Unlocking Kiwifruit Secrets: Introducing the 2024 Update of the F-751 Kiwifruit Quality Meter

Hunter Weber

April 8, 2024 at 6:40 pm | Updated June 4, 2024 at 9:35 pm | 22 min read

Dive into the heart of food quality technology with our recent webinar, spotlighting the groundbreaking update to the F-751 Kiwifruit Quality Meter. Hosted by Galen Geroge, our Director of Applied Science at Felix Instruments. This session is a must-watch for professionals and enthusiasts alike who are keen on the latest advancements in food quality assessment.

Why Watch?
– Learn Directly From the Experts: Galen brings a wealth of knowledge, sharing insights for the F-751 Kiwi Meter’s latest features and applications.
– Engage with Innovations: Discover how updated technology sets new standards in food quality and safety, significantly impacting the industry.
– Interactive Q&A: Benefit from a dedicated Q&A session where our experts address your questions and offer personalized insights.
– Practical Information: From technical troubleshooting to accessing additional resources, get practical information to enhance your understanding and use of the F. 751 Kiwi Food Quality Meter.

Links discussed in this webinar:

Slide deck:

Validation report:

Chemometrics Series:





Video Transcription:


All right.It is 4:00 pm on the East coast

of the United States, where I am.

And it is 9 a.m.

in Auckland, New Zealand,

according to my iPhone.

So welcome, everyone to today’s webinar.

We’re really excited today

to talk about the update of the F7

51 kiwifruit Quality Meter.

Before we get started,

I want to just cover a little bit of light

housekeeping today.

Our moderator is Hunter.

He is our marketing coordinator

at Helix Instruments and City Bioscience.

Hunter is going to be the one

that’s moderating the chat.

He’ll be posting

any kind of relevant links.

so if there’s any links

in the presentation,

he’ll post those in the chat

so that you can click on those.

You’re not gonna be able to interact

with them on my presentation.

So that’s where they will be.

If you guys, need to communicate with us,

that we’re having technical issues,

if you can’t hear me, if the video

goes out or if the presentation goes away,

please use the chat feature for,

for those kinds of, notifications.

If you have questions that pertain

to the actual content

of this presentation,

then please utilize the Q&A function

at the end of the presentation,

I will be opening up that Q&A function

and going through all the questions

one by one and answering those.

So if you post your question

into the chat, then I likely won’t see it.

And I won’t be able to answer it.

But always

be, you know,

aware that if you do have questions,

if you think of questions after this,

or if you had a question that got lost in,

in the mix, then please just feel free

to reach out to us directly.

And I’m happy to answer any questions

after this presentation, through email

or through a video call

or or anything like that.

So for any of you that aren’t familiar

with who I am, my name is Galen.

I am the director of applied science here

at Felix Instruments.

I’ve been with the company

for five years now.

My degrees are in biochemistry

and food science.

I’m an AI certified food scientist,

and my background is mostly in quality

and safety assessment in the food,

agriculture and cannabis industries.

And what I wanted to do today

was a little bit of a departure from

from what we’ve done in previous webinars.

If you haven’t joined one of our webinars


one that kind of goes along with this,

a little bit that you might interest

you is our whole series on key metrics

and modeling and how we actually do

the modeling process for,

these devices, the at 750 and the 751.

but what I wanted to do

today was just kind of

give you some background on our company,

in case you aren’t familiar

with who we are.

So we are Felix Instruments,

and we were established in 2012.

we are a, sister company or a subsidiary

company of Seed Bioscience.

And so CED Bioscience

was, founded in 1989,

and it started as a company

that created nondestructive research,

scientific research tools for plant

research, and plant phenotyping.

and so we’ve what we’ve done in since 2012

is we’ve applied that knowledge

that we gained over,

catering to plant researchers

in the agriculture or in the ecology,

and the environmental sciences,

kind of, realms.

We’ve taken that knowledge and we’ve

applied it to this new technology

that’s near spectroscopy technology

to help us create these cutting edge,

nondestructive tools for the produce

sectors of the agricultural industry.

And the way we’ve done that,

we’ve we’ve been able to do this,

basically this innovation

through collaboration with top researchers

and industry leaders across the globe.

