Multispectral imaging provides valuable information on the quality and safety of a vast array of materials from pharmaceuticals to raw meat and burned biscuits. The same techniques can be used to measure skin sensitivity to sticking plaster, detect counterfeit drugs and packaging and gain insight into historical artefacts such as medieval manuscripts and weapons. The VideometerLab 3 takes 19 images at 19 specific wavelengths in ultraviolet, visible and infrared light. With multivariate statistical analysis we can reveal hidden patterns in our 19-dimension colour space and find information that would be otherwise 'invisible' to our human eyes.

1. © analytikLtd
analytikLtd
Multispectral Imaging in Pharmaceuticals
Rapid Non-destructive Surface Analysis
Adrian Waltho, Analytik Ltd (Cambridge, UK)
adrian.waltho@analytik.co.uk
www.analytik.co.uk/multispectral-imaging
www.analytik.co.uk
Light reflectance surface mapping for accurate measurement
of colour, texture, shape, size and chemical composition

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• Nuanced, human-like analysis of
heterogeneous samples
• Objective, repeatable
measurements
• Non-destructive testing
• Rapid assessment
• No sample preparation
Why use Imaging?

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Traditional colour imaging
uses three broad bands of
colour
Red, Green and Blue
Traditional Colour Imaging

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Traditional Colour Imaging
• Broad RGB bands
• Chlorophyll a and b
give almost the same
signal
Chloro-a High Low High
Chloro-b High Low High

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Multispectral Imaging
Chloro-a High Low Low High
Chloro-b Med High Med Low
• Just 4 wavelength
bands with tightly
defined ranges
• Chlorophyll a and b
can easily be
distinguished

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Multispectral Imaging
• Many images at defined
wavelength bands
• Distinguish many different
chemical identities
• Wavelength range chosen
to maximise spectro-
chemical versatility
• UV-VIS-NIR wavelengths
Ultraviolet Near-Infrared
IR
Red
Yellow
Green
Blue
UV

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Multispectral Imaging with VL3
CCD
Camera
LEDs
Integrating
sphere

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Multispectral Imaging with VL3
• 19 LEDs
• Narrowband sequential
illumination, µs duration control
• Stable, long life, reproducible
• Integrating sphere
• Diffuse light on sample
• Precise illumination conditions
• 5Mp camera
• Broadband CCD captures
% reflected light at each
wavelength for every pixel
• 19 image spectral-spatial data set
CCD
Camera
LEDs
Integrating
sphere

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Multispectral Imaging with VL3

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Index of application areas
1. Incoming QC
1. API identification
2. API contamination checks (mixing)
3. API contaminating checks (leaking)
2. Finished product QC
1. Actives distribution
2. Coating quality
3. Counterfeit identification
1. Counterfeit product identification (spectral)
2. Counterfeit product identification (geometric)
3. Counterfeit packaging identification
www.analytik.co.uk

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In this example, our API is made up of a mix of Powder A
(active) and Powder B (inert) in some ratio. The Videometer
assigns these powders a number to quantify the ratio later
Incoming QC API Identification
Back to Index8

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In this image, we see that the mix is about 64% Powder A, and
has not become unmixed in transit
Incoming QC API Identification
Back to Index8

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This incoming API is a mix of two powders
Incoming QC API Contamination
Back to Index8

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This incoming API is a mix of two powders, which the
VideometerLab can demonstrate
Incoming QC API Contamination
Back to Index8

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It can also spot areas that are neither Powder A nor Powder B,
for example the fiber off a lab coat on the left hand side of this
image
Incoming QC API Contamination
Back to Index8

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The same process, demonstrating dust or grit which has
contaminated a powder
Incoming QC API Contamination
Back to Index8

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The Videometer is asked to identify the unknown powder on this label after
a spill may potentially have contaminated a batch of pharmaceutical
product
Incoming QC Powder Identification
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It is known to be either Powder A (left) or Powder B (right)
Incoming QC Powder Identification
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The image of the label is masked so that just the powder is seen
Incoming QC Powder Identification
Back to Index

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This is a little difficult to see, but the VideometerLab has identified each
‘blob’ of powder and given it a label so when we do statistical analysis we
can correlate each result to a physical ‘blob’. A detail on the left
demonstrates this
Incoming QC Powder Identification
Back to Index

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Here we see that the VideometerLab has identified that Powder A (left) is
more like the unknown substance than Powder B (right). We know this
because the left image is more orangey, which implies a positive match
Incoming QC Powder Identification
Back to Index

