This toolbox for analysing spectroscopic reaction data allows you to extract time-based trendlines from GRAMS multifiles. ReactionSleuth includes visual tools for selecting features and using them to create simple, or baseline-corrected, trendlines.
Complex features can be analysed using Peakfitting models created in GRAMS® to deconvolute complex spectra and generate trendlines based on individual peak areas. The PCA/Iterative Target Transformation capability allows you to extract principal components and synthetic spectra from highly complex data sets, along with trendlines showing their changing contribution over the course of the reaction. When time-varying spectroscopic data is available in a multifile format, the ReactionSleuth™ ActiveApp® offers a simple but powerful visual interface for a range of reaction analysis tools. ReactionSleuth™ displays the multifile as a 3D object which can be rotated and re-scaled to let you pick up reaction trends visually.
The secondary plot shows individual spectra from the dataset or it can be used to display trendlines. The simplest type of trendline graph the absorbance at a single frequency against time. The ReactionSleuth™ PeakPicker is a visual tool for visually selecting features for simple trending.
More complex features in a spectrum can be analysed by building a peak model using the GRAMS® Peak Fitting application. You can use a peak-fit model in ReactionSleuth™ to generate a set of trendlines based on peak area versus time – the lines appear on a list of lines available for display after the model has been run against the multifile dataset.
Both simple and peak-fitted trendlines can be used in the autoscaled display, and all of the calculated trendlines can be saved in ASCII files for later use in spreadsheets etc. When simple techniques are not enough, the ReactionSleuth™ PCA/Iterative Target Transform capabilities can be used to find trends in datasets and generate “synthetic spectra”. ReactionSleuth™’s simple visual interface gives you access to sophisticated mathematical methods, but leaves you in control of the way the methods are used.
Both the principal components and the synthetic spectra can be individually subtracted from the dataset. Once all of the calculated factors or spectra have been subtracted, the 3D display of the residuals gives a good indication of the quality of the fit and can be useful for identifying any small unmodelled features such as transient a intermediate.
Finally syntetic spectra of the species identified using the Target Transformation process can be exported as .SPC files and further processed in GRAMS or identified using Spectral ID.
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