And so we really want

to make this technology as,

with a global approach in mind and,

and everything that we do,

we want to make sure that it caters

to regions all across the world.

And so that’s what

we’ve taken as an approach.

And the other thing that to note

is that everything we do,

all the engineering, the design,

the manufacturing, the research,

everything takes place

in-house at our headquarters in Camis,

Washington, in the Pacific

Northwest of the United States.

we the way that we actually are able

to access all corners of the globe

is through our distribution partners.

And so we’ve got distribution partners

worldwide that allow,

anyone, anywhere

to access our technology,

and also give them localized

support and sales, help.

And so they’ll in New Zealand

specifically, we have Mac solutions.

they’re based in Christchurch

and they are, a company

that’s very familiar with

and our technology,

they’ve been dealing with that

for a while.

And they also have a lot of expertise

in the food industry

and shelf life testing

and things of that nature.

And so they’re our local distributor

for New Zealand.

and the other aspect of this is

that New Zealand isn’t the only region

we cater to, obviously.

So we actually have devices

that are deployed over

to over 500 companies and or 500 customers

in over 100 different countries,

and we have features in over 200

different peer reviewed publications.

And so these devices

have been really tried and tested across,

multiple different regions

and, and across time

to, you know, really become the robust

technology that they are now.

And the technology that I

referring to in that whole section

was the F7 50 and the F7 51.

So these are our near infrared

spectroscopy instruments.

They’re portable quality sensors.

And so what that means is we’re using near

infrared spectroscopy in combination

with a robust key metric calibration

to enable users to acquire data

nondestructively of internal

through quality indicators.

So we’re shining light in the visible

and near infrared spectrums

into the actual muscle car

tissue of the fruit.

And we’re getting information

about the chemical constituents

and the physical chemical properties

inside of that fruit without having

to actually cut it open or do anything,

any kind of destructive assessment.

And so this technology really is

a combination,

a marriage of the hardware aspect,

the actual spectroscopy

kind of aspect of it.

But then there’s also this really complex


of of creating these keemo metric models.

So that’s kind of where

we have to find this balance

and where we find most of the improvement

coming from,

in recent

years is on the key metric side of things.

And so that’s what

we’re going to be talking today about, in

later slides is I’ll be showing you

how we’ve improved the actual quality

of the models themselves with the,

kind of actions that we’ve taken

to increase, our data flow.

But before we get there, I

also want to just kind of lay out

why you might be interested

in this technology, what kind of benefits

it can provide to you.

So the very first thing I’ve already

mentioned this a couple times.

I’ve said this, this buzz word a couple

times is that it’s nondestructive, right.

We’re eliminating food waste.

For one thing, we’re eliminating

the actual waste is for it

in the form of the cost of the produce

that you’re destroying

when you do your quality testing.

But then we’re also enabling you to go out

and take measurements

and take as many measurements as you want,

without having to worry about,

you know, wasting that fruit

and not being able to monitor it

again at a later point in time.

So it enables you to actually see

progression of these fruit traits

in the same exact fruit over time,

which is something that’s really unique.

And and at the current state,

with the technology

and the destructive methods

that are being used, is just not possible.

So that’s one benefit of this,

this technology.

The other is the accuracy. So

right now

we have the ability to utilize

in this great age of technology.

We have, AI and machine learning methods

that we can utilize

to actually improve

the performance of the models.

So this technology spectroscopy

has been around for a while,

but we also haven’t really had the,

I guess, the software

kind of AI technical capabilities

to make these giant kind of data,

you know, databases

that allow us to incorporate tons

of different variability

and allow AI to learn

how all these different little variables,

things like regionality, seasonality,

temperature, variety,

all sorts of different, you know,

what stage of maturity the fruit is at all

these different variables.

The models now can learn

how these different variables impact

the actual spectra that’s received by the

instrument, and allow us to incorporate

and build all those different variables

and build these more robust data sets.

And so the accuracy is improving

because we’re able to actually use it.

The technology

and a wide variety of situations now

and not be worried

about whether or not it’s going to be less

accurate or more accurate

in this situation, because we’ve built up

this large database, whereas in the past

and I our calibrations are typically

very specific to a singular, you know,

condition, environmental condition, it’s

very controlled kind of in conditions.

