MATLAB: Shifting Baselines of Raman Spectra

detrendramanspectra

Hi,
I have been trying to detrend raman spectra to bring the baseline down and reduce the affects of flouresence on analysis but when I use the code pasted below (with various adjustments based on the data), it definitely does not come out right at all. Trying to get a xlim of 0-800 with wavenumber (col1) on the x axis and intensity (col2, what I need corrected) on the y axis. I am relatively new to the program but if anyone has advice I would greatly appreciate! (also attatched a csv of one of the data sets in case that helps)
t = 0:300;
dailyFluct = gallery('normaldata',size(t),2); 
sdata = cumsum(dailyFluct) + 20 + t/100;
mean(sdata)
figure
plot(t,sdata);
legend('Original Data','Location','northwest');
xlabel('Time (days)');
ylabel('Stock Price (dollars)');
detrend_sdata = detrend(sdata);
trend = sdata - detrend_sdata;
mean(detrend_sdata)
hold on
plot(t,trend,':r')
plot(t,detrend_sdata,'m')
plot(t,zeros(size(t)),':k')
legend('Original Data','Trend','Detrended Data',...
       'Mean of Detrended Data','Location','northwest')
xlabel('Time (days)'); 
ylabel('Stock Price (dollars)');

Best Answer

This is an interesting problem!
It took me a while to figure out the correct approach, however it is deceptively simple, and uses only core MATLAB functions (although it requires R2017b or later for the ischange function). The essence of it is to use the 'linear' option of ischange to get the slopes of the various line segments, then use hiscounts to find the most numerous slopes, corresponding to the linear baseline of your Raman signal data. After that, it¹s just a polyfit call to calculate the slope of the line to be detrended. It requires a bit of interaction at the outset to do the initial thresholding, with the rest taking care of itself.
The Code — 
D = csvread('raman-1 week.csv');
x = D(:,1);
y = D(:,2);
x = x(y >= 1E+4);                                           % Threshold Data

y = y(y >= 1E+4);                                           % Threshold Data
[Cp,Sl,Ic] = ischange(y,'linear');                          % Detect Changes, Calculates Slopes (& Intercepts)
[Cts,Edg,Bin] = histcounts(Sl, 50);                         % Histogram Of Slopes
[Max,Binmax] = max(Cts);                                    % Find Largest Bin
LinearRegion = (Bin==Binmax);                               % Logical Vector Of Values Corresponding To Largest Number Of Slopes
B = polyfit(x(LinearRegion), y(LinearRegion), 1)            % Linear Fit
L = polyval(B, x);                                          % Evaluate
yc = y - L;                                                 % Detrend
figure
plot(x, yc)
grid
The Plot —
That’s likely as close as it’s possible to get. I encourage you to experiment with it to fine-tune it to your requirements.
For comparison, the original signal is:
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