A step-by-step guide for anyone challenged by the many subtleties of sampling particulate materials. The only comprehensive document merging the famous works of P. Gy, I. Visman, and C.O. Ingamells into a single theory in a logical way - the most advanced book on sampling that can be used by all sampling practitioners around the world.
Table of Contents
Part I Introduction and a Management Strategy 1. Definition of Basic Terms and Symbols 2. A Management Strategy Part II Fundamental Statistical Concepts Used in the Theory of Sampling 3. Fundamental Statistical Concepts 4. A Logical Introduction to the Components of the Overall Estimation Error Part III Heterogeneity and Homogeneity 5. A Logical Introduction to the Notion of Heterogeneity 6. Heterogeneity of a Zero-Dimensional Lot: Constitution and Distribution Heterogeneities 7. Heterogeneity of a One-Dimensional Lot: Notion of Variography Part IV Sampling Errors Introduced by Various Forms of Heterogeneity 8. Sampling of One-Dimensional Lots: The Continuous Model 9. Sampling of Zero-Dimensional Lots: The Discrete Model 10. The Fundamental Sampling Error 11. Minimizing the Fundamental Sampling Error in Sampling Protocols 12. Other Approaches, a Strategy, and Cardinal Rules for the Estimation of the Variance of FSE 13. The Grouping and Segregation Error Part V Integration of Visman and Ingamells’s Works into the Theory of Sampling 14. The Works of Visman and Ingamells Relevant to the Theory of Sampling 15. Theoretical, Practical, and Economic Difficulties in Sampling for Trace Constituents 16. From Links between Gy and Ingamells to a Sampling Strategy Part VI The In-Situ Nugget Effect: A Major Component of the Random Term of a Variogram 17. The In-Situ Nugget Effect: A Transition Between Geostatistics and the Theory of Sampling Part VII The Capital Notion of Sampling Correctness 18. The Increment Materialization Error 19. Sampling Modes 20. The Increment Delimitation Error during Exploration, Mining, and Sampling Food and the Environment 21. The Increment Delimitation Error at a Processing Plant 22. The Increment Delimitation Error during Sampling at the Laboratory 23. The Increment Extraction Error during Exploration and Mining 24. The Increment Extraction Error: During Sampling in a Processing Plant 25. The Increment Extraction Error during Sampling at the Laboratory 26. The Increment Preparation Errors and the Notion of Sample Integrity Part VIII The Increment Weighting Error and the Weighing Error 27. The Increment Weighting Error 28. The Weighing Error Part IX Review of Some Notorious Sampling Problems 29. Sampling for the Determination of the Moisture Content 30. Peculiarities about the Sampling of Precious Metals 31. Sampling of Liquid and Solid Wastes and Sampling of the Environment 32. Solvable and Unsolvable Sampling Problems Part X Chronostatistics 33. A Strategy to Take Better Advantage of Existing Chronological Data 34. The Use of the Variogram to Elaborate Meaningful Process Control Charts 35. Case Studies where Variography is an Effective Tool to Discover and Quantify Structural Problems Part XI Homogenization 36. An Introduction to Homogenizing Processes 37. Bed-Blending Techniques Part XII Recommendations to Manufacturers of Sampling Equipment and to Engineering Firms 38. Recommendations for the Design, Installation, and Maintenance of Sampling Systems
Dr. Pitard is a well-known authority on sampling theory and practice. He has a Doctorate of Technologies from the University of Aalborg. He is the recipient of the famous Pierre Gy’s Gold Medal for excellence in promoting and his teaching of the Theory of Sampling all around the world.
"The unique feature of this well written comprehensive book is its step-by-step guidance in all thirty-eight chapters under twelve parts. Basic knowledge of calculus, matrix theory and mathematical statistics is essential to comprehend this book. ... I enjoyed reading this book. I recommend this book to engineers, geologists, chemists, environmental scientists, instructor of statistics courses and computing professionals."
-Ramalingam Shanmugam in Journal of Statistical Computation and Simulation, June 2020