage of contaminated kernels increases with an increase in concentration.}, number={6}, journal={JOURNAL OF FOOD PROTECTION}, author={Whitaker, TB and Johansson, AS}, year={2005}, month={Jun}, pages={1306–1313} }
 @article{vargas_whitaker_dos santos_slate_lima_franca_2005, title={Testing green coffee for ochratoxin A, part II: Observed distribution of ochratoxin A test results}, volume={88}, number={3}, journal={Journal of AOAC International}, author={Vargas, E. A. and Whitaker, T. B. and Dos Santos, E. A. and Slate, A. B. and Lima, F. B. and Franca, R. C. A.}, year={2005}, pages={780–787} }
 @article{whitaker_trucksess_giesbrecht_slate_thomas_2004, title={Evaluation of sampling plans to detect Cry9C protein in corn flour and meal}, volume={87}, number={4}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Trucksess, M. W. and Giesbrecht, F. G. and Slate, A. B. and Thomas, F. S.}, year={2004}, pages={950–960} }
 @article{vargas_whitaker_santos_slate_lima_franca_2004, title={Testing green coffee for ochratoxin A, Part I: Estimation of variance components}, volume={87}, number={4}, journal={Journal of AOAC International}, author={Vargas, E. A. and Whitaker, T. B. and Santos, E. A. and Slate, A. B. and Lima, F. B. and Franca, R. C. A.}, year={2004}, pages={884–891} }
 @article{trucksess_whitaker_slate_williams_brewer_whittaker_heeres_2004, title={Variation of analytical results for peanuts in energy bars and milk chocolate}, volume={87}, number={4}, journal={Journal of AOAC International}, author={Trucksess, M. W. and Whitaker, T. B. and Slate, A. B. and Williams, K. M. and Brewer, V. A. and Whittaker, P. and Heeres, J. T.}, year={2004}, pages={943–949} }
 @misc{whitaker_2003, title={Detecting mycotoxins in agricultural commodities}, volume={23}, ISSN={["1559-0305"]}, DOI={10.1385/MB:23:1:61}, abstractNote={It is difficult to obtain precise and accurate estimates of the true mycotoxin concentration of a bulk lot when using a mycotoxin-sampling plan that measures the concentration in a small portion of the bulk lot. A mycotoxin-sampling plan is defined by a mycotoxin test procedure and a defined accept/reject limit. A mycotoxin test procedure is a complicated process and generally consists of several steps: (a) a sample is taken from the lot, (b) the sample is ground (comminuted) in a mill to reduce particle size, (c) a subsample is removed from the comminuted sample, and (d) the mycotoxin is extracted from the comminuted subsample and quantified. Even when using accepted test procedures, there is variability associated with each step of the mycotoxin test procedure. Because of this variability, the true mycotoxin concentration in the lot cannot be determined with 100% certainty by measuring the mycotoxin concentration in a sample taken from the lot. The variability for each step of the mycotoxin test procedure, as measured by the variance statistic, is shown to increase with mycotoxin concentration. Sampling is usually the largest source of variability associated with the mycotoxin test procedure. Sampling variability is large because a small percentage of kernels are contaminated and the level of contamination on a single seed can be very large. Methods to reduce sampling, sample preparation, and analytical variability are discussed.}, number={1}, journal={MOLECULAR BIOTECHNOLOGY}, author={Whitaker, TB}, year={2003}, month={Jan}, pages={61–71} }
 @article{whitaker_richard_giesbrecht_slate_ruiz_2003, title={Estimating deoxynivalenol in shelled corn barge lots by measuring deoxynivalenol in corn screenings}, volume={86}, number={6}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Richard, J. L. and Giesbrecht, F. G. and Slate, A. B. and Ruiz, N.}, year={2003}, pages={1187–1192} }
