Hybrid regression–classification framework with probability-guided decision refinement for green tea quality assessment using electronic nose

Accurate prediction in tea quality assessment is essential for ensuring consistency between production standards and market expectations. However, conventional classification models often struggle to distinguish subtle aroma differences captured by electronic nose (e-nose) sensors, particularly in b...

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Bibliografiske detaljer

Principais autores:	Rini Handayani, Riyanarto Sarno, Dedy Rahman Wijaya, Aziz Fajar, Agus Tri Haryono, Sang-Seok Lee
Format:	Artigo
Sprog:	Inglês
Udgivet:	PeerJ Inc. 2026-05-01
Serier:	PeerJ Computer Science
Fag:	Electronic nose (e-nose) Green tea quality assessment Hybrid regression–classification framework Ensemble learning Balanced accuracy Confidence-guided decision refinement
Online adgang:	https://peerj.com/articles/cs-3890.pdf
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