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This is a Quantitative Study study.

Expanding Multivariate Analysis Principles in Conventional Chemometric Processes

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Perspective highlights the need to enhance multivariate analysis practices in chemometrics, particularly integrating the Rashomon effect.

The research emphasizes the need to capitalize on multivariate analysis within chemometric practices, enhancing scientific intuition.
Key enhancements include model selection and explanation, with a focus on comprehensive analysis involving matrix effects.
Discussion centers around utilizing techniques like outlier detection and assessment of sample similarity for improved model reliability.
Findings from this work integrate concepts from spectroscopic data, making them applicable to diverse chemometric data structures.

Kalivas et al. (2025) studied this question.

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