Could data analytics aid in the diagnosis of severe neurological diseases? In a recent study, a research group at Umeå University has conducted statistical data analysis of biomarkers from patients suffering from Amyotrophic lateral sclerosis (ALS, also known as Lou Gehrig’s disease) and Parkinson’s disease to investigate whether data analytics could help in the diagnosis of – and help distinguish between – the two diseases.
Making sure your data and processes from research and development through to production are compliant is essential in today's highly regulated life science, biopharma, pharmaceutical and food industries. But it's no easy task. Following all of the required steps and ensuring the integrity of your data at every stage is easier and more successful when you use a product designed to keep your data compliant.
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.
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.
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.
An important environmental issue that has come into focus is the increasing number of chemicals that we are exposed to in our everyday life. Chemicals are found in products ranging from cars and furniture to clothing and skincare, and are also by-products from combustion. The CAS REGISTRYSM, an international standard for chemical information, currently contains more than 134 million unique organic and inorganic chemical substances and more than 67 million sequences.
If data analytics were easy, everyone would do it, right? Well, what if were easy enough for anyone to do it? Can you image what sort insights you might glean from the vast pools of data your company collects about your manufacturing processes, sales or production outputs?
If all of your data stays hidden in the depths of some process control computer or in Excel spreadsheets on the manufacturing floor manager’s desk, are they doing anyone any good?
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.