 @article{whitaker_2003, title={Standardisation of mycotoxin sampling procedures: an urgent necessity}, volume={14}, ISSN={["1873-7129"]}, DOI={10.1016/S0956-7135(03)00012-4}, abstractNote={A mycotoxin sampling plan is defined by the mycotoxin test procedure (sample size, sample preparation method, and analytical method) and the accept/reject limit. Because of the variability associated with each step of the mycotoxin test procedure, the true mycotoxin concentration of a bulk lot cannot be determined with 100% certainty. As a result, some lots will be misclassified by the sampling program. Some good lots will be rejected by the sampling plan (seller’s risk or false positives) and some bad lots will be accepted by the sampling plan (buyer’s risk or false negatives). The magnitude of these risks is directly related to the magnitude of the variability associated with the mycotoxin test procedure. It is difficult for an exporter to have an effective control program when regulatory limits and sample designs differ greatly among trading countries. In order to facilitate trade and provide protection for the consumer, it would be desirable for all trading countries to have the same mycotoxin limits and sample plan. While standardization of sampling plans among trading nations is important, any standardised sampling plan must be designed to minimize both the seller’s and buyer’s risks to the lowest possible levels that resources will allow. Reducing the variability of the mycotoxin test procedure will reduce both the buyer’s and seller’s risks. It is important to understand the sources of error in the mycotoxin test procedure so the errors can be effectively reduced. The sampling step usually is the largest source of error due to the extreme mycotoxin distribution among kernels in the lot. As an example, sampling (5 kg), sample preparation (USDA subsampling mill and 250 g subsample), and analysis (TLC) accounted for 83%, 9%, and 8% of the total aflatoxin testing error, respectively, when testing raw shelled peanuts for aflatoxin. Examples are given to show how increasing sample size reduces sampling error; increasing the fineness of grind and using larger subsamples reduces sample preparation error, and increasing the number of aliquots analyzed and using improved technology (HPLC versus TLC) decreases analytical error. International organizations such as FAO/WHO have used scientific techniques to evaluate and design aflatoxin sampling plans for raw shelled peanuts traded in the export market.}, number={4}, journal={FOOD CONTROL}, author={Whitaker, TB}, year={2003}, month={Jun}, pages={233–237} }
 @article{whitaker_hagler_giesbrecht_johansson_2002, title={Sampling wheat for deoxynivalenol}, ISBN={0306467801}, DOI={10.1007/978-1-4615-0629-4_8}, abstractNote={The variability associated with testing wheat for deoxynivalenol (DON) was measured using a 0.454 kg sample, a Romer mill, 25 g of 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 found to be a function of the DON concentration and equations were developed to predict each variance component using regression techniques. The effects of sample size, subsample size, and number of aliquots on reducing the variability of the DON test procedure were also determined. Using the test procedure described above, the coefficient of variation (CV) associated with testing wheat at 5 ppm DON was found to be 13.4%. The CVs associated with sampling, sample preparation, and analysis were 6.3, 10.0, and 6.3%, respectively. The sample variations associated with testing wheat are relatively small when compared to CVs associated with testing other commodities for other mycotoxins such as aflatoxin in peanuts. Even with the use of a small sample size (0.454 kg), the sampling variation was not the largest source of error as found in other mycotoxin test procedures.}, journal={Mycotoxins and food safety  (Advances in experimental medicine and biology; v. 504)}, publisher={New York: Kluwer Academic/Plenum Publishers}, author={Whitaker, T. B. and Hagler, W. M. and Giesbrecht, F. G. and Johansson, A. S.}, editor={J. W. DeVries, M. W. Trucksess and Jackson, L. S.Editors}, year={2002}, pages={73–83} }
 @article{vandeven_whitaker_slate_2002, title={Statistical approach for risk assessment of aflatoxin sampling plan used by manufacturers for raw shelled peanuts}, volume={85}, number={4}, journal={Journal of AOAC International}, author={Vandeven, M. and Whitaker, T. and Slate, A.}, year={2002}, pages={925–932} }
 @article{whitaker_hagler_johansson_giesbrecht_trucksess_2001, title={Distribution among sample test results when testing shelled corn lots for fumonisin}, volume={84}, number={3}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Hagler, W. M. and Johansson, A. S. and Giesbrecht, F. G. and Trucksess, M. W.}, year={2001}, pages={770–776} }
