When it comes to creating an optimal manufacturing process that limits variation and conserves energy or resources, or a developing a new formula that is most likely to meet customer expectations, design of experiments (DOE) is an indispensable tool.
OPLS and PCA are two commonly used techniques to analyze genomics, metabolomics and other Omics data.
Do you know when to use OPLS-DA and when to use PCA data analysis techniques to makes sense of your Omics data? Find out how to uncover the differences in your data with these classification and discriminant analysis methods.
Over the last several years, the use of artificial intelligence (AI) in the pharma and biomedical industry has gone from science fiction to science fact. Increasingly, pharma and biotech companies are adopting more efficient, automated processes that incorporate data-driven decisions and use predictive analytics tools. The next evolution of this approach to advanced data analytics incorporates artificial intelligence and machine learning.
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.
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Whether you work in engineering, R&D, or a science lab, understanding the basics of experimental design can help you achieve more statistically optimal results from your experiments or improve your output quality.
Consumers expect a certain consistency in quality and taste from the food and beverage brands they love. But many factors can influence the way a product tastes when it reaches the consumer – ranging from the manufacturing process to seasonality of ingredients to storage temperatures. Similarly, a number of other factors may influence the overall quality attributes that matter, such as alcohol content of beer or stability of the whiskey aging process.
In the midst of a global COVID-19 pandemic, a top priority for many pharma and biopharma companies is to get a vaccine developed, produced and delivered to the public as quickly as possible. Ushering a vaccine through rigorous testing protocols and regulatory approvals is not an easy (or quick) effort, but incorporating advanced data analytics could help accelerate the process. Data analytics has proven effective in speeding vaccine development both by enabling more efficient Design of Experiments (DOE) and by creating rapid-scale production rollout processes.
You’ve probably heard the terms artificial intelligence (AI), machine learning (ML) and deep learning (DL) being used in conjunction with digital transformation and data science. You may be wondering what the relationship is between these subjects. How are businesses in industries ranging from biopharma to chemicals to food & beverage incorporating AI, machine learning and data science to improve their processes? Let’s take a look at what these terms mean and how businesses are using them to make more strategic decisions and improve production processes.
Digital transformation in biopharma promises to deliver exponential results and make new discoveries and solutions to complex problems a reality, but it requires companies to make big changes to get there—changes in processes as well as adoption of new technologies. For some companies and facilities, this is a bigger leap than for others. Depending on the level of digitalization and integration that currently exists within a company, the process can take from months to years.
Digital transformation in biopharma, like other industries, is accelerating as Pharma 4.0 and Industry 4.0 begin to take shape in companies of all sizes. But knowing that digital transformation is inevitable is one thing, successfully managing the transition process is another. What are the steps biopharma companies should be taking to ensure a smooth digital transformation?
Looking for ways to improve the efficiency of its power plant operations while reducing costs and environmental emissions, the Department of Power and Water at Michigan State University (MSU) began a study using multivariate data analytics that led to some surprising findings. The results have implications that could help other operators of large-scale power facilities reduce their carbon footprint and improve power plant operations.
Finding the right balance between efficient power output from boilers and other energy producing equipment while also reducing environmental emissions is an important objective for power plant operators. Governments and environmental agencies around the world establish emission standards as part of air pollution regulations, but finding the right way to meet the standards can vary greatly depending on location, equipment and other operating factors.