MATLAB: Vector Normalising & Measuring Euclidian distance of Raman (Intensity Vs Wavenumber) spectra.
Bioinformatics Toolboxeuclidian distancevectorvector normaisation
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The ‘baseline’ of your data appears to be something resembling a logistic curve, although perhaps reversed. You will likely have to fit the section where the x- values are greater than 1000, then evaluate it over the entire x-range. See if the approach in Shifting Baselines of Raman Spectra suggests a solution to your problem.
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
figureplot(x, yc)gridThe 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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