Thermo Scientific™ OMNIC™ FTIR Software Training Videos

The arrangement of the various software  parts is introduced. This video prepares the student to view the other training videos in the series.

Advantages and disadvantages of using the ATR sampling approach are discussed.

Extracting information through cursor interactions with spectral data is shown. Examples of how to use these tools with other functions are covered.

Overview of the experiment setup parameter window is covered. Navigating and understanding how experiment files are created.

The data point spacing in the spectrum is covered. Using the various approaches is discussed.

Working with spectra collected using DRIFTS (diffuse reflection) accessory is described with viewing in the Kubleka – Munk scale for easier comparisons with library data.

The Signal-Noise-Ratio is described along with the use or terms and the source of noise in the experiment.

The steps to produce a simple infrared spectrum of a sample after it has been prepared and inserted into the FTIR are demonstrated.

Improving the quality of the spectrum collected with increasing scans, and the time required, are discussed.

The effects of different resolution settings are shown and the need for correct resolution to meet the needs of the analysis is covered.

Understanding the various spectral formats available and when they should be used is shown.

Setting the optimum background refresh schedule and the benefits of the various options are shown.

Saving all parameters quick retrieval is discussed. Using the Experiment menu in OMNIC is shown.

An introduction to the settings available on the Bench Tab in the Experiment setup window is covered.

Understanding the correct spectral range for the optics selected is covered.

Adjusting the signal gain for optimal results is covered.

Setting the speed of the moving mirror in the interferometer and consequences are covered.

Adjusting the optical aperture in the system for optimal spectral quality is covered.

Understanding advanced collection parameters and setting them to optimize the analytical results are covered.

The Mercury GC software is covered showing the use of the analysis for discovering components collected during a gas chromatographic collection.

The Mercury TGA software is discussed talking about the use of the software in post collection to help identify the mixtures that evolved from the sample run.

Determining which corrections make spectral data better suited for analysis is demonstrated.

The various axis labels and proper use when displaying spectral data is covered.

The recommended procedure to correct spectral baselines is demonstrated.

Using software corrections to remove water vapor and carbon dioxide peaks from the sample spectrum is covered.

The suggested best use of subtraction to perform "non-chemical" spectral separations as an aid in identification of mixtures is shown.

Setting the criteria to search spectral libraries to identify unknown material is shown.

Applying regional search techniques to help identify unknown material in mixtures is shown.

The procedure to create custom search libraries to address specific needs is detailed.

The use of the peak labeling function in the OMNIC software is described.

Creating a custom template that generates reports containing all of the analytical information is shown.

Generating analysis reports and archiving them for future reference and printing is demonstrated.

An introduction to the use of the Pallet tools to extract quantitative information from spectral data is covered.

The application of the Statistical Spectra tool in OMNIC is covered and its use in determining new information is discussed.

The application of the Kramers-Kronig Analysis on specular reflection data is covered.

Setting up a Series collection is discussed. The correct time for the run and the interval is important.

The data analysis after collection will lead to discovery of new information.

The Peak Resolve software in OMNIC is explained and the recommended procedure is described.

Working with the TQ Analyst software

The Macro Editor is introduced describing the various parts and their use in creating and editing macros.

Creation of a simple macro is demonstrated.

The creation, conventions and use of macro variables is discussed.

Editing existing macros to expand their usefulness is shown. Advanced programming tools are demonstrated.

This goes over the procedure and logic in writing a peak height macro.

The use of subroutines in macros is explained.

The inclusion of decision statements in the macro code will direct the macro to complete the desired analysis.

This function can be very useful when performing repetitive tasks that require the results are output from the macro.

Using the Log File is covered, showing how to capture analytical information from spectral data for further use in the analysis.

The use of configuration settings to customize the OMNIC software for specific uses is introduced. The overview of the capabilities and interface is shown.

Demonstrates how global options will appear, where files will be saved, and how spectral tools will operate when their defaults are set and saved for future use.

Menu modifications are demonstrated, as well as adding and removing commands to simplify the operation and make the software targeted to the analyses being performed.

Adding, removing and rearranging the icon collection displayed on the tool bar is shown. Adding macros and other external programs for access in OMNIC is explained and demonstrated.

Creating a list of approved users and unique configurations to address the optimum settings for each situation is covered. Adding password protection and managing data files are outlined.

A preliminary discussion for using the TQ Analyst software, menus and tabs is gone over in detail.

Selecting the type of quantitative algorithm in the TQ Analyst software is explained to allow the best     quantitative model to be achieved.

Setting the correct path length for a calibration needs to be considered. The wrong setting can lead to poor results.

Step by step construction of a simple Beer’s law model is outlined.

The conclusion for creating a simple Beer’s law model from part I.

The creation of a classic least squares model is outlined in part I of this video.

The continuation of the classic least squares model from part I is discussed.

The SMLR modeling approach can be useful in evaluating a quantitative model that has limited data. Models that have small data sets can be created.

The details for building PLS or PCR models in the TQ Analyst software is discussed.

The collection of diagnostic tools to evaluate quantitative models constructed from the TQ Analyst software is described. Evaluating the results to improve the models created is shown.

A continued demonstration of the use and evaluation of available diagnostic tools.

Building and working with the qualitative approaches in the TQ Analyst software is covered.

A continuation of the discussion covering the qualitative analysis in the TQ Analyst software.