Thanks to visit codestin.com
Credit goes to github.com

Skip to content

oicurp/oicurp.github.io

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

28 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Chemometrics for Analytical Chemistry Using Python


Note: this might seem a lofty plan, as there're a lot to do. Currently only codes for I-MR chart are developped and stored under 4.6.Shewhart Charts.

Introduction

This repository contains Python codes for people who're interested in learning Chemometrics using Python as the tool in stead of commercial statistical softwares.The knowledge behind the Python codes are mainly based on the Chemometrics book by Miller [1].

One might find the codes useful in the following application pharmaceutical manfacturing areas:

  1. Control Charts (I-MR, or Shewhart charts) for process and quality monitoring (In-Process Control, Finished Product Testing, Stability Testing).
  2. Cusum chart for use in microbiological observations.
  3. Linear Regression, for analytical method validation or routine testing.
  4. Partial least squares regression for FT-NIR.
  5. etc

Intended Audiences

  1. People who want learn chemometrics and Python programming.
  2. People who work in pharmacetucal quality function, and who need to deal with statistical assessment without using commercial statistical software.

Environments

The codes are devloped with the following environments:

  1. IDE: VS Code 1.52.1.
  2. Interpreter: Python 3.6.5 from Anaconda 4.8.4

Table of Contents

  • 0.0.Data
  • 2.1.Mean and Std Deviation
  • 3.0.Significance Tests
  • 4.0.Quality Control of Analytical Measurements
  • 4.6.Shewhart Charts (I-MR)
  • 4.9.Average Run Length (CUSUM) charts
  • 4.10.Zone control charts (J-charts)
  • 5.0.Calibration Methods, Regression and Correlation
  • 8.0.Multivariate Analysis
  • 8.2.Intial Analysis
  • 8.3.Principal Component Analysis
  • 8.4.Cluster Analysis
  • 8.5.Discriminant Analysis
  • 8.6.K-nearest Neighbour Method
  • 8.7.Disjoint Class Modelling
  • 8.8.Regression Methos
  • 8.9.Multiple Linear Regression
  • 8.10.Principal Component Regression
  • 8.11.Partial Least-squares Regression
  • 8.12.Natual Computation Method

How to Use

to be continued...

References:

  1. Statistics and Chemometrics for Analytical Chemistry 6th edition by J Miller. Link
  2. European Pharmacopoeia 10.4: Process Analytical Technology (4.5.25)
  3. European Pharmacopoeia 10.4: Multivariate Statistical Process Control (4.5.2)

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published