On the west coast of southern Sweden, facing the expanse of the ocean, is the beautiful city of Gothenburg. Surrounded by a string of islands, this city has been the home for sailors and merchants, seafaring and shipping, since ancient times. One of the islands to the north of Gothenburg is the picturesque island of Tjörn. Once every year, Tjörn is the location for one of the most famous sailing races in Sweden – “Tjörn Runt” or “Around Tjörn”.
In life science biopharma manufacturing, demonstrating consistent, repeatable processes is essential both for regulatory compliance and product quality. Being able to create data-driven, performance-based objectives, and aligning the process control strategies with compliance and business performance objectives, allows companies to take their data analysis to the next level: the level at which it becomes meaningful for the company’s bottom line.
Product development and innovation are important elements for the survival of many companies. Whether introducing a new food flavor or adding new product features, understanding consumer preferences can help guide both design and production decisions. The right decisions can make a product launch more successful, and ultimately more profitable.
Multivariate data analysis (MVDA) is a statistical technique that can be used to analyze data with more than one variable in order to look for deviations and understand the relationships between the different data points. In practice, this can mean taking data from a number of different sources and turning it into meaningful information from which you can draw some conclusions.
In life science, biopharma and other areas of research, development and production, design of experiments (DOE) provides a systematic method to determine cause and effect relationships between factors and responses affecting a process, product or analytical system. But the key to understanding your results is effective analysis of your experimental data.
What do we mean by pre-processing of data, and why is it needed? Let's take a look at some data pre-processing methods and how they help create better models when using Principle Component Analysis (PCA) and other methods of data analytics.
In this blog post, we’ll take a closer look at a feature of the SIMCA data analytics software called the Omics skin. So what exactly is an “omics” skin?
In chemical manufacturing, the process involved in creating a breakthrough product often takes several years — with ongoing tests that may be based on trial and error as much as specifically applied knowledge. One area of development in the specialty chemicals market involves the creation of new new additives called plasticizers that can help resins or polymers retain a more supple or flexible nature.
Analyzing batch process data is a lot like juggling. You have multiple sets of data from different sources and in order to turn them into a meaningful presentation, you need a method of handling them to make sure they are all in the right place at the right time.
When to apply OPLS-DA vs PCA for metabolomics and other omics data analysis
Do you know when to use OPLS-DA and when to use PCA/SIMCA data analysis techniques? Find out how to uncover the differences in your data with these classification and discriminant analysis methods.