Least Cost Formulations Ltd

Virginia Beach, VA, United States

Least Cost Formulations Ltd

Virginia Beach, VA, United States
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Humrighouse B.,Pegasus Technical Services Inc. | Pemberton A.,Pegasus Technical Services Inc. | Gallardo V.,U.S. Environmental Protection Agency | Lindquist H.D.A.,U.S. Environmental Protection Agency | And 2 more authors.
Journal of AOAC International | Year: 2015

The method detection limit (MDL, 99% chance of detecting a positive result in a single replicate), as per the United States Code of Federal Regulations, was determined for a protocol using an ultrafiltration based automated waterborne pathogen concentration device. Bacillus anthracis Sterne strain spores were seeded at low levels into 100 L reagent water samples. Suspect colonies were confirmed through morphological, chemical, and genetic tests. Samples of 100 L (n = 14) of reagent water were seeded with five B. anthracis CFUs each. To confirm the estimated detection limit, a second set (n = 19) of 100 L reagent water samples were seeded at a higher level (7 CFUs). The second estimate of the MDL could not be pooled with the first, due to significant difference in variance. A third trial (n = 7) seeded with 10 CFUs produced an estimate of the MDL that could be pooled with the higher previous estimate. Another trial consisting of eight 100 L samples of tap water were seeded with approximately 7 CFUs. Recovery in these samples was not significantly different from the pooled MDL. Theoretically a concentration of 4.6 spores/100 L would be required for detection 95% of the time, based on a Poisson distribution. The calculated pooled MDL, based on experimental data was approximately 6 B. anthracis CFU/100 L (95% confidence interval 4.8 to 8.4). Detection at this level was achieved in municipal water samples.


Sun J.,University of Liverpool | Chernick M.R.,Lankenau Institute for Medical Research | LaBudde R.A.,Least Cost Formulations Ltd. | LaBudde R.A.,Old Dominion University
Journal of Biopharmaceutical Statistics | Year: 2011

An F statistic was proposed by Good and Chernick (1993) in an unpublished paper, to test the hypothesis of the equality of variances from two independent groups using the bootstrap; see Hall and Padmanabhan (1997), for a published reference where Good and Chernick (1993) is discussed. We look at various forms of bootstrap tests that use the F statistic to see whether any or all of them maintain the nominal size of the test over a variety of population distributions when the sample size is small. Chernick and LaBudde (2010) and Schenker (1985) showed that bootstrap confidence intervals for variances tend to provide considerably less coverage than their theoretical asymptotic coverage for skewed population distributions such as a chi-squared with 10 degrees of freedom or less or a log-normal distribution. The same difficulties may be also be expected when looking at the ratio of two variances. Since bootstrap tests are related to constructing confidence intervals for the ratio of variances, we simulated the performance of these tests when the population distributions are gamma(2,3), uniform(0,1), Student's t distribution with 10 degrees of freedom (df), normal(0,1), and log-normal(0,1) similar to those used in Chernick and LaBudde (2010). We find, surprisingly, that the results for the size of the tests are valid (reasonably close to the asymptotic value) for all the various bootstrap tests. Hence we also conducted a power comparison, and we find that bootstrap tests appear to have reasonable power for testing equivalence of variances. © Taylor & Francis Group, LLC.


Labudde R.A.,Least Cost Formulations Ltd | Labudde R.A.,Old Dominion University | Harnly J.M.,U.S. Department of Agriculture
Journal of AOAC International | Year: 2012

A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given. © 2012 Publishing Technology.


Trombley A.,Beacon Analytical Systems Inc. | Fan T.,Beacon Analytical Systems Inc. | Labudde R.,Least Cost Formulations Ltd | Shephard G.S.,South African Medical Research Council | And 2 more authors.
Journal of AOAC International | Year: 2011

The level of total aflatoxin contamination was analyzed in naturally contaminated and spiked samples of corn and peanut using the Aflatoxin Plate Kit. This kit is an enzyme-linked immunosorbent assay (ELISA) suitable for rapid testing of grains and peanuts. The assay was evaluated for ruggedness and linearity of the standard curve. The test kit results were then statistically evaluated for accuracy, precision, and correlation to a validated HPLC method (AOAC 994.08). The results were verified by an independent laboratory. © 2012 Publishing Technology.


Nelson M.T.,AOAC International | Labudde R.A.,Least Cost Formulations Ltd | Tomasino S.F.,U.S. Environmental Protection Agency | Pines R.M.,U.S. Environmental Protection Agency
Journal of AOAC International | Year: 2013

A multilaboratory study was conducted to determine the equivalence of the 3MTM PetrifiolmTM Aerobic Count Plate and standard plating methodology for measuring viable bacteria and spores recovered from hard-surface carriers (stainless steel and porcelain), also known as "control carrier counts," used in AOAC antimicrobial efficacy test methods. Six laboratories participated in the study in which carriers inoculated with Staphylococcus aureus, Pseudomonas aeruginosa, Salmonella enterica, and spores of Bacillus subtilis were evaluated using 3M Petrifilm Aerobic Count (AC) plates and standard plating side-by-side. The data were analyzed using a matched-pair t-test to determine the betweenmethod effect with confidence intervals. For all test organisms pooled across all laboratories, the mean difference in log10 concentration between the standard plate count method and 3M Petrifilm AC Plates was -0.012, with a 95% confidence interval of (-0.090, +0.066), which was well within the -0.5, +0.5 interval established as the acceptance criterion. The between-carrier SD averaged 0.139; the between-replicate SD was 0.050. The carrier reproducibility, given that a single replicate per carrier is done, was estimated to be 0.148. Although differences were seen in the final concentrations of the test organisms among laboratories, there were no statistical differences between the enumeration methods. Based on the results from this study, 3M Petrifilm AC Plates are equivalent to standard plating methodology and can be used as an alternative procedure for the enumeration of test organisms used in AOAC Methods 955. 14, 955. 15, 964.02, and 966.04.


Wehling P.,General Mills Inc. | LaBudde R.A.,Least Cost Formulations Ltd. | Brunelle S.L.,AOAC INTERNATIONAL | Nelson M.T.,AOAC INTERNATIONAL
Journal of AOAC International | Year: 2011

A statistical model is presented for use in validation of qualitative methods. This model, termed Probability of Detection (POD), harmonizes the statistical concepts and parameters between quantitative and qualitative method validation. POD characterizes method response with respect to concentration as a continuous variable. The POD model provides a tool for graphical representation of response curves for qualitative methods. In addition, the model allows comparisons between candidate and reference methods, and provides calculations of repeatability, reproducibility, and laboratory effects from collaborative study data. Single laboratory study and collaborative study examples are given.


PubMed | Least Cost Formulations Ltd
Type: Journal Article | Journal: Journal of AOAC International | Year: 2012

A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.

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