Very High Standard Deviation in Excitation Emission Matrix Measurement

In summary, the speaker is discussing their high standard deviations in Excitation-Emission Spectra for phenolic compounds in olive oil. They have tried various methods and tests, but still cannot explain the high s.d. They have also tried different spectrometers and settings, but have not been able to obtain a viable spectrum at low concentrations. They are seeking input and mention that the S/N ratio is within the normal range and they have analyzed a blank.
  • #1
JonasFnr
2
0
TL;DR Summary
I get very high standard deviations for phenolic compounds in EEM measurements of olive oil. Any thoughts/input highly appreciated.
Hi,

I obtain really high standard deviations in Excitation-Emission Spectra mainly for the phenolic compounds in olive oil (Em: 290-350nm).

Method:
I weigh 0.05g of olive oil and dilute it up to 25ml with cyclohexane to remain in the range of linearity for absorbance measurements to correct for filter effects.
To estimate the standard deviation of measurements, I made five equivalent samples and measured each five times.

Both within and between the samples the s.d. is very high in this area (13-20%).
All validity and calibration tests I have tried thus far seem to indicate that the device is working accurately.

I get those results both with a Shimadzu RF-6000 and an Aqualog (which already corrects for filter effects) from 200-800nm.

Settings for the Shimadzu: Datainterval: 2nm each, Scanspeed 6000nm/min, Ex. Bandwith 5nm, Em. Bandwith 3nm, Sensitivity: High.

I don't think the solvent, contamination, scattering, runtime-dependent spectrometer performance or photodecay of the phenols could explain this.
Is the proximity to the rayleigh scattering or are the settings an issue here?
I couldn't obtain a viable spectrum with any other settings for such low concentrations though, which I need to correct for filter effects.

I couldn't find anything on this in the literature, so any thoughts or input would be HIGHLY appreciated.

Thank you and kind regards.
 
Last edited:
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  • #2
What do you estimate for S/N?
 
  • #3
S/N ratio is in the normal range according to the device manufacturer (water raman peak).
 
  • #4
Have you analyzed a blank?
 

FAQ: Very High Standard Deviation in Excitation Emission Matrix Measurement

1. What is a very high standard deviation in Excitation Emission Matrix (EEM) measurement?

A very high standard deviation in EEM measurement refers to a large variation or spread in the data points collected from the EEM scan. It indicates that the measurements are not consistent and there is a high level of uncertainty in the results.

2. What causes a very high standard deviation in EEM measurement?

There are several factors that can contribute to a very high standard deviation in EEM measurement. These include instrument errors, sample preparation errors, and environmental factors such as temperature and pH. Additionally, the presence of impurities or contaminants in the sample can also affect the EEM measurements and lead to a high standard deviation.

3. How does a very high standard deviation affect the accuracy of EEM measurements?

A high standard deviation indicates a lack of precision and consistency in the EEM measurements. This can affect the accuracy of the results, as it becomes difficult to determine the true value of the measured data. It also makes it challenging to compare results from different experiments or samples.

4. Can a very high standard deviation be reduced in EEM measurements?

Yes, a high standard deviation can be reduced in EEM measurements by carefully controlling the experimental conditions, optimizing sample preparation techniques, and using high-quality instruments. Additionally, performing multiple measurements and taking the average can also help reduce the standard deviation and improve the accuracy of the results.

5. How can a very high standard deviation be interpreted in EEM measurements?

A very high standard deviation in EEM measurements can indicate that there is a significant amount of variability in the data, which could be due to experimental errors or sample heterogeneity. It is important to carefully evaluate the data and consider the potential sources of error before drawing any conclusions based on the results.

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