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.
From employing artificial intelligence (AI) to identify drug candidates to using big data to support continuous process manufacturing, the prospects for digital transformation in the biopharma industry are huge. Yet, biopharma and life sciences lag behind many other industries when it comes to digital transformation.
In the midst of a global crisis, many industrial manufacturing operations— including those in the chemical industry— are faced with shortages of supplies and equipment, or staff reductions, and finding it difficult to keep operations working as normal. Are there process improvements or tools that can be used to manage production more efficiently during this time of COVID-19 (and moving forward)?
While many other industries have implemented multivariate data analysis software for process optimization and control, it is still not very common in the pulp and paper industry. However, multivariate data analysis has a very promising potential for both cost reductions and quality improvements in pulp and paper mills. No capital investments are needed, the implementation can be done remotely, and the software typically requires no permits.