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  • Jordan Gunn

Hyperspectral Imaging in the Pharmaceutical Industry: A Review


The average person in the United States spends $1,200/yr on prescription medicine. This constitutes the largest pharmaceutical market in the world, accounting for 40% of the worldwide market, and nearing $500 billion in revenue. It almost goes without saying that this provides ample opportunity for profit in the production of pharmaceuticals. But that process doesn’t come without its own challenges.


In this blog we’ll look at some of those challenges, and how state-of-the-art technology in the form of hyperspectral imaging can help to overcome some of those challenges.


The process of producing pharmaceuticals is highly regulated by the FDA, who is charged with ensuring that drugs are, among other things, safe for approval, and that they are manufactured, labeled, and marketed in a satisfactory manner.


Additionally, as multi-billion dollar companies with reputations at stake, they harbor their own set of standards for the quality control of the drugs they produce. This ultimately results in a dizzying number of parameters being monitored in their production. Chief among these are what is referred to as Critical Process Parameters (CPP), which are variables that can have an appreciable effect on Critical Quality Attributes (CQA). Some of these CPP’s include:

The methods that are used to monitor these parameters vary. Some are chemical-based tests, some are ELISA-based tests, and some are spectroscopic. There are teams of people conducting these tests in unison, with each having their own role in the overall safety and quality of their products. Two potential issues arise with this process

1) The sheer volume of tests being conducted

2) The need for overarching monitors as insurance


To put it plainly, there isn’t much data these pharmaceutical companies wouldn’t be interested in seeing on their products. After all, they go out of their way to individually test for dozens and dozens of parameters. Additionally, there are a number of unknown variables in the production of numerous drugs simultaneously, and considering the stakes, these companies wish to have as much control and as much insurance on production as they can reasonably provide.


Hyperspectral imaging, and specifically AI-assisted hyperspectral imaging, have key attributes that provide an opportunity to address these issues head-on. These are, namely

-An ability to test a multitude of parameters

-An ability to monitor multiple parameters simultaneously

-An ability to be placed at-line or in-line

-The removal of sample prep and consequently human error

-The ability to add redundancy

-The ability to recognize fingerprints and deviations in fingerprints

-The ability to monitor homogeneity


Because hyperspectral imaging setups can be outfitted with a variety of sensors they are extremely versatile with respect to the parameters they can analyze. They can also monitor numerous parameters at the same time, and map these parameters across and between samples giving information about homogeneity. Hyperspectral imaging cameras can be placed throughout the production process, and can run continuously and without human direction. With the addition of artificial intelligence engines, this process has the ability to learn about product composition even amidst, weak, overlapping, and unknown signals. This allows for the detection of unknown abnormalities.

Overall the new-age technology present within AI-assisted hyperspectral imaging provides a unique opportunity for the producers of pharmaceuticals to monitor new parameters and add redundancy to parameters being tested with other methods. In an industry with such high stakes, it makes sense for these companies to adopt AI-assisted hyperspectral imaging to protect both their own bottom line, as well as the end-users of their products.

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