THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method was originally developed in animal breeding for estimation of breeding values and is now widely used in many areas of research. It does not, however, seem to have gained the same popularity in plant breeding and variety testing as it has in animal breeding. In plants, application of mixed models with random genetic effects has up until recently been mainly restricted to the estimation of genetic and non- genetic components of variance, whereas estimation of genotypic values is mostly based on a model with fixed effects. This paper reviews recent developments in the application of BLUP in plant breeding and variety testing. These include the use of pedigree information to model and exploit genetic correlation among relatives and the use of flexible variance-covariance structures for genotype-by-environment interaction. We demonstrate that BLUP has good predictive accuracy compared to other procedures. While pedi- gree information is often included via the so-called numerator relationship matrix ðAÞ, we stress that it is frequently straightforward to exploit the same infor- mation by a simple mixed model without explicit reference to the A-matrix.

https://www.esalq.usp.br/departamentos/lce/arquivos/aulas/2012/LCE5872/Piepho%20et%20al.,%202008.pdf

BLUP for phenotypic selection in plant breeding and variety testing

The thin layer drying characteristics of hazelnuts during roasting

Mycotoxins are fungal metabolites commonly occurring in food, which pose a health risk to the consumer. Maximum levels for major mycotoxins allowed in food have been established worldwide. Good agricultural practices, plant disease management, and adequate storage conditions limit mycotoxin levels in the food chain yet do not eliminate mycotoxins completely. Food processing can further reduce mycotoxin levels by physical removal and decontamination by chemical or enzymatic transformation of mycotoxins into less toxic products. Physical removal of mycotoxins is very efficient: manual sorting of grains, nuts, and fruits by farmers as well as automatic sorting by the industry significantly lowers the mean mycotoxin content. Further processing such as milling, steeping, and extrusion can also reduce mycotoxin content. Mycotoxins can be detoxified chemically by reacting with food components and technical aids; these reactions are facilitated by high temperature and alkaline or acidic conditions. Detoxification of mycotoxins can also be achieved enzymatically. Some enzymes able to transform mycotoxins naturally occur in food commodities or are produced during fermentation but more efficient detoxification can be achieved by deliberate introduction of purified enzymes. We recommend integrating evaluation of processing technologies for their impact on mycotoxins into risk management. Processing steps proven to mitigate mycotoxin contamination should be used whenever necessary. Development of detoxification technologies for high-risk commodities should be a priority for research. While physical techniques currently offer the most efficient post-harvest reduction of mycotoxin content in food, biotechnology possesses the largest potential for future developments.

/pdf/impact-of-food-processing-and-detoxification-treatments-on-2974len175.pdf

Impact of food processing and detoxification treatments on mycotoxin contamination.

Bacterial spores are among the most resistant of all living cells to biocides, although the response depends on the stage of sporulation. The development of resistance to some agents such as chlorhexidine occurs much earlier in sporulation than does resistance to glutaraldehyde, which is a very late event. During germination or outgrowth or both, resistance is lost and the cells become as susceptible to biocides as nonsporulating bacteria. Mechanisms of spore resistance to, and the action of, biocides are discussed, and possible means of enhancing antispore activity are considered. The clinical and other uses of sporicidal and sporostatic chemical agents are described.

Bacterial spores and chemical sporicidal agents.

Measurements obtained with a simple rigorometer (muscle tension measuring device) were found to correlate well with measurements of the relative ATP/IMP content ratio (258/250 nm absorbance ratio of perchloric extracts), muscle pH, and visual measurements of stiffness in whole fish (tilapia, Areochromis aureus) during rigor development. These simple tests yielded measurements which clearly differentiated the effects of ante-mortem stress on the rate of onset of rigor mortis and associated biochemical changes in the fish muscle.

An Evaluation of Simple Methods for Following Rigor Development in Fish

Preface.Introduction.The Completely Randomized Design.Linear Models for Designed Experiments.Testing Hypotheses and Determining Sample Size.Methods of Reducing Unexplained Variation.Latin Squares.Split-Plot and Related Designs.Incomplete Block Designs.Repeated Teatments Designs.Factorial Experiments, the 2n System.Factorial Experiments, the 3n System.Analysis of Experiments Without Designed Error Terms.Confounding Effects with Blocks.Fractional Factorial Experiments.Response Surface Designs.Plackett-Burmann Hadamard Plans.The General Pn and Nonstandard Factorials.Factorial Experiments with Quantitative Factors.Plans for Which Run Order is Important.Supersaturated Plans.Sequences of Fractions of Factorials.Multi-Stage xperiments.Orthogonal Arrays and Related Structures.Factorial Plans Derived via Orthogonal Arrays.Experiments on the Computer.

