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Adventitious Presence (AP) Testing for The Seed Industry

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Are you confident in the genetic quality of your seed lots? Could your conventional seed or export lots have transgenic contamination within them? Join Joseph Lopez, DNA Lab Manager and Anna Doornink, Supervisor I, DNA QA Laboratory in this on-demand webinar to learn more about this important quality check for your seed lots.

 Discussion topics will include:

  • AP testing methods
  • Advantages and disadvantages of the different methods
  • The importance of semi-quantitative AP testing
  • Seed industry needs for AP testing

Let's start the conversation

Contact us with your questions or testing needs!

 

Begin Transcript:

 

Sarah Curran: All right. Good morning everyone thank you for joining us for our presentation of Adventitious Presence or AP testing for the Seed Industry. My name is Sarah Curran. I am a marketing communication specialist with Eurofins Scientific, and I'll be here to do just a few housekeeping items before I hand over to Joseph and Anna to begin our presentation. I'll give you a few moments to look over the contents of our presentation before we get started today.Our presenters today will be Joseph Lopez, DNA lab manager. Joseph received his PhD in plant physiology and biotechnology from Bharathiar University and his Master's and Bachelor's of Science in Botany from Mahatma Gandhi University. Joseph has more than 10 years of laboratory operations management experience in seed and food testing laboratories. His career includes time as operations head for multiple seed and food laboratories at SGS group in India, and serving as a faculty member at Birla Institute of Technology and Sciences in India, and Vellore Institute of Technology.

 

Anna Doornink, first supervisor in DNA QA laboratory, received registered genetic technologist and Bachelor's of Science degree in Biotechnology from the University of Wisconsin in River Falls, and also minored in biology, chemistry, and animal science. She joined Eurofins BioDiagnostics in 2005 and is currently serving as co-chair of the Society of Chemical Seed Technologists on the Genetic Technology Committee. Before we start our presentation today, I'd like to make sure that everyone knows the webinar is being recorded. A copy of the slides and the recording will be available within one to two business days and both the copy of the slides and the recording will be emailed directly to you and everyone that registered even if they are not attending. There will be time for Q and A following the presentation. To submit your questions, please use the questions box in your GoToWebinar panel. You can type a question in under the question section and hit enter to submit. At the end of the presentation, I will pitch those questions to our presenter.

 

A little bit about Eurofins before we get started, Eurofins is driven by our mission to contribute to global health by offering the highest quality testing, training, auditing, and consulting services. We strive to listen to our customers' needs and not simply meet but exceed expectations. Eurofins BioDiagnostics consists of three commercial testing laboratories located throughout the United States. Our scientists have years of experience with a comprehensive portfolio of laboratory testing methods from plant disease diagnosis to animal genetic selection to a wide variety of seed health testing options for many different crop species. At this time, I will hand over our presentation to Joseph and Anna.

 

Joseph Lopez: Thank you, Sarah. Good morning all. Genetically modified crops. Genetically modified crops are produced by introducing a foreign gene of interest in a plant system. Example is an insect-resistant and herbicide-resistant genes. GM crops are mainly cultivated in North and South Americas and to some extent, in the rest of the world. The demand for GM seeds are huge in North and South Americas. The GM seeds are being produced in open field and the adjacent fields maybe of the same crop with different GM events, so this may lead to the contamination of a specific GM event with other GM events of the same crop. Contamination may occur through different means. It could be through a cross-pollination in agricultural practices and through logistics. Here comes the importance of having a seed quality to ensure that none of the unwanted GM events are contaminated with a seed lot of an intended event before it is placed in the market.

 

We would like to take you through various genetic testing methods available and emphasize which is the suitable method used for AP testing. Isozyme and SNP testing is to determine the genetic purity of a seed lot irrespective of GM or non-GM seed lot. Herbicide bioassay is to detect the herbicide tolerance of a GM plant. Strip test is a rapid on-site test to detect the trade protein in a GM plant or seed. ELISA is a common lab test to detect and quantify the trade protein in a GM plant or seed. PCR testing, so this is the appropriate method used for AP testing. None of the other genetic tests mentioned above are not applicable for an AP testing. PCR technology-based testing is the correct method used for the AP detection and quantification in a seed lot.