But this allows us to use it

in a variety of applications.

And that leads us to the versatility.

So these instruments

can be used out in the field.

They can be used in the pack house

at the retail outlet.

As an importer

you can use it to inspect incoming lots,

all sorts of different applications.


any point in that food supply chain.

And the reason we’re able

to do that is because of these

really robust data sets.

We’ve also integrated some features

into the device itself to allow for

compensation for different lighting

conditions and things of that nature.

So we’ve got this great marriage of

we know we can deal and compensate

for all these different variabilities

that might be present

in your specific situation.

So what we’re trying to do is create this,

this product

that works for everyone in any situation.

And lastly, what this can give

you is just a wealth of insights.

This technology,

because it’s nondestructive,

because of all these things I mentioned

before, it allows you to go out and

and rethink how you sample.

So and you know, and traditionally

we’re sampling as little fruit as we can


still trying to maintain representation

in our sample set.

But the reality is we’re never going

to be able to hit that representation

mark, because we’re always

going to be encouraged to reduce our ways

to reduce our costs of testing,

our costs of wasting fruit.

So now with this technology

allows you to do is go out

and actually sample as many fruit

as you want to try to get

as a better, more representative idea

of what’s going on with the fruit

that is in your field, in your orchard,

or the incoming fruit

that’s coming from the orchards

or whatever you’re importing.

All of that allows you to go out

and sample more and get a

and be more informed, essentially.

So this technology

has been really groundbreaking for

for the agricultural field

because it’s just it’s it’s a total

change up to what we’ve been doing for the

last, you know, 30 years now.

So when

I’ve been talking a lot

about the modeling,

and I haven’t really explained too much

about what goes into that modeling,

besides, we’re building a big data sets

and we’re we’re adding a whole bunch

of data from different regions and seasons

and things like that.

But I wanted to give you a specific

look into how we’ve been developing

these models in beach.

And as transparent as we can.

So specifically for New Zealand,

what we’ve been doing is, working really

closely with a independent third party,

collaborator of ours called Start Afresh.

And so they are a totally unbiased,


unit that is actually investigating


different kinds of spectroscopy

pieces of equipment.

and in that we were a part

of that kind of investigation.

And this started back, in the earliest,

data collection was back in 2020.

So at the F7 50 and the F7 51,

we were, started

fresh, was helping us to collect

both spectral data and reference data

using the current standardized methods

for reference testing, from,

you know, early season

through to, you know, the harvest stage

and even some post-harvest data.

So from 2020 all the way now through 2023

and continuing on in 2024,

we’ve been collecting data.

And in 2020, we just did gold in green.

And in 2021 is when we started collecting

red kiwi fruit data.

So right now in this updated model,

we have four seasons

worth of data in the gold

and the green models.

And we have three seasons now

worth of data in the red kiwi fruit.

And I’ll show you the impact that those

have towards the end of this presentation.

But there is some significant

differences that you’ll see,

especially in the red kiwi fruit

now that we’ve added in a third season.

so just something to note

how we built the models.

I will also mention later,

but New Zealand isn’t

the only region that we’re catering to.

It is just, you know, one of the largest

kiwi fruit growing regions in the world.

And we are, you know, collecting

massive amount of data from them.

But we do have data from other regions

in our model as well,

which I’ll mention later.

So I want to get

into the nitty gritty details

of of exactly

what performance looks like and how we

measure performance of our models.

And I want to explain this graph

the statistics to you and everything.

so let’s just start off

by going with the table to the right.

I’ll explain

some of the statistics to you.

And from there

I will explain what this graph means.

And and kind of how we interpret

this graph

that we and that we, that we create

and how we actually even created it.

That being said, I’m

not going to go through

we have an entire internal validation

document that I’ve put together,

that goes through

every single model that we have.

And so if you want to view that and review

all of the different models

and look at all these performance

statistics that will be available

for download online from our website,

if you so.

But for the sake of keeping

this presentation, not, you know, 2.5

hours long,

we are going to only focus on a few of the

we’re going to

I’m actually

just going to focus on dry matter

for gold, red and green

and just kind of show you an example

for each of what we did

for our validation testing.