 @article{whitaker_freese_giesbrecht_slate_2001, title={Sampling grain shipments to detect genetically modified seed}, volume={84}, number={6}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Freese, L. and Giesbrecht, F. G. and Slate, A. B.}, year={2001}, pages={1941–1946} }
 @article{whitaker_wu_peterson_giesbrecht_johansson_2001, title={Variability associated with the official USDA sampling plan used to inspect export wheat shipments for Tilletia controversa spores}, volume={50}, ISSN={["1365-3059"]}, DOI={10.1046/j.1365-3059.2001.00640.x}, abstractNote={The variability associated with estimating the true concentration of teliospores of dwarf bunt (Tilletia controversa) per 50 g of wheat (TC concentration) in an export wheat shipment was studied by measuring the TC concentration in 16 test samples (50 g) taken from each of 137 export shipments. The variability among the 16 TC test sample results, as measured by the standard deviation, was found to increase with TC concentration. The functional relationship was approximately linear in a full‐log plot and regression analysis was used to determine the coefficients of the regression equation. Using statistical theory, the regression equation was modified to predict the standard deviation among test sample sizes other than the 50 g size used in this study. The standard deviation and coefficient of variation associated with using a 50 g test sample to estimate the true TC concentration of a wheat shipment with 2000 spores per 50 g were estimated to be 1062·8 and 53·1%, respectively. Increasing test sample size to 1600 g reduced the standard deviation and coefficient of variation to 187·9 and 9·4%, respectively.}, number={6}, journal={PLANT PATHOLOGY}, author={Whitaker, TB and Wu, J and Peterson, GL and Giesbrecht, FG and Johansson, AS}, year={2001}, month={Dec}, pages={755–760} }
 @article{park_whitaker_giesbrecht_njapau_2000, title={Performance of three pneumatic probe samplers and four analytical methods used to estimate aflatoxins in bulk cottonseed}, volume={83}, number={5}, journal={Journal of AOAC International}, author={Park, D. L. and Whitaker, T. B. and Giesbrecht, F. G. and Njapau, H.}, year={2000}, pages={1247–1251} }
 @article{whitaker_2000, title={Sampling agricultural commodities for mycotoxins}, volume={83}, number={5}, journal={Journal of AOAC International}, author={Whitaker, T. B.}, year={2000}, pages={12451245} }
 @article{whitaker_hagler_giesbrecht_johansson_2000, title={Sampling, sample preparation, and analytical variability associated with testing wheat for deoxynivalenol}, volume={83}, number={5}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Hagler, W. M. and Giesbrecht, F. G. and Johansson, A. S.}, year={2000}, pages={1285–1292} }
 @article{johansson_whitaker_hagler_giesbrecht_young_bowman_2000, title={Testing shelled corn for aflatoxin, Part I: Estimation of variance components}, volume={83}, number={5}, journal={Journal of AOAC International}, author={Johansson, A. S. and Whitaker, T. B. and Hagler, W. M. and Giesbrecht, F. G. and Young, J. H. and Bowman, D. T.}, year={2000}, pages={1264–1269} }
 @article{johansson_whitaker_giesbrecht_hagler_young_2000, title={Testing shelled corn for aflatoxin, Part II: Modeling the observed distribution of aflatoxin test results}, volume={83}, number={5}, journal={Journal of AOAC International}, author={Johansson, A. S. and Whitaker, T. B. and Giesbrecht, F. G. and Hagler, W. M. and Young, J. H.}, year={2000}, pages={1270–1278} }
 @article{johansson_whitaker_giesbrecht_hagler_young_2000, title={Testing shelled corn for aflatoxin, Part III: Evaluating the performance of aflatoxin sampling plans}, volume={83}, number={5}, journal={Journal of AOAC International}, author={Johansson, A. S. and Whitaker, T. B. and Giesbrecht, F. G. and Hagler, W. M. and Young, J. H.}, year={2000}, pages={1279–1284} }
 @article{bechtel_wilson_eustace_behnke_whitaker_peterson_sauer_1999, title={Fate of dwarf bunt fungus teliospores during milling of wheat into flour}, volume={76}, ISSN={["1943-3638"]}, DOI={10.1094/CCHEM.1999.76.2.270}, abstractNote={ABSTRACT}, number={2}, journal={CEREAL CHEMISTRY}, author={Bechtel, DB and Wilson, JD and Eustace, WD and Behnke, KC and Whitaker, T and Peterson, GL and Sauer, DB}, year={1999}, pages={270–275} }