 

Adventitious presence. What is adventitious presence? A general definition says AP is an unintentional presence of unwanted GM traits in seed lot. On the other hand, a low level presence of unwanted GM events in a seed lot. Why do we do adventitious presence testing? Can anyone spot the GMO contamination in this seed lot? Probably no. It's already said, PCR testing is the appropriate method for AP testing of a seed lot. Now, the question comes, which PCR test is most appropriate as various methods are available? If the AP level requirement is zero, then qualitative or quantitative PCR tests may be used to detect an AP, but the most of the case, the regulations and the consumer requirement is some level of AP is allowed in a GM seed lot. In this case, semi-quantitative PCR method is the most appropriate method for an AP testing.

 

The semi quantitative PCR test is a sort of qualitative test. It is done with multiple sub-samples, and the data are analyzed statistically, and the results represents the entire seed lot. Whereas, the qualitative and quantitative PCR methods are performed on a single bulk sample. Quantification PCR method, qualitative PCR method gives a presence or absence results whereas, the quantitative PCR quantify the AP level. However, the quantitative method may lead into an our estimation of AP as the multiple copies of the transgene maybe integrated into a plant system. By qualitative and quantitative PCR method, at times, a bad lot can be accepted or a good lot can be rejected.

 

Having said semi-quantitative PCR testing is the appropriate method, let's see the importance of this method. It is suitable method in most of the AP testing where some level of AP is allowed in a GM seed lot. The method is flexible to design the number of sub-samples and the number of seeds per sub-sample based on the seed lot characteristics and the requirement of AP threshold level by the regulation of the customers. The seed characteristics here I will mention is like an understanding about the seed lot contamination level, so a producer can plan a different pool sizes and number of seeds for the AP testing.

 

As we generate multiple presence and absence results by this method, the data can be analyzed statistically to achieve a much more accurate results. The use of multiple samples and statistical analysis will minimize the sampling and the testing error. It is the most reliable and acceptable AP test method used for the seed lot samples, which minimize the risk of producer and consumer. We'll just go for a polling question, and you can see a polling on your screen, and you can take your time. I mean, in 30 seconds here, you can just do your polling. Over to Sarah.

 

Sarah Curran: Thank you, Joseph. Yep, so we're going to take a quick break for a polling question here, get some more information from our audience really quickly. Our question is, which AP testing method do you use or have you used previously? I'll give everyone about 30 seconds to respond to this poll. Yeah, and as you can see, if you have conducted a different type of testing, you can submit what you have done into the question or chat boxes. I'll give everyone just a few more seconds to respond. All right, and with that, I will close our poll and show the results. It looks like about 30% have not conducted AP testing yet. With that, I will hand it back over to Anna and Joseph.

 

Anna Doornink: Okay. Thank you, Sarah. This is Anna, and I'm going to be talking about our lab workflow. We are going to step through the processes of you do have a sample that you would like to submit for AP testing, and we are going to talk through the stages and the decisions and just the conversations that need to happen from submitting and actually, before obtaining your sample, to submitting your sample, to receiving your report, and what all that information means.

 

Before you actually obtain your sample, we do have to talk about some questions. You have to understand, where is your seed destined for? Where is the most appropriate testing coming from for your seed lot? How much seed do you need to test? Is there a threshold that you're trying to meet? Does your market have an established threshold of 5% or 1% or 0.1%? These are all the questions that you need to ask and also understand to help us design an appropriate test for you.

 

We're to start off by obtaining your sample. You have a very large seed lot, so you have to draw a representative sample from that seed lot to send in for testing. There's several increments that that seed sample goes through. We are referencing the ISTA or the International Seed Testing Association standard for seed sampling methods. They do have a very comprehensive method in place. I urge you to review their method if you would like more information on how to appropriately sample your seed lots. The take home message from this is making sure that is a randomized, unbiased sampling method. Every seed should have equal opportunity to be tested or to be drawn to be tested, and also along the lines of making sure you have an appropriate randomized sample is ensuring proper labeling and sealing of the samples through every step of the process from your large seed lot all the way down to your bulk sample that you're going to be submitting.

 

Once you have achieved your sample that you're going to be submitting from the lab, then we take over, and we maintain those same sampling standards. We have randomized unbiased sampling that we use to create our test sub-samples. This is where we are taking your large bulk sample. For this example, I'll just use a 3,000-seed test, so you'll submit a single bag of 3,000 seeds, and then we'll create sub-samples based off of the testing plan that was identified best for your samples. We'll create those test samples, and then from that, we'll actually have a test portion of each of those sub-samples that we are working with to obtain the DNA to do the testing.

