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?
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.
In this blog post we will take a closer look at OPLS*, or Orthogonal PLS, a method to model process data. The advantage of OPLS compared to PLS is that you can uncover hidden details and get a more precise understanding of your data – all of which will help you build better predictive models of your processes.
Worldwide demand for energy escalates every year, and the consumption of fossil fuels continues to increase despite the growing supply of alternative energy options. Globally, about 81 percent of energy comes from a finite supply of fossil fuels like oil, coal and natural gas. Fossil fuels are used to heat homes, run vehicles, power industry and manufacturing, and provide electricity.
All manufacturing industries need good control and good overview of their production processes. As already discussed in a previous blog post, SIMCA-online enables you to apply advanced multivariate data analytics in real time to monitor your production processes, for example to make sure that your production process is behaving as it should or that the quality is what it should be.