 @article{whitaker_hagler_giesbrecht_1999, title={Performance of sampling plans to determine aflatoxin in farmers' stock peanut lots by measuring aflatoxin in high-risk- grade components}, volume={82}, number={2}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Hagler, W. M. and Giesbrecht, F. G.}, year={1999}, pages={264–270} }
 @article{whitaker_hagler_giesbrecht_dorner_dowell_cole_1998, title={Estimating aflatoxin in farmers' stock peanut lots by measuring aflatoxin in various peanut-grade components}, volume={81}, number={1}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Hagler, W. M. and Giesbrecht, F. G. and Dorner, J W. and Dowell, F. E. and Cole, R. J.}, year={1998}, pages={61–67} }
 @article{giesbrecht_whitaker_1998, title={Investigations of the problems of assessing aflatoxin levels in peanuts}, volume={54}, ISSN={["0006-341X"]}, DOI={10.2307/3109780}, abstractNote={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.}, number={2}, journal={BIOMETRICS}, author={Giesbrecht, FG and Whitaker, TB}, year={1998}, month={Jun}, pages={739–753} }
 @article{whitaker_trucksess_johansson_giesbrecht_hagler_bowman_1998, title={Variability associated with testing shelled corn for fumonisin}, volume={81}, number={6}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Trucksess, M. W. and Johansson, A. S. and Giesbrecht, F. G. and Hagler, W. M. and Bowman, D. T.}, year={1998}, pages={1162–1168} }
 @article{whitaker_giesbrecht_wu_1996, title={Suitability of several statistical models to simulate observed distribution of sample test results in inspections of aflatoxin-contaminated peanut lots}, volume={79}, number={4}, journal={Journal of AOAC International}, author={Whitaker, T. and Giesbrecht, F. and Wu, J.}, year={1996}, pages={981} }
 @article{whitaker_horwitz_albert_nesheim_1996, title={Variability associated with analytical methods used to measure aflatoxin in agricultural commodities}, volume={79}, number={2}, journal={Journal of AOAC International}, author={Whitaker, T. and Horwitz, W. and Albert, R. and Nesheim, S.}, year={1996}, pages={476} }
 @article{whitaker_springer_defize_dekoe_coker_1995, title={Evaluation of sampling plans used in the United States, United Kingdom, and the Netherlands to test raw shelled peanuts for aflatoxin}, volume={78}, number={4}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Springer, J. and Defize, P. R. and DeKoe, W. J. and Coker, R.}, year={1995}, pages={1010} }
 @article{whitaker_wu_dowell_hagler_giesbrecht_1994, title={Effects of sample size and sample acceptance level on the number of aflatoxin-contaminated farmers' stock lots accepted and rejected at the buying point}, volume={77}, number={6}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Wu, J. and Dowell, F. E. and Hagler, W. M., Jr. and Giesbrecht, F. G.}, year={1994}, pages={1672} }
 @article{whitaker_giesbrecht_wu_hagler_dowell_1994, title={Predicting the distribution of aflatoxin test results from farmers' stock peanuts}, volume={77}, number={3}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Giesbrecht, F. G. and Wu, J. and Hagler, W. M., Jr. and Dowell, F. E.}, year={1994}, pages={659} }
 @article{whitaker_dowell_hagler_giesbrecht_wu_1994, title={Variability associated with sampling, sample preparation, and chemical testing for aflatoxin in farmers' stock peanuts}, volume={77}, number={1}, journal={Journal of AOAC International}, author={Whitaker, T. B. and Dowell, F. E. and Hagler, W. M., Jr. and Giesbrecht, F. G. and Wu, J.}, year={1994}, pages={107} }
 @article{whitaker_dickens_giesbrecht_1991, title={Variability associated with determining grade factors and support price of farmers stock peanuts}, volume={18}, DOI={10.3146/i0095-3679-18-2-15}, abstractNote={Abstract}, number={2}, journal={Peanut Science}, author={Whitaker, T. B. and Dickens, J. W. and Giesbrecht, F. G.}, year={1991}, pages={122} }
 @article{whitaker_1990, title={Reply to "Reaction to a paper by Whitaker and Dickens on aflatoxin testing plans for shelled peanuts in the U.S. and the export market"}, volume={73}, number={5}, journal={Journal of the Association of Official Analytical Chemists}, author={Whitaker, T. B.}, year={1990}, pages={812} }