What Does a Test Tell You? How Sampling and Testing Protect Grain Safety and Quality

Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jim Lenz, GEAPS (00:02):
Every day in this industry, millions of
decisions are made about grain.
Decisions about storing,blending, shipping, drying,
feeding, and processing.
But behind those decisions issomething many people
underestimate.
Data.
And in grain handling, one testor one mis test can change
everything.
In this episode of the wholebrain podcast, we explore a

(00:24):
topic that affects everyelevator, every mail, every
processor, and every safetymanager.
Sampling and testing shape thequality and safety of green.
My guest today is Dr.
Richard Walter, AssociateProfessor in the Department of
Agriculture and BiosystemsEngineering at Iowa State
University, and one of thenation's leading researchers in
the measurement system, samplingcarrying, quality management,

(00:45):
and great feed.
She joins me to break down whattesting actually tells us why
consistency matters.
Garbage, garbage out, realfacilities.
And how even simple samplingpractices can prevent quantity
mistakes.
Stay tuned.

(01:17):
Hello and welcome to the show.
You're listening to the WholeGrain Podcast, where we explore
the people, innovations, andideas shaping the grain handling
and processing industry.
This show brings grainprofessionals together from 94
countries around the world.
My name is Jim Lenz, your hostand director of global education
and training at GEAPS, wherethe mission of the Grain

(01:39):
Elevator and Processing Societyis to champion, connect, and
serve the global grain industryand our members.
Today we're taking a deep diveinto the world of sampling,
testing, and data-drivendecision making with Dr.
Gretchen Mosher.
Whether you run a countryelevator, manage a terminal,
operate a processing plant, orwork in quality or safety, this
episode will help you see yourtesting program perhaps in a

(02:03):
whole new way.
And as always, we'll wrap upwith reflection questions you
can use with your team to turntoday's insights into action.
All that and more coming upnext.
She advised her time betweenteaching research administration

(02:36):
service.
She spent her career exploringhow we can improve safety and
quality across the grain andfeed industries.
Dr.
Mosher, welcome to the show.

Dr. Gretchen Mosher (02:47):
Thank you for having me, Jim.
It's a pleasure to be here.

We are going to discuss the role of sampling
and testing and managing safetyand quality of green.
Let's start the big picture.
An important part of managingand predicting the quality and
safety of grain and feedproducts is testing.
From your perspective, what'sthe real value that testing
provides to grain handlers?

I think the primary value is the
information that we get from thetest, right?
People make better decisionswhen they have better data and
better information.
And the test is kind of anall-in-one.
You can get a lot ofinformation from a very few
number of tests that you can useto make storage, blending, end

(03:32):
use decisions about commoditygrain and oil seeds as they come
into the elevator.
So I think that's the primaryvalue, it's the information that
the test provides.

Good to make decisions with sound data.
Absolutely.
When you speak with industryprofessionals, what are some of
the more common, or at leastmaybe you've heard some common
misconceptions you hear abouttesting?

Well, I think many see testing as a way
to catch them in a negativesituation or potentially test
for negative aspects in thegrain or oil seeds.
And I think I see it both ways.
I think you also look forthings that are where they
should be, right?

(04:16):
You're verifying and validatingthat the grain you're bringing
in, the oil seeds you'rebringing in, fall within the
parameters that you expect themto.
And so it is a a bit about riskmanagement too, right?
And and you catch things earlyso you can keep them from
becoming big things, right?
When you test on a regularbasis.

(04:37):
I think the other misconceptionis that it is expensive.
It is, it there is a cost, itis true, but it's an investment
and it's really part of a largerquality management system is
testing and the sample that isneeded.
Most tests in the grain andfeed industry are

(04:58):
non-destructive.
Not all of them, it's true, butmany of them are.
And so you're not takingproduct out of commerce, right?
You're testing it, you'reputting it back in and running
it through.
And that's another advantage, Ithink, when you're working with
non-destructive testing, likesome of the examples I'll talk
about today.
Those are the two primarymisconceptions that I would like

(05:20):
that I feel comfortableaddressing.

As you said, testing could be expensive.
Certainly, there is anevolution of these automation
sampling pieces of equipmentthat are out there.
It's pretty interesting howit's done now compared to many
times how it's done in the past,although people aren't all
using automated procedures.
But testing isn't cheap.
I mean, you need equipment,people, time.
For those running elevators orprocessing facilities, why

(05:45):
should they still considerinvesting in a testing strategy?

I would argue the primary reason why
folks should potentially investin some form of testing strategy
is the risk managementpotential.
It doesn't happen all the time,but generally, what has been
characteristic of most largeproblems that are sourced out of
grain-based ingredients, theystarted small.

