Hello everyone, thank you for coming to my presentation. My name is Weiwei Jiang.
from the university of melbourne. Today I'm
going to present you our paper titled by Probing Sucrose
Contents in Everyday Drinks using Miniaturized
Near Infrared Spectroscopy Scanners. So first of all an overview for our paper
In this paper we propose a generalizable way to estimate sugar
concentrations in everyday drinks using miniaturized
NIRS. We have tested the our method in two cases in
Study one we have tested the in controlled conditions
where we achieved 0.29 g/100ml.
We have tested our method in field with everyday drinks, where we
achieved 2.0 g/100ml.
So we also tested the classification models we can
successfully classify everyday drinks with very
high accuracy. Most importantly our method is generalizable to
other liquid contents such as alcohols milks and perfumes.
Next for the motivation of this work. First of all, we all
familiar with a lot of sensing technology nowadays.
Some are very popular, for example, the computer vision
which is also very useful nowadays. For example, if you have
a watermelon then you take a photo of the watermelon
they run some deep learning algorithms then you can say okay it's a watermelon.
So it's quite straightforward and popular and also
very useful nowadays. However what if you have an
object that you want to know the contents of
the object, the material of the object, then it might be a little bit
challenging using the computer vision. So again for the same example if I make
the watermelon into watermelon juice then it's look like a red liquid
and with not too many features about appearance.
And also the alcohols another alcohol they look very similar to each other so
in that case, it's more challenging to use computer
vision based on the appearance. To handle this issue we use a
method called near infrared spectroscopy.
NIRS is a technique that can acquire material level information
instead of the appearance only. It utilizes
multiple wavelengths in the near infrared region. So imagine you
have a scanner and transmit different
wavelengths of lights to scan the material and at some
wavelengths the lights will be absorbed more than
others. So in that case you can get a spectrum look like this
And in some wavelengths you have lower absorbance some will have higher
absorbance and this absorbance spectrum will correlate to the material that you
are scanning. So with this principle, we want to
build a method for liquid sensing which should
be non-invasive and also generalizable.
Here we start with the sugar contents, we want to estimate sugar concentrations
in drinks. Then I'm going to show you this is also
generalizable to other liquid contents as well.
We made this prototype looks like this. It's like a clamp
If you are drinking using a tube if you have some other liquids running the
tube in general.
Then you can attach this
clamp on the tube, then you scan the tube then
you get the information for the liquids. So that's the prototype we have.
Next we have done some modeling based on this prototype at Study 1.
We have prepared 25 different solutions with different concentrations, sugar
concentrations, from 0 to 20 g/100ml.
And in the spectrum you can see with
different concentrations, the spectrum varies with different
levels. Then we have run a linear regression
model to analyze those wavelengths and you can see there are three peaks
that maximizes the response for the sugar contents.
Using this spectrum with different machine learning models we have
achieved 0.29 g/100ml RMSE.
for the best model we have here.
For the study 2, we have done some
field tests with 18 everyday drinks that you can get in
local supermarkets. You might be familiar with some of those
drinks like the coca-cola something like that.
We have tested those drinks under 9 conditions different temperatures
different ambient illuminance, the performance varies a little bit
but there's no big impact. So overall, we can see the performance
for everyday drinks is less than 2.0 g/100ml RMSE.
For the same thing same method we also
tried different liquid contents, glucose,
fructose, those are different sugars. Alcohol, perfume and milk.
They all work very well using the same method the same prototype
we have here. We have more results that you can refer to, in the paper
We have classification models that can
also achieve very high accuracy for classifications
of those drinks. We also have some limitations
basically from the hardware customization that it can be
addressed with further the prototyping or
massive production like that.
That's the end of my talk,
thank you for your attention.