 

When your sample is received here at Eurofins BioDiagnostics, it goes through a inspection process. We also inspect the documents that come with it. Every sample is entered into our database, and it receives a unique identifier, what we identify as the BDI number. Every sample has that unique number, and the testing plan is recorded on what's called a sample entry report. This is emailed to the customer. It is documenting the tests that are being performed on your sample. Then, also at the beginning of this process, we also identify our lab workflow sheets that are specifying the tests to be completed.

 

Then, the sample moves to the lab, and this is where we are creating our sub samples that we are working with. As I mentioned before, if we're doing a 3,000-seed test, we may be performing 30 pools of 100 seeds or it might be 12 pools of 250 seeds per pool. All of that is identified in those initial conversations that we're having with a customer to identify that best testing plan. We'll create our sub-samples, and then we actually grind that sample, that pool of 100 seeds into a fine homogeneous powder. That is actually very important moving forward into the process for our DNA extraction. We are ensuring that every single seed is ground into that fine homogeneous powder so that when we take our sub-sample of that powder, as you can see, that's illustrated in the picture that we have here. We're using just a small amount of that ground powder to move forward into our testing. We want to ensure that we have a piece of every single seed in that ground powder that we are using.

 

We will load our plates that's identified with our unique sample IDs into a 96-well plate. Once that plate is loaded with the samples, we also include both positive and negative controls. The positive controls are positive for the targets that we're testing for on the specific sample set, and the positive controls are created by spiking one positive seed that is heterozygous for the target that we are looking for in 99 negative seeds. Again, we use the appropriate target pool size for the pool sizes that we're testing, so if we're doing pools of 250, we will have positive controls that are spiked at one positive seed and 249 negative seeds.

 

Once our plate is completely finished, loaded with samples and positive controls, then it moves to DNA extraction. We perform DNA extraction using validated DNA extraction methods, and these will vary. Since we deal with both seed and leaf tissue in our lab, we do have unique methods that are used between those two tissue types, and we also have unique methods for our oil seeds versus our starch seeds. We find that we have better optimization of achieving quality DNA by using different extraction methods. We do go through some container changes through the extraction method, so as you can see, we use our liquid handling robots to do transfers and also dispenses of the reagents that are used in our extraction. We, again, maintain a unique identifier on each of those plates.

 

Once the extractions are complete, we do do a quality check, again, for quality and for quantity of DNA. Then, we move to the polymerase chain reaction or PCR phase of the testing. PCR is done on our 384-well thermal cyclers. This is an example or a graphic of a thermal cycler that we have in the lab. Again, we have a container change from a 96-well plate to a 384-well format, and so, we map those 96-well plates onto our 384-well, so we know exactly which sample is in, which location on that 384-well plate. We select the appropriate targets, again, that we are testing for. Our methods are all validated by trait providers, and we maintain proficiencies with these trait providers ongoing, so we do proficiencies every year to make sure that we are still meeting the standards set up by the trait provider for our testing. Now, Joseph is going to discuss PCR.

 

Joseph Lopez: Thank you, Anna. The basic understanding of genetic modification in a plant system is shown here, so this will help you guys to understand that how a test is done on a PCR technology or a thermal cycler. Here, the transgene is the portion, which is introduced into a plant system by biological or mechanical means. I'm not going into detail into that how it's going to introduce or how it's getting integrated into the plant system. A semi-quantitative AP testing, so mainly, it targets these DNA sequences in the transgene ocean. The majority of the time, the AP testing is done with an even specific method, which targets the junction between the plant genome and the transgene portion. It should be somewhere here or it can be somewhere this portion.

 

This junction detects exactly the event, which I mean, like a MON 531, a specific event. In this construct, Cry1 Ac is the trait-specific gene so which produces the insect-resistant gene. P35S is a promoter sequence, which initiate the Cry1 Ac and activate and produce an mRNA and then a trait-specific protein, which is an insect-resistant protein. TNOS is a terminator sequence, which terminates the production of the trait-specific protein. P35S and TNOS are generic sequences present in most of the GM crops and events. An Npt II gene is an antibiotic-resistant gene, which is used for a selection of GMO plant during the developmental stage. At times, so this sequence, the Npt II sequence may be used to target for an AP test or a GMO test.

