Rapid classification of tea using laser-induced breakdown spectroscopy coupled with a BP neural network optimized by improved SSA

The LIBS combined with ISSA optimized BP neural network was used to identify seven types of tea. The classification accuracy rate of the ISSA-BP model was 99.1%, which was superior to other models.

Authors

Muhua Liu (ORCID: https://orcid.org/0000-0002-4707-1354)
Mingyin Yao (ORCID: https://orcid.org/0000-0002-9850-4826)
Lin Huang (ORCID: https://orcid.org/0000-0002-4434-7861)
Jiang Xu (ORCID: https://orcid.org/0000-0002-3744-4989)
Tianbing Chen

Institutions

Jiangxi Agricultural University (CN)

Publication Details

Journal: Journal of Analytical Atomic Spectrometry
Published: 2025-01-01
DOI: https://doi.org/10.1039/d5ja00282f
Primary Topic: Spectroscopy and Chemometric Analyses
Type: article
Field-Weighted Citation Impact: 0.00

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article

Rapid classification of tea using laser-induced breakdown spectroscopy coupled with a BP neural network optimized by improved SSA

Muhua Liu, Mingyin Yao, Lin Huang, Jiang Xu et al.

Jan 1, 2025

Journal of Analytical Atomic Spectrometry

Spectroscopy and Chemometric Analyses

article

Rapid classification of tea using laser-induced breakdown spectroscopy coupled with a BP neural network optimized by improved SSA

Muhua Liu, Mingyin Yao, Lin Huang, Jiang Xu, Tianbing Chen

January 1, 2025article en

Abstract

The LIBS combined with ISSA optimized BP neural network was used to identify seven types of tea. The classification accuracy rate of the ISSA-BP model was 99.1%, which was superior to other models.

Journal of Analytical Atomic SpectrometryVol. 40(12)

Jiangxi Agricultural University (CN)

Openalex Percentile: Top 69%

Spectroscopy and Chemometric Analyses

DOI Source OpenAlex

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