So in a validation, what we’re doing

just kind of some background

of in a validation,

we are taking a small subset of data

from the most recent season, 2023.

And what we’re doing is we’re removing it

from the model training set.

So the data

that’s actually used to create the model

where we’re taking a small subset

of data out of that.

And putting it aside, and we’re building

the model independently.

And then we’re going to take this data

and we’re going to test it

against the model and see how it performs.

And so that’s what we’ve done here.

And I’ve split it up in a way

that we’re actually looking at the two

different instrument types

f 750 and f 751.

And we’re comparing those performances as

well as looking at the overall accuracy.

So in the table we’re looking at

we have the average for the data sets.

The standard deviation.

And then we have the Rmse CV

which is the root mean square

error of the validation set.

And so this statistic

is very important to us.

The Rmse CV is

to be considered

essentially the average error

that you can expect.

So imagine if you see a statistic

in a publication that says, you know,

we’re confident that this prediction

that this, reading

was 1.0 plus or minus,

and then there’s an error

that’s kind of what you, what we interpret

this, value can be interpreted as.

So Rmse is what you would consider as the

error of the instrument, the r squared.

Most of you you’re probably familiar

with the r squared statistic.

It’s a correlation coefficient.

and then RPD is a statistic that we use

that is not necessarily

a it’s a it’s a unitless measure.

It’s essentially looking at the,

the Rmse in comparison

to the standard deviation.

So what we’re looking at is, is the MSI

low, is the error low in comparison

to the standard deviation.

And for the RPD, 

a value of 1.5 or higher means that the,

the predictions of this of this model

are really robust.

And they, they are

the error is essentially much lower

than the standard deviation.

And so that’s higher

RPD is better always.

So that being said,

if we look at our gold dry matter model

right here, we can see that both the F 750

and the f 751 data sets align

very well with each other.

They’re not drastically different.

They’re they have no, difference in bias.

So there’s not one data set that’s

predicting much higher than the other one

or anything like that.

They’re both predicting very, similarly,

the data sets that we use,

the reference data sets, you can see that

the averages are both very similar.

it’s represented

across as wide of a range as we can,

as we basically can, measure.

So from around a little over

10 to 22 dry matter.

And what we’re seeing is the standard

deviations are slightly lower in our

in the instrument, the predicted versus

the reference method.

But pretty much very similar, 

which is a very good sign.

And then our Rmse is

which is what we think

is almost the most critical thing for us

is very, very good.

So we’re seeing very similar performance,

both with an error of,

you know, around .7.66

for that seven, 50.71 for the 751.

So that’s saying that on a given

prediction for a single measurement

that you can expect that if you were to

then take that exact same fruit

and measure

it analytically by slicing the center out,

and then drying it in a dehydrator

and weighing it and finding

the loss and weight,

from during the drying process,

that you would get a value

from that reference process,

you would get a value

that’s within plus or -0.66.

or sorry, I guess reverse of that.

This prediction from that instrument

would be within plus or -0.66 of whatever.

That reference value was so very,

very accurate for our dry

matter models and very similar performance

out of the instruments.

Our, our squareds are really high,

almost 0.9, which means

that we have a really good correlation,

strong correlation in this data.

And our our speeds are all above 1.5.

These are almost at three.

So that’s really, really means

that this is a really really robust model.

For our green


we have so it’s the same kind of graph.

You can see there’s a little bit more

spread here a little bit more variation.

and that’s mostly in our F 751.

So the f seven and 51 is around

0.8 for our MSI.

and 0.66 is the same, value

as it was for the gold.

So it’s also at 2.66 IMC.

Our standard deviations once again match

up really well with our reference method.

Our r squared values are very high

still 0.83 and 0.9 for the f 750.

so both of these again

predicting very similarly,

both giving very accurate, results.

And both, you know,

I would trust either of these instruments

to be predicting green dry

matter properties again really high.

And then our red model.

So our red model

now is at a Rmse of around

and so we’re looking at an error,

an average error

that’s much lower than it was.

And last season,

which you’ll see here in the next slide,

R squared is again around 0.9.

I guess I wouldn’t be exaggerating

when I say that these kinds of models are.

This is kind of the ideal model

to see when you’re building models for.

