Hula Hoops

[zogtrog@yahoo.co.uk]

I am trying to sort potato chips or crisps called hula hoops using the scio as a demo project. What I want to do is to create a model that allows me to sort out each hoop by flavour. I created a single list parameter that can contain one of three values Beefy, saltNvinegar and plain. I am having trouble adding in more samples after I create a single sample for a flavour, I get errors in the scio lab.If I try to add in another sample of the saltNVinegar flavour material it won’t let me do this as it claims each sample has to be unique. Do I have to a second parameter such as a serial number so every sample is  unique.

[Ayelet]

Hi zogtrog,

 

We recommend adding a new attribute: ‘Sample name’ or ‘Sample number’. This should solve the problem.

We would also recommend scanning in various experiment set-ups, for example ground form of crisps or smashed form of potato chips.

Also, scanning in direct contact or close (0-5mm) to the sample.

Following that, you should evaluate the model performance for each set-up.

 

Feel free to contact us with any further question.

With your permission we can copy your data collection and give you some feedback.

 

Ayelet

The ConsumerPhysics Team

[zogtrog@yahoo.co.uk]

Thanks for the reply, all I needed to do was to do was to add in a numeric field for packet number, and then the scio lab would then allow me to add in new samples. Maybe you could add in an autonumber field type. I have been scanning plastics today and I managed to get some nice spectrums. It would be very nice if I could get the spectral data so I can try to apply some mathematics to the derived data. I understand this is a researcher feature, but there is no way I can afford the reasearcher licence. I would like to be able to subtract one spectrum from another, generate cepstrums and apply fourier filters to the data. Maybe it would be possible to have the data for a small sample set to play around with ?

I am very happy to share anything I do with the scio community.

At the moment I am delighted to have recieved my SCIO and I am just experimenting with it to find out what it can do.

 

Jack

[Ayelet]

Hi Jack,

We are very excited to see our developer community growing every day and we are thrilled that you are a part of it.

Just to remind you of the current mathematical algorithms as part of our pre-processing method options: 

Processed (only): Assumes Beer-Lambert model is valid, and transforms the measured signal to be linear with concentration by doing a log transform and adjusting the result for noise and deviations from the model. 

Normalized (only): Performs normalization of the signal. This is meant to compensate for changing measurement conditions (e.g. varied scanning distances) that typically occur from sample to sample. Y axis still means reflectance but in normalized units instead of raw reflectance.

Both Processed and Normalized: First assumes Beet-Lambert model (Processed) and then normalizes the results to compensate for differences in the optical path between samples. This is useful, for example, when there is variation in the thickness of the samples.

Both (log)R))” and Normalized: Similar to Processed and Normalized, uses a more aggressive form of Processed. Adds more noise, but in some cases may be the only way to create a good model.

Typically, different models and types of samples will require different pre-processing methods. You should both choose the pre-processing method to match your experimental setup and optimize the performance of your model. If you planned and gathered your data correctly, these efforts will coincide.

An expert mode of SCiO lab which enable users to apply different mathematical algorithms such as: Log, Derivative and SNV, will be released in the near future.

 

 

Attached is a screenshot of this new feature.

Ayelet,

The ConsumerPhysics Team

• This reply was modified 8 years, 10 months ago by Ayelet.

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