# [SciPy-User] technical question: normed exponential fit for data?

Thu Mar 24 12:19:05 CDT 2011

```Hi,

> Hmm. I certainly wouldn't have come to that conclusion looking at the
> data. At least for #0 (high concentration?), the linear fit is
> substantially better.

yes, of course, there is always a better fit possible, no doubt. When
I perform a fit for only a single data series, I can optimize both c0
and c1 in the equation:
f(x) = c[0] * scipy.exp(c[1]*x)

It is just my observation that with a factor c[1]=0.1 the fit works
very well for a broad range of concentrations.

However, c[0] needs to be different, and that is my problem. Can I
somehow normalize the measurement values in order to compensate for
different temperatures?

>> Now comes the tricky part: I'd like to use this knowledge for a
>> temperature compensation because I only need to determine the
>> concentration. The temperature of the reaction is measured
>> simultaneously but might vary in the range of +-3K. In terms of assay
>> performance, that makes a huge difference due to the 10%/K so that I'd
>> need to compensate for it.
>>
>> How can I use my calibration measurement to find a function which I
>> could use to compensate for varying temperatures?
>
> Can you do more calibrations with different concentrations? For any
> given temperature, you essentially only have three data points with
> which to determine the relationship between concentration and photon
> count. That's pretty difficult without any theory to help you fill in
> the gaps.

These experiments are very expensive, a single series from above is
about 300€. So, I was hoping there is something I can learn from these
already. It needs not be perfect, a procedure outline would be nice so
that I could justify to spend even more money on this.

But, as stated above, right now I only have a "feeling" that I can do
something about it but it's still hidden in the mist...

Thanks for taking the time to think about it!
```