Chromatography Toolbox
| Open-source code for processing chromatography and mass spectrometry data in the MATLAB programming environment |
Download
Select the Download ZIP button on this page or visit the MATLAB File Exchange to download a copy of the current release.
Features
| Import Data |
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| Baseline Correction | |||||||||||
| Curve Fitting | |||||||||||
| Visualize | |||||||||||
System Requirements
Current release stable on the following systems:
- MATLAB 2013b+
Documentation
Visit the wiki for a full list of methods and options.
Getting Started
Initialize Toolbox
Add the @Chromatography folder to your MATLAB path and run the following code in the MATLAB command window:
obj = Chromatography();
Find out which version is currently installed using the command below:
obj.version
ans =
'0.1.51'
Load Data
Import raw data files into the MATLAB workspace:
% Import Agilent '.D' files
data = obj.import('.D');
% Append data with Thermo '.RAW' files
data = obj.import('.RAW', 'append', data);
Baseline Correction
Calculate baselines for the total ion chromatograms (TIC) in all samples:
data = obj.baseline(data,...
'samples', 'all',...
'ions', 'tic',...
'smoothness', 1E7,...
'asymmetry', 5E-6);
Smoothing
Apply a smoothing filter to the total ion chromatograms (TIC) in all samples:
% Small amount of smoothing
data = obj.smooth(data,...
'samples', 'all',...
'ions', 'tic',...
'smoothness', 10,...
'asymmetry', 0.5);
% Heavy amount of smoothing
data = obj.smooth(data,...
'samples', 'all',...
'ions', 'tic',...
'smoothness', 1000,...
'asymmetry', 0.5);
Reset Data
Reset data to its original state with the command:
data = obj.reset(data);
Plotting
Plot all total ion chromatograms (TIC) in a stacked layout:
fig = obj.visualize(data,...
'samples', 'all',...
'ions', 'tic',...
'layout', 'stacked',...
'scale', 'normalized',...
'xlim', [5,45],...
'colormap', 'jet',...
'legend', 'on');
Plot the total ion chromatogram (TIC) for a single sample and save as a JPG (400 DPI):
fig = obj.visualize(data,...
'samples', 4,...
'ions', 'tic',...
'xlim', [2,50],...
'color', 'black',...
'legend', 'on',...
'export', {'MyFileName', '-djpeg', '-r400'});
Plot selected extracted ion chromatograms (XIC) for selected samples and save as a PNG (150 DPI):
fig = obj.visualize(data,...
'samples', [1:2,6,9:10],...
'ions', [10:50,55,59,100:200],...
'layout', 'stacked',...
'scale', 'full',...
'xlim', [10,30],...
'colormap', 'winter',...
'legend', 'on',...
'export', {'MyFileName', '-dpng', '-r150'});
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Using Smoothing Algorithms to Reduce Baseline Noise in Chromatography 使用平滑算法减少色谱中的基线噪声 介绍 虽然大多数现代数据分析系统足以确定峰面积和峰高，但必须使用适当的积分参数才能进行良好的量化。 结果将取决于： • 系统如何过滤或平均短期噪声 • 数据收集频率 • 用于处理数据的计算机算法 短期噪声（也称为高频噪声，>
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If you are not the author of this article and you wish to reproduce material from it in a third party non-RSC publication you must formally request permission using Copyright Clearance Center. Go to o
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摘要： BACKGROUND: Efavirenz (EFV) is one of the preferred components of first-line antiretroviral treatment. EFV is characterized by a long plasma half-life (40-55 hours) with large interpatient variabi