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 bioprocessing today, a shift is happening that takes the ability to monitor, optimize and control processes to the next level. Whereas in the past manufacturers aspired to measure data in order to find out why a bioprocess action happened (using descriptive and diagnostic analytics), today we are able to use predictive analytics to determine what will happen in a bioprocess based on specific process data measured in real-time. This migration “up the food chain” to a higher level of data analytics requires automation, ongoing process monitoring and the ability to make adjustments in real-time.
At the heart of any process used to manufacture biological products is a bioreactor setup that supports a stable and reproducible biologically active environment. The bioreactor provides a controlled environment to achieve optimal growth for the particular cell cultures being used.
Biopharmaceutical companies today are challenged to develop high producing cell lines as quickly as possible. Commercially available media may fall short of performance expectations required to meet targets. The alternative —fully customized media and feed development — requires significant funding, time and in-house expertise in media development.
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.
Principal component analysis, or PCA, is a statistical procedure that allows you to summarize the information content in large data tables by means of a smaller set of “summary indices” that can be more easily visualized and analyzed. The underlying data can be measurements describing properties of production samples, chemical compounds or reactions, process time points of a continuous process, batches from a batch process, biological individuals or trials of a DOE-protocol, for example.
In pharmaceutical and other industries that rely on spectroscopy and multivariate calibration for quality control of manufacturing processes, optimizing the analysis of spectral data is imperative. Using a tool that is specifically designed with spectral analytics in mind can make the job faster, easier and more reliable.
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.
For manufacturing companies, process control is essential— even for those producing low-cost items such as small plastic parts. That’s because even when units are small and inexpensive, the cost of defects becomes exponentially higher when they reach the next manufacturing step at another plant.
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?