similar to: quantitative spectra analysis

Hi all, I'll have to filter Raman spectra and I would like to do so in R. I'm trying to figure out how. Maybe one of you could help me ... This is what I have to do: General: separate the real spectrum (the set of peaks) from two contaminations: a drifting background and (white) noise Constraints (which should help the task): A - the spectrum is positive B - the spectrum is long

Dear all, This is potentially a bug in spec.pgram, when the number of samples is odd,spec.pgramreturns a different result withfast = TRUE, the example below contains the two varieties with a reference spectrum calculated manually. the number of returned spectra is also larger (50 compared to 49) whenfast = TRUE x <- rnorm( 99 ) plot(spec.pgram(x, taper = 0 , detrend = FALSE , plot =

I would really appreciate it if someone can give suggestions on how to do spectra fitting in R using ordinary least square fitting and non-negativity constraints. The lm() function works well for ordinary least square fitting, but how to specify non-negativity constraints? It wouldn't make sense if the fitting coefficients coming out as negative in absorption spectra deconvolution. Thanks.

Hi all, I need to insert the name of spectra in a stacked plot obtained with hyperspec. I use this command plot(spectra [c(-1:-4, -6:-8, -10:-12, -14:-16)], stacked = T) but, in this way R draw nameless spectra on the Y axis. How can I solve the problem? Thank you for any suggestion. Best regards, Roberto -- View this message in context:

Dear collegues, I´ ve worked with near infrared (NIR) spectroscopy to assess chemical, physical, mechanical and anatomical properties of wood. I use "The Unscrambler" software to correlate the matrix of dependent variables (Y) with the matrix of spectral data (X) and I would like to migrate to R. The matrix of spectral variables is very large (2345 columns and n lines, where n =

Hi Folks... No code to troubleshoot here. I need some suggestions about the right terminology to use in further searching, and any suggestions about R pkgs that might be appropriate. I am in the planning stages of a project in which IR, NMR and other spectra (I'm a chemist) would be collected on various samples, and individual samples would be followed over time. The spectra will be feature