Contributed by: Balakrishnan
I am Balakrishnan, working as a Scientist in R&D. I completed my PhD in Organic chemistry and worked in the medicinal and analytical field. I have 12 years combined experience in the pharma and Agri-food business. Basically, I am a researcher that tends to conduct a lot of animal trials for product development. So, Data science helped in three different things.
1. Statistical analysis of trial results,
2. Project measure analysis,
3. PCA for dimension reduction
I found Principal Component Analysis is a very useful tool for dimension reduction, especially in measuring the outcome of animal trial results, thus getting maximum accuracy.
Talking about the business problem, we have to conduct many animal trials to check the efficacy of developed products. Several parameters need to be evaluated to assess the product efficacy in blood, tissues or matrix, resulting from 15-20 variables.
Every animal trial experiment goes for at least two months to six months. The whole process is highly laborious to check every parameter and thus time-consuming.
I performed PCA, where variables were reduced to 60%, helping to reduce manpower significantly, time by 50% & cost to 60%. The purpose of the study is to screen different molecules, and reducing them to 8 critical variables helps to speed up the project significantly. That is, Variables were reduced to 8 to get 85% accuracy.
Our organization has decided to apply data analytics in every process followed in trial and production to improve process efficiency.
This application has been proposed & we are planning to present it in the inter-business unit to other fellow researchers across countries. This will be a new initiative for our business as well.
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