Chromatography Data Systems

This topic presents a number of use cases that can be satisfied using the Tetra Data Platform.

Example: Understand System Utilization

Empower’s built-in usage metrics tool sometimes do not fully satisfy your needs. Although it does display some relevant usage data, it is not enough to make actionable decisions, and it is unable to be configured. Life Sciences companies would benefit from better understanding system usage to improve operations and make more informed purchasing decisions. You can configure a usage metrics Spotfire dashboard template to illustrate usage by system, project, user, and more. Users can (de)select fields to sort and understand usage data in a number of different ways.

The diagram below shows a visualization in Spotfire, containing usage metrics such as the injection count per system, the total injection time a system has run as well as the system usage per user. This can be configured to show additional statistics related to the system usage metrics based on your specific business need and logic.


System Usage Metric

In the diagram above you can see usage metrics on your HPLC systems.

  • Top left is the total injection time for a system
  • Bottom left is the injection frequency per user
  • Top and bottom right display two visualization options for the total number of injections run by system.
  • Sidebar provides filter options which can be used for you to isolate specific systems and narrow down the visuals to your desired system, project or site.

Example: Data Access and Search

Empower, currently does not provide a visualization option where sample metadata and result data can be visualized easily for multiple injections without opening of and switching between multiple tabs. Moreover, the capability to visualize and select channels are not possible in batch. Further, loading data into a platform like JMP/R/Python takes manual effort (in some cases, up to 2-3 hours per analysis).

With TetraScience you can configure a Spotfire dashboard template allowing for side-by-side chromatogram visualization based on sample, channel, and other metadata selection, and more.

Data retrieved from the TetraScience Platform can be retrieved in a hierarchical manner, leading with Project, Sample Set, Injections, and then Results. This will provide the capability to visualize data without the usual hurdle of opening multiple windows and tabs and having to constantly switch between them. This will also allow your team to search across metadata and quickly retrieve desired Empower files.

The diagram below represents the starting point of interacting with your Empower data in Spotfire through the Tetra Data Platform. You can begin by searching all of your projects, sample sets, injections and then get your results and chromatograms. This allows you to then use the other dashboards to narrow down your search further.


Search all your projects, sample sets and injections

The diagram below represents the visualization of Chromatograms, injection metadata (injection IDs, injection times, injection volume, dilution, etc.), and also the resulting peak tables. Again the sidebar can be used to narrow down by project, sample set, system name, sample name, analyte name, or any other available metadata you see fit to use. This can be achieved all on the same screen, and you will not need to switch between any windows or tabs.


Overlay your chromatograms across projects and view results (peak)

With your results data available, you are able to dive deeper into these data sets and perform analysis. For example, beblow is a diagram where Peak Area is plotted against Retention Time, colored by different analyte. This is a great way to detect anomolies by looking at the clusters. For example, there are two clusters forming with color red, indicating that something has changed.


Peak Area vs Retention Time

Below are two analysis you can create for control charting applications. The first dashboard plots how Peak Area evolves over time for different columns. The second dashboard plots how suitability parameters such as tailing factor evolves over time for different columns. Typically this kind of analysis can only be performed on Excel spreadsheet. Such method takes a significant amount of time, is error prone, not real time and difficult to share.

Leveraging tools like Spotfire, you are able to perform large scale visualization and analytics on your Empower data, download the search results and share the dashboard within your organization.


Peak Area vs time per column


Control charting: Tailing factor vs time per column

Use Cases

Besides the examples listed above, here are some other use cases. In the following use cases, user means Principal Investigator (PI), Scientist, QA Technician

  1. Integration of Chromatographic data with 3rd party visualization and business intelligence tools.
  • Automatic transfer of chromatographic data from Empower to visualization software such as TIBCO Spotfire, Dotmatics Vortex, Tableau, etc.
  • Automatic transfer of chromatographic data from Empower to ELN and LIMS systems
  1. Search an injection.
    • The user is able to search injections based on available metadata (e.g. sample name, date, parent sample set, parent project, etc.) across all connected Empower instances.
  2. Multi-site data access
    • Users are able to search, analyze and retrieve data from multiple sites via a centralized platform.
  3. Batch control
    • User interrogates the metadata stored in a data lake to locate all the chromatograms of a product, or batch or samples.
    • The user collects chromatograms of a product with multiple batches, each batch has multiple samples, each sample has multiple injections
    • The user looks at the product performance by comparing the different batch results
    • When an aberrant result is observed, the user would drill down to the chromatogram or compare multiple chromatograms (chromatogram overlay) of the product to see peak shape or hidden peaks.
  4. Method Development
    • User query for a method name
    • Varying experimental parameters (temp, column, mobile phase etc), viewing of the chromatograms can help to see the effect of the changes
  5. Method reliability – (e.g. impact of the column)
    • What testing has been performed using a particular column
    • What and how other parameters of the run changed on the column