How can I normalize the IR spectrum of different yeast species? In literature, I usually see "Amide I" peak used to normalize. However, I am wondering, in this way don't we risk losing the differences on this peak?
Normalizing FTIR spectra of different yeast species can be challenging because the spectra can vary due to differences in cell morphology, biochemical composition, and growth conditions. The Amide I peak is a common peak used for normalization because it is a strong, broad peak that is associated with the protein backbone vibrations, which are present in most biological samples.
However, you are correct that using Amide I peak for normalization can potentially mask differences in this peak between different yeast species, especially if the protein composition is significantly different.
One alternative approach is to use the so-called "internal standard" method, which involves choosing a peak that is present in all the spectra and does not vary significantly between samples. This peak can be used as a reference peak to normalize the spectra.
One example of an internal standard peak in yeast spectra is the beta-glucan peak, which is present in the 1100-1200 cm^-1 range and is associated with the polysaccharide component of the yeast cell wall. This peak is relatively constant between different yeast species and can be used for normalization, especially if the cell wall composition is not significantly different between the samples.
Another option is to use a multi-peak normalization method, where several peaks in the spectra are used for normalization to ensure that the differences in individual peaks are not masked. This approach can be more robust but requires careful selection of peaks that are stable across different samples.
It's important to keep in mind that the choice of normalization method can depend on the specific research question and the variability of the samples.
It's always a good idea to test different normalization methods and compare the results to ensure that the normalization does not introduce artifacts or bias into the data.