View sensory profiling data, analyze and generate statistics, as well as create various graphs or scatter plots, with this reliable tool.
- PanelCheck
- Version :1.4.2
- License :GPL
- OS :Windows All
- Publisher :Oliver Tomic
PanelCheck Description
PanelCheck is a simple to use tool, designed for analyzing sensory data and calculate the parameters according to various plot algorithms. The application can generate a large series of plot graphs, which allow you to visualize sensory profiling data. The statistics are designed to offer information on the assessor and panel performance.
Quick data import and analysis
PanelCheck only works with data imported from external file and does not feature an editor for you to modify the values. Therefore, you can easily import data from text files or from Excel spreadsheets; the program supports tab, comma and semicolon delimited files. Microsoft Excel is required for importing a spreadsheet.
Once the file is imported, you can map the columns, in order to match the desired parameters. Three variables are needed for the algorithms, namely assessors, samples and replicates. You may assign any of the columns contained within the file to either of the parameters.
Data analysis and plot generator
After analyzing the imported data, PanelCheck can instantly generate the plots. You may access them by clicking the entry in the sensory data list. The assessor, attribute and sample values are displayed in their corresponding columns, for you to view and filter. You can include or exclude any of the values from the plot, with one simple click.
The program can generate line plots, correlations, profile, eggshell, F&P, MSE and p-MSE graphs, in Univariate mode. Tucker-1 and Manhattan plots can be viewed in the Multivariate mode, while Standardized or STATIS tables can be accessed from Consensus tab. Moreover, you may view the statistic graphs, generated according to the ANOVA method in the Overall section.
Standardization and methods
PanelCheck is a reliable tool for data analysis and plot generating, which can handle even large amounts of data. It can apply variable standardization and generate the graphs based on sample averages or replicates. Moreover, it can apply several ANOVA methods in order to obtain a general trend based on all the entries.