(06:12):
And had they been caught at thetime that they initially
surfaced, it would have been afairly small amount of product
to move out or to re-divert to alesser, a less sensitive
audience, if you will.
And so being able to catch thesmall things before they become
big things is the first thing.

(06:33):
I also think with theregulatory compliance that we
have now, post-FSMA, Food SafetyModernization Act, a testing
plan represents the so-calleddue diligence that a firm or an
organization performs so thatthey can say with confidence
that things are clean, if youwill.

(06:54):
They are non-contaminated, thehazard is not present, the
adulterant is not present, thequality factors are where they
need to be for most optimalstorage, handling, and
processing.
And so it is an investment, butit is a solid investment, I
think, when you consider thatthe inventory that any given

(07:17):
facility holds is at timesmillions of dollars in in
inventory, right?
So you'd like to know what'sup, you know, what's up with
that inventory.
Does it fit where you'd like itto be or does it not?
And if not, how far off does itdeviate?
Right.
So those are all things, piecesof information that can be

(07:38):
provided with a regular testingstrategy.

I like that you pointed that out, due
diligence.
So, yes, uh big component andrationale for developing that
testing strategy.
In your mind, when is testingmost critical?
Or is it critical during allstages?
For example, you know, atharvest intake, during storage,
or before a grain goes out thedoor?

Good question.
I think for certain, testingincoming product is something
that you should be doing.
And if you're you're measuringthat incoming product as an
elevator to determine where thediscounts ought to be taken.
So clearly, that's a good timeto take a sample.
You have a good baseline forthis is at receipt, right?

(08:23):
So it wasn't yours, now it is,it's in your possession.
So clearly, incoming product.
But I would also say it doesnot hurt to take maybe a smaller
number of samples of outgoingproduct.
Again, particularly if you'resending it up the supply chain
or down the supply chain, if youwill, to the vendor.
And corn and oil seeds andsoybeans and wheat change as

(08:48):
they're held in storage.
And so you took that step oftesting when it was incoming,
depending on how long you haveheld it, it would make sense, in
my opinion, to also test it onits way out, particularly if
it's headed somewhere wherethey're going to look at the
quality and they're gonna say,you know, it doesn't meet our

(09:09):
standards, right?
Or and I think if you have thatrecord that says, in fact, it
does, when it left our facility,here's how we tested it, here's
where our data fall, here'swhere those parameters are.
I think it really gives you agood bargaining and leverage
power uh when you're workingwith vendors upstream to the

(09:29):
next level.

Excellent.
Thank you for sharing.
Something you've emphasized inyour research is the importance
of valid measurement procedures.
Why is this such a big deal inour industry?

I think that's a a great question
because when I teachundergraduates, I use a term
with them related to qualityprotocols and data that says
garbage in, garbage out, right?
You really take care to have ahigh level of confidence in your

(10:01):
results.
To do that, you really need asample that is taken in the same
way again and again, right?
The same protocol, the samemethodology.
When you use those standardizedprocesses, you can have a
higher confidence that when youdo see differences or deviation
from where it needs to be, youknow that it's actually the

(10:24):
sample that is the problem,right?
Not the fact that you had sixdifferent people testing it in
six different ways, right?
It is, you know, you know thatthose differences are actual
differences based on thevariability in the in the
material rather than throughmeasurement error, receiving

(10:44):
error, you know, errors in inhow people do things.
And that's important, uh,number one.
And as an example, low-levelanalytes or things that we look
for when we test adulterants orwhatever that are non-uniformly
distributed are highlyinfluenced by sampling
procedures.
So when you change the way yousample, it could have a pretty

(11:09):
large impact on the finding.
And in the grain and oil seedhandling industry, these types
of low-level analytes that arenon-uniformly distributed
include big things, um,mycotoxin, genetic traits,
foreign material,microbiological or microbiology

(11:33):
contamination.
All of these things areconsidered, with the potential
exception of foreign material,are considered hazards, are
often on the list of preventivecontrols for grain and feed
handling industries.
And therefore, the the sampleprotocol and measurement
procedures are our big deal.

(11:54):
Um, and when you do it the sameway every time, you know that
that that variability is notpart of the equation.
Uh, there's always variabilityin processes and procedures.
But even if you take care tominimize that, but minimizing
that variability is critical tofind the real differences,

(12:15):
right?
When they exist.

That makes sense.
What are some of the risks whena facility relies on poor
sampling methods or uncalibratedequipment?
You touched on this a littlebit.