And then I r spectroscopy applications.

these very, very, good correlations

that are on

a, you know, a one, a 1 to 1 slope.

And they’re all really tightly packed

on that line.

It’s really an ideal situation to be in.

So these models have been trained in us

that they are now able

to compensate for all sorts of conditions

because these are a random data set.

So this data contains data that is from,

you know, different fields,

different conditions, different times,

different phases

throughout the maturity process.

So, you know, it’s totally random.

But this model has been able to compensate

for all those variables

and is still able to predict really well,

on a, on a single scan basis.

Now, comparing

where we’re at since last year,

the first thing I want to point out

is that we are essentially stable from

for the gold and green models, for dry

matter and bricks from 2023 to 2024.

Now, this to me is an indicator

that we are at a point where

it’s going to be difficult

to overcome, that

inherent inaccuracies

that are in the reference method.

once you start getting low enough,

you know, it’s essentially impossible

for us to have a zero error

because there’s inherent inaccuracies

in the actual reference method itself.

So for us to be able to 

do a error measurement

with the an error

spectroscopy, is just not feasible.

So we’re getting to a point

where it seems like we’re kind of

just in a stable stability,

you know, area where

we’re not seeing much improvement

in the dry matter in bricks.

But we also have now the ability

to explore

even more complex

AI and machine learning methods.

And so what we’re looking at right now

with, some research partners in Australia

and central Queensland is looking

at other types of neural networks,

and how those can improve

the performance of our models.

so what we’re going to see is from here

is most likely

just some more improvement,

but up to a threshold,

there’s always going to be a threshold

that we can’t overcome.

because there is, as I mentioned,

inaccuracy in the reference method itself.

that being said, you know, it’s

really good accuracy that we’re seeing out

of these instruments.

you know, we’re seeing if for bricks even,

you know, everything’s,

you know,

pretty much exactly one or under,

and, for a hue

that was one that we actually did see

pretty good improvement from 2023 to 2024.

So in the gold hue,

you see that we’ve now reduced our

our error down to about 1.2, 1.3.

and so that is a major improvement

from the previous year of two, you know,

being over two, in the accuracy.

So being able to more accurately assess

the internal color

of those gold kiwifruit,

with the devices now,

so the major,

I guess,

kind of revelation here is that, you know,

adding in the fourth season of data

might not have increased the accuracy

significantly for gold and green,

but what it’s doing is it’s building up

that robustness,

that resiliency

to predicting future seasons.

So when this device is now used to predict

for this season or for next season,

it’s going to be much more accurate

and not require

as much updating and fine tuning

and all of those things.

It’s going to be a much more robust model.

And the thing that’s probably going to be

what helps us improve

it is going to be further research

into more complex machine

learning methodologies.

the data collection at this point, 

building up a larger

data set isn’t necessarily

going to improve the model significantly

and might allow us to,

get some more representation.

But even then, it’s not going to,

I guess, necessarily

improve the model significantly

in any way.

the big update here

is that the red kiwifruit has improved

pretty significantly.

So we went from last year the, the

the model only had two seasons

worth of data, right, 2021 and 2022.

And our dry matter marks

were above one and everything was kind of

sitting above one for bricks as well.

and this year

when we went and updated it, our,

our masses are now 0.8

for less than 0.8 for dry matter.

And we’re sitting below one

for our bricks, values, for our bricks.

Ramses. So that’s great.

The little bit of glaring, I guess, kind

of, information here that everyone

might be kind of concerned about.

Is this hue reading for the red kiwifruit?

Now, this is kind of an experimental model

that we decided to just pursue because

we were collecting the data for it.


but for the red hue, it’s just in, in,

in, monumentally complex fruit to measure

with a reference method,

which is a, usually a colorimeter,

because coloration is, is so variable

within the fruit itself.

With the red kiwifruit,

it’s impossible for us

to use our instrument

as a direct comparison to the Colorimeter.

The Colorimeter is a much smaller

scan window.

It’s only getting a very specific point

to the flash,

which might have a little bit of gold,

a little bit of red in it.

but when we use our device,

it’s really getting a much bigger picture.

and so that is why we’re not

we’re really not seeing much accuracy,

at all from this around 15,

which is in hue, it’s in hue angles.

