Users can now analyze time-series process conditions as well as quality repeats in the latest version release of FormuSense!

In addition, a new set of standard statistics can be calculated on process conditions, quality properties, and ratios – each in the context of any descriptor variable. Raw data plots may also be generated in this same context to give users further insight into characteristics of their imported data.

Complete version release notes are provided below.
 

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Release Notes

Below is an expandable summary of new features added and changes implemented in FormuSense as part of the version released in June 2025. This version is referred to as the Time-Series Release.

What’s Changed

Import
    • Formulations
      • Removed the requirement to specify Ingredient Class Names. Note: When Ingredient Class Names are omitted, FormuSense groups all ingredients into a single Ingredient Class called “Unspecified”.
    • Quality
      • Removed the requirement to provide unique Formulation Names in Quality data in order to allow intentional replicates to be imported and treated as such. Note: When a Formulation Name occurs more than once in Quality, FormuSense appends each additional occurrence of the Formulation Name, creates a new descriptor variable called Master Formulation to readily identify all related replicates, and replicates formulation(s) ingredients and process conditions where relevant.

What’s New

Import
    • Time-Series – Process
      • Import dynamic process conditions by specifying the data source as “Time-series – Process”.
Data
    • Process Conditions
      • Features – Calculate features from time-series process conditions. This feature extraction tool is available when time-series data has been imported. Users may configure multiple features (such as average, standard deviation, minimum, etc.) for any/all time-series process variable(s). These features can be calculated across the entire trajectory, and/or for specific process stages where relevant. Plots are available to assist users on the selection of appropriate time-series features. Alternatively, FormuSense can suggest features that have the highest likelihood to improve a PLS model on provided data.
      • Stats – Calculate standard statistics (mean, standard deviation, etc.) on variables in the active data block on the basis of any descriptor variables (including product family, replicates, etc.).
    • Quality
      • Stats – Calculate standard statistics (mean, standard deviation, etc.) on variables in the active data block on the basis of any descriptor variables (including product family, replicates, etc.).
Ratios
    • Ingredients
      • Stats – Calculate standard statistics (mean, standard deviation, etc.) on variables in the active data block on the basis of any descriptor variables (including product family, replicates, etc.).
    • Ingredient Classes
      • Stats – Calculate standard statistics (mean, standard deviation, etc.) on variables in the active data block on the basis of any descriptor variables (including product family, replicates, etc.).