I I did.
I actually wrote a paper onthis with my colleague u
Charles Herberg and a graduatestudent, where we actually
simulated what we called randomand systematic error in our
ability to segregate soybeans bygeographic area.
The the risk of a facilityrelying on poor sampling methods

(12:49):
or uncalibrated equipment isthat they risk make a large
error in the decisions that theymake.
So, as I said earlier, I wrotea paper on this where we
simulated uh random andsystematic errors.
Uh, we looked at its impact,the impact of those random and

(13:09):
systematic errors on whether wecould segregate and isolate and
differentiate soybeans comingdifferent uh geographic areas.
Random errors include usererrors, people doing different
things, environmental effects,and equipment uh deterioration.
That equipment deteriorationcould be the fact that the power

(13:33):
source is uh not consistent andtherefore the light is not
consistent and it's notmeasuring in the same direction.
Those are random errors that donot occur with any kind of a
routine way.
Systematic errors includeinstrument measurement bias, so
an instrument that reads not asaccurately at higher levels of

(13:57):
measurement, for example, orvariances in how we standardize
those instruments.
So if we use differentstandardization sets of grain
samples to standardize, therecan be differences in readings
across several locations.
Those are systematic errors.
Our simulation found that itcan have a big impact on what

(14:23):
the final result looks like andcan muddy the waters even
further in a situation where thedifferentiation is not always
clear.
Furthermore, sometimes errorsthat we think are random,
meaning that they occur not on aregular pattern, are actually

(14:43):
systematic, meaningenvironmental effects such as
temperature or equipmentdeterioration could very
possibly be based on what wecall a non-random pattern.
So this idea that these thingsare happening on a regular
basis, and if you're totallyunaware of them, they're
influencing your decision makingand you don't even know it,

(15:05):
right?
You don't even know that thesethings are happening.
So good and valid samplingmethods help not necessarily,
it's not going to say, oh, bythe way, here's your error,
right?
But when you see the behaviorof the data when you use those
those uh protocols, it shouldgive you cause to pause, if you

(15:27):
will, and think, I wonder what'sgoing on here.
We usually see this.
Today we're seeing thesethings.
What's happening, right?
And if you can say, I know thatit is not my measurement
protocol, it's not the samplingtechnique, it's not the testing
technique, it's got to be fromsomewhere else.
And it really begins that rootcause analysis on where the

(15:48):
problem is.
And without that or sampling,you must first rule out the
sample, uh the sampling method,right?
And that's harder to do thanmore difficult than just
finding, you know, finding theproblem with the product.
And that's what you want tofind, right?
You don't want to find thatit's just that you have not
calibrated your instrument inyou know for too long.

(16:11):
And that's why you you'reseeing these random or
systematic errors.

What advice would you give to an operations
manager to ensure their testingprocess is consistent and
reliable?

Two things.
First, I think it is really agood practice to take a
composite sample, meaning asample that's got bits and
pieces from each load thatyou've brought in, potentially
in a five-gallon or a 10-gallonbucket, once or twice a day
during times of low risk, justfor maintenance samples.

(16:46):
And then when the situationdemonstrates a higher level of
risk, compiling and testingseveral composite samples a day
would not be outrageous, I don'tthink.
So that's the first thing.
Second, to consider atwo-tiered approach.
Determine a fairlystraightforward and rapid,
non-destructive measurement forall samples that you take,

(17:10):
moisture or test weight orsomething that potentially could
serve as an indicator thatsomething else might be
happening to kind of maintainand establish a pattern of what
the product is looking like.
And then if that first tiermeasurement reads outside of
your parameters, you can setthat sample aside for later,

(17:31):
right?
For further testing, maybe amore comprehensive or a more
analytical or uh near infraredtest for things, you know, like
protein, oil, fiber, toxin, thattype of thing.
Because clearly you can't dothat while you're receiving a
lot of product at a time, as wedo during harvest.
So having that two-tieredapproach, having one thing that

(17:53):
you're already going to collectand to use that as kind of your
your indication that somethingelse might be happening, and
record that data.
And then when things falloutside of that spectrum, test
just a subgroup, a smallersubgroup of samples for the
higher level tests would be mysuggestion.

That makes sense.
Thank you.
Let's say you've run the test.
Now comes a question what doyou do with the data?
How can grain handlers use thatinformation, improve safety or
product quality?

Well, to begin, I will first uh share an
analogy from a colleague of minewho said if you collect data
and do not use it or do nothingwith it, you might as well put a
pile of money on the floor andlight it on fire.
And it is true, data areexpensive, no matter what,
right?
Collecting the data are timeconsuming.

