Spectroscopic Calibration

Spectroscopic regression models as an alternative to lab analyses

WHY SPECTROSCOPIC CALIBRATION?

More and more companies in chemical, pharma and food industries opt for (on-line) NIR calibration models as an alternative to time-consuming and expensive lab analyses. The method is applied to process control as well as quality control of feedstock and final product and also allows process automation. Due to continuous chemometrical developments, the number of applications of spectroscopic calibration – especially NIR – continues to increase, despite the strong overlap of spectral bands and peaks in the NIR region.  Multivariate calibration requires quite a bit of expertise and know-how, for which this training lays a solid foundation.

COURSE SET-UP

Calibration is more than pumping data through some software. In this course we will go through the different steps required for a successful Spectroscopic Calibration: from sample selection over validation and interpretation of the models, up to guidelines and recommendations for the maintenance and update of calibration models in the future. Since emphasis will be put on practice, theoretical aspects will be alternated with practical exercises.

OBJECTIVE

Participants will develop a feel for the multivariate approach to spectroscopic calibration, gain insight into the underlying methods, learn to perform a multivariate calibration in “normal” situations and recognise problem situations.

INTENDED AUDIENCE AND PRIOR KNOWLEDGE

If your aim is to perform multivariate calibrations and/or to properly interpret the results, this course will satisfy your needs. No particular prior knowledge is required. The course is at master’s level.

COURSE CONTENTS

Day 1:

  • NIR introduction
  • Exploratory Multivariate Analysis
    • Visualisation of information in big data sets
    • Principal Component Analysis (PCA)
    • Cluster analysis: searching for groups of similar samples

Day 2:

  • Basic principles of calibration techniques
    • Multiple Linear Regression (MLR)
    • Principal Component Regression (PCR)
    • Partial Least Squares (PLS)
  • Interpretation of calibration models
  • Model validation
  • Preprocessing and scaling of spectra

Day 3:

  • Detection of outliers and non-linearities
  • Prediction with calibration models
  • Selection of calibration samples
  • Standardisation of calibration models
  • Monitoring the performance of (on-line) calibration models

PRACTICAL

Each course day will be held from 9 am to about 4.30 pm. The course fee includes handouts, lunches and the individual follow-up coaching.

Spectroscopic Calibration - CQ Consultancy

Table of Contents

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