 

A typical PCR reaction consists of a genomic DNA, which is extracted from the samples. It's a double-stranded DNA helix molecule, and primers and probe. These are small pieces of DNA, which is specific to this DNA sequences present in the transgene I've shown in the previous slide. The nucleotides are a small pieces of DNA, which is act as a building block to form a complete DNA, and Taq polymerase is an enzyme required to add the nucleotides onto a primer and grow into a complete double-stranded DNA. This reaction happens in a microtube in a buffer, so it's a solution, which is required to perform this reaction. The similar reaction is, in fact, happening in a plant cell. This is an artificial method of the DNA replication or doubling outside the plant system.

 

PCR chain reaction, it's a three-step reaction performed on a thermal cycler, a PCR machine Anna has shown in her previous slide. Denaturation is the first step where the double-stranded DNA splits into single-stranded DNA at the temperature of around 94 to 96 degrees centigrade. Annealing is the second step where the target-specific primer or probe are getting attached to the single-stranded DNA where it finds complementary sequences at a temperature of around 68 degrees centigrade. The elongation is the third step where the nucleotides, the small pieces we had added into the reactions are getting attached to the primer, and it elongates and forming a complete double-stranded DNA. These three steps reaction recur for many cyclers and form millions of copies of double-stranded DNA. These DNAs can be detected by measuring the fluorescence on a fluorescence spectrophotometer or it can be run it on an agarose gel, and we can visualize under maybe a UV light, so over to Anna.

 

Anna Doornink: Okay. Once our PCR is complete, then we move on to data analysis. Our plates are read in a fluorescent microplate reader, and that's capturing the data, and then we go through an analysis. This is an example of a scatter plot that we received from our analysis. You can see that we have two groupings. We have a green group and then also a blue group. The blue group is our positives, and that's where we also want to see our positive controls. This group right here is our positive wells and our positive controls. The green group down here is our negative samples and our negative controls. This is also why we include our positive controls from the very beginning of the process. This ensures that we have a process control, that those same controls are going through the same stages as our samples all the way through so that we ensure that we have valid testing with our controls, reading as they should.

 

One thing I forgot to highlight on here is that below the scatter plot is we receive a printout that is a well by well positive or negative call, and that is important for the reporting aspect. Once we move to the reporting, the data is again reviewed, and this is also where we apply a statistical analysis to our samples. A report is prepared and double-checked prior to the final data being sent to the customer. Once the customer receives their reports, we are always available to do consultation. There may be some surprises for the customer if they are not expecting a certain type of result. Then, we may need to do follow-up testing or some additional testing to get them the appropriate data that they need for their sample.

 

We are going to know step through what a report looks like. This is an example of a report that would be issued from Eurofins BioDiagnostics. Now, I know there's a lot of information on this slide, so we are going to take some time and walk through what all this information means. Okay, so at the beginning, we have identified each of our sample numbers. They each have a unique sample number, unique variety, and then we have our testing plan highlighted, how many seeds that we tested, the number of pools that were tested, and the number of seeds per pool. Then, under this next section is number of positive pools for each target, so we would identify the unique targets in each of these columns, and then, the final portion is our statistical analysis. We use SeedCalc here at Eurofins BioDiagnostics. There are other methods that are available for doing a statistical analysis, but SeedCalc is the one that we have chosen to use.

 

We're going to walk through some of these examples of, and what these mean for our customers. Sample number one, you can see that in these stats here on the side, that this sample has 10 positive pools and 20 negative pools. Now, you may be looking at the number of contaminants and saying, "Well, that adds up to more than 10," and you're correct. That does add up to more than 10, but what that means is that there were several pools that had multiple targets positive for that pool. What this analysis is measuring is the total number of positive pools. A positive pool may have two or three or even more targets present in that pool, but it's still just one positive pool, so it only gets counted once.

 

The second sample you can see did not have any positives, so we report there are 30 negative pools, zero positive pools, and we say none detected as the estimated contamination level within this sample. Now, we do not say zero because zero would mean that you have tested every single seed that's in that lot. Now, again, if you do that, you would not have a product to sell. That is why we have the qualifier of the less than 0.1% under the probable contamination range.

 

All right, so moving on to sample number three, you can see that again there's a few pools that had some contamination. We had three positive pools, but then we also had 30 positive pools under target number six. This is highlighted in a different color. This is telling us that we have an intended target that is in this particular sample. We've known that by either the variety that's listed or communication from the customer that this should be an intended target, so we do not include that in the statistical analysis. That target is supposed to be there. It's not considered contamination, so that is where this statement in blue is verifying that that particular target is not being included in the statistical analysis.

