A Primer on Theory and Uncertainty of Grain Sampling

Transcription

1 A Primer on Theory and Uncertainty of Grain Sampling Kirk Remund Monsanto Jean-Louis Laffont Dupont Pioneer International Seed Testing Association Statistics and GMO Technical Committees presented at CLI/AACCI Global Detection Methods and Reference Materials Symposium Minneapolis, Minnesota October 16, 2015

2 Uncertainty in Grain Sampling and Testing Grain Lot 0.07% 0.05% 0.09% 0.03% 0.00% Steps to reduce uncertainty 1. Good sampling practice 2. Increase test sample size

3 Uncertainty in Grain Sampling and Testing Grain Lot Sample 0.05% 0.09% > 0.10% Assay (PCR) 0.11% Sample Prep

4 Today s Discussion One Grain Testing Objective: Bulk Properties Question 1: How do I get a representative sample? Question 2: How many seeds do I need to test? Helpful Tool: SeedCalc

5 Assessing Bulk Grain Properties Average properties across the entire lot. Not identifying/quantifying hot spots Examples: moisture, protein, biotech trait low level presence (LLP), biotech trait purity Grain Lot 0.05% Same sampling approach works for all bulk properties

6 How to obtain a grain sample for bulk characteristics? Grain/Seed lot (specified quantity of grain physically and uniquely identifiable) Primary/Increment samples (obtained during one single sampling action) [e.g., probe or pelican sampler]) Composite sample (by combining and mixing primary samples) Submitted sample (whole composite sample or sub-sample reduction) Working sample (number seeds/grains actually tested) cargo hold, barge, silo, truck Various standards (e.g., FOSFA) have limits on lot size Divide Sample Bulk sampling approach of organizations including ISTA, USDA GIPSA, FOSFA, ISO 24333

7 3rd dim. (most heterogeneity) Sampling static grain Two Dimensional Sampling Lot is oriented in three dimensions but there is sufficient access to allow complete sampling through one of the dimensions to reduce the sampling dimension to two It is important that the sampler allow for material to be taken through the entire dimension to reduce bias Example: Sample trucks, barges or bins with safe top access Manual or pneumatic probes (e.g., Seedburo ) Randomly select positions of K primary/increment samples (or grid) 1 st dim. Take vertical samples through 3 rd dimension Reference A Primer for Sampling Solids, Liquids and Gases, Patricia L. Smith

8 Static Sampling Example: Truck Sampling Top view of trucks Sampling bulk containers (e.g., trucks or bins) Often reasonable approach if heterogenuity occurs as horizontal or inverted cone layers Sampling collection point: probe the depth of the container Reference A Primer for Sampling Solids, Liquids and Gases, Patricia L. Smith

9 3rd dim. Sampling flowing grain One Dimensional Sampling Lot is oriented in three dimensions but there is sufficient access to allow complete sampling through two of the dimensions to reduce the sampling dimension to one Used to sample stream of material as lot is conveyed between steps of a process (sample through time) Must sample entire cross-section of stream Examples: ship cargo hold, filling/emptying bin, filling/emptying truck Samplers: automated sampler (Gamet ), pelican sampler, ellis sampler Slice samples taken through two dimensions in time 1 st dim. Grain Flow Reference A Primer for Sampling Solids, Liquids and Gases, Patricia L. Smith

10 How many seeds/grains do we test? We know the math and statistics Acceptance sampling using the Binomial distribution for testing seed pools (LLP impurity testing) or individual seed (trait purity testing) Remund, Laffont et al., Seed Science Research, 2001 & 2005 papers Work of ISTA Statistics Committee

11 Acceptance sampling for qualitative assays (available for quantitative assays also) SEED GRAIN SEED GRAIN GRAIN GRAIN LOT Adequate Sampling Method X DEVIANT SEEDS FOUND X > c SAMPLE OF SEEDS (n = sample size) X c Goal: Find adequate n and c too much not too much REJECT LOT ACCEPT LOT Work of ISTA Statistics Committee

12 Chance of Accepting Lot want these What is adequate n and c? Operating Characteristic Curve Tool whatever (uncertainty) don t want these 100% 80% What is Adequate? Choose n and c to satisfy the consumer and the producer. 60% 40% 20% Practical production practice Consumer (Buyer) does not want these Choice of number of tested seeds (n) and number of allowable deviant seeds (c) changes shape of curve 0% Seller Threshold Buyer Threshold True Impurity in Lot Work of ISTA Statistics Committee Illustrative example

13 Chance of Accepting Lot want these What is adequate? Operating Characteristic Curve Tool whatever (uncertainty) don t want these 100% 80% 60% 40% 20% High chance of accepting lot that meets current production practices High chance of rejecting lot that does not meet consumer requirement What is Adequate? Choose n and c to satisfy the consumer and the producer. Choice of number of tested seeds (n) and number of allowable deviant seeds (c) changes shape of red curve 0% Seller Threshold Buyer Threshold True Impurity in Lot Work of ISTA Statistics Committee Illustrative example

14 Can we have a zero buyer threshold? No. Census Test We would need to test every seed/grain in the grain lot. Work of ISTA Statistics Committee

15 Probability of Accepting Lot (%) Zero Tolerance Testing Plan [no tolerance for any deviant seeds/grains in sample (c=0)] 100% producer=0.5% consumer=1.0% 90% 80% 70% 60% 50% 40% 30% 20% Producer is not happy because acceptable lots are rejected most of the time. Consumer is satisfied with low chance of accepting low quality lot. 10% 0% 0.00% 0.25% 0.50% 0.75% 1.00% 1.25% 1.50% 1.75% 2.00% True Impurity Level (%) Work of ISTA Statistics Committee

16 Seedcalc Seed/Grain Testing Plan Tool It can help you determine the sample size and accept/reject criteria Work of ISTA Statistics Committee

17 Uncertainty in grain sampling/testing can be managed Grain Lot 0.07% 0.09% 0.12% 0.11% 0.05%

18 Thanks for your attention Cross country season! Questions?