(18:49):
And if you are not committed tousing them, using the data
results that you have foroperational and daily decision
making, why are you collectingit?
Right.
And to think about when youcollect how long do you store
things, how long do you keepstuff?
Those are more sophisticatedquestions.

(19:10):
But ultimately, commit to usingthat data to make operational
decisions on a daily basis.
For example, when and where andhow long to store, um, how much
to dry?
Do we send it to the wet bin?
Do we send it to the dry bin?
In merchandising decisions, youknow, when you're loading the

(19:31):
train, when you're loading thetruck, uh, what do we put in
there to optimize blending andso on?
Things like that.
All of those decisions aredata-based decisions.
And when you collect the datathat you need to make those
decisions, it makes sense and itis logical that you will use
the data that you collect.

(19:53):
And that's those are just a fewsuggestions on how to use the
the data you collect on a on adaily, your kind of daily
operational data.

So that's why you encourage using test data to
support continuous improvementat first and facilities then?

Exactly.
Exactly.
And I think there are a lot offacilities out there that are
doing a great job with that.
They they have a much betterhandle on what they have and how
much they can blend and andwhat they can hold and what they
can merchandise when they knowwhat's in their inventory.
Again, it isn't, you know, overa million dollars of inventory,
at least at minimum, for mostfacilities.

(20:31):
Keeping good care of that andmanaging it appropriately should
be a priority.

So let's move the bigger picture, talk about,
let's talk about industry andregulatory connections.
Beyond the day-to-dayoperations, what role does
testing play in meetingregulatory requirements?
I know you mentioned thingslike FSMA or other export market
standards.

That's a good question.
With FSMA, the test data thatare collected, you know, at
receipt provide some of the bestproof, quote unquote, that an
organization has thought throughfood and feed safety hazards at
their facility and takenactions to both monitor and
remove hazardous hazardousmaterial as warranted.

(21:16):
Further, when it doesn't happenvery often, and I know this,
but outbreaks and recalls dooccur.
And when they do, your testdata provide that solid
documentation that your productpotentially is not among that
which is being recalled, right?
That is one of the best cardsyou can hold in your hand,

(21:39):
right?
Is to lay that card on thetable and say, we know that our
product is not there becausewe've kept we've kept good track
of it, right?
And I think this is a goodpoint to mention.
It's your processes that tellyou that, right?
Your documented processes thattell you where your product is
and where it's not.

(22:01):
And sometimes where it is notis the more important
designation that you can you canmake.
Is it we know it's not herewhere all of these other
recalled products are located,right?
Your processes and the recordof that process is is what you
have.
Uh and that is especially true,I think, with um export market

(22:24):
standards, particularly as itrelates to transgenic material
and things like that.
It is those records thatprovide you that documented data
that you need to be able tosay, hey exporter, here's where
we've held this.
We have the records todemonstrate that we have held

(22:45):
this non-genetically modifiedproduct in this bin.
We've held these food-gradesoybeans in this bin, which is a
food grade bin.
We have not put food grade ornon-food grade things into the
bin.
We've not hauled in a containerthat is non-food grade.
So we feel nearly positive thatthis these recalled things are

(23:08):
not uh not among our products,right?
Not we we did not contribute tothis problem or something like
that.
But it provides a really stronghand to play.

Yeah, good record keeping is critical.

It is.

Do you see testing practices changing in
the future as technologyadvances?
For example, real-time digitalmonitoring or automation.

Absolutely.
I think we we change practicesall the time.
I think an area of interest forme personally is kind of
machine vision and and real-timesensoring and and uh
monitoring, potentially with abit of automation involved.
Uh and I have no doubt thatwill probably change the

(23:51):
sampling and testing practices.
But I also would like to pointout that we're making the same
decisions with the same datawe've always had, right?
It it is no matter how wemeasure that data, whether it's
through an automated sensor orwith a human sensor, right?

(24:12):
It it's the same data.
Uh that could changemoderately, but I think the
practice of removing theembedded errors in the sampling
process is something that I seedefinite definite change.
I've seen it even in the 15years that I've been in this
industry, that it has changeddramatically.

(24:33):
And I expect we have moredramatic change on the way
because given all the newtechnologies available.

Yeah, that's a good great uh offering there.
And uh that makes me wonder.
Not every listener has a largebudget or access to cutting-edge
labs.
What are two or three practicalsteps that any facility, big or
small, can take right now toimprove their testing practices?

That's a great question.
I would begin by saying even ifyou don't have a lot of
equipment, take care of theequipment you have, do the
routine maintenance, do youknow, perform the calibration
procedures, ensure that theequipment you have is measuring
things in a valid and accurate,precise way.