 

Moving on to sample number four, you can see that we have zero negative pools and 30 positive pools. For one target, every single pool was positive. Now, in this case, we are not able to give you a statistical analysis, and the reason behind that is we don't know where the contamination stops, so to speak, so we would need to come back and do further testing on this sample. We may need to do a fewer number of seeds per pool. We may need to do more seeds total to be tested to achieve where that contamination level could be, and then again, for sample number five, we have just one positive pool. You can see it was positive for multiple traits, but it's still just one positive pool. This sample also had an intended target.

 

We are going to use this information as I had mentioned before in SeedCalc, and that provides us the estimate and the probable contamination range that you are just viewing on the previous slide that is highlighted in this pink color. Using SeedCalc, we plug in the numbers that we are using, so the number of pools that we tested is 30 pools. The number of seeds per pool is 100 seeds for a total of 3,000-seed test. In this instance, we had 23 positive pools. Positive also equals deviant for SeedCalc nomenclature, so the information that we are given is the percent computed in the sample is the percentage of AP and the given sample at 95% confidence level. You can see that that's highlighted down here, the 95%. That information is calculated to be 1.44% is the computed percent contamination in this 3,000-seed test.

 

Now, we also give you an upper bound of true percent impurity, and this means it's the projected maximum percentage of AP in the seed lot from this sample at 95% confidence level. Again, that is circled in blue down below by the upper bound of true percent impurity, and this is calculated to be less than 2.14%. I'm going to flip back to the previous slide and show you where those numbers are entered. This is your estimated contamination level, so from the previous slide, this would be the 1.44% would be entered in this column, and the probable contamination range is that upper bound so that less than 2.14% would be entered in this probable contamination range column.

 

We also have another example of zero positive pools out of 30 that were tested. Our testing plan is still the same. We still have 30 pools of 100 seeds per pool for a 3,000-seed test, and this time, we do not have any deviant pools, so we enter zero. The information that we are given is the computed percent in the sample does say zero, but the upper bound says less than 0.1%. Again, we do not say zero on the report because that's implying that you have tested every single seed in that seed lot, so you have to have ... That was not the case. We did not test every single seed, so that is why we use the upper bound of the less than 0.1%. Now, Joseph is going to walk us through some of that statistical data that we were just discussing and how our answers are arrived by that.

 

Joseph Lopez: Thank you, Anna. We'll just go through the statistical tools, so how it works with the testing plan, designing of the testing plan. Anna was mentioning about the SeedCalc, so it is a beautiful software. It's developed by the seed industry and especially for the AP testing needs. The beauty of this software is that a consumer or a producer can plan or design their tests based on the seed lot characteristics or the regulations or the requirement of the AP threshold. I'm just trying to make you understand in a simple way that, so how am number of seeds tested will impact on the risk of producer and consumer.

 

Here, the thin line, the black line shows that a test conducted with 400 individual seed with just one positive seed as deviant, so deviant here, it's a positive seed. The dotted line is, again, it's a test conducted with 400 seeds, but here, there are four individual seeds that are positive. These two testing plants are considered as a core testing plants with a less number of seeds. In one case, you can see the lot is accepted at the threshold level of 0.5 percentage for the producer, but whereas, it may end up with having more than two percentage of the AP contamination in that lot, so the consumer is in the risk.

 

Whereas, in this case, the thin black line, the 400-seed test with just one positive deviant here, the producer is at the risk because only a 40 percentage of the acceptance at the threshold level of 0.5 percentage, but for the consumer, it almost meets the requirement of one percentage of the threshold. Generally, the AQL, the acceptable quality level is considered as a customer set threshold, and LQL is the consumer set threshold for any given seed lot. Generally, it's just double than the AQL. The LQL will be just double than the AQL. In nutshell, like testing at 3,000 individual seeds, so this is an ideal situation and a good testing plan, which minimize the risk of producer and the consumer, so you can see that even up to 21 individual seeds can be positive. The tolerance level by testing a 3,000 individual seeds, so this is the testing plan, which can be designed using this software, and we can minimize the producer and the consumer's risk.

