Research Topic · Peer-Reviewed

Principal Component Analysis

Principal component analysis is a statistical technique that reduces the dimensionality of complex datasets by transforming correlated variables into a smaller set of uncorrelated components that capture the most important patterns of variation. Research published in New Developments in Chemistry and its affiliated …

Curated from this journal's research 📚 12 peer-reviewed articles cited Cited 69× across the literature 🔖 ISSN 2377-2549 🗓 Reviewed June 2026

Overview

Principal component analysis is a statistical technique that reduces the dimensionality of complex datasets by transforming correlated variables into a smaller set of uncorrelated components that capture the most important patterns of variation. Research published in New Developments in Chemistry and its affiliated journals applies this method across diverse fields including agricultural genetics, environmental monitoring, and public health assessment. Studies have employed principal component analysis to evaluate genetic diversity in crop varieties such as Assam chilli and cocoyam germplasm, to screen sugar beet and sorghum genotypes under stress conditions, and to characterize vineyard clusters through spectroscopic data. The technique has also been applied to analyze spatial patterns in urban-rural land dynamics, to assess relationships between environmental noise and depression at the municipal level, to profile metabolic changes in fish following toxicant exposure, and to examine water quality variations in industrially impacted river systems. Additionally, researchers have used principal component analysis to validate psychological assessment instruments and to develop disease severity classification systems. This analytical approach proves valuable because it allows investigators to identify underlying structure in multidimensional datasets, facilitating interpretation of complex biological, environmental, and socioeconomic phenomena while reducing computational complexity.

Research published in this journal

12 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 12 articles above have been cited 69 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Principal Component Analysis, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in New Developments in Chemistry (ISSN 2377-2549).

Journal editorial board
Annarita Del Gatto · Italy Bharat Gurale · United States Palani ELUMALAI · United Kingdom

This page summarises published research for orientation; it is not medical or professional advice.