So, 15 degrees of, accuracy here.

so that will, you know,

we’ll continue to explore that route.

But, as far as I know right now,

that’s not really,

something that’s really critical for,

for people that are growing red

kiwifruit to understand, quite yet

I’m sure that they’ll nail down

a process, for a reference methodology,

sometime in the near future.

And once that is, occurs,

then we will be able

to kind of build up this model a lot more,

but overall really happy

with the performance of these models.

I mean, everything

being at pretty much at or

below one for, accuracy.

as far as, the,

the rmsd values are concerned,

that’s just really

great performance overall.

Now, what are

we going to do in the future from here?

Where are we going from here?

So the, industry itself,

you know, as, as industry members

that are on this, on this webinar

right now,

you have, you know, some decisions

to make after this information

that I’ve presented to you.

this technology really lends itself

as a excellent monitoring tool

for your harvest monitoring.

You know,

it can help reduce your testing costs.

It can allow you to get better insights

so you know exactly

when you need to send fruit in to test.

To get that, go ahead to harvest,

for the post-harvest supply chain,

you know, it just allowing you to sample

at a much higher rate.

So you really are confident

in the fruit that you are receiving

that it’s, the highest possible quality.

Same with importers

and all sorts of things.

And even laboratories can help use this

as a, as a kind of,

monitoring or assessment tool and,

and their own way

to help them, kind of scale, you know,

scale their operations as well. So

what we need to do moving forward,

I’ve already mentioned

we’ve been building up

these really robust data sets.

I also told you I would mention other

regions that we do include in our model.

So we have collected data from, 

some data from South Korea.

We’ve collected data from Italy.

We’ve collected data from Chilean, fruit,

Greece as well.

but, what we’re doing

now is where

we need to expand our horizons more.

And so we need to continue to collect

that data set, but from a wider range.

So, you know, France, 

as a, as a major growing region as well,

that we need to collect data from.

So we’re going to continue

to pursue those leads to make sure

that these models

are continuously updated and improved,

and that they work really well

for everyone.

That being said, Regionality

and the kiwifruit,

may or may not present itself

as much of an issue with this device.

It really requires us

to have to go out there and

do some actual testing

and do some experimentation.

So, what we’ll do in the future is,

you know, continue to update these models

as we have will,

you know, from our partners,

every time we collect data,

we always will use

that to update our models

and provide these updates for free.

and then we’ll also be exploring,

like I said, those advanced,

more advanced neural network machine

learning approaches to key, metrics

to help improve this model performance.

So really, what you can be sure of

is that the quality,

the accuracy that I just showed you,

the accuracy that’s in the report,

the independent report

that I’ve, put out on our website,

you can be sure that things are only going

to continue to get better.

And in this case, in some cases,

like from last year, this year,

at the very least, remain stable,

where they are at

and things aren’t going to get worse.

And so, that’s kind of the message

that I wanted to put out,

is that we’re at this level

where we’re getting really great accuracy.

Now for the kiwi fruit.

This is a technology

that’s very mature at this point.

It’s now four years in the making.

We’re in our fifth year

of data collection.

We have data that we’re collecting right

now actively, for the red kiwi fruit.

and we’re also doing some more testing.

So, really,

this product is gone from something

that was, you know, just an idea.

It’s now this very, very mature product

that is ready to be used

and all sorts of different applications.

And so if you have questions

about whether or not this

how this will fit into your operation,

you know, if you have your own quality

management system or your own farm,

management system, that you want this data

to be automatically integrated into,

we can do things like that.

We have all sorts of options for, for, 

anyone that’s

really going to be handling kiwi fruit

at any point in the supply chain.

So, we’re really excited to continue

to see this, this technology grow.

And we’re putting every effort into

make sure you’re making sure that it works

for everyone as best as it possibly can.

And we’re really committed to that

that mission.

So really excited about this.

We’re happy to be, you know,

releasing this this will be available

to download this, app.

If you already own a device,

you can download the app and just

replace the old app on your device

or put it,

you know, just put the new app on

and delete the old app.

and if you are interested in purchasing a

kiwi fruit


or if you’re just interested in pricing,

any new shipments of these will all have

this brand new model on them.

and so you can be rest assured

that everything will have the latest

and greatest on it

when it get shipped out.