 

Having said about the total number of seeds tested, so now, here comes the number of pools per test per seed. Here are the scenario. There are two scenarios. In one scenario, the dotted line, the total number of seeds remain 3,000 but it's divided into six different pools of 500 seeds each and a tolerance level up to five deviant pools, the positive pools. This testing plan is, again, a producer's risk were the acceptance of the seed lot at 0.5 percentage. Threshold level is just 40 percentage, but for the consumer, it doesn't have any impact because it passes at the one percentage level of threshold.

 

The second scenario, you can see the same number of seeds like 3,000 seeds are divided into 60 different seed pools of 50 seeds each and the tolerance level up to seven deviant pools. This is a testing plan, which low ... I mean, it's the low risk for producer as well as consumer, you can see. For the producer, the lot passes at 90 percentage confidence level, and it passes for the consumer at the threshold at one percentage level. This is the importance of the number of seeds tested and the number of pools per seeds tested, so over to Anna.

 

Anna Doornink: Okay. Now, we are going to go through our testing capabilities here at Eurofins BioDiagnostics. Here is a list of the different crops that we do have validated methods for AP testing. We are going to be walking through a cotton example of how to design our testing plan, but we do have these testing plans available for both corn and soy and all of these crops that are listed. This is an example of a chart. It was actually taken from the ISAAA website, which is the International Service for Acquisition of Agri-biotech Applications. This table was taken from their website for the cotton traits that are on the market today.

 

Now, you may be looking at all this and saying, "That is a lot of different traits. How do I know what I need to test?" That is going to be on this slide. You see that we have two groupings. We have a conventional seed lot that has highlights of this dark blue, and then we have a GM or a traited seed lot that has highlights of this dark orange. If you have a conventional seed lot, which means it's not trait-containing, you want to do an AP screen on your seed to see if there's any contamination. We would recommend testing for five different targets, and that includes 35S, TNOS, Pat, 2m epsps, and MON 88913. You can see by the blue highlights that you are covering all the different event-specific traits that are on the market today by testing for those five different targets for a conventional seed lot.

 

Now, you may be thinking, "Well, I have a traited product. It contains ..." We will pick COT102, so it is a VIPCOT variety that you have. So, if you choose to test for TNOS, that would not be advantageous for you because COT0 102 contains TNOS. All of your pools would come up positive for TNOS, so this is where you have to look at doing event-specific testing. You would test for all of these different events that are highlighted in orange. We do suggest also testing for that intended trait to confirm that that is the variety that you are working with, and there may be some surprises if you see something that ... All pools tested positive for 88913, and that may be a surprise for you, and you could trace back and see, did a sample switch happen? Did a bin switch happen? Was there mislabeling along the way of the journey that you're seed had taken from the field to being submitted for a testing sample? That is why we recommend testing for all intended traits as well for your GM or your trait-containing seed lots.

 

Joseph Lopez: We would like to summarize this presentation by emphasizing that adventitious presence testing is an important quality check, and semi-quantitative AP testing is the best option for the seed lot, and using appropriate and validated methods are very important in performing the adventitious presence testing. Having a good laboratory practices like having a system like ISO 17025 or any related quality management system, so it's key for the integrity and the quality of the results from a laboratory. Selection of correct test plan and statistical analysis plays a crucial role in AP testing.

 

I just want to, I mean, talk about the sources from where we have gathered the information and the materials for this presentation. We have gathered the information from an International Service for the Acquisition of Agri-biotech Applications, ISAAA, and International Seed Testing Association, ISTA, about the sampling method. We have gathered the statistical analysis, the test plan designing details from a paper published by Remund, Dixon, Wright, Larry, the statistical consideration in seed purity testing for transgenic traits published in Seed Science Research, 2001, and SeedCalc. It's the software we have used for showing the data analysis as well as the designing of the test plan, so which is available in the ISTA's site. With this, we are concluding this presentation, and we are glad to hear questions from you. Thank you.

 

Sarah Curran: All right. Thank you, Anna and Joseph. We do have a few questions from the audience. I'll give everyone more time to continue submitting questions. Go ahead and type those into the question box in your GoToWebinar dashboard or you can send it to me over the chat function as well. To get us started, we have a couple questions that have already come in. Someone has asked, "How much seeds should I send for an adventitious presence test?"

