Multi-Sample Analysis

MDC Investigator™ provides an array of tools for an analyst performing multi-sample analyses including:

Built-in calculations and plots of common statistics and trends for assisting multi-sample analysis.
Interactive visualization for comparing pairwise differences among samples and sample classes.
Multi-class analysis tools with principal component analysis (PCA), Fisher ratios, F-tests, and machine learning.
Tools for detecting common and unique compounds across many samples based on statistical characteristics, and chromatographic and mass spectral information.

1D and 2D Peak Table Support

MDC Investigator performs multiple-sample analysis directly on peak tables acquired by both one-dimensional and two-dimensional chromatographic and spectrometric techniques (GC-MS, LC-MS, GCxGC, LCxLC, etc.). With the functionality enabled by Smart Feature Templates™ and alignment algorithms, MDC Investigator provides a high degree of selectivity and implicit cross-sample matching of peaks in peak tables.

Data Visualization and Analytics

Visualizing your data is easy with MDC Investigator. The software includes a variety of customizable charts to highlight interesting trends, features, outliers, and class differences.

Find Common and Unique Compounds

MDC Investigator's Find Compounds tool aids in the discovery of common and unique compounds using statistical characteristics, and chromatographic and mass spectral information. It also provides quick access to detailed compound information.

Machine Learning

Perform advanced classification and analysis using the Investigator ML™ machine learning tool. This tool performs supervised pattern recognition and classification using a variety of algorithms and supports visualization and reporting on the chromatograms, features, feature patterns, classification models, and classification results.

Alignment

MDC Investigator has the capabilities to perform peak alignment across multiple samples using automated template matching and transformation algorithms. The aggregated peak tables after alignment can be exported for further modeling and analysis.