But Hunter will post this, 

link on the chat.

And if you want to just get pricing

or if you want to just, you know,

get some questions answered, offline,

then feel free to click that link

and fill out some information,

and then we’ll get in touch with you

and help,

make sure that we can properly inform you

on on how this technology can be utilized

in your specific application.

otherwise, you know, we have a lot of

other exciting things, coming soon.

on the note of farm management systems,

we do have an upcoming, release for

a, big update to our fruit maps platform,

which is a orchard management system.

it’s a software online application

that you can use to track

all of your harvest information.

And so that will be coming soon.

So please follow us on our social media

or just check out our website,

sign up for our newsletter.

Our we have great newsletters,

a lot of really interesting,


that we, we to discuss in our newsletters.

you can do that through our website.

and yeah,

thank you all so much for joining today.

I’m going to go ahead and open up

the Q&A and answer any questions you have.

And, Hunter, if you could, if you want to

just throw my email up in the chat,

so, people have access to be able

to email me if they want, questions,

if they think of them after the,

the session today, then

I’m happy to answer those.

the first

question is, oh, this is a great question.

And I’m glad this is this was asked,

does the d750 need to be updated?

the firmware does the firmware need

to be updated to use the new Kiwi models?

So this app is using the same firmware

that we’ve been using, for a while now.

So there should be no firmware

update needed if you are on the latest

firmware already

that is available on our website.

if you have already updated your unit,

then then there will be no other

further updates

needed. It’s just as simple.

Copy and paste the new app file

onto your SD

card of your device.

The next question,

what are the prediction ranges?

The minimum and to maximum values

for, the, dry matter

and soluble solids I’m

guessing has what that the DZ and the SS.

so, for dry

matter, it depends on which variety

we’re looking at.

but typically we’re looking at anywhere

from like ten

to mid 20s for dry matter.

for brix actually much low.

We much lower,

so probably around 3 to 4 up to,

the 20s, up to,

you know, up to like low 20s for Brix.

and that is dependent on which variety.

But, most of them

have approximately that range,

but you’ll be able to see from the,

the independent validation report

that I, that I put up on the website.

you’ll be able to see from that

on those graphs, the ranges, because

the validation sets we use encompass

the entire range of, possible prediction.

So, and that, that range

that’s covered is the minimum and maximum

values of that range.

So that was a great question.

Thank you both.

Those are really good questions.

Appreciate it.

So if we don’t have any other questions

we can go ahead and wrap up.

I just want to thank everyone again.

We have one more here.

did you include pre harvest readings

and how long in storage

did you follow the fruit.

So pre harvest readings.

Yes. That was really the initial

kind of goal

of this technology was only to be used

in this pre harvest application.

But then we kind of expanded it

into a more post harvest stuff as well.

So everything we’ve done for data

collection has been included

from early early season pre harvest

like all the way through

basically to the post harvest stage.

Now in post harvest, that’s kind of where

it gets a little more a little messier.

We’ve done studies

where we’re putting things in cold store

and we’re taking things out and taking,

you know, at different temperatures,

basically to help, you know, and build

that temperature variability

into the models.

We’ve also, done a lot of, post harvest,

retail actual like retail outlets testing

where we’re going out and from retail

outlets, we’re picking fruit and we’re

testing it in our laboratory and house.

so we’ve really kind of

got everything up through that,

you know, everything from the early stage

all the way through to the very end

consumer end

point, in that, in that, range.

All right.

So if there are no other questions, then

we’ll go ahead and and today’s webinar.

But like I said, if you have any

if you think of any other questions

please feel free to reach out to us.

you can reach out to me directly

if you would like.

and I’m happy to answer any questions

that you might have.

also, if you are interested

in the modeling stuff,

all the modeling things that I talked

about, we do have a really comprehensive

webinar series on our YouTube page

that covers all the basics

that you need to know about how modeling

is conducted with Nar spectroscopy,

all the considerations you have to make,

and everything, very comprehensive

and, very useful to help learn about this

technology and how it works. So.

All right, well, that being said,

thank you, everybody, and,

have a great rest of your day.


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