 

Anna Doornink: That is an excellent question. That is where we go back to the slide that I had shown from your sample to your report. This is where I will ask you many different questions. Are you trying to reach a specific threshold? Where is your seed destined for? Is it destined for export? Then, I would recommend a 3,000-seed test. It's not an easy answer to that question, but we would have to find out more information. The other part of that is also, if your seed has been tested before, do you have an idea of what the contamination level could be, because that will also establish the appropriate testing plan to meet the criteria that you're trying to establish for your seed.

 

Sarah Curran: All right. We did have one question come in as well about whether or not the webinar will be recorded or shared after this presentation. Just to answer those questions, yes, it will be posted on our website in the resource center. A copy of the slides and the recording will also be sent to everyone who registered for the presentation. We have another question from the audience. If they have a small seed lot and cannot provide 3,000 seeds, do you have capacity to test a small lot seed?

 

Anna Doornink: Yes, and what we recommend for small seed lots is not testing more than 10% of the seed that you have. Obviously, you still have to have seed to produce and to further on into subsequent generations for this product, so if you do have a small seed lot, do reach out to us. We can test definitely fewer than 3,000 seeds. We recommend not going above 10% of the total seed lot that you have.

 

Sarah Curran: All right. Dave has submitted a question. He asked, "Is it possible to have false positives when testing seeds?" He asked about alfalfa in particular.

 

Anna Doornink: Okay. Joseph?

 

Joseph Lopez: Yeah. I mean, yes. It's possible that we can have a false positive at times. This is where the good laboratory practices comes in place, one, where in the lab, we ensure that the samples are not contaminated by any other means, and we maintain the integrity of the samples, but the false positive can be from the samples submitted where, I mean it's from a contamination of a dust or it's a contamination of any other source like, so this may lead into a false positive. Actually, the seed may not be positive, but from the particular like dust can lead it to a false positive.

 

Sarah Curran: All right. Another question has come in. Someone has asked, "How many pools would you recommend I test for in a semi-quantitative analysis for conventional corn?"

 

Anna Doornink: Okay, so again, I would ask the question of, what's the threshold that you're trying to meet, if it is conventional corn, and if ... Basically, if you're going to be doing export or to have the most stringent level of testing, a 3,000-seed test is really the industry accepted standard, like I said, especially for export, and that will get you down to less than 0.1% contamination if no detects or positives are found in your seed lot. Now, conventional production in the U.S. is difficult. It's not impossible, but it is difficult, so again, you just want to make sure that we are testing to the level that your seed is destined for, making sure that we are establishing that appropriate testing plan.

 

Sarah Curran: Great. Let's see. Well, Anna and Joseph, maybe you guys can answer this question too. It's a pretty easy one, but they asked which Eurofins office can they contact about this testing.

 

Anna Doornink: Sure, so you can contact the Eurofins BioDiagnostics River Falls location, and you can actually ask to speak to either Joseph or myself, Anna, and we are happy to answer any questions that you have about AP testing.

 

Sarah Curran: Great. All right. I'll give everyone a couple of minutes to continue submitting questions. We have quite a few that relate directly to cost and a few other case-by-case specific questions, so everyone is aware, we will be sending these questions directly to our presenters after this webinar, so for some of these that are more specific to your business, we'll have Anna and Joseph follow up with you directly to answer your questions further. Earlier, someone asked about the 95% confidence level. They have concerns that in many places, they're requiring "100% assurance." Is there a reason that we use 95% as a rule of thumb?

 

Anna Doornink: What's that? 95% is, again, it's an industry standard for the testing plan. 100% is, well, not achievable.

 

Sarah Curran: Right.

 

Anna Doornink: That would mean testing everything, and then, there would be nothing left. There'd be no product left to sell, so the 95%, it's an industry standard.

 

Joseph Lopez: Yep.

 

Anna Doornink: Joseph, you want to-

 

Joseph Lopez: It's a industrial driven standard like for any that, I mean the import/export or domestic trading, so the 95 percentage confidence level is an industry standard. We always ... The customer as well as the producer and the customer will always look for that, so that's the reason why we test the majority of the samples at the 95 percentage of the confidence level.

 

Sarah Curran: All right. Okay, and with that, I will conclude our presentation at this time. I'd like to say a very big thank you to Anna and Joseph for sharing their time with us today. Thank you to everyone who attended. We will be following up with your questions one-by-one after this presentation, and you'll also receive a copy of the recording and slide. With that, we will conclude our broadcast today. Have a great day, everyone.

 

Anna Doornink: Thank you.

 

Joseph Lopez: Yeah. Thank you, everyone.