(25:21):
Nearly all facilities have amoisture meter, for example, a
moisture uh instrument.
I don't know the number whocalibrate these instruments on a
regular basis.
And it makes a big differencein how well they read, right?
And how well and how accuratethose uh readings are.
And when you ensure that thatequipment is in is calibrated

(25:44):
and in good working order,that's the first step.
That way, when you perform yourtest, you can feel confident
that the data output is valid.
Second, double dip on yourdata, use data you're already
collecting for operationalreasons.
Moisture, test weight, uh,grading factors like foreign

(26:07):
material, broken kernels, thattype of thing, damage.
Use these measures as kind ofthat first tier operational
decision, right?
Um make a decision about whereyou're going to put things and
think about scenarios ahead oftime.
Where you where are you goingto divert this load that has a

(26:28):
lot of damage versus one that isa high moisture load or you
know, whatever you're lookingat.
Uh, when you take those initialsamples, use that basic data
that you have to predict larger,um kind of establish the
patterns that you can expect.

(26:48):
And so when you see somethingthat is outside of that
boundary, you can take actionand investigate a bit.
And finally, I think it is agood practice, and I will repeat
this again, to take a compositesample even during times when
the hazard level is low.
Run your basic tests on thatcomposite sample.

(27:09):
And then if there's no need tofollow up, you know, record it,
note, note it, and then ifwarranted, dump that grain back
into the pit and go on with yourday, right?
If there's a problem, then youkeep the sample and you have it
as your record.
You don't even need tonecessarily test it that day, or

(27:30):
maybe you send it off site fortesting if you're a small
facility.
But having the composite sampleis a cheap way to uh ensure
that the loads that have comethrough your facility are quote
unquote clean.
That is a really good practiceand habit to get into is taking

(27:51):
once or twice daily compositesample and just checking it,
making sure there are no majorproblems.
So those three things take careof the equipment, use your
basic data, and then make thatpractice a habit of taking a
composite sample once or twice aday.

It's terrific.
Three big tips there for anyoperation and facility,
regardless of budget, somethingthat all can consider and
implement.
Now, Dr.
Mosher, this has beenincredibly insightful.
Testing can sometimes feel likea burden, but you've really
highlighted how it's a criticaltool for both safety and
profitability in our industry.

(28:29):
Before we wrap up, what's onething you'd like listeners to
remember the next time they facea decision about testing at
their facility?

You have total control over the quality
of your data.
And people make betterdecisions when they use valid
data to make those decisions.
So utilize the inventory thatyou have and the data and
information that it provides tomake better decisions.

That's fantastic.
Great advice, and others canimplement this, our listeners.
You've been a terrific guest inthe Whole Grain Podcast.
Thank you for joining us today.

You're very welcome.

That brings us to the end of today's episode of
the GEAPS Whole Grain Podcast.
A huge thank you to Dr.
Gretchen Mosher for joining usand for sharing her expertise on
one of the most critical andoften overlooked aspects of
grain operations, the role ofaccurate sampling and testing.
As we heard today, reliabledata doesn't just protect
product quality, it protectsyour inventory, your customers,

(29:29):
your reputation, and your bottomline.
A strong testing program is oneof the best tools any facility
can have for managing safety,reducing risk, and making
informed decisions every singleday.
Before we wrap up, here are afew reflection questions you can
discuss with your team.
What data do we collect today?
And are we actually using it toguide decisions?

(29:52):
How consistent are our samplingprocedures across ships,
seasons, and employees?
What risks could we?
Reduced with more routinecomposite sampling or better
calibration practices.
Where does testing give usleverage with suppliers,
customers, or regulatoryrecords?
And how can we strengthen thatdocumentation?

(30:13):
And finally, what smallimprovements to our current
testing workflow could make thebiggest impact?
And remember, Jeeps is here tosupport your growth.
Explore over 25 online andon-demand courses built for
green professionals, hands-ontraining programs with industry
experts, a searchable librarythat continues to grow,

(30:36):
comprised of over 200 technicaland operational videos,
interactive webinars andmaintenance state learning, our
next gen path for interns andearly career professionals,
globally recognized credentialsfor emerging leaders and
industry veterans, the GEAPSDigital Grain Glossary, local
chapters for networking andprofessional development, and of

(30:57):
course GEAPS Exchange, theindustry's premier annual event.
Visit GEAPS.com, that'sGEAPS.com to learn more about
how you can connect, grow, andstay ahead.
I'm Jim Lenz, Director ofGlobal Education and Training at
GEAPS.
Thank you for joining us, anduntil next time, keep feeding,

(31:19):
fueling, and clothing the world.

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What Does a Test Tell You? How Sampling and Testing Protect Grain Safety and Quality

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