Applications of Chemometrics for Quality by Design in the Pharmaceutical Industry
Learn how to use chemometrics for vendor selection, raw material selection, reverse engineering, and PAT for Quality by Design in Pharmaceutical Industry.
Training on PAT application using NIR Spectroscopy
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. The following videos are related to the application of machine learning in scientific research.
Predicting unknown material properties using PLS Regression
Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models. Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical. This video shows Data based prediction of material properties.
Principal Component Analysis to Determine Drug-Exceipient Miscibility
The principal components of a collection of points in a real p-space are a sequence of direction vectors, where the vector is the direction of a line that best fits the data while being orthogonal to the first vectors. Here, a best-fitting line is defined as one that minimizes the average squared distance from the points to the line. These directions constitute an orthonormal basis in which different individual dimensions of the data are linearly uncorrelated. Principal component analysis (PCA) is the process of computing the principal components and using them to perform a change of basis on the data, sometimes using only the first few principal components and ignoring the rest.
Training on Failure Mode Effect Analysis
Introduction to Six Sigma
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