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.
In manufacturing and other industries that have complex processes, knowing which variables have the most impact on quality and at what point, or knowing which combination of variables to change in order to improve your process, can have a huge impact on the overall quality or profitability of your manufacturing process. But without making expensive and time-consuming changes in the physical processes in order to test all possible scenarios, how can you identify and predict the variables that have the most significant impact on your outputs?
Using real-time data analytics monitoring has become the accepted way to monitor processes in several industries. The goal is to detect and diagnose issues as they happen, which is a great leap forward compared to traditional analysis conducted in retrospect. This has been highlighted in a previous blog post.