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One of these pills is not like the others; it has none of the actives that
would normally be found in a pill of this type
Product QC Actives Distribution
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The VideometerLab clearly demonstrates which pill this is
Product QC Actives Distribution
Back to Index

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The VideometerLab can also be used to check distribution of actives on
individual pills. In this image, the more orangey a pixel the less the active
concentration. This distribution can be quantified easily.
Product QC Actives Distribution
Back to Index

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These minitabs should normally have a coating on them to delay their
release into the bloodstream. The tabs on the left are coated, on the right
are uncoated
Product QC Coating Quality
Back to Index

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The VideometerLab quantifies this by replacing each pixel with a false-
colour measurement of ‘degree of coating’ – if there is a good chance the
tab is coated it is blue, but if there is not it will be red
Product QC Coating Quality
Back to Index

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This then allows an operator to immediately know if a batch of minitabs
were improperly coated during production
Product QC Coating Quality
Back to Index

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Visually, there is not a lot of difference between the genuine
Viagra (left) and the counterfeit (right).
Counterfeit Spectral Identification
Back to Index8
Presented side-by-side like
this one might see that
the counterfeit is slightly
darker, but there would be
no way a human operator
could make that
distinction without a
control sample to contrast
the counterfeit to

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At 700nm there is absolutely no doubt there is a difference
between the left and right pill
Counterfeit Spectral Identification
Back to Index9
If the MHRA had the
spectral response of
Viagra at 700nm on file,
this would be enough to
prove the right pill was a
fake, and only takes an
operator scrolling through
the NIR wavelengths to
see. Yet even this is
impossible with traditional
RGB photography

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A skilled operator could use spectral information gathered by the
Videometer to select the most relevant wavelength straightaway
Counterfeit Spectral Identification
Back to Index10
That 700nm shows the biggest difference
between the counterfeit (blue) and the three
genuine (all others) is confirmed by the
subjective visual analysis of the previous slide

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Alternatively, an operator could ask the VideometerLab to look for
differences automatically, using an ‘MNF’ statistical transformation
Counterfeit Spectral Identification
Back to Index11

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However, the VideometerLab is at its most powerful when it has
been taught what ‘genuine’ and ‘counterfeit’ look like beforehand
Counterfeit Spectral Identification
Back to Index12

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What has occurred in this image
is that the VideometerLab was
taught what a genuine and fake
Viagra looked like, then asked to
recolour the image so that each
pixel which is more ‘Viagra-like’
than ‘fake-like’ is orangeyred and
each pixel which is closer to
counterfeit than genuine is
bluewhite
Statistically, this process is
equivalent to a normalised
canonical discriminant analysis
Counterfeit Spectral Identification
Back to Index13

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The transformation does not look for any difference – only those
which are relevant. See how similar the transformation of these
two genuine Viagra pills is
Counterfeit Spectral Identification
Back to Index14

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This information could be used to produce a single number which you could
think of as ‘chance of being genuine’ and present to a jury. In the image below,
each of the pills on the right is certain to be genuine, whereas the pill on the
left is certain not to be.
If you knew –for example - that even the worst possible deviation of genuine
Viagra would never fall below 0.5, you would be able to build an unassailable
case that the leftmost pill was counterfeit from this single image
Counterfeit Spectral Identification
Back to Index15

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Though the VideometerLab is clearly successful at identifying genuine from
counterfeit when the fakes are bad, in a lab setting it is more likely they will be
visually similar, as with these Casadex. Even looking at spectra a human
operator could make a mistake, since they are quite similar with a moderately
high deviation
Counterfeit Spectral Identification
Back to Index17
Genuine spectra are on top in red and teal,
counterfeit spectra are blue and tan on bottom

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The VideometerLab correctly identifies the genuine Casadex pills in orange and
the counterfeits in blue.
Counterfeit Spectral Identification
Back to Index19

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The VideometerLab could be used to build a case on a number of other criteria.
For example, the counterfeit Viagra is ever so slightly less eccentric and bigger
than the genuine. All this information can be automatically generated.
Counterfeit Geometric Identification
Back to Index16
Length (mm) 15.17 14.48 14.48 14.48
Width (mm) 10.88 10.58 10.58 10.58
Ratio 0.72 0.73 0.73 0.73
Area (mm) 117.0 110.1 109.6 109.5

