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VeriVin and Openvino are teaming up to perform a series of experiments using VeriVin's prototype Raman spectrometer.
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Do coloured wine bottles protect wine from oxidation, and what is the measurable effect of lightstrike from different types of lighting?
Which bottle closures better promote desirable wine evolution?
Can we create a unique digital fingerprint for a wine, using a spectrometer, and represent this vinoprint on the blockchain as a non-fungible token?
About VeriVin
Experiment Overview
Can we bottle 640 bottles of wine, using four different bottle colours, four different closures, and four different light sources, and perform simultaneous experiments?
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Bottles | |
Natural cork | 32 |
Portocork | 32 |
Tapicork | 32 |
Screwcap | 32 |
total | 128 |
Preliminary testing
VeriVin Through-Bottle Analysis of MTB Wines – A first Test – 17/3/19
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Since the final output is a fairly mundane table of classification as shown here, we detail some of the actual analytical components used within the model.
A First Simple Model (figures right)
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This is what a single data point on these Latent-Variable graphs actually looks like. A spectral Raman response of 512 data points, with a wavelength assigned to each.
Adding more/ similar wines to the model (figures right)
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Note that measurements can vary and some are more clearly classified than others. To ensure that one measurement is classified correctly, we can set a threshold, which the measurement has to pass to classify as wine X. An ideal result has measurements of one class above the threshold and all others below, as shown here (note, 2014-1 and -2 as separate classes). Keep in mind the lowest (blue) data point here, could be under the threshold for some measurement and then would be marked as unclassified if not going above the threshold for any class.
Measurements taken two weeks previously applied to the first simple model (no 2016):
The model results table correctly classifies this wine. However, over the time the bottle was open, the wine may have changed, for it appears outside the calibration MTB2014-1 data shown above. There are other experimental reasons for an overtime drift but these were due to alignment changes, which will clearly not occur in the final device.
