Users can easily differentiate samples and identify marker peaks using univariate and multivariate analysis tools.
Implement discoveries from Statistical Analysis Mode to discriminate unknown samples.
Polystyrene (heated and unheated) polymers were separated into two groups (heated or unheated) by multivariate analysis (PLS-DA) of the MALDI mass spectrum (Score Plot). A Loading Plot can be used to confirm which peaks (marker peaks) affect the differences between the two groups.
Using marker peaks identified by multivariate analysis to create a discriminant model and discriminate (SVM) between heated and unheated polymers in a polystyrene mass spectrum, obtained separately, resulted in correct discrimination of all polymers. By using eMSTAT Solution in combination with MALDI mass spectrometry, which can easily measure samples with large molecular weight, in addition to synthetic polymers, a wide variety of samples, such as protein, fat, or sugar samples, can be easily differentiated.
Easy Differentiation for Beef Classification
Flexible sample grouping based on registered quality information facilitates biomarker discovery.
Extracts from commercial beef (Tasmanian and A5/A4 grade Wagyu beef) were analyzed in a DPiMS-2020 mass spectrometer. The resulting spectra were then analyzed by PLS discriminant analysis. A Score Plot confirms grouping into three groups and a Loading Plot confirms which metabolite peaks affect grouping.
With eMSTAT Solution, spectra obtained by convenient metabolite analysis in a DPiMS-2020 spectrometer can be used to easily differentiate between differences in food, plant, and other samples, and screen for information about metabolites that contribute to differentiation.