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Updated: Oct 29, 2025

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

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Published on: October 5, 2016

Combinatorial projection pursuit analysis for exploring multivariate chemical data.

Peter D Wentzell¹, Thays R Gonçalves², Makoto Matsushita²

¹Trace Analysis Research Centre, Department of Chemistry, Dalhousie University, PO Box 15000, Halifax, NS B3H 4R2, Canada.

Analytica Chimica Acta

|July 12, 2021

Summary

This summary is machine-generated.

Combinatorial projection pursuit analysis (CombPPA) enhances kurtosis-based projection pursuit analysis (kPPA) by exploring local minima for improved multivariate data visualization and class separation. This method aids in discovering alternative data partitions beyond the global minimum.

Keywords:

Cluster analysis Data visualization Dimensionality reduction Exploratory data analysis Grape juice Projection pursuit analysis Unsupervised classification

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Area of Science:

Multivariate Data Analysis
Exploratory Data Analysis
Chemometrics

Background:

Kurtosis-based projection pursuit analysis (kPPA) visualizes multivariate data and aids in unsupervised class separation, complementing methods like principal components analysis (PCA).
Standard kPPA identifies a single projection (global minimum kurtosis), which may not align with researcher-defined classifications when multiple partitioning schemes are possible.

Purpose of the Study:

Introduce combinatorial projection pursuit analysis (CombPPA) to explore numerous local minima of kPPA, enabling the discovery of alternative class separations.
Provide researchers with a more comprehensive exploratory data analysis tool for uncovering diverse data structures.

Main Methods:

CombPPA interrogates multiple local minima identified by the kPPA optimization algorithm.
Procrustes rotation is employed to map and compare solutions from different local minima.
Enhanced kPPA algorithm improvements focus on increasing the orthogonality of obtained solutions.

Main Results:

Demonstrated the ability of CombPPA to reveal alternative projections and class separations not found by standard kPPA.
Successfully applied CombPPA to cluster grape juice samples based on visible spectroscopy data, identifying partitions by type (organic/non-organic) and brand.
Showcased the visualization of different projection combinations, including desired partitions, through the Procrustes analysis of local minima.

Conclusions:

CombPPA offers a robust method for fully probing the landscape of interesting projections in multivariate data.
The technique effectively addresses the limitation of standard kPPA by allowing exploration beyond the global minimum.
CombPPA, coupled with enhanced kPPA, provides a powerful, truly exploratory approach for uncovering complex data structures and classifications.