Introduction to multivariate analysis in spectroscopy using Aspen Unscrambler

In this training, learn how to analyze problems that involve many variables at once using multivariate analysis tools through Aspen Unscrambler. Understand how to perform exploratory analysis using Principal Component Analysis (PCA) to reduce the dimensionality of complex datasets. Data processing/transformations are then applied to remove extraneous sources of noise or signals not relevant to the objective. This includes data standardization, derivatives, scatter corrections, among other methods. With properly treated data, Partial Least Squares Regression (PLSR) is used to relate predictor variables with response variables to build a model, which then used for prediction of new samples.  

 

Audience:

Engineers and scientists responsible for process troubleshooting, analysis, and monitoring

Training Details

  • Course Id:

    MVA101

  • Duration:

    2 day(s)

  • CEUs Awarded:

    1.4

  • Level:

    Introductory

Benefits

  • The user will obtain a general familiarity with the methods for analyzing data from a multivariate perspective.  
  • Visualize and manipulate data in Unscrambler to prepare the information for further analysis 
  • Learn how to handle and build models on sensory, near-infrared (NIR), and Fourier Transform (FTIR) data  

Approach

  • Clear guidance on fundamental topics 
  • Multivariate analysis workflows 
  • Hands-on workshops 
  • Experienced instructor-guided demonstrations 

Pre-requisites

Some familiarity with multivariate analysis is helpful but not essential

Agenda

Introduction to multivariate analysis 

  • Explain the purpose of using multivariate analysis 
  • Learn how to organize data and navigate the Unscrambler dashboard 

Principal Component Analysis 

  • Discuss the concept of Principal Component Analysis and the advantages to handle multivariate data 
  • Apply PCA to a standard dataset and interpret results 
  • Interpret residual plots to estimate the number of components needed 
  • Workshop: Interpreting PCA of Sensory Data (Peas) 
  • Workshop: Application of PCA to FTIR Data (Vegetable Oils) 
  • Workshop: Projecting New Data onto Existing PCA Model (Vegetable Oils) 

Outlier Diagnostics 

  • Learn how to identify outliers using analysis plots 

Data Preprocessing 

  • Transform spectral data using processing methods to remove additive and multiplicative effects 
  • Compare different methods and evaluate their effects on the data 
  • Workshop: Application of Various Pre-Treatments to NIR Spectra (Wheat) 

Regression Modeling 

  • Explore Partial Least Square Regression and its uses for model building and prediction 
  • Evaluate the uncertainty of predictions using root-mean-square error values and deviations 
  • Identify patterns in scores and loadings which can be interpreted similarly to PCA 

Validation Methods 

  • Import an Equipment Set Profile 
  • Workshop: Multivariate Calibration and Validation (Wheat NIR) 

Register for a Class

Date Class Type Location Price Language
Date(s): 02/2/2026 - 02/3/2026 Type: Public Virtual Location: Virtual-EMEA Price: (USD) 2000.00 Language: English Register
Date(s): 02/2/2026 - 02/3/2026 Type: Public Virtual Location: Virtual-EMEA Price: (USD) 2000.00 Language: English Register
Date(s): 04/13/2026 - 04/14/2026 Type: Public Virtual Location: Virtual-EMEA Price: (USD) 2000.00 Language: English Register
Date(s): 02/24/2026 - 02/25/2026 Type: Public Virtual Location: Virtual-Americas Price: (USD) 2000.00 Language: English Register
Date(s): 04/14/2026 - 04/15/2026 Type: Public Virtual Location: Virtual-Americas Price: (USD) 2000.00 Language: English Register
Date(s): 02/24/2026 - 02/25/2026 Type: Public Classroom Location: 2500 Citywest Blvd, Suite 1600
Houston , Texas USA 77042 		Price: (USD) 2000.00 Language: English Register
Date(s): 04/14/2026 - 04/15/2026 Type: Public Classroom Location: 2500 Citywest Blvd, Suite 1600
Houston , Texas USA 77042 		Price: (USD) 2000.00 Language: English Register

Aspen Technology, Inc. awards Continuing Education Units (CEUs) for training classes conducted by our organization. One CEU is granted for every 10 hours of class participation.