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The Videometer can tell counterfeit from real even without opening the
packaging
Counterfeit Packaging Identification
Back to Index21
The next slides show a Cialis blister packet.
The counterfeit packet is on the left, the
genuine on the right.
Just as an aside, the Videometer usually has
no trouble imaging through plastic. If – for
whatever reason – you do not want to
break the seal on a blister pack the
Videometer can still return high-quality
results through the plastic windows

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The simplest way to tell the genuine from the counterfeit would be to use the
Videometer to build up a model of what a real tablet looks like, then image
through the blister pack
Counterfeit Packaging Identification
Back to Index24

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The genuine tablet is on the right, the counterfeit on the left (you can probably
confirm this visually on the previous slide). The Videometer can quickly
demonstrate this, even to an unskilled audience
Counterfeit Packaging Identification
Back to Index25

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An operator might want to confirm this by using either a statistical analysis (left
image shows the imaging equivalent of a Principle Component Analysis, a
Minimum Noise Frequency distribution) or by reading a spectrum (right,
genuine tablet in blue)
Counterfeit Packaging Identification
Back to Index26

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However, if we pretend for a moment that this is impossible – maybe the
counterfeit blister packet has been used completely before being confiscated –
the Videometer is still a powerful tool for detecting differences
Counterfeit Packaging Identification
Back to Index27
The next slides show a series of differences
which an operator was able to pick up
between the two packets. The operator was
moderately skilled at using the Videometer,
but completely untrained at counterfeit
pharmaceutical detection – all of these
differences were pointed out by the
Videometer and it is certain that if it were
operated by someone who knew something
about counterfeiting many more differences
could be discovered
In every image, the counterfeit is always
presented on the left, genuine on the right

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The spectral response of the ‘sun’ and the ‘swirl’ is different between packets.
This is easy to see visually, but the Videometer could prove the fake falls well
outside normal manufacturing error bounds
Counterfeit Packaging Identification
Back to Index28
Swirl
Sun

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The spectral response of the ‘sun’, genuine spectrum in blue
Counterfeit Packaging Identification
Back to Index29

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The spectral response of the ‘swirl’, genuine spectrum in blue
Counterfeit Packaging Identification
Back to Index30

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The texture of the counterfeit breaks down at 700nm, but does not for the genuine
Counterfeit Packaging Identification
Back to Index31

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The wear on the lettering is different, but this is only provable at spatial resolutions
of about 100um. The Videometer can resolve as low as 45um
Counterfeit Packaging Identification
Back to Index32

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Additionally, the lettering is printed in subtly different fonts – the genuine ‘C’ is
slightly less eccentric and is more correctly justified
Counterfeit Packaging Identification
Back to Index33
Genuine
Tablet ‘C’
Counterfeit
Tablet ‘C’
Length 1
(mm)
4.00 4.41
Length 2
(mm)
4.13 4.51
Ratio 0.97 0.98
Angle of
intersection
0.01 -2.53

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Sometimes it is obvious the font is different without using analytics. It is very easy
to pull information from the Videometer to present in this way
Counterfeit Packaging Identification
Back to Index34
Detail from translation
(counterfeit left)
Detail from expiry date
(counterfeit bottom)

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The welds on the front behave differently in profile – presumably the welding
process is different between genuine and counterfeit manufacture
Counterfeit Packaging Identification
Back to Index35

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This difference is absolutely stark in profile (blue is genuine). What you are looking at is the average
intensity of the pixels the small lines in the previous slide pass over. The huge drop in the middle of the
counterfeit (red) profile is the line passing over the weld, which is much less neat than the genuine weld
Counterfeit Packaging Identification
Back to Index36
As an aside, the fact
the red line continues
for longer than the
blue indicates the
counterfeit weld is
c.1mm larger. This is
confirmed visually or
by the Videometer
software

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Conclusions and Further Actions
Back to Index
• The Videometer is a powerful tool in pharmaceutical QC and anti-counterfeiting
applications. As well as being cutting edge in its own right as an analytical
instrument, one major advantage it has over the competition is the fact that the
images it produces are extremely accessible, which means it can be operated and
interpreted far faster and by less skilled technicians than conventional techniques
37
• The Videometer used in this presentation
was absolutely ‘pure’, in the sense that it
was given no prior information about what
these drugs should look like, other than the
training demonstrated in each short
section. If an operator could take the time
to train the VideometerLab on a library of
drug samples, you could scale up the
precision of all of these demonstrations by
an order of magnitude