Planning, construction, and statistical analysis of comparative experiments

The variability associated with testing lots of shelled corn for aflatoxin was investigated. Eighteen lots of shelled corn were tested for aflatoxin contamination. The total variance associated with testing shelled corn was estimated and partitioned into sampling, sample preparation, and analytical variances. All variances increased as aflatoxin concentration increased. With the use of regression analysis, mathematical expressions were developed to model the relationship between aflatoxin concentration and the total, sampling, sample preparation, and analytical variances. The expressions for these relationships were used to estimate the variance for any sample size, subsample size, and number of analyses for a specific aflatoxin concentration. Test results on a lot with 20 parts per billion aflatoxin using a 1.13 kg sample, a Romer mill, 50 g subsamples, and liquid chromatographic analysis showed that the total, sampling, sample preparation, and analytical variances were 274.9 (CV = 82.9%), 214.0 (CV = 73.1 %), 56.3 (CV = 37.5%), and 4.6 (CV = 10.7%), respectively. The percentage of the total variance for sampling, sample preparation, and analytical was 77.8, 20.5, and 1.7, respectively.

/pdf/testing-shelled-corn-for-aflatoxin-part-i-estimation-of-1pa6h03uvn.pdf

Testing shelled corn for aflatoxin, part I: estimation of variance components.

Carbohydrates are known to be important precursors in the development of roasted peanut quality. However, little is known about their genotypic variation. A better understanding of the role of carbohydrates in roasted peanut quality requires first an understanding of the genotypic variation in the soluble carbohydrate components. Ion exchange chromatography was used to isolate 20 different carbohydrate components in 52 genotypes grown in replicated trials at two locations. Inositol, glucose, fructose, sucrose, raffinose, and stachyose were quantitated, and 12 unknown peaks were evaluated on the basis of the peak height of the unknown relative to the cellobiose internal standard peak height. Peaks tentatively identified as verbascose and ajugose could not be properly integrated because of tailing. Of the 18 carbohydrates that were estimated, 9 exhibited significant variation between test environments, 5 among market types, 14 among genotypes within market types, and 11 exhibited some significant form of genotype x environment interaction. Genotypes accounted for 38-78% of the total variation for the known components, suggesting that broad-sense heritability for these components is high. The observed high genotypic variation in carbohydrate components is similar to the high genotypic variation observed for the sweetness attribute in roasted peanuts, which raises the question regarding possible interrelationships. The establishment of such interrelationships could be most beneficial to peanut breeding programs to ensure the maintenance of flavor quality in future peanut varieties.

Investigations into genotypic variations of peanut carbohydrates.

The variability associated with testing wheat for deoxynivalenol (DON) was measured using a 0.454 kg sample, Romer mill, 25 g 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 a function of the DON concentration and equations were developed to predict each variance component using regression techniques. The effect of sample size, subsample size, and number of aliquots on reducing the variability of the DON test procedure was also determined. For the test procedure, the coefficient of variation (CV) associated with testing wheat at 5 ppm was 13.4%. The CVs associated with sampling, sample preparation, and analysis were 6.3, 10.0, and 6.3%, respectively. For the sample variation, a 0.454 kg sample was used; for the sample preparation variation, a Romer mill and a 25 g subsample were used; for the analytical variation, the Romer Fluoroquant method was used. The CVs associated with testing wheat are relatively small compared to the CV associated with testing other commodities for other mycotoxins, such as aflatoxin in peanuts. Even when the small sample size of 0.454 kg was used, the sampling variation was not the largest source of error as found in other mycotoxin test procedures.

Francis G. Giesbrecht

Papers

An Evaluation of Simple Methods for Following Rigor Development in Fish

Planning, construction, and statistical analysis of comparative experiments

Testing shelled corn for aflatoxin, part I: estimation of variance components.

Investigations into genotypic variations of peanut carbohydrates.

Sampling, sample preparation, and analytical variability associated with testing wheat for deoxynivalenol.